Growth and Product Metrics Analysis Questions

Analysis skills specific to growth and product contexts: interpreting funnel metrics, cohort and retention analyses, attribution of acquisition versus activation, detecting seasonality and external event impacts, and diagnosing conversion or engagement changes. Candidates should be able to form hypotheses about what drove changes, propose targeted follow up analyses or A B tests, and identify which additional metrics are needed to evaluate unit economics and growth efficiency.

MediumTechnical

42 practiced

Given two years of daily signups, describe methods to detect seasonality and quantify the impact of an external event (e.g., a marketing promotion or holiday). Discuss time-series decomposition, intervention analysis, and which visualization(s) you would show to stakeholders.

MediumTechnical

45 practiced

Compare multi-armed bandit approaches to traditional A/B testing for optimizing sign-up flows. When is a bandit appropriate for growth experiments and what are the trade-offs in terms of statistical guarantees and exploration?

MediumTechnical

62 practiced

Write a SQL query to compute DAU, MAU, and DAU/MAU stickiness for each day over the last 90 days. Use events table:

events(user_id bigint, event_time timestamp, event_name varchar)

Count a user as active if they have any event that day. Output date, dau, mau, dau_mau_ratio (rounded).

HardTechnical

50 practiced

Explain Pareto/NBD (or BG/NBD) models for forecasting active users and LTV. Describe data preparation steps (eventization), core parameters, assumptions, and how you would validate model forecasts against holdout cohorts.

EasyTechnical

39 practiced

You have an events table with schema:

events(event_id bigint, user_id bigint, event_name varchar, event_time timestamp)

Event names include 'visit', 'signup', 'activation', 'purchase'. Write a SQL query (ANSI SQL) to compute daily funnel conversion rates for the last 14 days: distinct users who visited → distinct users who signed up → distinct users who activated → distinct users who purchased. Show per-day counts and conversion percentages (visit→signup, signup→activation, activation→purchase, visit→purchase).

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