Task-Boundary Mode
How task-instance boundaries are drawn from the event stream. Applies to every Task SoP, Step SoP, and Variants view.
Triage
📝 Task SoP
Mined from 685 instances
Manage Work List
How 8 analysts execute this task across 275 cases — auto-generated Standard Operating Procedure with adherence scoring.
Sub-Task Filter
Reshapes all tabs below to just this anchor within the composite task.
82.5
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
7.0 min
Avg Task Duration
0.0 min median
1.3
Avg Sub-Steps per Instance
12.6% have multiple sub-steps
51.7%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
51.7% of instances follow this exactly
Manage Work List — Mined SoP
The most common sub-step sequence observed across 971 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
0.0s
Phobos
57%
Variant Explorer
Reference
Manage Work List Reference Path
502 instances · 7 analysts
This is the mined reference path — 502 instances across 7 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Manage Work List.
Genuine Complexity
Complex-Case Execution Path
186 instances · 7 analysts
· Δ adherence -0.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 82.5 (delta -0.0). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
161 instances · 8 analysts
· Δ adherence +6.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.3 vs reference 82.5 (delta +6.8). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
16 instances · 4 analysts
· Δ adherence +1.7
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
View Worklist
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 84.2 vs reference 82.5 (delta +1.7). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
15 instances · 5 analysts
· Δ adherence +7.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.5 vs reference 82.5 (delta +7.0). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
6 instances · 4 analysts
· Δ adherence -15.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
5 instances · 2 analysts
· Δ adherence -1.5
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Add New Case
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 81.0 vs reference 82.5 (delta -1.5). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
4 instances · 2 analysts
· Δ adherence -15.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
2 instances · 2 analysts
· Δ adherence -7.5
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
2 instances · 2 analysts
· Δ adherence -15.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to collaboration.merck.com
2 instances · 1 analysts
· Δ adherence -15.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
collaboration.merck.com
extra step (reference ends at step 1)
Cross-App Swivel — 1 analysts pivot into collaboration.merck.com during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
2 instances · 2 analysts
· Δ adherence -7.5
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Microsoft Excel
2 instances · 2 analysts
· Δ adherence -15.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Microsoft Excel
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Excel during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 1,031 | 81.4% | 87.4% |
| Veeva Safety | 179 | 14.1% | 12.6% |
| Microsoft Teams | 14 | 1.1% | 0.0% |
| collaboration.merck.com | 9 | 0.7% | 0.0% |
| Acrobat | 9 | 0.7% | 0.0% |
| Microsoft Excel | 7 | 0.6% | 0.0% |
| Microsoft Word | 4 | 0.3% | 0.0% |
| Microsoft Outlook | 3 | 0.2% | 0.0% |
| YoudaoDict | 2 | 0.2% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.2% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 56 |
| Phobos | Microsoft Teams | 8 |
| Phobos | Microsoft Excel | 5 |
| Acrobat | Veeva Safety | 4 |
| Veeva Safety | Microsoft Teams | 4 |
| Phobos | collaboration.merck.com | 4 |
| Microsoft Teams | Veeva Safety | 4 |
| Veeva Safety | Microsoft Word | 3 |
| Veeva Safety | Acrobat | 3 |
| Phobos | Acrobat | 3 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,669 events 239 cases 44.4 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
452×
pyzeClick
<a>
"Show more"
165×
pyzeClick
<span>
"Adverse Events"
117×
pyzeClick
<span>
"Narrative"
108×
pyzeClick
<span>
"Submissions & Distributions"
93×
pyzeClick
<li>
"Narrative"
92×
pyzeClick
<span>
"Products"
86×
pyzeClick
<span>
"Case Assessment Results"
83×
Phobos DOM 3,345 events 275 cases 83.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
316×
pyzeClick
<button>
"Send to process"
219×
pyzeClick
<a>
"Cases"
180×
pyzeClick
<button>
"Save Comments"
172×
pyzeClick
<button>
"Advance"
140×
pyzeClick
<a>
"Cases Coordinator"
129×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
127×
pyzeClick
<a>
"Inbox"
103×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 103 events 39 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
1,283 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 96 events 50 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
990 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 90 events 41 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
775 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 71 events 33 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 59 events 34 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
usc-excel.officeapps.live.com Document 34 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 15 events 8 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.04
Swivel Rate
events in non-primary app / total
0.90
Zero-Edit Visit Rate
inspection-only clicks
1.01
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.90
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.90
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.01
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.04
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
10
13
02
03
01
05
12
09
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 10 | 61 | 6.8 | 84.7 | Zero-Edit Visits | 0.00 | 0.00 | 2 |
|
Reference SoP
1
View Processing Routed
Analyst 10's Dominant Sequence
— 61 of 61 instances
1
Filter Cases
· reference: View Processing Routed
|
||||||||
| ▸ | Analyst 13 | 184 | 20.4 | 83.2 | Zero-Edit Visits | 0.05 | 0.01 | 67 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 66 of 184 instances
1
View Processing Routed
This analyst exhibits 22 distinct sequences across their 184 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 96 | 13.7 | 82.8 | Zero-Edit Visits | 0.06 | 0.00 | 99 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 47 of 96 instances
1
View Processing Routed
This analyst exhibits 15 distinct sequences across their 96 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 149 | 12.4 | 82.8 | Zero-Edit Visits | 0.03 | 0.00 | 1,831 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 101 of 149 instances
1
View Processing Routed
This analyst exhibits 13 distinct sequences across their 149 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 173 | 13.3 | 82.7 | Zero-Edit Visits | 0.03 | 0.00 | 74 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 110 of 173 instances
1
View Processing Routed
This analyst exhibits 14 distinct sequences across their 173 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 58 | 5.8 | 82.2 | Zero-Edit Visits | 0.04 | 0.00 | 953 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 40 of 58 instances
1
View Processing Routed
This analyst exhibits 7 distinct sequences across their 58 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 82 | 9.1 | 81.5 | Zero-Edit Visits | 0.08 | 0.00 | 1,089 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 47 of 82 instances
1
View Processing Routed
This analyst exhibits 19 distinct sequences across their 82 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 168 | 11.2 | 81.0 | Zero-Edit Visits | 0.06 | 0.00 | 2,715 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 91 of 168 instances
1
View Processing Routed
This analyst exhibits 23 distinct sequences across their 168 instances; the dominant one is shown.
|
||||||||
81.4
Avg Adherence Score (0-100)
Median 82.5 · Range 57.5–90.0
10.5 min
Avg Task Duration
0.0 min median
1.2
Avg Sub-Steps per Instance
9.1% have multiple sub-steps
—
Reference Path Coverage
552 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 552 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 564 | 83.1% | 4.5% |
| Veeva Safety | 83 | 12.2% | 95.5% |
| Microsoft Teams | 8 | 1.2% | 0.0% |
| Acrobat | 6 | 0.9% | 0.0% |
| collaboration.merck.com | 4 | 0.6% | 0.0% |
| Microsoft Word | 4 | 0.6% | 0.0% |
| Microsoft Excel | 4 | 0.6% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.3% | 0.0% |
| Microsoft Outlook | 2 | 0.3% | 0.0% |
| tools.meddra.org | 1 | 0.1% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 26 |
| Phobos | Microsoft Teams | 5 |
| Phobos | Microsoft Excel | 4 |
| Microsoft Teams | Veeva Safety | 3 |
| Acrobat | Veeva Safety | 3 |
| Veeva Safety | Microsoft Word | 3 |
| Microsoft Word | Acrobat | 3 |
| Phobos | Acrobat | 2 |
| Phobos | collaboration.merck.com | 2 |
| Veeva Safety | Microsoft Teams | 2 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,233 events 186 cases 42.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
440×
pyzeClick
<a>
"Show more"
131×
pyzeClick
<span>
"Adverse Events"
100×
pyzeClick
<span>
"Narrative"
94×
pyzeClick
<li>
"Narrative"
85×
pyzeClick
<span>
"Submissions & Distributions"
76×
pyzeClick
<li>
"Details"
75×
pyzeClick
<span>
"Products"
72×
Phobos DOM 2,427 events 209 cases 61.0 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
289×
pyzeClick
<button>
"Send to process"
191×
pyzeClick
<button>
"Save Comments"
166×
pyzeClick
<a>
"Cases"
159×
pyzeClick
<button>
"Advance"
135×
pyzeClick
<a>
"Cases Coordinator"
121×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
109×
pyzeClick
<a>
"Inbox"
87×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 81 events 31 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
877 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 78 events 42 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
839 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 63 events 27 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
521 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 55 events 31 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
Microsoft Excel Document 52 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
usc-excel.officeapps.live.com Document 28 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 14 events 7 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.04
Swivel Rate
events in non-primary app / total
0.99
Zero-Edit Visit Rate
inspection-only clicks
1.02
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 1.00
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 1.00
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.02
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.04
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
| 2331410 | Analyst 01 | 58.5 | 2.7m | 5 | 0.80 | 0.00 | 1 |
| 2343258 | Analyst 13 | 59.2 | 4.9m | 9 | 0.67 | 0.00 | 3 |
| 2316969 | Analyst 01 | 60.0 | 1.3m | 4 | 0.75 | 0.00 | 1 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
02
01
05
03
09
13
12
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 02 | 49 | 7.0 | 82.2 | Zero-Edit Visits | 0.01 | 0.00 | 67 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 47 of 49 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 49 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 114 | 8.8 | 81.9 | Zero-Edit Visits | 0.02 | 0.00 | 9 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 110 of 114 instances
1
View Processing Routed
This analyst exhibits 5 distinct sequences across their 114 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 42 | 4.2 | 81.8 | Zero-Edit Visits | 0.02 | 0.00 | 124 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 40 of 42 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 42 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 111 | 9.2 | 81.6 | Zero-Edit Visits | 0.03 | 0.00 | 1,782 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 101 of 111 instances
1
View Processing Routed
This analyst exhibits 7 distinct sequences across their 111 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 102 | 7.3 | 81.1 | Zero-Edit Visits | 0.05 | 0.00 | 2,686 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 91 of 102 instances
1
View Processing Routed
This analyst exhibits 10 distinct sequences across their 102 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 73 | 8.1 | 80.8 | Zero-Edit Visits | 0.05 | 0.00 | 24 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 66 of 73 instances
1
View Processing Routed
This analyst exhibits 8 distinct sequences across their 73 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 61 | 6.8 | 80.4 | Zero-Edit Visits | 0.08 | 0.00 | 1,083 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 47 of 61 instances
1
View Processing Routed
This analyst exhibits 13 distinct sequences across their 61 instances; the dominant one is shown.
