Design fundraising experiment and guardrails
Overview
This question evaluates experimental design, statistical power and sample-size calculation, selection of primary and guardrail metrics, segmentation with multiple-testing control or hierarchical modeling, stopping rules for sequential analysis, and revenue-impact calculations for A/B testing of email solicitations.
Capital One
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Analytics & Experimentation
0
0
You plan to A/B test two online solicitation variants for the nonprofit's email campaign (subject line + suggested amount). Assume baseline conversion is 5.0% and Variant B is expected to improve conversion by 10% relative. Per-person reach cost is 80 (assume unchanged by variant). There is no capacity constraint online. Tasks:
- Define the primary success metric and at least two guardrail metrics that protect long-term health (e.g., unsubscribe rate, complaint rate). Justify each.
- Calculate the minimum sample size per arm to detect the expected lift (two-sided test, 95% confidence, 80% power). Show the formula and numeric result; state any approximations.
- Describe how you would segment results by donor tier (H vs L) without inflating false positives. Include your multiple-testing or hierarchical modeling approach.
- Outline a stopping rule and the risk of peeking. How would you handle uneven email deliverability across segments?
- If Variant B raises conversion but lowers average gift by 5%, show how you would recompute the decision using net revenue per reached recipient.