Analyze Revenue Shifts to Identify Cannibalization Effects
Quick Overview
Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.
Analyze Revenue Shifts to Identify Cannibalization Effects
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
##### Scenario
Management wants evidence that revenue growth from one creation_source is caused by cannibalization from other sources rather than organic growth.
##### Question
Design an analysis to demonstrate whether next year’s revenue increase for a specific creation_source is offset by declines in other sources.
##### Hints
Discuss causal inference approach: difference-in-differences, control groups, fixed effects, time-series regression, or experiment with traffic re-allocation.
Quick Answer: Evaluates revenue cannibalization analysis across creation sources. Strong answers define Source A, other-source revenue, total revenue, cannibalization ratio, and net lift, then use panel, rollout, difference-in-differences, or synthetic-control evidence to separate source shifting from organic growth.
Analyze Revenue Shifts to Identify Cannibalization Effects
Management observes strong revenue growth from one creation_source, such as a channel where revenue-generating units are created. They want to know whether this growth is net new revenue or whether it cannibalizes revenue from other sources.
Constraints & Assumptions
Treat
creation_source
as mutually exclusive for each revenue-generating unit.
Revenue is measured over time by source and can be aggregated by user cohort, geography, market, or another panel unit.
The goal is to distinguish organic growth from source shifting.
Aim for causal evidence if the source exposure or product change can be varied.
Clarifying Questions to Ask Guidance
What product or business change caused Source A to grow?
Are sources mutually exclusive and consistently logged over time?
What unit of analysis can form a panel: users, markets, cohorts, products, or geographies?
Was there a rollout, experiment, policy change, or external event that can support causal identification?
Part 1 - Define Metrics and Estimands
How would you define revenue metrics and cannibalization?
What This Part Should Cover Guidance
Revenue from Source A, revenue from other sources, and total revenue.
Incremental change in Source A, offsetting change in other sources, cannibalization ratio, and net lift.
Unit, time window, cohort, and denominator choices.
Part 2 - Analyze the Revenue Shift
Design an analysis to determine whether next year's revenue increase for Source A is offset by declines in other sources.
What This Part Should Cover Guidance
Time-series and panel decomposition by source, market, cohort, and user segment.
Difference-in-differences, synthetic control, rollout analysis, or holdout design when feasible.
Controls for seasonality, pricing, user growth, mix shift, marketing, and macro trends.
Part 3 - Validate Causality and Risks
How would you validate whether observed cannibalization is causal rather than coincidental?
What This Part Should Cover Guidance
Pre-trend checks, placebo tests, falsification outcomes, sensitivity analysis, and robustness across segments.
Instrumentation checks and source-definition stability.
Interpretation when total revenue rises, stays flat, or falls.
What a Strong Answer Covers Guidance
A strong answer defines cannibalization precisely, decomposes source and total revenue, uses panel or experiment evidence to estimate net lift, and avoids mistaking source mix shifts for causal harm.
Follow-up Questions Guidance
What if Source A revenue rises and total revenue also rises?
How would you handle users who switch sources multiple times?
What would convince you the growth is mostly cannibalization?