|
||||||||
85.8
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
0.7 min
Avg Task Duration
0.0 min median
1.6
Avg Sub-Steps per Instance
21.1% have multiple sub-steps
—
Reference Path Coverage
204 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 204 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
Filter Cases
2
View Worklist
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 234 | 72.9% | 97.2% |
| Veeva Safety | 64 | 19.9% | 2.8% |
| collaboration.merck.com | 5 | 1.6% | 0.0% |
| Microsoft Teams | 5 | 1.6% | 0.0% |
| Acrobat | 3 | 0.9% | 0.0% |
| Microsoft Excel | 3 | 0.9% | 0.0% |
| YoudaoDict | 2 | 0.6% | 0.0% |
| Notepad | 1 | 0.3% | 0.0% |
| ONENOTE | 1 | 0.3% | 0.0% |
| Microsoft Outlook | 1 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 19 |
| Veeva Safety | Microsoft Teams | 3 |
| Veeva Safety | Acrobat | 2 |
| YoudaoDict | Veeva Safety | 2 |
| Phobos | collaboration.merck.com | 2 |
| Microsoft Excel | Veeva Safety | 2 |
| Phobos | Microsoft Teams | 2 |
| collaboration.merck.com | Microsoft Excel | 2 |
| Veeva Safety | gpteal.merck.com | 1 |
| Veeva Safety | Notepad | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 1,463 events 100 cases 40.7 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
56×
pyzeClick
<a>
"Cases"
53×
pyzeClick
<button>
"Send to process"
38×
inputld
<select>
"LIKE · = · !="
34×
pyzeClick
<a>
"Cases Coordinator"
23×
pyzeClick
<a>
"Rules"
23×
pyzeClick
<a>
"Worklist Open"
23×
pyzeClick
<button>
"Re-Route"
23×
Veeva Safety DOM 765 events 82 cases 3.8 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Show more"
46×
pyzeClick
<span>
"Complete"
34×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
25×
pyzeClick
<span>
"Documents"
23×
pyzeClick
<span>
"Submissions & Distributions"
23×
pyzeClick
<li>
"Narrative"
22×
Microsoft Teams App 34 events 17 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
350 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 31 events 20 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
365 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Acrobat App 27 events 11 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
466 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 25 events 13 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
2329445 (v2.1)
339×
file
Priorización
179×
file
Case tracking list
126×
file
Daily tracking list (PV-SMART) copy
32×
Microsoft Word Document 12 events 4 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 10 events 8 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
gpteal.merck.com DOM 5 events 2 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.08
Swivel Rate
events in non-primary app / total
0.60
Zero-Edit Visit Rate
inspection-only clicks
1.02
Avg Revisit Depth
max repeats of any activity
0.01
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.60
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.60
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.02
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.08
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.01
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2301418 | Analyst 09 | 58.0 | 0.4m | 4 | 0.75 | 0.19 | 1 |
| 2291017 | Analyst 02 | 58.1 | 3.0m | 8 | 0.88 | 0.00 | 1 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
03
01
12
05
13
02
10
09
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 03 | 14 | 3.5 | 93.0 | Zero-Edit Visits | 0.04 | 0.00 | 44 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 03's Dominant Sequence
— 10 of 14 instances
1
Filter Cases
This analyst exhibits 3 distinct sequences across their 14 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 12 | 1.7 | 90.1 | Swivel Rate | 0.14 | 0.04 | 5 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 01's Dominant Sequence
— 8 of 12 instances
1
Filter Cases
This analyst exhibits 5 distinct sequences across their 12 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 10 | 2.0 | 89.0 | Zero-Edit Visits | 0.12 | 0.00 | 5 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 12's Dominant Sequence
— 8 of 10 instances
1
Filter Cases
This analyst exhibits 3 distinct sequences across their 10 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 7 | 7.0 | 88.9 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 05's Dominant Sequence
— 7 of 7 instances
1
Filter Cases
|
||||||||
| ▸ | Analyst 13 | 45 | 5.6 | 87.6 | Zero-Edit Visits | 0.11 | 0.02 | 34 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 13's Dominant Sequence
— 37 of 45 instances
1
Filter Cases
This analyst exhibits 9 distinct sequences across their 45 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 17 | 3.4 | 86.1 | Zero-Edit Visits | 0.21 | 0.00 | 25 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 02's Dominant Sequence
— 12 of 17 instances
1
Filter Cases
This analyst exhibits 6 distinct sequences across their 17 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 10 | 61 | 6.8 | 84.7 | Zero-Edit Visits | 0.00 | 0.00 | 2 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 10's Dominant Sequence
— 61 of 61 instances
1
Filter Cases
|
||||||||
| ▸ | Analyst 09 | 38 | 4.2 | 79.8 | Zero-Edit Visits | 0.12 | 0.01 | 21 |
|
Reference SoP
1
Filter Cases
2
View Worklist
Analyst 09's Dominant Sequence
— 18 of 38 instances
1
Filter Cases
This analyst exhibits 13 distinct sequences across their 38 instances; the dominant one is shown.
|
||||||||
82.3
Avg Adherence Score (0-100)
Median 82.5 · Range 62.5–90.0
4.3 min
Avg Task Duration
0.0 min median
1.2
Avg Sub-Steps per Instance
13.5% have multiple sub-steps
—
Reference Path Coverage
215 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 215 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Re-Routing
2
View ID
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 233 | 87.6% | 55.1% |
| Veeva Safety | 32 | 12.0% | 44.9% |
| Microsoft Teams | 1 | 0.4% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 11 |
| Phobos | Microsoft Teams | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 736 events 47 cases 23.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Cases"
59×
pyzeClick
<button>
"Proceed"
39×
pyzeClick
<a>
"Cases Coordinator"
28×
pyzeClick
<a>
"Re-Routing Section"
25×
pyzeClick
<button>
"Re-Route"
24×
inputld
<select>
"LIKE · = · !="
23×
pyzeClick
<select>
"Re-route to · agueeroa · agueroma · aladria8 · alfaroh · al"
20×
pyzeClick
<button>
"Advance"
14×
Veeva Safety DOM 472 events 40 cases 4.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
56×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
21×
pyzeClick
<a>
"Show more"
17×
pyzeClick
<li>
"Narrative"
15×
pyzeClick
<span>
"Submissions & Distributions"
13×
pyzeClick
<div>
"Verdict Comment: duplicate for case# 2353378 (ina"
12×
Microsoft Teams App 23 events 7 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
256 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 9 events 4 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
40 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 7 events 5 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
subj
Incorrect article uploaded in CLIC 940845
37×
subj
Untitled
8×
subj
<EVA Lit> Case Number I-0000317670, CSD 2025-11-03, MK-3475, Serious:
2×
subj
PRODUCTION System - AE Alert - CLIC #940845 - PEMBROLIZUMAB;
1×
usc-excel.officeapps.live.com Document 6 events 1 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
Microsoft Word Document 3 events 2 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
gpteal.merck.com DOM 3 events 1 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
TextInputHost App 3 events 2 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.03
Swivel Rate
events in non-primary app / total
0.96
Zero-Edit Visit Rate
inspection-only clicks
1.0
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.96
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.96
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.00
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.03
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2314753 | Analyst 01 | 62.5 | 0.8m | 3 | 0.67 | 0.00 | 1 |
| 2347380 | Analyst 05 | 62.5 | 1.2m | 3 | 0.67 | 0.00 | 1 |
| 2353093 | Analyst 12 | 62.5 | 0.8m | 3 | 0.67 | 0.00 | 1 |
| 2352746 | Analyst 02 | 62.5 | 1.1m | 3 | 0.67 | 0.00 | 1 |
| 2355714 | Analyst 02 | 67.5 | 0.3m | 2 | 0.50 | 0.00 | 1 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
13
01
09
03
02
12
05
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 13 | 66 | 11.0 | 82.9 | Zero-Edit Visits | 0.01 | 0.00 | 10 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 13's Dominant Sequence
— 56 of 66 instances
1
View Re-Routing
This analyst exhibits 6 distinct sequences across their 66 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 47 | 9.4 | 82.6 | Zero-Edit Visits | 0.01 | 0.00 | 60 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 01's Dominant Sequence
— 42 of 47 instances
1
View Re-Routing
This analyst exhibits 4 distinct sequences across their 47 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 28 | 4.7 | 82.5 | Zero-Edit Visits | 0.00 | 0.00 | 8 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 09's Dominant Sequence
— 27 of 28 instances
1
View Re-Routing
This analyst exhibits 2 distinct sequences across their 28 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 24 | 12.0 | 82.2 | Zero-Edit Visits | 0.02 | 0.00 | 4 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 03's Dominant Sequence
— 22 of 24 instances
1
View Re-Routing
This analyst exhibits 3 distinct sequences across their 24 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 30 | 7.5 | 82.0 | Zero-Edit Visits | 0.05 | 0.00 | 7 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 02's Dominant Sequence
— 23 of 30 instances
1
View Re-Routing
This analyst exhibits 6 distinct sequences across their 30 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 11 | 5.5 | 81.4 | Zero-Edit Visits | 0.06 | 0.00 | 1 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 12's Dominant Sequence
— 9 of 11 instances
1
View Re-Routing
This analyst exhibits 3 distinct sequences across their 11 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 9 | 4.5 | 78.6 | Zero-Edit Visits | 0.13 | 0.00 | 828 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 05's Dominant Sequence
— 7 of 9 instances
1
View Re-Routing
This analyst exhibits 3 distinct sequences across their 9 instances; the dominant one is shown.
|
||||||||
82.4
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
5.6 min
Avg Task Duration
0.0 min median
1.3
Avg Sub-Steps per Instance
12.9% have multiple sub-steps
51.4%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
51.4% of instances follow this exactly
Manage Work List — Mined SoP
The most common sub-step sequence observed across 971 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
0.0s
Phobos
57%
Variant Explorer
Reference
Manage Work List Reference Path
499 instances · 7 analysts
This is the mined reference path — 499 instances across 7 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Manage Work List.
