← Capital One Interview Insights
I jumped straight into throwing out numbers, which was a mistake.
Start by clarifying the business goal—likely maximizing long-term customer value, not just conversion. Then propose a data-driven framework that segments customers by predicted value and price sensitivity, and test discount levels via controlled experiments to find the optimal incentive per segment.
Pro tip: Emphasize that discounts are an investment with a measurable ROI; tie every discount decision to incremental lift in LTV or retention, not just initial conversion. Also, mention the importance of guardrails to prevent margin erosion and discount abuse.
Clarify the primary goal (e.g., maximize LTV, acquisition, or retention) and constraints (e.g., margin thresholds, budget). Align with stakeholders on what success looks like.
Use available data to segment new customers by characteristics like acquisition channel, demographics, browsing behavior, and predicted value. Identify segments with different price sensitivities.
For each segment, estimate how discount depth affects conversion and long-term value. Use historical data, cohort analysis, or run small-scale tests to measure elasticity.
Implement A/B tests offering different discount levels to each segment. Measure incremental impact on conversion, repeat purchase, and LTV, while monitoring margin.
Based on experiment results, set discount levels per segment that maximize the objective. Continuously monitor and adjust as market conditions or customer behavior change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.