Design Experiments for Email Campaign & Messaging Update
Quick Overview
Evaluates experiment design for concurrent email and in-product messaging launches with possible interaction effects. Strong answers use factorial designs, power calculations, interaction terms, sequential testing, and guardrails.
Design Experiments for Email Campaign & Messaging Update
Company: LinkedIn
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
Scenario: Marketing is running an email campaign while Product ships a new messaging function; you must test both.
Question 1: How would you design experiments to isolate each feature’s effect while accounting for potential interaction?
Question 2: Detail how you would calculate sample size, statistical power, and test duration.
Question 3: Discuss strategies for handling interaction effects, such as factorial or staggered rollouts.
Question 4: Explain considerations for sequential testing and maintaining statistical validity.
Quick Answer: Evaluates experiment design for concurrent email and in-product messaging launches with possible interaction effects. Strong answers use factorial designs, power calculations, interaction terms, sequential testing, and guardrails.
Experiment Design for Concurrent Email Campaign and Messaging Feature
Marketing will run an email campaign at the same time Product ships a new in-product messaging feature. You need to measure the causal impact of each initiative on key metrics such as activation, engagement, or revenue, while allowing for the two initiatives to interact.
Constraints & Assumptions
Estimate the email effect, messaging-feature effect, and possible interaction.
Use sticky assignment and clear exposure definitions.
Include sample size, power, duration, and sequential-testing considerations.
Account for overlap between marketing and product surfaces.
Clarifying Questions to Ask Guidance
Are all users eligible for both email and messaging feature exposure?
What is the primary metric and the decision horizon?
Can email sending be randomized independently of feature access?
Are there frequency caps, compliance, or notification fatigue constraints?
Part 1 - Experiment Design
How would you isolate each feature's effect while accounting for interaction?
What This Part Should Cover Guidance
Prefer a 2x2 factorial design with email on/off and feature on/off.
Define treatment cells, randomization unit, eligibility, exposure, and intent-to-treat analysis.
Estimate main effects and interaction effects.
Include guardrails for unsubscribes, spam complaints, latency, and user experience.
Part 2 - Sample Sizing and Duration
How would you calculate sample size, power, and test duration?
What This Part Should Cover Guidance
Use baseline rate, MDE, variance, alpha, power, allocation ratio, and expected trigger rate.
Power the interaction only if it is decision-critical.
Account for delayed outcomes, seasonality, and email send windows.
Run SRM and logging checks.
Part 3 - Interaction and Sequential Testing
How would you detect interaction effects and handle sequential testing?
What This Part Should Cover Guidance
Use regression or factorial ANOVA-style terms for interaction.
Consider staggered rollouts or holdouts if factorial design is not feasible.
Use pre-specified monitoring boundaries or alpha-spending for repeated looks.
Avoid peeking-driven launch decisions.
Follow-up Questions Guidance
What if the email works only when the messaging feature is enabled?
How would you analyze users who never open the email?
What would you do if guardrails fail in one cell but not another?