Genuine Complexity
Complex-Case Execution Path
186 instances · 7 analysts
· Δ adherence +0.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 82.4 (delta +0.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
161 instances · 8 analysts
· Δ adherence +6.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.3 vs reference 82.4 (delta +6.9). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
11 instances · 4 analysts
· Δ adherence +2.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
View Worklist
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 85.2 vs reference 82.4 (delta +2.8). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
10 instances · 5 analysts
· Δ adherence +6.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 89.2 vs reference 82.4 (delta +6.8). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
6 instances · 4 analysts
· Δ adherence -14.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
5 instances · 2 analysts
· Δ adherence -1.4
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Add New Case
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 81.0 vs reference 82.4 (delta -1.4). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
4 instances · 2 analysts
· Δ adherence -14.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
4 instances · 2 analysts
· Δ adherence +7.6
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Re-Route Case
reference at this step: View Processing Routed
2
View Re-Routing
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 90.0 vs reference 82.4 (delta +7.6). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
4 instances · 1 analysts
· Δ adherence +0.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Worklist
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 82.4 (delta +0.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Microsoft Teams
2 instances · 2 analysts
· Δ adherence -7.4
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
Microsoft Teams
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Microsoft Teams during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
2 instances · 2 analysts
· Δ adherence -14.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 2 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Cross-App Swivel
Cross-App Lookup to collaboration.merck.com
2 instances · 1 analysts
· Δ adherence -14.9
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
collaboration.merck.com
extra step (reference ends at step 1)
Cross-App Swivel — 1 analysts pivot into collaboration.merck.com during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 1,031 | 80.7% | 87.4% |
| Veeva Safety | 187 | 14.6% | 12.6% |
| Microsoft Teams | 15 | 1.2% | 0.0% |
| collaboration.merck.com | 10 | 0.8% | 0.0% |
| Acrobat | 9 | 0.7% | 0.0% |
| Microsoft Excel | 7 | 0.5% | 0.0% |
| Microsoft Word | 5 | 0.4% | 0.0% |
| Microsoft Outlook | 3 | 0.2% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.2% | 0.0% |
| YoudaoDict | 2 | 0.2% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 59 |
| Phobos | Microsoft Teams | 8 |
| Phobos | Microsoft Excel | 5 |
| Veeva Safety | Microsoft Teams | 5 |
| Acrobat | Veeva Safety | 4 |
| Phobos | collaboration.merck.com | 4 |
| Microsoft Teams | Veeva Safety | 4 |
| Microsoft Word | Acrobat | 3 |
| Phobos | Acrobat | 3 |
| Veeva Safety | Acrobat | 3 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,669 events 239 cases 44.4 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
452×
pyzeClick
<a>
"Show more"
165×
pyzeClick
<span>
"Adverse Events"
117×
pyzeClick
<span>
"Narrative"
108×
pyzeClick
<span>
"Submissions & Distributions"
93×
pyzeClick
<li>
"Narrative"
92×
pyzeClick
<span>
"Products"
86×
pyzeClick
<span>
"Case Assessment Results"
83×
Phobos DOM 3,345 events 275 cases 83.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
316×
pyzeClick
<button>
"Send to process"
219×
pyzeClick
<a>
"Cases"
180×
pyzeClick
<button>
"Save Comments"
172×
pyzeClick
<button>
"Advance"
140×
pyzeClick
<a>
"Cases Coordinator"
129×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
127×
pyzeClick
<a>
"Inbox"
103×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 103 events 39 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
1,283 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 96 events 50 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
990 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 90 events 41 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
775 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 71 events 33 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 59 events 34 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
usc-excel.officeapps.live.com Document 34 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 15 events 8 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.05
Swivel Rate
events in non-primary app / total
0.90
Zero-Edit Visit Rate
inspection-only clicks
1.01
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.90
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.90
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.01
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.05
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
10
13
01
02
03
05
12
09
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 10 | 61 | 6.8 | 84.7 | Zero-Edit Visits | 0.00 | 0.00 | 2 |
|
Reference SoP
1
View Processing Routed
Analyst 10's Dominant Sequence
— 61 of 61 instances
1
Filter Cases
· reference: View Processing Routed
|
||||||||
| ▸ | Analyst 13 | 184 | 20.4 | 83.1 | Zero-Edit Visits | 0.06 | 0.01 | 74 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 65 of 184 instances
1
View Processing Routed
This analyst exhibits 24 distinct sequences across their 184 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 173 | 13.3 | 82.7 | Zero-Edit Visits | 0.03 | 0.00 | 74 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 110 of 173 instances
1
View Processing Routed
This analyst exhibits 15 distinct sequences across their 173 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 96 | 13.7 | 82.6 | Zero-Edit Visits | 0.06 | 0.00 | 113 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 47 of 96 instances
1
View Processing Routed
This analyst exhibits 16 distinct sequences across their 96 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 149 | 12.4 | 82.6 | Zero-Edit Visits | 0.03 | 0.00 | 1,852 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 100 of 149 instances
1
View Processing Routed
This analyst exhibits 14 distinct sequences across their 149 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 58 | 5.8 | 82.2 | Zero-Edit Visits | 0.04 | 0.00 | 953 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 40 of 58 instances
1
View Processing Routed
This analyst exhibits 7 distinct sequences across their 58 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 82 | 9.1 | 81.5 | Zero-Edit Visits | 0.08 | 0.00 | 1,089 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 47 of 82 instances
1
View Processing Routed
This analyst exhibits 19 distinct sequences across their 82 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 168 | 11.2 | 80.9 | Zero-Edit Visits | 0.06 | 0.00 | 1,294 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 90 of 168 instances
1
View Processing Routed
This analyst exhibits 26 distinct sequences across their 168 instances; the dominant one is shown.
|
||||||||
81.3
Avg Adherence Score (0-100)
Median 82.5 · Range 57.5–90.0
7.9 min
Avg Task Duration
0.0 min median
1.2
Avg Sub-Steps per Instance
9.6% have multiple sub-steps
—
Reference Path Coverage
552 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 552 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 564 | 82.0% | 4.5% |
| Veeva Safety | 89 | 12.9% | 95.5% |
| Microsoft Teams | 9 | 1.3% | 0.0% |
| Acrobat | 6 | 0.9% | 0.0% |
| collaboration.merck.com | 5 | 0.7% | 0.0% |
| Microsoft Word | 5 | 0.7% | 0.0% |
| Microsoft Excel | 4 | 0.6% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.3% | 0.0% |
| Microsoft Outlook | 2 | 0.3% | 0.0% |
| tools.meddra.org | 1 | 0.1% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 28 |
| Phobos | Microsoft Teams | 5 |
| Phobos | Microsoft Excel | 4 |
| Microsoft Word | Acrobat | 3 |
| Veeva Safety | Microsoft Teams | 3 |
| Veeva Safety | Microsoft Word | 3 |
| Acrobat | Veeva Safety | 3 |
| Microsoft Teams | Veeva Safety | 3 |
| Microsoft Teams | collaboration.merck.com | 2 |
| Phobos | Acrobat | 2 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,233 events 186 cases 42.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
440×
pyzeClick
<a>
"Show more"
131×
pyzeClick
<span>
"Adverse Events"
100×
pyzeClick
<span>
"Narrative"
94×
pyzeClick
<li>
"Narrative"
85×
pyzeClick
<span>
"Submissions & Distributions"
76×
pyzeClick
<li>
"Details"
75×
pyzeClick
<span>
"Products"
72×
Phobos DOM 2,427 events 209 cases 61.0 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
289×
pyzeClick
<button>
"Send to process"
191×
pyzeClick
<button>
"Save Comments"
166×
pyzeClick
<a>
"Cases"
159×
pyzeClick
<button>
"Advance"
135×
pyzeClick
<a>
"Cases Coordinator"
121×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
109×
pyzeClick
<a>
"Inbox"
87×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 81 events 31 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
877 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 78 events 42 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
839 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 63 events 27 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
521 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 55 events 31 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
Microsoft Excel Document 52 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
usc-excel.officeapps.live.com Document 28 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 14 events 7 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.04
Swivel Rate
events in non-primary app / total
0.99
Zero-Edit Visit Rate
inspection-only clicks
1.02
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 1.00
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 1.00
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.02
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.04
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
| 2345982 | Analyst 03 | 57.5 | 20.9m | 6 | 0.83 | 0.00 | 1 |
| 2331410 | Analyst 01 | 58.5 | 2.7m | 5 | 0.80 | 0.00 | 1 |
| 2343258 | Analyst 13 | 59.2 | 4.9m | 9 | 0.67 | 0.00 | 3 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
02
01
05
03
09
13
12
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 02 | 49 | 7.0 | 82.2 | Zero-Edit Visits | 0.01 | 0.00 | 67 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 47 of 49 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 49 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 114 | 8.8 | 81.9 | Zero-Edit Visits | 0.02 | 0.00 | 9 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 110 of 114 instances
1
View Processing Routed
This analyst exhibits 5 distinct sequences across their 114 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 42 | 4.2 | 81.8 | Zero-Edit Visits | 0.02 | 0.00 | 124 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 40 of 42 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 42 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 111 | 9.2 | 81.4 | Zero-Edit Visits | 0.04 | 0.00 | 1,803 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 100 of 111 instances
1
View Processing Routed
This analyst exhibits 8 distinct sequences across their 111 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 102 | 7.3 | 80.9 | Zero-Edit Visits | 0.06 | 0.00 | 1,264 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 90 of 102 instances
1
View Processing Routed
This analyst exhibits 11 distinct sequences across their 102 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 73 | 8.1 | 80.6 | Zero-Edit Visits | 0.06 | 0.00 | 30 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 65 of 73 instances
1
View Processing Routed
This analyst exhibits 9 distinct sequences across their 73 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 61 | 6.8 | 80.4 | Zero-Edit Visits | 0.08 | 0.00 | 1,083 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 47 of 61 instances
1
View Processing Routed
This analyst exhibits 13 distinct sequences across their 61 instances; the dominant one is shown.
|
||||||||
85.8
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
0.7 min
Avg Task Duration
0.0 min median
1.6
Avg Sub-Steps per Instance
21.1% have multiple sub-steps
—
Reference Path Coverage
204 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 204 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Worklist
2
Filter Cases
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 234 | 72.9% | 97.2% |
| Veeva Safety | 64 | 19.9% | 2.8% |
| collaboration.merck.com | 5 | 1.6% | 0.0% |
| Microsoft Teams | 5 | 1.6% | 0.0% |
| Acrobat | 3 | 0.9% | 0.0% |
| Microsoft Excel | 3 | 0.9% | 0.0% |
| YoudaoDict | 2 | 0.6% | 0.0% |
| TextInputHost | 1 | 0.3% | 0.0% |
| Notepad | 1 | 0.3% | 0.0% |
| ONENOTE | 1 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 19 |
| Veeva Safety | Microsoft Teams | 3 |
| YoudaoDict | Veeva Safety | 2 |
| Veeva Safety | Acrobat | 2 |
| Phobos | collaboration.merck.com | 2 |
| Microsoft Excel | Veeva Safety | 2 |
| collaboration.merck.com | Microsoft Excel | 2 |
| Phobos | Microsoft Teams | 2 |
| Veeva Safety | collaboration.merck.com | 1 |
| Veeva Safety | Notepad | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 1,463 events 100 cases 40.7 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
56×
pyzeClick
<a>
"Cases"
53×
pyzeClick
<button>
"Send to process"
38×
inputld
<select>
"LIKE · = · !="
34×
pyzeClick
<a>
"Cases Coordinator"
23×
pyzeClick
<a>
"Rules"
23×
pyzeClick
<a>
"Worklist Open"
23×
pyzeClick
<button>
"Re-Route"
23×
Veeva Safety DOM 765 events 82 cases 3.8 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Show more"
46×
pyzeClick
<span>
"Complete"
34×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
25×
pyzeClick
<span>
"Documents"
23×
pyzeClick
<span>
"Submissions & Distributions"
23×
pyzeClick
<li>
"Narrative"
22×
Microsoft Teams App 34 events 17 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
350 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 31 events 20 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
365 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Acrobat App 27 events 11 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
466 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 25 events 13 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
2329445 (v2.1)
339×
file
Priorización
179×
file
Case tracking list
126×
file
Daily tracking list (PV-SMART) copy
32×
Microsoft Word Document 12 events 4 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 10 events 8 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
gpteal.merck.com DOM 5 events 2 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.08
Swivel Rate
events in non-primary app / total
0.60
Zero-Edit Visit Rate
inspection-only clicks
1.02
Avg Revisit Depth
max repeats of any activity
0.01
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.60
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.60
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.02
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.08
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.01
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2301418 | Analyst 09 | 58.0 | 0.4m | 4 | 0.75 | 0.19 | 1 |
| 2291017 | Analyst 02 | 58.1 | 3.0m | 8 | 0.88 | 0.00 | 1 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
03
01
12
05
13
02
10
09
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 03 | 14 | 3.5 | 93.0 | Zero-Edit Visits | 0.04 | 0.00 | 44 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 03's Dominant Sequence
— 10 of 14 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 3 distinct sequences across their 14 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 12 | 1.7 | 90.1 | Swivel Rate | 0.14 | 0.04 | 5 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 01's Dominant Sequence
— 8 of 12 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 5 distinct sequences across their 12 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 10 | 2.0 | 89.0 | Zero-Edit Visits | 0.12 | 0.00 | 5 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 12's Dominant Sequence
— 8 of 10 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 3 distinct sequences across their 10 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 7 | 7.0 | 88.9 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 05's Dominant Sequence
— 7 of 7 instances
1
Filter Cases
· reference: View Worklist
|
||||||||
| ▸ | Analyst 13 | 45 | 5.6 | 87.6 | Zero-Edit Visits | 0.11 | 0.02 | 34 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 13's Dominant Sequence
— 37 of 45 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 9 distinct sequences across their 45 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 17 | 3.4 | 86.1 | Zero-Edit Visits | 0.21 | 0.00 | 25 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 02's Dominant Sequence
— 12 of 17 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 6 distinct sequences across their 17 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 10 | 61 | 6.8 | 84.7 | Zero-Edit Visits | 0.00 | 0.00 | 2 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 10's Dominant Sequence
— 61 of 61 instances
1
Filter Cases
· reference: View Worklist
|
||||||||
| ▸ | Analyst 09 | 38 | 4.2 | 79.8 | Zero-Edit Visits | 0.12 | 0.01 | 21 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 09's Dominant Sequence
— 18 of 38 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 13 distinct sequences across their 38 instances; the dominant one is shown.
