Start by framing the problem around customer lifetime value (LTV) and price elasticity, then propose a phased approach: first analyze historical data and run a conjoint survey to estimate willingness-to-pay, then design a controlled A/B test with gradual rollout to measure demand elasticity, churn, and revenue impact across segments. Finally, recommend a data-driven pricing strategy with guardrail metrics and a rollback plan.
Pro tip: Emphasize the importance of measuring long-term effects (e.g., churn, brand perception) and not just short-term revenue; also, consider competitive dynamics and the risk of alienating price-sensitive segments.
Clarify the goal (e.g., maximize revenue or profit) and establish guardrail metrics like churn rate, customer satisfaction, and conversion rate to ensure the price change doesn't harm the business.
Use historical data, competitive analysis, and conjoint surveys to estimate how demand varies with price across different customer segments.
Implement a randomized controlled trial (A/B test) with multiple price points (e.g., 10%, 15%, 25% increases) and a control group, ensuring proper randomization and sufficient sample size.
Measure the impact on key metrics (revenue, churn, LTV) overall and by segment; identify which segments are most sensitive and which price increase maximizes revenue without excessive churn.
Based on experiment results, recommend a phased rollout to a small percentage of users, monitor guardrail metrics, and be prepared to roll back if negative effects emerge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.