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Determine Revenue and Cost Components for Credit-Card Issuer

Last updated: Mar 29, 2026

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Revenue and Cost Components for Credit-Card Issuer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  • medium
  • Capital One
  • Analytics & Experimentation
  • Data Scientist

Determine Revenue and Cost Components for Credit-Card Issuer

Company: Capital One

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario A credit-card company is evaluating whether to offer a new 1%-cashback card alongside its existing no-cashback card. ##### Question What are the main revenue and cost components for a credit-card issuer? Given: both cards charge the same interest rate y% on carried balances and earn x% interchange on transactions. Non-cashback users carry an average balance of $1,000. How large must the average balance on the cashback card be for the product to break even? ##### Hints List interchange, interest income, default risk, rewards cost, servicing. Set profit_no_cashback = profit_cashback and solve for required balance.

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Determine Revenue and Cost Components for Credit-Card Issuer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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|Home/Analytics & Experimentation/Capital One

Determine Revenue and Cost Components for Credit-Card Issuer

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Capital One
Aug 4, 2025, 10:55 AM
mediumData ScientistOnsiteAnalytics & Experimentation
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0

Determine Revenue and Cost Components for Credit-Card Issuer

Credit-Card Issuer Unit Economics and Break-even Analysis

Scenario

A card issuer is considering launching a new 1% cashback card alongside its existing no-cashback card. Both cards:

  • Earn x% interchange on purchase transactions
  • Charge the same interest rate y% on carried balances (APR)

For the existing no-cashback card, users carry an average balance of $1,000.

Assumption (to close the math cleanly): For a typical revolver, purchase volume over the period is roughly comparable to the average carried balance for that period (i.e., spend ≈ balance), so interchange and rewards can be modeled per dollar of average balance. Time bases (e.g., annual) are consistent across rates.

Tasks

  1. List the main revenue and cost components for a credit-card issuer.
  2. Using the setup above, compute how large the average balance on the 1% cashback card must be for the product to break even with the no-cashback card.

Optional generalization: If spend-to-balance ratio r = (spend)/(average balance) is known rather than assuming r ≈ 1, express the break-even in terms of r.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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