|
||||||||
82.2
Avg Adherence Score (0-100)
Median 82.5 · Range 62.5–90.0
4.3 min
Avg Task Duration
0.0 min median
1.2
Avg Sub-Steps per Instance
13.5% have multiple sub-steps
—
Reference Path Coverage
215 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 215 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Re-Routing
2
View ID
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 233 | 86.9% | 55.1% |
| Veeva Safety | 34 | 12.7% | 44.9% |
| Microsoft Teams | 1 | 0.4% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 12 |
| Phobos | Microsoft Teams | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 736 events 47 cases 23.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Cases"
59×
pyzeClick
<button>
"Proceed"
39×
pyzeClick
<a>
"Cases Coordinator"
28×
pyzeClick
<a>
"Re-Routing Section"
25×
pyzeClick
<button>
"Re-Route"
24×
inputld
<select>
"LIKE · = · !="
23×
pyzeClick
<select>
"Re-route to · agueeroa · agueroma · aladria8 · alfaroh · al"
20×
pyzeClick
<button>
"Advance"
14×
Veeva Safety DOM 472 events 40 cases 4.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
56×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
21×
pyzeClick
<a>
"Show more"
17×
pyzeClick
<li>
"Narrative"
15×
pyzeClick
<span>
"Submissions & Distributions"
13×
pyzeClick
<div>
"Verdict Comment: duplicate for case# 2353378 (ina"
12×
Microsoft Teams App 23 events 7 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
256 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 9 events 4 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
40 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 7 events 5 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
subj
Incorrect article uploaded in CLIC 940845
37×
subj
Untitled
8×
subj
<EVA Lit> Case Number I-0000317670, CSD 2025-11-03, MK-3475, Serious:
2×
subj
PRODUCTION System - AE Alert - CLIC #940845 - PEMBROLIZUMAB;
1×
usc-excel.officeapps.live.com Document 6 events 1 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
TextInputHost App 3 events 2 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
gpteal.merck.com DOM 3 events 1 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Microsoft Word Document 3 events 2 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.03
Swivel Rate
events in non-primary app / total
0.96
Zero-Edit Visit Rate
inspection-only clicks
1.0
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.96
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.96
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.00
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.03
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2347380 | Analyst 05 | 62.5 | 1.2m | 3 | 0.67 | 0.00 | 1 |
| 2353093 | Analyst 12 | 62.5 | 0.8m | 3 | 0.67 | 0.00 | 1 |
| 2314753 | Analyst 01 | 62.5 | 0.8m | 3 | 0.67 | 0.00 | 1 |
| 2352746 | Analyst 02 | 62.5 | 1.1m | 3 | 0.67 | 0.00 | 1 |
| 2333518 | Analyst 05 | 67.5 | 824.2m | 2 | 0.50 | 0.00 | 1 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
13
01
09
03
02
12
05
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 13 | 66 | 11.0 | 82.9 | Zero-Edit Visits | 0.01 | 0.00 | 10 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 13's Dominant Sequence
— 56 of 66 instances
1
View Re-Routing
This analyst exhibits 6 distinct sequences across their 66 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 47 | 9.4 | 82.6 | Zero-Edit Visits | 0.01 | 0.00 | 60 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 01's Dominant Sequence
— 42 of 47 instances
1
View Re-Routing
This analyst exhibits 5 distinct sequences across their 47 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 28 | 4.7 | 82.5 | Zero-Edit Visits | 0.00 | 0.00 | 8 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 09's Dominant Sequence
— 27 of 28 instances
1
View Re-Routing
This analyst exhibits 2 distinct sequences across their 28 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 24 | 12.0 | 82.2 | Zero-Edit Visits | 0.02 | 0.00 | 4 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 03's Dominant Sequence
— 22 of 24 instances
1
View Re-Routing
This analyst exhibits 3 distinct sequences across their 24 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 30 | 7.5 | 81.4 | Zero-Edit Visits | 0.07 | 0.00 | 21 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 02's Dominant Sequence
— 23 of 30 instances
1
View Re-Routing
This analyst exhibits 7 distinct sequences across their 30 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 11 | 5.5 | 81.4 | Zero-Edit Visits | 0.06 | 0.00 | 1 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 12's Dominant Sequence
— 9 of 11 instances
1
View Re-Routing
This analyst exhibits 3 distinct sequences across their 11 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 9 | 4.5 | 78.6 | Zero-Edit Visits | 0.13 | 0.00 | 828 |
|
Reference SoP
1
View Re-Routing
2
View ID
Analyst 05's Dominant Sequence
— 7 of 9 instances
1
View Re-Routing
This analyst exhibits 3 distinct sequences across their 9 instances; the dominant one is shown.
|
||||||||
81.0
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
9.9 min
Avg Task Duration
0.0 min median
1.8
Avg Sub-Steps per Instance
35.1% have multiple sub-steps
58.7%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
58.7% of instances follow this exactly
Manage Work List — Mined SoP
The most common sub-step sequence observed across 687 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
0.0s
Phobos
67%
Variant Explorer
Reference
Manage Work List Reference Path
403 instances · 7 analysts
This is the mined reference path — 403 instances across 7 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Manage Work List.
Genuine Complexity
Complex-Case Execution Path
74 instances · 6 analysts
· Δ adherence +4.7
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 85.7 vs reference 81.0 (delta +4.7). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
42 instances · 6 analysts
· Δ adherence +1.3
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.3 vs reference 81.0 (delta +1.3). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Reviewer Sweep
Reviewer Verification Pass
14 instances · 5 analysts
· Δ adherence +1.7
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
3
View Re-Routing
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.3 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 5 analysts across 14 cases.
Genuine Complexity
Complex-Case Execution Path
14 instances · 3 analysts
· Δ adherence +3.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
View Worklist
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.9 vs reference 81.0 (delta +3.0). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
7 instances · 5 analysts
· Δ adherence +2.6
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.6 vs reference 81.0 (delta +2.6). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
6 instances · 4 analysts
· Δ adherence -13.5
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Genuine Complexity
Complex-Case Execution Path
5 instances · 2 analysts
· Δ adherence +0.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Add New Case
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 81.0 vs reference 81.0 (delta +0.0). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
4 instances · 3 analysts
· Δ adherence +9.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
View Re-Routing
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 90.0 vs reference 81.0 (delta +9.0). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Reviewer Sweep
Reviewer Verification Pass
4 instances · 3 analysts
· Δ adherence +4.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
3
View Re-Routing
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.0 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 3 analysts across 4 cases.
Reviewer Sweep
Reviewer Verification Pass
4 instances · 4 analysts
· Δ adherence +4.0
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.0 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 4 analysts across 4 cases.
Reviewer Sweep
Reviewer Verification Pass
4 instances · 2 analysts
· Δ adherence +4.7
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
3
View Processing Routed
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.5 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 2 analysts across 4 cases.
Backfill Loop
Mid-Flow Data Backfill
3 instances · 3 analysts
· Δ adherence -0.6
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
3
View Re-Routing
extra step (reference ends at step 1)
4
Filter Cases
extra step (reference ends at step 1)
5
View Re-Routing
extra step (reference ends at step 1)
Backfill Loop — instances revisit the same activity 3.7× on average, consistent with mid-flow detours to fetch or enter data that wasn't ready at the right step. Ordering issue, not an app issue — 3 analysts across 3 cases. Suggests upstream data readiness or UX sequencing is at fault.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 1,031 | 81.4% | 87.4% |
| Veeva Safety | 179 | 14.1% | 12.6% |
| Microsoft Teams | 14 | 1.1% | 0.0% |
| collaboration.merck.com | 9 | 0.7% | 0.0% |
| Acrobat | 9 | 0.7% | 0.0% |
| Microsoft Excel | 7 | 0.6% | 0.0% |
| Microsoft Word | 4 | 0.3% | 0.0% |
| Microsoft Outlook | 3 | 0.2% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.2% | 0.0% |
| YoudaoDict | 2 | 0.2% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 56 |
| Phobos | Microsoft Teams | 8 |
| Phobos | Microsoft Excel | 5 |
| Microsoft Teams | Veeva Safety | 4 |
| Acrobat | Veeva Safety | 4 |
| Veeva Safety | Microsoft Teams | 4 |
| Veeva Safety | Phobos | 4 |
| Phobos | collaboration.merck.com | 4 |
| Veeva Safety | Acrobat | 3 |
| Veeva Safety | Microsoft Word | 3 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,669 events 239 cases 44.4 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
452×
pyzeClick
<a>
"Show more"
165×
pyzeClick
<span>
"Adverse Events"
117×
pyzeClick
<span>
"Narrative"
108×
pyzeClick
<span>
"Submissions & Distributions"
93×
pyzeClick
<li>
"Narrative"
92×
pyzeClick
<span>
"Products"
86×
pyzeClick
<span>
"Case Assessment Results"
83×
Phobos DOM 3,345 events 275 cases 83.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
316×
pyzeClick
<button>
"Send to process"
219×
pyzeClick
<a>
"Cases"
180×
pyzeClick
<button>
"Save Comments"
172×
pyzeClick
<button>
"Advance"
140×
pyzeClick
<a>
"Cases Coordinator"
129×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
127×
pyzeClick
<a>
"Inbox"
103×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 103 events 39 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
1,283 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 96 events 50 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
990 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 90 events 41 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
775 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 71 events 33 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 59 events 34 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
usc-excel.officeapps.live.com Document 34 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 15 events 8 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.06
Swivel Rate
events in non-primary app / total
0.93
Zero-Edit Visit Rate
inspection-only clicks
1.28
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.93
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.93
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.28
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.06
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2281696 | Analyst 02 | 54.4 | 4.8m | 13 | 0.77 | 0.28 | 2 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
10
05
03
01
02
09
12
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 10 | 58 | 6.4 | 84.6 | Zero-Edit Visits | 0.00 | 0.00 | 4 |
|
Reference SoP
1
View Processing Routed
Analyst 10's Dominant Sequence
— 58 of 58 instances
1
Filter Cases
· reference: View Processing Routed
|
||||||||
| ▸ | Analyst 05 | 46 | 4.6 | 81.6 | Zero-Edit Visits | 0.03 | 0.00 | 952 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 37 of 46 instances
1
View Processing Routed
This analyst exhibits 8 distinct sequences across their 46 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 118 | 9.8 | 81.6 | Zero-Edit Visits | 0.04 | 0.00 | 1,831 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 91 of 118 instances
1
View Processing Routed
This analyst exhibits 18 distinct sequences across their 118 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 129 | 9.9 | 81.6 | Zero-Edit Visits | 0.03 | 0.00 | 77 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 89 of 129 instances
1
View Processing Routed
This analyst exhibits 22 distinct sequences across their 129 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 62 | 8.9 | 80.7 | Zero-Edit Visits | 0.08 | 0.00 | 99 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 38 of 62 instances
1
View Processing Routed
This analyst exhibits 18 distinct sequences across their 62 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 132 | 8.8 | 80.2 | Zero-Edit Visits | 0.07 | 0.00 | 2,716 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 78 of 132 instances
1
View Processing Routed
This analyst exhibits 26 distinct sequences across their 132 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 59 | 6.6 | 79.8 | Zero-Edit Visits | 0.11 | 0.00 | 1,090 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 34 of 59 instances
1
View Processing Routed
This analyst exhibits 22 distinct sequences across their 59 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 83 | 9.2 | 78.5 | Zero-Edit Visits | 0.11 | 0.01 | 71 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 36 of 83 instances
1
View Processing Routed
This analyst exhibits 34 distinct sequences across their 83 instances; the dominant one is shown.
