Everything in Analytics & Metrics
Amplitude reduces MCP tool overload by grouping actions around usage
Amplitude redesigned its MCP server after endpoint-level tools created context bloat and tool-choice errors.
Jev catches changes in an analytics test, but false alarms matter
Amplitude reports a synthetic analytics benchmark. Strong recall alone does not prove reliable production analysis.
Define and calculate Weekly Active Users
Define a meaningful active action, count distinct users over a rolling 7-day window, and exclude bots and background syncs.
Use Difference-in-Differences without an A/B test
Give a scenario like a region-wide launch, apply Difference-in-Differences comparing treated vs control over time, and state the parallel-trends assumption.
Determine A/B test sample size
Define baseline rate, minimum detectable effect, significance (alpha), and power (1-beta); smaller effects and stricter thresholds need more users.
Design analytics event schema validation
Define a schema registry, validate at both client (fast feedback) and server (authoritative gate), and quarantine failures to a dead-letter store.
Calculate MRR with SQL including annual plans
Sum monthly_price for subscriptions active this month, filter on start and end dates, and normalize annual plans by dividing annual price by 12.
Interpret a p-value in an A/B test
Define it as P(data this extreme | null true), interpret 0.03 against a 0.05 threshold, and state what it is NOT.
Define a consistent day across timezones
Store events in UTC, capture the local/source timezone, then convert to a single reporting timezone at query time.
Implement CDC from OLTP to warehouse
Contrast log-based CDC with query-based timestamp polling, cover deletes and load on source, then pick log-based for minimal impact.
Design a multi-touch attribution model
Pick a model (time-decay, position-based, or data-driven Shapley), stitch touchpoints by user identity into ordered paths, then assign fractional credit.
Reframe time series for a tree model
Lag and rolling-window features, calendar and cyclical encodings, then split chronologically to avoid leakage.
Find leading indicators of long-term churn
Cohort renewers vs churners, compare first-30-day engagement depth and breadth, validate correlations and check causality.
Calculating Daily Active Users in SQL
Need per-event user_id and timestamp and a clear active definition; count distinct user_id within the day in a fixed timezone.
Client vs server tracking: pros, cons, examples
Client-side wins on UI context but loses data to blockers and tampering; server-side wins on reliability and trust but misses pure UI events.
Building a conversion funnel in SQL
Count distinct users reaching each ordered step, compute step-over-step conversion; the biggest drop-off is the lowest consecutive ratio.
Bundled analytics vs warehouse-native trade-offs
Warehouse-native gives one source of truth and SQL flexibility but shifts modeling, performance, and UX onto your team; bundled tools are turnkey but siloed.
Investigating analytics vs database count gaps
Causes include ad-blocker loss, differing identity logic, timezone mismatches, filtering, and pipeline delay; investigate by aligning definitions and tracing one user.
Architecting multi-touch attribution
Ingest touchpoints, resolve to one identity, order into paths, apply a model; last-touch is trivial, time-decay needs the full path.
SQL for a three-step onboarding funnel
Anchor the 30-day signup cohort, count distinct users reaching each later step in timestamp order; conversion is each step over the prior.
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