Audience Segmentation and Cohorts Questions

Covers methods for dividing users or consumers into meaningful segments and analyzing their behavior over time using cohort analysis. Candidates should be able to choose segmentation dimensions such as demographics, acquisition channel, product usage, geography, device, or behavioral attributes, and justify those choices for a given business question. They should know how to design cohort analyses to measure retention, churn, lifetime value, and conversion funnels, and how to avoid common pitfalls such as Simpson's Paradox and survivorship bias. This topic also includes deriving behavioral insights to inform personalization, content and product strategy, marketing targeting, and persona development, as well as identifying underserved or high value segments. Expect discussion of relevant metrics, data requirements and quality considerations, approaches to visualization and interpretation, and typical tools and techniques used in analytics and experimentation to validate segment driven hypotheses.

MediumTechnical

0 practiced

Define lifetime value (LTV) in the context of cohort analysis. Explain the difference between cohort-level LTV and per-user predictive LTV, list the minimum data requirements to compute cohort LTV, and describe common pitfalls when comparing LTV across acquisition channels.

MediumTechnical

0 practiced

Write a SQL query (standard SQL / BigQuery style) to compute a weekly retention cohort matrix showing the percentage of users in each signup week who returned in subsequent weeks. Schema:

users(user_id bigint, signup_at date, acquisition_channel string)
events(event_id bigint, user_id bigint, event_name string, event_timestamp timestamp)

Requirements: cohorts by signup week for the last 12 signup weeks, retention by week-number after signup, distinct users per week, and percentage values. Explain assumptions about timezone, users with multiple events per week, and how you handle users who never return.

MediumTechnical

0 practiced

Given two pandas DataFrames in Python: users (user_id, signup_date) and transactions (user_id, amount, occurred_at), implement a function that computes 30-day cumulative cohort LTV for each signup week. Use a vectorized pandas approach and return a DataFrame where each row is a signup_week and columns are cumulative day-1..day-30 LTV per user averaged across the cohort. Briefly explain complexity and memory considerations.

MediumTechnical

0 practiced

Given an events table that records user activity, write a SQL query to flag churned users defined as users with no events in the last 30 days but who were active in the prior 60 days. Schema:

events(user_id bigint, event_timestamp timestamp, event_type string)

Also describe limitations of this definition and alternatives you might use for subscription and non-subscription products.

HardTechnical

0 practiced

You must decide whether to invest in micro-segmentation (many tiny segments) or broader segments for marketing spend. Outline a quantitative framework to compare the cost and benefit, including how to compute break-even CAC uplift for a micro-segment and what data you need. Mention practical constraints that might prevent pursuing micro-segmentation.

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