|
||||||||
80.9
Avg Adherence Score (0-100)
Median 82.5 · Range 57.5–85.0
12.6 min
Avg Task Duration
0.0 min median
1.5
Avg Sub-Steps per Instance
26.7% have multiple sub-steps
—
Reference Path Coverage
460 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 460 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 563 | 83.2% | 30.5% |
| Veeva Safety | 83 | 12.3% | 69.5% |
| Microsoft Teams | 8 | 1.2% | 0.0% |
| Acrobat | 6 | 0.9% | 0.0% |
| collaboration.merck.com | 4 | 0.6% | 0.0% |
| Microsoft Word | 4 | 0.6% | 0.0% |
| Microsoft Excel | 3 | 0.4% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.3% | 0.0% |
| Microsoft Outlook | 2 | 0.3% | 0.0% |
| tools.meddra.org | 1 | 0.1% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 26 |
| Phobos | Microsoft Teams | 5 |
| Phobos | Microsoft Excel | 3 |
| Microsoft Word | Acrobat | 3 |
| Veeva Safety | Microsoft Word | 3 |
| Acrobat | Veeva Safety | 3 |
| Microsoft Teams | Veeva Safety | 3 |
| Veeva Safety | Microsoft Teams | 2 |
| Phobos | collaboration.merck.com | 2 |
| Phobos | Acrobat | 2 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,233 events 186 cases 42.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
440×
pyzeClick
<a>
"Show more"
131×
pyzeClick
<span>
"Adverse Events"
100×
pyzeClick
<span>
"Narrative"
94×
pyzeClick
<li>
"Narrative"
85×
pyzeClick
<span>
"Submissions & Distributions"
76×
pyzeClick
<li>
"Details"
75×
pyzeClick
<span>
"Products"
72×
Phobos DOM 2,427 events 209 cases 61.0 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
289×
pyzeClick
<button>
"Send to process"
191×
pyzeClick
<button>
"Save Comments"
166×
pyzeClick
<a>
"Cases"
159×
pyzeClick
<button>
"Advance"
135×
pyzeClick
<a>
"Cases Coordinator"
121×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
109×
pyzeClick
<a>
"Inbox"
87×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 81 events 31 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
877 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 78 events 42 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
839 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 63 events 27 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
521 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 55 events 31 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
Microsoft Excel Document 52 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
usc-excel.officeapps.live.com Document 28 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 14 events 7 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.04
Swivel Rate
events in non-primary app / total
0.99
Zero-Edit Visit Rate
inspection-only clicks
1.19
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.99
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.99
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.19
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.04
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
| 2331410 | Analyst 01 | 58.5 | 2.7m | 5 | 0.80 | 0.00 | 1 |
| 2343258 | Analyst 13 | 59.2 | 4.9m | 9 | 0.67 | 0.00 | 3 |
| 2317182 | Analyst 09 | 60.0 | 3.5m | 4 | 0.75 | 0.00 | 1 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
05
02
01
03
09
13
12
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 05 | 39 | 3.9 | 81.8 | Zero-Edit Visits | 0.02 | 0.00 | 126 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 37 of 39 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 39 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 40 | 5.7 | 81.6 | Zero-Edit Visits | 0.01 | 0.00 | 67 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 38 of 40 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 40 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 95 | 7.3 | 81.3 | Zero-Edit Visits | 0.02 | 0.00 | 11 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 89 of 95 instances
1
View Processing Routed
This analyst exhibits 7 distinct sequences across their 95 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 101 | 9.2 | 81.3 | Zero-Edit Visits | 0.03 | 0.00 | 1,783 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 91 of 101 instances
1
View Processing Routed
This analyst exhibits 7 distinct sequences across their 101 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 89 | 6.4 | 80.5 | Zero-Edit Visits | 0.06 | 0.00 | 2,687 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 78 of 89 instances
1
View Processing Routed
This analyst exhibits 10 distinct sequences across their 89 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 47 | 5.2 | 79.7 | Zero-Edit Visits | 0.07 | 0.00 | 20 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 36 of 47 instances
1
View Processing Routed
This analyst exhibits 10 distinct sequences across their 47 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 49 | 5.4 | 79.2 | Zero-Edit Visits | 0.10 | 0.00 | 1,084 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 34 of 49 instances
1
View Processing Routed
This analyst exhibits 14 distinct sequences across their 49 instances; the dominant one is shown.
|
||||||||
81.5
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
1.0 min
Avg Task Duration
0.0 min median
2.2
Avg Sub-Steps per Instance
41.7% have multiple sub-steps
—
Reference Path Coverage
120 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 120 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Worklist
2
Filter Cases
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 180 | 69.0% | 94.5% |
| Veeva Safety | 59 | 22.6% | 5.5% |
| collaboration.merck.com | 5 | 1.9% | 0.0% |
| Microsoft Excel | 4 | 1.5% | 0.0% |
| Microsoft Teams | 4 | 1.5% | 0.0% |
| Acrobat | 3 | 1.1% | 0.0% |
| YoudaoDict | 2 | 0.8% | 0.0% |
| Notepad | 1 | 0.4% | 0.0% |
| ONENOTE | 1 | 0.4% | 0.0% |
| gpteal.merck.com | 1 | 0.4% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 17 |
| Veeva Safety | Acrobat | 2 |
| Phobos | Microsoft Teams | 2 |
| YoudaoDict | Veeva Safety | 2 |
| Phobos | Microsoft Excel | 2 |
| Phobos | collaboration.merck.com | 2 |
| collaboration.merck.com | Microsoft Excel | 2 |
| Microsoft Excel | collaboration.merck.com | 1 |
| Veeva Safety | collaboration.merck.com | 1 |
| Veeva Safety | ONENOTE | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 1,463 events 100 cases 40.7 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
56×
pyzeClick
<a>
"Cases"
53×
pyzeClick
<button>
"Send to process"
38×
inputld
<select>
"LIKE · = · !="
34×
pyzeClick
<a>
"Cases Coordinator"
23×
pyzeClick
<a>
"Rules"
23×
pyzeClick
<a>
"Worklist Open"
23×
pyzeClick
<button>
"Re-Route"
23×
Veeva Safety DOM 765 events 82 cases 3.8 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Show more"
46×
pyzeClick
<span>
"Complete"
34×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
25×
pyzeClick
<span>
"Documents"
23×
pyzeClick
<span>
"Submissions & Distributions"
23×
pyzeClick
<li>
"Narrative"
22×
Microsoft Teams App 34 events 17 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
350 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 31 events 20 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
365 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Acrobat App 27 events 11 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
466 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 25 events 13 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
2329445 (v2.1)
339×
file
Priorización
179×
file
Case tracking list
126×
file
Daily tracking list (PV-SMART) copy
32×
Microsoft Word Document 12 events 4 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 10 events 8 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
gpteal.merck.com DOM 5 events 2 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.12
Swivel Rate
events in non-primary app / total
0.77
Zero-Edit Visit Rate
inspection-only clicks
1.13
Avg Revisit Depth
max repeats of any activity
0.02
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.77
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.77
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Swivel Rate
value 0.12
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Revisit Depth
value 1.13
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Cross-App Time Share
value 0.02
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2301418 | Analyst 09 | 58.0 | 0.4m | 4 | 0.75 | 0.19 | 1 |
| 2291017 | Analyst 02 | 58.1 | 3.0m | 8 | 0.88 | 0.00 | 1 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
05
12
03
10
01
02
09
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 05 | 3 | 3.0 | 87.5 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 05's Dominant Sequence
— 3 of 3 instances
1
Filter Cases
· reference: View Worklist
|
||||||||
| ▸ | Analyst 12 | 6 | 1.5 | 84.8 | Zero-Edit Visits | 0.11 | 0.00 | 4 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 12's Dominant Sequence
— 2 of 6 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 4 distinct sequences across their 6 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 4 | 1.3 | 84.7 | Zero-Edit Visits | 0.12 | 0.00 | 44 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 03's Dominant Sequence
— 2 of 4 instances
1
Filter Cases
· reference: View Worklist
2
View Worklist
· reference: Filter Cases
This analyst exhibits 3 distinct sequences across their 4 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 10 | 58 | 6.4 | 84.6 | Zero-Edit Visits | 0.00 | 0.00 | 4 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 10's Dominant Sequence
— 58 of 58 instances
1
Filter Cases
· reference: View Worklist
|
||||||||
| ▸ | Analyst 01 | 7 | 1.2 | 83.8 | Zero-Edit Visits | 0.24 | 0.06 | 3 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 01's Dominant Sequence
— 2 of 7 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 6 distinct sequences across their 7 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 6 | 1.5 | 79.9 | Zero-Edit Visits | 0.36 | 0.00 | 14 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 02's Dominant Sequence
— 2 of 6 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 5 distinct sequences across their 6 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 27 | 3.9 | 78.8 | Zero-Edit Visits | 0.15 | 0.02 | 21 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 09's Dominant Sequence
— 7 of 27 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 13 distinct sequences across their 27 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 9 | 2.2 | 63.7 | Zero-Edit Visits | 0.58 | 0.11 | 32 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 13's Dominant Sequence
— 1 of 9 instances
1
View Processing Routed
· reference: View Worklist
2
Filter Cases
3
View Processing Routed
4
Filter Cases
5
View Re-Routing
6
Filter Cases
7
View Re-Routing
8
Filter Cases
9
View Re-Routing
10
Filter Cases
11
View Re-Routing
This analyst exhibits 9 distinct sequences across their 9 instances; the dominant one is shown.
