Easy everything in Growth & Experimentation
Why consistent user IDs matter in experiments
The user id seeds deterministic bucketing and ties events to one person across devices; achieve it via authenticated ids and anonymous-to-known stitching.
Trace an event from click to analysis
Client SDK captures and batches, a collection endpoint ingests, a stream and ETL enrich and load into a warehouse for analysis.
Instrument a first-full-song activation event
Define 'full song' server-side, emit a typed event with user, song, and context, dedupe the first-time flag.
Lifecycle of a feature flag experiment from creation to cleanup
Tests operational rigor across the full flag lifecycle. A strong answer covers six stages: SDK instrumentation with event tracking, phased rollout, monitored experiment, ship/kill decision, and code cleanup.
How would you implement a simple feature flag system?
Tests runtime config separation and distributed hygiene. Outline: control service, datastore, API, SDK, and update mechanism; local evaluation with cached state; short-lived flags. Red flag: static config files requiring redeployment are not feature flags.
What differentiates leading and lagging indicators for subscriptions?
Leading indicators forecast; lagging indicators confirm. Subscriptions: activation rate leads, MRR lags.
Why is extrapolating 5% weekly growth naive for annual forecasting?
This tests knowledge of extrapolation limits. A strong answer cites market saturation, seasonality, and channel exhaustion as invalidating factors, and notes that compounding 5% over 52 weeks magnifies error.
What data and approach for a simple 30-day DAU forecast?
Tests forecasting from sessionized logs without overengineering. Cite timestamped events, a 30 min session rule, and a regression baseline with day-of-week, recent totals, scored with MAE. Red flag: deep learning before a baseline or ignoring privacy hashing.
Implement a 10% onboarding tutorial feature flag and track success
Tests lightweight experiment design and metric selection beyond vanity numbers. Strong answers cover user-ID bucketing, conversion tracking, and guardrail metrics like drop-off. Red flag: manual toggles or click-through without downstream outcomes.
How would you instrument a key activation milestone event?
Tests schema design beyond a bare event name. A strong answer includes the event key, user ID, timestamp, and properties like project_id and is_first_project. Red flag: omitting the user ID or sending only a human-readable label without structured context.
Model a 3-month 20% promo discount and apply it at billing
This tests separating coupon rules from per-user redemptions. Good answers use a coupons table for the 20%/3-month rule, a redemptions table for usage, and apply the discount to the first three invoices. A red flag is hard-coding the discount on the user row.
What schema changes are needed to add a Pro subscription tier?
This tests normalization of billing data versus hardcoding tiers. Add a plans table with integer cents pricing, link subscriptions via plan_id, and leave users untouched. Red flag: adding a tier string column to users or storing prices in subscriptions.
Describe the data model and backend logic for a daily login bonus.
This tests streak state machines and calendar edge cases. A strong answer stores last_login_utc and streak_count, uses UTC day buckets, resolves timezones per user tz, and needs no leap-year logic.
Describe the end-to-end data flow for tracking a 'Share' button click
Payload carries event type, user ID, timestamp, device, content; client batches with retry; backend validates and lands in a partitioned store.
How do you determine if a user is 'new' for a setup guide?
This tests whether you separate account age from user state for onboarding. Good answers compare created_at (brittle) with a persistent flag (idempotent) and consider milestones. A red flag is using a timestamp as a permanent new proxy without managing reruns.
How would you instrument a 4-step onboarding wizard?
Track Step Started and Step Completed with step_index and flow_variant; tie via distinct_id.
What is a p-value in A/B tests and what does threshold mean?
P-value is the chance of this or more extreme result if the null is true; the 5% threshold is the accepted false-positive rate.
How do you set up a button color A/B test?
Tests whether you can define a valid experiment, not just a color change. A strong answer covers hypothesis, randomization, primary metric, sample size, and significance threshold.
What framework decides between low-effort/low-impact and high-effort/high-impact experiments?
This tests structured experiment sequencing beyond gut instinct. A strong answer picks ICE, RICE, or PIE; scores both experiments by impact, confidence, and effort or reach; then weighs opportunity cost and bandwidth.
Which three data sources would you analyze to improve activation?
This tests whether you ground hypotheses in diverse evidence before experimenting. A strong answer names qualitative feedback, funnel metrics, and behavioral analytics as distinct inputs.
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