Capital One·Data Scientist·Onsite - Product Sense / Strategy·Intermediate

Intermediate

May 2026

Summary

Capital One data scientist case study round, focused on a marketing analytics scenario about waiving annual fees. Pretty heavy on experimentation design, which I should've seen coming given the role.

Questions Asked (1)

You're evaluating a marketing campaign that waives the first-year annual fee for new cardholders. Design an A/B test to measure its impact on card acquisitions and spending. Walk through your primary metrics, guardrail metrics, how you'd size the experiment, and what post-experiment analysis would look like.

A/B Testing & ExperimentationProduct Analytics & MetricsPricing & Monetization

Author's notes

I started with conversion rate as the primary metric and revenue lift as a secondary, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clearly defining the experiment's goal and the target population, then outline the primary and guardrail metrics that capture both acquisition and spending impacts. Explain how you would determine sample size and duration, and finally describe the analysis plan including segmentation and long-term value considerations.

Pro tip: Emphasize the importance of considering the long-term value of cardholders, not just first-year metrics, and discuss how to account for potential cannibalization or selection bias. Also, mention the need to monitor for novelty effects and ensure the experiment runs long enough to capture seasonal variations.

1. Define Objective and Hypothesis

Clearly state the goal: measure the impact of waiving the first-year annual fee on card acquisitions and spending. Formulate a hypothesis, e.g., waiving the fee will increase new cardholders and their spending.

2. Select Primary and Guardrail Metrics

Choose primary metrics such as number of new cardholders (acquisition) and total spend per cardholder in the first year. Guardrail metrics include profitability, default rates, and customer satisfaction to ensure no negative side effects.

3. Design Experiment and Determine Sample Size

Randomly assign eligible customers to control (no fee waiver) and treatment (fee waiver) groups. Calculate required sample size using power analysis, considering baseline conversion rates, expected lift, and desired power (e.g., 80%) and significance level (e.g., 5%).

4. Run Experiment and Monitor

Launch the experiment, ensuring proper randomization and data collection. Monitor for data quality issues and early signs of guardrail violations, but avoid peeking at primary metrics to prevent bias.

5. Analyze Results and Derive Insights

After the experiment, compare primary metrics between groups using statistical tests. Conduct segmentation analysis (e.g., by customer tenure, credit score) and assess long-term value. Evaluate guardrail metrics to ensure no harm.

Key Points to Mention

Randomization and control group setup
Primary metrics: acquisition rate, spending per cardholder
Guardrail metrics: profitability, default rate, customer satisfaction
Sample size calculation and power analysis
Segmentation analysis and long-term value assessment
Potential biases: novelty effect, selection bias, cannibalization

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