|
||||||||
80.9
Avg Adherence Score (0-100)
Median 82.5 · Range 54.4–90.0
8.8 min
Avg Task Duration
0.1 min median
3.1
Avg Sub-Steps per Instance
63.6% have multiple sub-steps
—
Reference Path Coverage
107 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 107 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 288 | 87.8% | 92.4% |
| Veeva Safety | 37 | 11.3% | 7.6% |
| Microsoft Teams | 2 | 0.6% | 0.0% |
| Microsoft Outlook | 1 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 13 |
| Veeva Safety | Phobos | 3 |
| Microsoft Outlook | Veeva Safety | 1 |
| Phobos | Microsoft Outlook | 1 |
| Veeva Safety | Microsoft Teams | 1 |
| Phobos | Microsoft Teams | 1 |
| Microsoft Teams | Phobos | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 736 events 47 cases 23.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Cases"
59×
pyzeClick
<button>
"Proceed"
39×
pyzeClick
<a>
"Cases Coordinator"
28×
pyzeClick
<a>
"Re-Routing Section"
25×
pyzeClick
<button>
"Re-Route"
24×
inputld
<select>
"LIKE · = · !="
23×
pyzeClick
<select>
"Re-route to · agueeroa · agueroma · aladria8 · alfaroh · al"
20×
pyzeClick
<button>
"Advance"
14×
Veeva Safety DOM 472 events 40 cases 4.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
56×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
21×
pyzeClick
<a>
"Show more"
17×
pyzeClick
<li>
"Narrative"
15×
pyzeClick
<span>
"Submissions & Distributions"
13×
pyzeClick
<div>
"Verdict Comment: duplicate for case# 2353378 (ina"
12×
Microsoft Teams App 23 events 7 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
256 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 9 events 4 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
40 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 7 events 5 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
subj
Incorrect article uploaded in CLIC 940845
37×
subj
Untitled
8×
subj
<EVA Lit> Case Number I-0000317670, CSD 2025-11-03, MK-3475, Serious:
2×
subj
PRODUCTION System - AE Alert - CLIC #940845 - PEMBROLIZUMAB;
1×
usc-excel.officeapps.live.com Document 6 events 1 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
TextInputHost App 3 events 2 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
gpteal.merck.com DOM 3 events 1 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Microsoft Word Document 3 events 2 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.05
Swivel Rate
events in non-primary app / total
0.86
Zero-Edit Visit Rate
inspection-only clicks
1.82
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.86
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.86
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.82
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.05
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2281696 | Analyst 02 | 54.4 | 4.8m | 13 | 0.77 | 0.28 | 2 |
| 2347380 | Analyst 05 | 62.5 | 1.2m | 3 | 0.67 | 0.00 | 1 |
| 2314753 | Analyst 01 | 65.5 | 1.6m | 5 | 0.40 | 0.00 | 3 |
| 2327846 | Analyst 13 | 66.3 | 1.2m | 16 | 0.00 | 0.00 | 9 |
| 2082933 | Analyst 02 | 66.9 | 8.0m | 8 | 0.50 | 0.00 | 2 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
03
01
13
09
02
12
05
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 03 | 13 | 6.5 | 82.7 | Zero-Edit Visits | 0.04 | 0.00 | 4 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 03's Dominant Sequence
— 4 of 13 instances
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
This analyst exhibits 8 distinct sequences across their 13 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 27 | 5.4 | 81.8 | Zero-Edit Visits | 0.01 | 0.00 | 63 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 01's Dominant Sequence
— 18 of 27 instances
1
View Re-Routing
This analyst exhibits 9 distinct sequences across their 27 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 27 | 4.5 | 81.3 | Zero-Edit Visits | 0.02 | 0.01 | 19 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 13's Dominant Sequence
— 8 of 27 instances
1
View Re-Routing
This analyst exhibits 16 distinct sequences across their 27 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 16 | 2.7 | 80.9 | Zero-Edit Visits | 0.00 | 0.00 | 8 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 09's Dominant Sequence
— 9 of 16 instances
1
View Re-Routing
This analyst exhibits 4 distinct sequences across their 16 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 16 | 4.0 | 78.9 | Zero-Edit Visits | 0.14 | 0.02 | 18 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 02's Dominant Sequence
— 4 of 16 instances
1
View Re-Routing
This analyst exhibits 10 distinct sequences across their 16 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 4 | 2.0 | 78.8 | Zero-Edit Visits | 0.21 | 0.00 | 2 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 12's Dominant Sequence
— 1 of 4 instances
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
This analyst exhibits 4 distinct sequences across their 4 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 05 | 4 | 2.0 | 75.6 | Zero-Edit Visits | 0.21 | 0.00 | 826 |
|
Reference SoP
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
4
Filter Cases
5
View ID
Analyst 05's Dominant Sequence
— 1 of 4 instances
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
This analyst exhibits 4 distinct sequences across their 4 instances; the dominant one is shown.
|
||||||||
80.9
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
8.0 min
Avg Task Duration
0.0 min median
1.9
Avg Sub-Steps per Instance
35.3% have multiple sub-steps
58.4%
Reference Path Coverage
Instances following mined SoP exactly
Reference Baseline
58.4% of instances follow this exactly
Manage Work List — Mined SoP
The most common sub-step sequence observed across 685 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
0.0s
Phobos
67%
Variant Explorer
Reference
Manage Work List Reference Path
400 instances · 7 analysts
This is the mined reference path — 400 instances across 7 analysts follow this exact sequence. Treat as the proposed Standard Operating Procedure for Manage Work List.
Genuine Complexity
Complex-Case Execution Path
74 instances · 6 analysts
· Δ adherence +4.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 85.7 vs reference 80.9 (delta +4.8). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
42 instances · 6 analysts
· Δ adherence +1.5
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 82.3 vs reference 80.9 (delta +1.5). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Reviewer Sweep
Reviewer Verification Pass
14 instances · 5 analysts
· Δ adherence +1.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
3
View Re-Routing
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.3 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 5 analysts across 14 cases.
Genuine Complexity
Complex-Case Execution Path
14 instances · 3 analysts
· Δ adherence +3.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
View Worklist
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.9 vs reference 80.9 (delta +3.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
7 instances · 5 analysts
· Δ adherence +2.7
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 83.6 vs reference 80.9 (delta +2.7). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Cross-App Swivel
Cross-App Lookup to Veeva Safety
6 instances · 4 analysts
· Δ adherence -13.4
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Navigate to Cases
extra step (reference ends at step 1)
Cross-App Swivel — 4 analysts pivot into Veeva Safety during this task (0% of task time in non-primary apps, 50% of events outside the primary system). Pattern is consistent with data lookup or cross-referencing rather than a system defect — lighter-weight than a Silent Workaround.
Linked Remediation Opportunities
Genuine Complexity
Complex-Case Execution Path
5 instances · 2 analysts
· Δ adherence +0.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Processing Routed
2
Add New Case
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 81.0 vs reference 80.9 (delta +0.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Genuine Complexity
Complex-Case Execution Path
4 instances · 3 analysts
· Δ adherence +9.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
Filter Cases
reference at this step: View Processing Routed
2
View Re-Routing
extra step (reference ends at step 1)
Genuine Complexity — this sequence differs from the reference path (2 sub-steps vs 1 in reference) but shows no strong pain-point signals: adherence score 90.0 vs reference 80.9 (delta +9.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.
Reviewer Sweep
Reviewer Verification Pass
4 instances · 3 analysts
· Δ adherence +4.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
3
View Re-Routing
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.0 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 3 analysts across 4 cases.
Reviewer Sweep
Reviewer Verification Pass
4 instances · 2 analysts
· Δ adherence +4.8
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
3
View Processing Routed
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.5 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 2 analysts across 4 cases.
Backfill Loop
Mid-Flow Data Backfill
3 instances · 3 analysts
· Δ adherence -0.5
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Filter Cases
extra step (reference ends at step 1)
3
View Re-Routing
extra step (reference ends at step 1)
4
Filter Cases
extra step (reference ends at step 1)
5
View Re-Routing
extra step (reference ends at step 1)
Backfill Loop — instances revisit the same activity 3.7× on average, consistent with mid-flow detours to fetch or enter data that wasn't ready at the right step. Ordering issue, not an app issue — 3 analysts across 3 cases. Suggests upstream data readiness or UX sequencing is at fault.
Reviewer Sweep
Reviewer Verification Pass
3 instances · 3 analysts
· Δ adherence +4.1
Observed Sequence (vs Reference SoP on left)
differs from reference
matches reference
1
View Re-Routing
reference at this step: View Processing Routed
2
Re-Route Case
extra step (reference ends at step 1)
Reviewer Sweep — a viewing-heavy pattern with 3.0 avg sub-steps and zero edits captured. Consistent with a downstream reviewer re-opening the task to verify earlier work rather than progressing new data. 3 analysts across 3 cases.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 1,031 | 80.7% | 87.4% |
| Veeva Safety | 187 | 14.6% | 12.6% |
| Microsoft Teams | 15 | 1.2% | 0.0% |
| collaboration.merck.com | 10 | 0.8% | 0.0% |
| Acrobat | 9 | 0.7% | 0.0% |
| Microsoft Excel | 7 | 0.5% | 0.0% |
| Microsoft Word | 5 | 0.4% | 0.0% |
| Microsoft Outlook | 3 | 0.2% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.2% | 0.0% |
| YoudaoDict | 2 | 0.2% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 59 |
| Phobos | Microsoft Teams | 8 |
| Veeva Safety | Microsoft Teams | 6 |
| Veeva Safety | Phobos | 5 |
| Phobos | Microsoft Excel | 5 |
| Phobos | collaboration.merck.com | 4 |
| Acrobat | Veeva Safety | 4 |
| Microsoft Teams | Veeva Safety | 4 |
| Veeva Safety | Microsoft Word | 3 |
| Veeva Safety | Acrobat | 3 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,669 events 239 cases 44.4 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
452×
pyzeClick
<a>
"Show more"
165×
pyzeClick
<span>
"Adverse Events"
117×
pyzeClick
<span>
"Narrative"
108×
pyzeClick
<span>
"Submissions & Distributions"
93×
pyzeClick
<li>
"Narrative"
92×
pyzeClick
<span>
"Products"
86×
pyzeClick
<span>
"Case Assessment Results"
83×
Phobos DOM 3,345 events 275 cases 83.3 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
316×
pyzeClick
<button>
"Send to process"
219×
pyzeClick
<a>
"Cases"
180×
pyzeClick
<button>
"Save Comments"
172×
pyzeClick
<button>
"Advance"
140×
pyzeClick
<a>
"Cases Coordinator"
129×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
127×
pyzeClick
<a>
"Inbox"
103×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 103 events 39 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
1,283 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 96 events 50 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
990 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 90 events 41 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
775 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 71 events 33 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 59 events 34 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
usc-excel.officeapps.live.com Document 34 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 15 events 8 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.06
Swivel Rate
events in non-primary app / total
0.93
Zero-Edit Visit Rate
inspection-only clicks
1.28
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.93
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.93
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.28
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.06
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2281696 | Analyst 02 | 54.4 | 4.8m | 13 | 0.77 | 0.28 | 2 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
10
05
01
03
02
09
12
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 10 | 58 | 6.4 | 84.6 | Zero-Edit Visits | 0.00 | 0.00 | 4 |
|
Reference SoP
1
View Processing Routed
Analyst 10's Dominant Sequence
— 58 of 58 instances
1
Filter Cases
· reference: View Processing Routed
|
||||||||
| ▸ | Analyst 05 | 46 | 4.6 | 81.6 | Zero-Edit Visits | 0.03 | 0.00 | 952 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 37 of 46 instances
1
View Processing Routed
This analyst exhibits 8 distinct sequences across their 46 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 129 | 9.9 | 81.6 | Zero-Edit Visits | 0.03 | 0.00 | 77 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 89 of 129 instances
1
View Processing Routed
This analyst exhibits 22 distinct sequences across their 129 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 118 | 9.8 | 81.4 | Zero-Edit Visits | 0.04 | 0.00 | 1,852 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 90 of 118 instances
1
View Processing Routed
This analyst exhibits 19 distinct sequences across their 118 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 61 | 8.7 | 80.6 | Zero-Edit Visits | 0.08 | 0.01 | 108 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 38 of 61 instances
1
View Processing Routed
This analyst exhibits 17 distinct sequences across their 61 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 132 | 8.8 | 80.1 | Zero-Edit Visits | 0.07 | 0.00 | 1,294 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 77 of 132 instances
1
View Processing Routed
This analyst exhibits 27 distinct sequences across their 132 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 59 | 6.6 | 79.8 | Zero-Edit Visits | 0.11 | 0.00 | 1,090 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 34 of 59 instances
1
View Processing Routed
This analyst exhibits 22 distinct sequences across their 59 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 82 | 9.1 | 78.2 | Zero-Edit Visits | 0.12 | 0.01 | 78 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 35 of 82 instances
1
View Processing Routed
This analyst exhibits 36 distinct sequences across their 82 instances; the dominant one is shown.
