This is a lot to hold in your head at once.
Start by clarifying the business objective and constraints, then propose a data-driven framework that covers diagnosis, segmentation, experiment design, and decision criteria. Emphasize how you would balance short-term revenue gains with long-term customer retention and product value perception.
Pro tip: Anchor your analysis in customer willingness-to-pay and elasticity, and always include a holdout group to measure long-term effects—leadership will appreciate the rigor and foresight.
Understand why leadership wants to raise prices: revenue targets, cost pressures, or value enhancements. Analyze current pricing, customer segments, usage patterns, and competitive landscape to establish a baseline.
Use historical data, surveys (e.g., Van Westendorp), and conjoint analysis to estimate how demand changes with price. Identify which segments are most and least price-sensitive.
Based on elasticity and business goals, decide on the magnitude and structure of the price increase (e.g., flat increase, tiered pricing) and which customer segments to apply it to (e.g., new vs. existing, heavy vs. light users).
Randomize eligible customers into treatment (new price) and control (old price) groups. Define primary metrics (e.g., conversion, retention, revenue per user) and guardrail metrics (e.g., churn, customer satisfaction). Ensure sufficient power and duration.
Compare treatment vs. control on key metrics, assess statistical significance, and evaluate long-term impact via holdout. Weigh revenue gains against churn risks and provide a clear go/no-go recommendation with rollout plan.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.