Calculate Profitability and Break-Even for Lyft Partnership Campaign

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 Calculate Profitability and Break-Even for Lyft Partnership Campaign states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Calculate Profitability and Break-Even for Lyft Partnership Campaign

Company: Capital One

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: easy

Interview Round: HR Screen

##### Scenario Assess profitability of launching a new credit card and potential partnership campaign with Lyft. ##### Question With 500,000 active cards, $2 monthly swipe revenue per card, a $79 annual membership fee, $15 monthly interest revenue per card, and a $5 monthly fraud-prevention cost per card, what is the annual profit? If we run a Lyft co-marketing campaign costing $25 million per year, how many incremental cardholders are required to at least break even on that campaign? Based on your calculations, would you recommend partnering with Lyft? Explain your reasoning. ##### Hints Translate every figure to annual dollars, build a per-card contribution margin, compute break-even incremental volume, then weigh strategic pros/cons.

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 Calculate Profitability and Break-Even for Lyft Partnership Campaign states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Calculate Profitability and Break-Even for Lyft Partnership Campaign

Credit Card Unit Economics and Lyft Co‑Marketing Break‑Even

Context

You are evaluating the current profitability of a credit card portfolio and considering a potential co‑marketing partnership with Lyft. Assume all figures below apply uniformly to active cardholders and there are no other costs unless specified.

Given

  • Active cards: 500,000
  • Per-card revenues and costs:
    • Swipe (interchange) revenue: $2 per month
    • Membership fee: $79 per year
    • Interest revenue: $15 per month
    • Fraud-prevention cost: $5 per month
  • Proposed Lyft co‑marketing campaign cost: $25 million per year

Tasks

  1. Compute the current annual profit for the 500,000 active cards.
  2. Compute how many incremental active cardholders are required to at least break even on the $25 million Lyft campaign (assume new cardholders have the same unit economics as existing ones).
  3. Based on your calculations, would you recommend partnering with Lyft? State assumptions and key considerations in your reasoning.

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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