|
||||||||
80.7
Avg Adherence Score (0-100)
Median 82.5 · Range 57.5–85.0
9.5 min
Avg Task Duration
0.0 min median
1.5
Avg Sub-Steps per Instance
27.4% have multiple sub-steps
—
Reference Path Coverage
460 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 460 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Processing Routed
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 570 | 82.3% | 47.0% |
| Veeva Safety | 89 | 12.8% | 53.0% |
| Microsoft Teams | 9 | 1.3% | 0.0% |
| Acrobat | 6 | 0.9% | 0.0% |
| collaboration.merck.com | 5 | 0.7% | 0.0% |
| Microsoft Word | 5 | 0.7% | 0.0% |
| Microsoft Excel | 3 | 0.4% | 0.0% |
| usc-excel.officeapps.live.com | 2 | 0.3% | 0.0% |
| Microsoft Outlook | 2 | 0.3% | 0.0% |
| Quality Vault | 1 | 0.1% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 28 |
| Phobos | Microsoft Teams | 5 |
| Microsoft Word | Acrobat | 3 |
| Phobos | Microsoft Excel | 3 |
| Veeva Safety | Microsoft Word | 3 |
| Veeva Safety | Microsoft Teams | 3 |
| Acrobat | Veeva Safety | 3 |
| Microsoft Teams | Veeva Safety | 3 |
| Microsoft Teams | collaboration.merck.com | 2 |
| Phobos | Acrobat | 2 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Veeva Safety DOM 3,233 events 186 cases 42.1 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
440×
pyzeClick
<a>
"Show more"
131×
pyzeClick
<span>
"Adverse Events"
100×
pyzeClick
<span>
"Narrative"
94×
pyzeClick
<li>
"Narrative"
85×
pyzeClick
<span>
"Submissions & Distributions"
76×
pyzeClick
<li>
"Details"
75×
pyzeClick
<span>
"Products"
72×
Phobos DOM 2,427 events 209 cases 61.0 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
289×
pyzeClick
<button>
"Send to process"
191×
pyzeClick
<button>
"Save Comments"
166×
pyzeClick
<a>
"Cases"
159×
pyzeClick
<button>
"Advance"
135×
pyzeClick
<a>
"Cases Coordinator"
121×
pyzeClick
<select>
"Select Attachment description · Legal · Legal Pre-Sca"
109×
pyzeClick
<a>
"Inbox"
87×
Microsoft Word Document 106 events 43 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
V-Narrative 533164 (English) (26378749_0_4_342805)
659×
file
V-Narrative 545329 (English) (26567747_0_2_344796)
215×
file
V-Narrative 538518 (English) (26459147_0_1_344628)
198×
file
V-Narrative 525091 (English) (26253532_0_1_338428)
197×
file
V-Narrative 542172 (English) (26517690_0_2_345412)
181×
file
V-Narrative 542162 (English) (26518018_0_1_342783)
168×
file
V-Narrative 530716 (English) (26341165_0_2_337732)
159×
file
V-Narrative 524911 (English) (26250161_0_1_339456)
142×
Acrobat App 81 events 31 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
877 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Teams App 78 events 42 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
839 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 63 events 27 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
521 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 55 events 31 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
Microsoft Excel Document 52 events 24 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
usc-excel.officeapps.live.com Document 28 events 6 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
ONENOTE Document 14 events 7 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.05
Swivel Rate
events in non-primary app / total
0.99
Zero-Edit Visit Rate
inspection-only clicks
1.19
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.99
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.99
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.19
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.05
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2345982 | Analyst 03 | 57.5 | 20.9m | 6 | 0.83 | 0.00 | 1 |
| 2334048 | Analyst 12 | 57.5 | 3.1m | 6 | 0.83 | 0.00 | 1 |
| 2354204 | Analyst 13 | 57.5 | 0.2m | 4 | 0.75 | 0.00 | 2 |
| 2331410 | Analyst 01 | 58.5 | 2.7m | 5 | 0.80 | 0.00 | 1 |
| 2343258 | Analyst 13 | 59.2 | 4.9m | 9 | 0.67 | 0.00 | 3 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
05
02
01
03
09
13
12
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 05 | 39 | 3.9 | 81.8 | Zero-Edit Visits | 0.02 | 0.00 | 126 |
|
Reference SoP
1
View Processing Routed
Analyst 05's Dominant Sequence
— 37 of 39 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 39 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 02 | 40 | 5.7 | 81.6 | Zero-Edit Visits | 0.01 | 0.00 | 67 |
|
Reference SoP
1
View Processing Routed
Analyst 02's Dominant Sequence
— 38 of 40 instances
1
View Processing Routed
This analyst exhibits 3 distinct sequences across their 40 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 01 | 95 | 7.3 | 81.3 | Zero-Edit Visits | 0.02 | 0.00 | 11 |
|
Reference SoP
1
View Processing Routed
Analyst 01's Dominant Sequence
— 89 of 95 instances
1
View Processing Routed
This analyst exhibits 7 distinct sequences across their 95 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 101 | 9.2 | 81.1 | Zero-Edit Visits | 0.04 | 0.00 | 1,804 |
|
Reference SoP
1
View Processing Routed
Analyst 03's Dominant Sequence
— 90 of 101 instances
1
View Processing Routed
This analyst exhibits 8 distinct sequences across their 101 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 09 | 89 | 6.4 | 80.3 | Zero-Edit Visits | 0.07 | 0.00 | 1,265 |
|
Reference SoP
1
View Processing Routed
Analyst 09's Dominant Sequence
— 77 of 89 instances
1
View Processing Routed
This analyst exhibits 11 distinct sequences across their 89 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 13 | 47 | 5.2 | 79.3 | Zero-Edit Visits | 0.08 | 0.00 | 28 |
|
Reference SoP
1
View Processing Routed
Analyst 13's Dominant Sequence
— 35 of 47 instances
1
View Processing Routed
This analyst exhibits 12 distinct sequences across their 47 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 12 | 49 | 5.4 | 79.2 | Zero-Edit Visits | 0.10 | 0.00 | 1,084 |
|
Reference SoP
1
View Processing Routed
Analyst 12's Dominant Sequence
— 34 of 49 instances
1
View Processing Routed
This analyst exhibits 14 distinct sequences across their 49 instances; the dominant one is shown.
|
||||||||
81.5
Avg Adherence Score (0-100)
Median 82.5 · Range 39.4–97.5
1.0 min
Avg Task Duration
0.0 min median
2.2
Avg Sub-Steps per Instance
41.7% have multiple sub-steps
—
Reference Path Coverage
120 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 120 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Worklist
2
Filter Cases
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 180 | 69.0% | 94.5% |
| Veeva Safety | 59 | 22.6% | 5.5% |
| collaboration.merck.com | 5 | 1.9% | 0.0% |
| Microsoft Excel | 4 | 1.5% | 0.0% |
| Microsoft Teams | 4 | 1.5% | 0.0% |
| Acrobat | 3 | 1.1% | 0.0% |
| YoudaoDict | 2 | 0.8% | 0.0% |
| TextInputHost | 1 | 0.4% | 0.0% |
| Notepad | 1 | 0.4% | 0.0% |
| ONENOTE | 1 | 0.4% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 17 |
| Phobos | collaboration.merck.com | 2 |
| Veeva Safety | Acrobat | 2 |
| Phobos | Microsoft Teams | 2 |
| Phobos | Microsoft Excel | 2 |
| collaboration.merck.com | Microsoft Excel | 2 |
| YoudaoDict | Veeva Safety | 2 |
| Phobos | Acrobat | 1 |
| gpteal.merck.com | Veeva Safety | 1 |
| Acrobat | Veeva Safety | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 1,463 events 100 cases 40.7 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<button>
"Proceed"
56×
pyzeClick
<a>
"Cases"
53×
pyzeClick
<button>
"Send to process"
38×
inputld
<select>
"LIKE · = · !="
34×
pyzeClick
<a>
"Cases Coordinator"
23×
pyzeClick
<a>
"Rules"
23×
pyzeClick
<a>
"Worklist Open"
23×
pyzeClick
<button>
"Re-Route"
23×
Veeva Safety DOM 765 events 82 cases 3.8 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Show more"
46×
pyzeClick
<span>
"Complete"
34×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
25×
pyzeClick
<span>
"Documents"
23×
pyzeClick
<span>
"Submissions & Distributions"
23×
pyzeClick
<li>
"Narrative"
22×
Microsoft Teams App 34 events 17 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
350 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 31 events 20 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
365 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Acrobat App 27 events 11 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
466 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Excel Document 25 events 13 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
file
2329445 (v2.1)
339×
file
Priorización
179×
file
Case tracking list
126×
file
Daily tracking list (PV-SMART) copy
32×
Microsoft Word Document 12 events 4 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
Microsoft Outlook Message 10 events 8 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
gpteal.merck.com DOM 5 events 2 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.12
Swivel Rate
events in non-primary app / total
0.77
Zero-Edit Visit Rate
inspection-only clicks
1.13
Avg Revisit Depth
max repeats of any activity
0.02
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
0
10
20
30
40
50
60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.77
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.77
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Swivel Rate
value 0.12
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Revisit Depth
value 1.13
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Cross-App Time Share
value 0.02
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2342889 | Analyst 13 | 39.4 | 4.0m | 15 | 0.93 | 0.40 | 3 |
| 2356569 | Analyst 13 | 52.4 | 5.8m | 6 | 0.67 | 0.42 | 1 |
| 2353963 | Analyst 13 | 57.1 | 2.1m | 7 | 0.86 | 0.13 | 1 |
| 2301418 | Analyst 09 | 58.0 | 0.4m | 4 | 0.75 | 0.19 | 1 |
| 2291017 | Analyst 02 | 58.1 | 3.0m | 8 | 0.88 | 0.00 | 1 |
Users on This Task
8 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
05
12
03
10
01
02
09
13
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 05 | 3 | 3.0 | 87.5 | Zero-Edit Visits | 0.00 | 0.00 | 0 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 05's Dominant Sequence
— 3 of 3 instances
1
Filter Cases
· reference: View Worklist
|
||||||||
| ▸ | Analyst 12 | 6 | 1.5 | 84.8 | Zero-Edit Visits | 0.11 | 0.00 | 4 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 12's Dominant Sequence
— 2 of 6 instances
1
Filter Cases
· reference: View Worklist
This analyst exhibits 4 distinct sequences across their 6 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 03 | 4 | 1.3 | 84.7 | Zero-Edit Visits | 0.12 | 0.00 | 44 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 03's Dominant Sequence
— 2 of 4 instances
1
Filter Cases
· reference: View Worklist
2
View Worklist
· reference: Filter Cases
This analyst exhibits 3 distinct sequences across their 4 instances; the dominant one is shown.
|
||||||||
| ▸ | Analyst 10 | 58 | 6.4 | 84.6 | Zero-Edit Visits | 0.00 | 0.00 | 4 |
|
Reference SoP
1
View Worklist
2
Filter Cases
Analyst 10's Dominant Sequence
— 58 of 58 instances
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Filter Cases
· reference: View Worklist
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| ▸ | Analyst 01 | 7 | 1.2 | 83.8 | Zero-Edit Visits | 0.24 | 0.06 | 3 |
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Reference SoP
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Analyst 01's Dominant Sequence
— 2 of 7 instances
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· reference: View Worklist
This analyst exhibits 6 distinct sequences across their 7 instances; the dominant one is shown.
