Intermediate everything in Product Management, page 3
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.
Implementing a consistent-assignment A/B test
Need an assignment service, exposure logging, and event tracking; ensure stickiness by hashing a stable user id; analyze conversion per variant.
The multiple comparisons problem in A/B testing
Many tests at alpha 0.05 inflate the chance of a false positive; mitigate with Bonferroni or FDR control plus pre-registered metrics.
Visualizing a correlation with a third variable
A scatter plot with a trend line shows the relationship; encode network type by color or facets to expose a lurking variable.
Cohort analysis for an onboarding change
A cohort groups users by a shared start trait; compare pre and post Jan-1 signup cohorts on retention by age.
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles