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Microsoft·Product Manager·Onsite - Product Sense / Strategy·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Interviewed at Microsoft for what seemed like a product or marketing analytics role. Single question about A/B testing email campaigns, pretty focused, nothing too wild but it made me think harder than expected.

Questions Asked (1)

Q1

You have two versions of an email campaign. How would you figure out which one drives more sales?

A/B Testing & ExperimentationProduct Analytics & Metrics
Author's notes

My first instinct was to just say 'run an A/B test' and I basically did say that, but then they kept probing and I realized I hadn't thought through the setup at all.

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AI HintsAI Generated

Suggested Approach

Frame your answer around a structured A/B test: define a clear success metric (e.g., sales per recipient), randomly split the audience, and run the test long enough to achieve statistical power. Then analyze the results with significance testing and consider practical significance before deciding which version to roll out.

Pro tip: Mention that you'd pre-register the hypothesis, primary metric, and minimum detectable effect to avoid p-hacking and ensure the test is trustworthy. Also note that you'd check for novelty effects and segment-level insights to inform future campaigns.

1. Define the Hypothesis and Success Metrics

Clearly state what you're testing (e.g., subject line, creative, CTA) and define the primary metric (e.g., conversion rate, sales per email) and secondary metrics (e.g., open rate, click-through rate).

2. Design the Experiment

Randomly assign a representative sample of your audience to version A or B, ensuring both groups are statistically equivalent. Determine sample size and test duration based on desired power and minimum detectable effect.

3. Run the Test and Collect Data

Execute the campaign, ensuring no contamination between groups. Monitor for technical issues and collect data on the primary and secondary metrics.

4. Analyze Results for Statistical Significance

Use appropriate statistical tests (e.g., t-test, chi-square) to determine if the difference in sales is significant. Calculate confidence intervals and p-values.

5. Interpret and Decide

Assess practical significance (e.g., lift size, cost-benefit) and consider segment-level effects. Decide whether to roll out the winning version, iterate, or run further tests.

Key Points to Mention

  • Randomization and control group to avoid bias
  • Statistical power and sample size calculation
  • Primary metric: sales per recipient or total sales
  • Statistical significance vs. practical significance
  • Segmentation analysis to understand heterogeneous treatment effects
  • Avoiding common pitfalls like peeking, multiple comparisons, and novelty effects

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.