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| ▸ | Analyst 02 | 6 | 1.5 | 79.9 | Zero-Edit Visits | 0.36 | 0.00 | 14 |
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Reference SoP
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2
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Analyst 02's Dominant Sequence
— 2 of 6 instances
1
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· reference: View Worklist
This analyst exhibits 5 distinct sequences across their 6 instances; the dominant one is shown.
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| ▸ | Analyst 09 | 27 | 3.9 | 78.8 | Zero-Edit Visits | 0.15 | 0.02 | 21 |
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Reference SoP
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View Worklist
2
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Analyst 09's Dominant Sequence
— 7 of 27 instances
1
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· reference: View Worklist
This analyst exhibits 13 distinct sequences across their 27 instances; the dominant one is shown.
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| ▸ | Analyst 13 | 9 | 2.2 | 63.7 | Zero-Edit Visits | 0.58 | 0.11 | 32 |
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Reference SoP
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View Worklist
2
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Analyst 13's Dominant Sequence
— 1 of 9 instances
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View Processing Routed
· reference: View Worklist
2
Filter Cases
3
View Processing Routed
4
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5
View Re-Routing
6
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7
View Re-Routing
8
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9
View Re-Routing
10
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11
View Re-Routing
This analyst exhibits 9 distinct sequences across their 9 instances; the dominant one is shown.
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80.8
Avg Adherence Score (0-100)
Median 82.5 · Range 54.4–90.0
9.0 min
Avg Task Duration
0.1 min median
3.1
Avg Sub-Steps per Instance
62.9% have multiple sub-steps
—
Reference Path Coverage
105 sub-task instances
Reference Baseline
Manage Work List — Mined SoP
The most common sub-step sequence observed across 105 task instances. Use the dropdown on the right to compare any deviation to this baseline.
1
View Re-Routing
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Filter Cases
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View Re-Routing
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View ID
No deviation variants observed for this task in the current cut — every instance follows the reference path.
Application Mix
Which applications analysts touch while executing this task, measured by event count and active dwell time.
| Application | Events | Event Share | Dwell Share |
|---|---|---|---|
| Phobos | 281 | 87.0% | 91.9% |
| Veeva Safety | 39 | 12.1% | 8.1% |
| Microsoft Teams | 2 | 0.6% | 0.0% |
| Microsoft Outlook | 1 | 0.3% | 0.0% |
Top Cross-App Transitions (within task instance)
Consecutive events that cross application boundaries within a task instance — the signal where swivel-chair patterns live.
| From | To | Transitions |
|---|---|---|
| Phobos | Veeva Safety | 14 |
| Veeva Safety | Phobos | 4 |
| Phobos | Microsoft Outlook | 1 |
| Phobos | Microsoft Teams | 1 |
| Microsoft Teams | Phobos | 1 |
| Microsoft Outlook | Veeva Safety | 1 |
| Veeva Safety | Microsoft Teams | 1 |
On-Case Application Journey (±15 min around anchor)
Apps the same analyst touched on the same case within ±15 minutes of the task anchor. Captures work that spans task boundaries — the Veeva/Acrobat/Outlook/Word context surrounding the anchor click.
Case counts per app are not mutually exclusive — a case that touched Veeva AND Phobos in this window is counted in both rows. Overlap reflects real cross-app work, not double-counted cases.
Phobos DOM 736 events 47 cases 23.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<a>
"Cases"
59×
pyzeClick
<button>
"Proceed"
39×
pyzeClick
<a>
"Cases Coordinator"
28×
pyzeClick
<a>
"Re-Routing Section"
25×
pyzeClick
<button>
"Re-Route"
24×
inputld
<select>
"LIKE · = · !="
23×
pyzeClick
<select>
"Re-route to · agueeroa · agueroma · aladria8 · alfaroh · al"
20×
pyzeClick
<button>
"Advance"
14×
Veeva Safety DOM 472 events 40 cases 4.5 min
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
pyzeClick
<span>
"Complete"
56×
pyzeClick
<span>
"Continue"
29×
pyzeClick
<li>
"Delete"
26×
pyzeClick
<li>
"Details"
21×
pyzeClick
<a>
"Show more"
17×
pyzeClick
<li>
"Narrative"
15×
pyzeClick
<span>
"Submissions & Distributions"
13×
pyzeClick
<div>
"Verdict Comment: duplicate for case# 2353378 (ina"
12×
Microsoft Teams App 23 events 7 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
256 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
collaboration.merck.com App 9 events 4 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
40 granular events captured, but no DOM, document, or message-level metadata is available for this application. Instrumenting this app directly with Pyze would unlock element-level detail.
Microsoft Outlook Message 7 events 5 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
subj
Incorrect article uploaded in CLIC 940845
37×
subj
Untitled
8×
subj
<EVA Lit> Case Number I-0000317670, CSD 2025-11-03, MK-3475, Serious:
2×
subj
PRODUCTION System - AE Alert - CLIC #940845 - PEMBROLIZUMAB;
1×
usc-excel.officeapps.live.com Document 6 events 1 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
olk Message 5 events 1 cases
Message-level capture
Task Mining captures email subjects. Case numbers frequently appear in the subject line.
Microsoft Word Document 3 events 2 cases
Document-level capture
Task Mining captures the specific file open during work — file names often encode case context.
gpteal.merck.com DOM 3 events 1 cases
DOM-level capture
Pyze JS captures every element interaction — button labels, field IDs, section panels.
TextInputHost App 3 events 2 cases
App-level capture
Only application name and dwell time available. Direct instrumentation would unlock deeper detail.
Adherence Vectors
Each task instance is scored on four behavioral vectors. Lower values are more adherent to the mined SoP.
0.05
Swivel Rate
events in non-primary app / total
0.87
Zero-Edit Visit Rate
inspection-only clicks
1.82
Avg Revisit Depth
max repeats of any activity
0.00
Cross-App Time Share
time outside primary app
Meta Score Distribution
Instance count in each 10-point adherence bucket. Right-skewed = healthy execution; long left tail = concentrated pain points.
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10
20
30
40
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60
70
80
90
Top Concern Vector
Zero-Edit Visit Rate — current value 0.87
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Vector Contribution to Adherence Loss
Each vector's weighted contribution to lost adherence. The top row is your highest-leverage fix.
Zero-Edit Visit Rate
value 0.87
Investigate why analysts inspect-and-leave without editing — usually missing upstream data or unclear UI affordances.
Revisit Depth
value 1.82
High revisit depth = mid-flow rework. Trace which sub-step is being repeated; usually a data-readiness gap upstream.
Swivel Rate
value 0.05
Reduce cross-app hops by surfacing the secondary-app data inline (API integration or pre-fetched context panel).
Cross-App Time Share
value 0.00
Time outside primary app dominates this score. Identify which secondary app(s) and whether their function can be in-lined.
Lowest-Adherence Instances
The 5 task instances that scored lowest. These are the concrete cases worth investigating to understand the worst patterns.
| Case | Analyst | Score | Duration | Sub-Steps | Swivel | X-App | Revisit |
|---|---|---|---|---|---|---|---|
| 2281696 | Analyst 02 | 54.4 | 4.8m | 13 | 0.77 | 0.28 | 2 |
| 2347380 | Analyst 05 | 62.5 | 1.2m | 3 | 0.67 | 0.00 | 1 |
| 2314753 | Analyst 01 | 65.5 | 1.6m | 5 | 0.40 | 0.00 | 3 |
| 2327846 | Analyst 13 | 66.3 | 1.2m | 16 | 0.00 | 0.00 | 9 |
| 2082933 | Analyst 02 | 66.9 | 8.0m | 8 | 0.50 | 0.00 | 2 |
Users on This Task
7 analysts executed this task during the pilot. Scatter plots productivity (instances per active day) against adherence (meta score). Top-right = healthy high-volume analysts; bottom-right = firefighters; top-left = careful low-volume; bottom-left = struggling.
Adherence (meta score) →
Productivity (instances / active day) →
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01
13
09
12
02
05
User-by-User Breakdown
Click any analyst to see their dominant sequence vs the reference SoP.
| Analyst | Instances | Inst / Day | Adherence | Weakest Vector | Swivel | X-App | Min | |
|---|---|---|---|---|---|---|---|---|
| ▸ | Analyst 03 | 13 | 6.5 | 82.7 | Zero-Edit Visits | 0.04 | 0.00 | 4 |
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Reference SoP
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View ID
Analyst 03's Dominant Sequence
— 4 of 13 instances
1
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3
View Re-Routing
This analyst exhibits 8 distinct sequences across their 13 instances; the dominant one is shown.
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| ▸ | Analyst 01 | 27 | 5.4 | 81.8 | Zero-Edit Visits | 0.01 | 0.00 | 63 |
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Reference SoP
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View ID
Analyst 01's Dominant Sequence
— 18 of 27 instances
1
View Re-Routing
This analyst exhibits 8 distinct sequences across their 27 instances; the dominant one is shown.
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| ▸ | Analyst 13 | 26 | 4.3 | 81.3 | Zero-Edit Visits | 0.03 | 0.01 | 18 |
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Reference SoP
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3
View Re-Routing
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View ID
Analyst 13's Dominant Sequence
— 8 of 26 instances
1
View Re-Routing
This analyst exhibits 15 distinct sequences across their 26 instances; the dominant one is shown.
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| ▸ | Analyst 09 | 16 | 2.7 | 80.9 | Zero-Edit Visits | 0.00 | 0.00 | 8 |
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Reference SoP
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View ID
Analyst 09's Dominant Sequence
— 9 of 16 instances
1
View Re-Routing
This analyst exhibits 4 distinct sequences across their 16 instances; the dominant one is shown.
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| ▸ | Analyst 12 | 4 | 2.0 | 78.8 | Zero-Edit Visits | 0.21 | 0.00 | 2 |
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Reference SoP
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View ID
Analyst 12's Dominant Sequence
— 1 of 4 instances
1
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3
View Re-Routing
This analyst exhibits 4 distinct sequences across their 4 instances; the dominant one is shown.
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| ▸ | Analyst 02 | 15 | 3.8 | 78.2 | Zero-Edit Visits | 0.15 | 0.02 | 27 |
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Reference SoP
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View ID
Analyst 02's Dominant Sequence
— 4 of 15 instances
1
View Re-Routing
This analyst exhibits 9 distinct sequences across their 15 instances; the dominant one is shown.
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| ▸ | Analyst 05 | 4 | 2.0 | 75.6 | Zero-Edit Visits | 0.21 | 0.00 | 826 |
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Reference SoP
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3
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View ID
Analyst 05's Dominant Sequence
— 1 of 4 instances
1
View Re-Routing
2
Filter Cases
3
View Re-Routing
This analyst exhibits 4 distinct sequences across their 4 instances; the dominant one is shown.
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