Identify behavioral differences between two user groups combining session replays with quantitative metrics. Uses mcp__Amplitude__get_session_replays, mcp__Amplitude__query_amplitude_data.
Precision in group definition is the foundation of useful comparison. Ambiguous groups produce ambiguous insights.
For each group, specify:
Common useful group pairs:
Use mcp__Amplitude__query_amplitude_data to compute key behavioral metrics for each group. Run parallel queries for Group A and Group B:
Compute for each metric: Group A value, Group B value, absolute difference, relative difference (%). This sets up the side-by-side comparison table.
Use mcp__Amplitude__get_session_replays to find recordings for users in each group. Filter by the segment criteria defined in Step 1.
Watch 5-10 sessions per group. For each session, note:
Do not summarize individual sessions — look for what recurs across sessions. A behavior that appears in 3+ Group A sessions but not in Group B sessions is a meaningful differentiator.
After watching replays, synthesize patterns by group:
Group A patterns (e.g., converters):
Group B patterns (e.g., churners):
Synthesize the behavioral differences between the two groups into a ranked list. For each difference:
The most powerful findings are those confirmed by both quantitative data and session replay evidence.
For the top 3-5 differentiators, compute the gap quantitatively:
The gap quantification transforms observations into leverage: if you could move Group B toward Group A on metric X, what would the business impact be?
Based on the differences identified, recommend specific product changes that could move Group B toward Group A's behavioral patterns:
mcp__Amplitude__get_session_replays — find and watch session recordings for both user groupsmcp__Amplitude__query_amplitude_data — pull quantitative behavioral metrics for both groupsmcp__Amplitude__get_event_properties — discover user properties for group segmentationmcp__Amplitude__get_context — get projectId and organization context (always first)mcp__Amplitude__query_chart — build funnel or retention charts broken down by the two groupsThe output combines a comparison table with narrative insights.
Structure:
| Metric | Group A | Group B | Difference |
|---|---|---|---|
| Avg session depth (events) | 24 | 9 | Group A 2.7x deeper |
| Feature breadth (features/week) | 6.2 | 1.8 | Group A 3.4x broader |
| Time to aha moment | 8 min | 31 min | Group A 4x faster |
| Week 4 retention | 68% | 12% | +56pp |