Retentioneering Product Analytics
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral...
Install
npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analytics -g -a claude-code -y- Repository
- retentioneering/retentioneering-tools943
- Category
- Data and ML
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,231 tokens
- Runs scripts
- Yes, review before use
What it does
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.
Retentioneering product analytics
Objective
Turn event-level behavioral data into a reproducible answer to a product question, why users convert, churn, loop, or abandon, using user trajectories, transitions, funnels, and behavioral segments.
Do not merely generate visualizations. Connect each output to the question, separate observation from interpretation, and never present path correlations as causal effects.
Bundled references (read on demand, not upfront)
| File | Read it when |
|---|---|
references/api-map.md | before writing any Retentioneering call, verified signatures, argument conventions, return shapes for 5.x |
references/analysis-recipes.md | after the question is clear, 10 field-tested patterns (R1, R10) with skeletons and pitfalls |
references/gotchas-and-validation.md | before executing (API gotchas G1, G10) and before presenting (integrity checklist B1, B10) |
scripts/inspect_event_log.py | step 2, automated data profiling and schema suggestion |
Required event-log semantics
Minimum: a path identifier (user or session), an event name, a timestamp (or a reliable order column, see gotcha G2 for order-only data). Useful extras: session id, segment attributes (device, source, plan), event properties, conversion labels.
Workflow
1. Environment
- Confirm the package:
python -c "import retentioneering; print(retentioneering.__version__)". Expect 5.x; this skill's API map is version-verified for 5.0, on a different major version, trust installed docstrings over the map. - Locate the event data (CSV / Parquet / frames in existing code). Never modify inputs.
- Do not invent methods: anything not in
references/api-map.mdmust be verified against the installed package before use.
