tushaarmehtaa User insights
Start from a product decision and metric definition. Do not turn convenient columns or arbitrary time thresholds into personas.
Choose a mode
- Question framing: turn a product concern into measurable questions.
- Segmentation: identify behaviorally meaningful groups from distributions or justified business rules.
- Retention/cohort: measure return behavior from event history.
- Activation/funnel: locate progression and drop-off.
- Feature adoption/pathing: understand use sequences and value realization.
- Churn/monetization: analyze decline, cancellation, expansion, and revenue behavior.
- Qualitative synthesis: connect interviews, support, or survey evidence to behavioral patterns.
- Audit: validate an existing query, dashboard, segment, or conclusion.
Workflow
- State the product decision, population, behavior, time window, and action the analysis may trigger.
- Inspect schemas, event taxonomy, identity model, account/user relationships, plan history, billing/refunds, timezone, retention policy, and available qualitative evidence. Match the query language to the actual system.
- Audit data quality before analysis: event semantics, duplicate/late events, nulls, bots/internal/test accounts, identity merges, plan changes, censoring, seasonality, and instrumentation changes.
- Define every metric with numerator, denominator, eligibility, window, and unit of analysis. Prefer event history over current user snapshots for trends and retention.
- For segments, inspect distributions and use quantiles, clusters, or business thresholds only when interpretable and justified. Test sensitivity to reasonable boundary changes. Small groups and ties need explicit handling.
- For retention, build cohort-period activity from events and distinguish classic, rolling, and bounded retention. Do not infer historical retention from
last_active_at. - For decline or churn risk, compare each subject with its own prior behavior or an appropriate matched baseline; do not call low cumulative usage a decline.
- Execute queries only when access is available. Otherwise return executable queries and expected result shapes without fabricating counts.
- Quantify sample size, uncertainty, missingness, and alternative explanations. Treat observational associations as non-causal.
- Connect each finding to a decision, mechanism, proposed action, and validation method. Prefer experiments or staged tests for causal recommendations.
Privacy and safety
- Minimize selected fields; avoid
SELECT *and raw email unless the task requires identifiable outreach and access is authorized. - Aggregate or pseudonymize outputs where possible.
- Respect consent, retention, deletion, and access-control boundaries.
- Do not label individuals with sensitive or stigmatizing inferred traits.
Load conditional reference
Read analysis patterns for calibrated segmentation, event-based cohort SQL, decline analysis, query review, and action design. The reference contains patterns to adapt, not fixed thresholds or a separate workflow.
Output contract
Distinguish two states:
- Executed analysis: evidence ledger, metric definitions, data-quality findings, results with denominators/uncertainty, limitations, and decisions.
- Query plan: schema mapping, executable queries, expected columns, validation queries, and interpretation rules—no invented results.
For either state, include justified segments/cohorts only, privacy notes, alternative explanations, recommended action, and how to test it.
Verify
- Queries use real schema fields and correct grain.
- Trends and retention use event history rather than snapshots.
- Cohort denominators and maturity/censoring are correct.
- Segment thresholds are justified and sensitivity-tested.
- Counts reconcile to eligible-population totals without unintended overlap.
- PII is minimized and access assumptions are explicit.
- Observational results are not presented as causal.
- Recommendations state evidence, mechanism, uncertainty, and validation.