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).

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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What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Capital One.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Capital One during technical interviews.

Evaluate merchant partnership for high-value customers

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

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.
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
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”
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).

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.

Tasks

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.

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

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”

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).

Evaluate merchant partnership for high-value customers

Quick Overview

Partnership evaluation to acquire high‑LTV cardholders: Home‑sharing (RH) vs Big‑box retailer

Context

Tasks

Solution

Submit Your Answer to Earn 20XP

Evaluate merchant partnership for high-value customers

Quick Overview

Partnership evaluation to acquire high‑LTV cardholders: Home‑sharing (RH) vs Big‑box retailer

Context

Tasks

Solution

Submit Your Answer to Earn 20XP