InterviewDB Experience

Analytics Trial: Design an Analytics Funnel for a Free-Trial Conversion Flow

Interview Experience

Problem

Your product has a free trial that converts to paid. Define the metrics and instrumentation for tracking the trial conversion funnel, diagnosing drop-offs, and running an A/B test on the trial length (7 days vs. 14 days).

Funnel stages:
Landing -> Sign-up -> Trial Start -> Feature Activation -> Trial End -> Paid Conversion

Your task: Define (1) the key metrics at each stage, (2) the events you would log with required properties, (3) a SQL query to compute stage-by-stage conversion rates, and (4) the A/B test design for trial length.

sql
-- Events table
events(user_id, event_name, properties JSONB, created_at)

Follow-ups

  1. What is your randomization unit for the A/B test — user, session, or device? Why?
  2. How do you detect and handle users who sign up under multiple accounts to extend their trial?
  3. If feature activation rate drops after a UI change, how do you distinguish a causal effect from a seasonal trend?
  4. What is the minimum detectable effect size you would target, and how does that determine sample size?

Full Details

Problem

Your product has a free trial that converts to paid. Define the metrics and instrumentation for tracking the trial conversion funnel, diagnosing drop-offs, and running an A/B test on the trial length (7 days vs. 14 days).

Funnel stages:
Landing -> Sign-up -> Trial Start -> Feature Activation -> Trial End -> Paid Conversion

Your task: Define (1) the key metrics at each stage, (2) the events you would log with required properties, (3) a SQL query to compute stage-by-stage conversion rates, and (4) the A/B test design for trial length.

sql
-- Events table
events(user_id, event_name, properties JSONB, created_at)

Follow-ups

  1. What is your randomization unit for the A/B test — user, session, or device? Why?
  2. How do you detect and handle users who sign up under multiple accounts to extend their trial?
  3. If feature activation rate drops after a UI change, how do you distinguish a causal effect from a seasonal trend?
  4. What is the minimum detectable effect size you would target, and how does that determine sample size?

About This Question

This is a candidate experience report from a mercor interview.

It covers the following topics: Other, Sql .