Evaluate merchant partnership for high-value customers

Quick Overview

This question evaluates skills in customer lifetime value modeling, marketing experiment design, acquisition economics, funnel and credit-risk analysis, fraud and operational-cost assessment, and measurement planning for partnership decisions.

Evaluate merchant partnership for high-value customers

Company: Capital One

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

C1 (a credit-card issuer) is considering partnerships with a home‑sharing platform (Rent‑a‑Home, RH) and big-box retailers (Costco‑like) to attract high‑LTV cardholders. Define “high value” quantitatively (e.g., 12‑month net contribution after CAC and expected loss, or 24‑month CLV). List and justify the top factors you would analyze before signing: overlap and incremental reach, expected average spend and category mix, interchange by MCC, partner commission/revenue share, promo mechanics (e.g., 30% off RH), cannibalization of existing spend, breakage, approval/activation/usage funnels, credit risk and expected loss, fraud risk, operational/servicing costs, and legal/regulatory constraints. Propose a measurement plan: primary KPI(s), guardrails, experiment design (randomized offer with holdout), sample size and power assumptions, and how you’d detect/adverse‑select against “credit‑card gamers.” Specify data needed, key segments (new vs. existing, peak vs. off‑peak), and your go/no‑go thresholds (e.g., payback within 12 months and minimum IRR).

Quick Answer: This question evaluates skills in customer lifetime value modeling, marketing experiment design, acquisition economics, funnel and credit-risk analysis, fraud and operational-cost assessment, and measurement planning for partnership decisions.

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Oct 13, 2025, 9:49 PM
hardData ScientistTechnical ScreenAnalytics & Experimentation
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Partnership evaluation to acquire high‑LTV cardholders: Home‑sharing (RH) vs Big‑box retailer

Context

C1, a credit‑card issuer, is considering marketing partnerships with:

  • Rent‑a‑Home (RH), a home‑sharing platform, and
  • A Costco‑like big‑box retailer, with the goal of attracting and growing high‑LTV cardholders.

Assume both partners can target their users with issuer‑funded or partner‑funded offers and can support randomized treatments. The issuer can track applications, approvals, activations, spend, and credit outcomes by MCC.

Tasks

  1. Define "high value" quantitatively for this context (e.g., 12‑month net contribution after CAC and expected loss; 24‑month CLV). State your formulas and any assumptions.
  2. List and justify the top factors to analyze before signing, including at minimum:
    • Audience overlap and incremental reach
    • Expected average spend and category mix
    • Interchange by MCC
    • Partner commission/revenue share
    • Promo mechanics (e.g., 30% off RH) and breakage
    • Cannibalization of existing spend
    • Approval/activation/usage funnels
    • Credit risk and expected loss
    • Fraud risk
    • Operational/servicing costs
    • Legal/regulatory constraints
  3. Propose a measurement plan covering:
    • Primary KPI(s)
    • Guardrail metrics
    • Experiment design (randomized offer with holdout)
    • Sample size and power assumptions
    • How to detect and adverse‑select against “credit‑card gamers”
  4. Specify the data needed, key segments to cut (e.g., new vs existing customers; peak vs off‑peak periods), and your go/no‑go thresholds (e.g., 12‑month payback and minimum IRR).
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