Metrics question
A B2B payment cards issuing company has observed a sudden drop in the use of their cards on e-commerce platforms, in the month of april. Analyze the problem using appropriate assumptions. Suggest solutions to get the usage back on track.
- PayPal
- American Express
- Stripe
- Metrics
- Hard
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What this question tests
Root-cause analysis of a transaction volume drop for a payments product, combined with solution design.
How to approach it
- Quantify the drop precisely: total transaction count, dollar volume, or both, and whether it is specific to April or a continuing trend.
- Segment the drop by merchant category, transaction size, and client cohort to see if it is broad or concentrated.
- Check for external seasonality, since April often includes tax season in the US, which can temporarily reduce business discretionary spending.
- Check for internal causes: a recent fraud rule tightening, a fee increase, or a technical issue causing checkout failures.
- Compare decline rate versus historical baseline, since a rising decline rate points to a fraud-rule or processing issue rather than genuine demand drop.
- Propose next steps based on the most likely cause, such as loosening an overly aggressive fraud rule or a targeted win-back outreach.
What a strong answer includes
- Separates seasonal explanations, tax season, from product-caused ones, fraud rules or technical issues, showing real payments-industry knowledge.
- Uses decline rate as a specific, diagnostic metric that distinguishes fewer attempts from more attempts rejected.
- Segments by client cohort to check whether this is systemic or concentrated, before proposing a fix.
- Proposes cause-specific next steps rather than one generic fix for all scenarios.
Common mistakes
- Jumping to a fix like a promotion without diagnosing whether this is seasonal, technical, or fraud-policy driven.
- Ignoring decline rate as a distinct signal from transaction volume.
- Treating all B2B clients as one segment instead of checking for concentration.
Likely follow-up questions
- How would you separate a seasonal effect from a genuine product issue?
- What would you do if the drop was concentrated in one merchant category only?
- How would you validate a fraud rule change was the actual cause?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop