What specific techniques have you used while optimizing price per person?

Situation: I was working on a subscription service where we needed to adjust the price per user to maximize revenue while staying competitive. Task: My goal was to identify the optimal price point that balanced user acquisition and profitability. Action: I first performed a cohort analysis to segment users by usage patterns and willingness to pay. Then I applied price elasticity modeling to estimate how demand would change with price adjustments. I ran A/B tests on a subset of users, varying the price and measuring conversion rates, churn, and lifetime value. I also used clustering to identify high‑value segments that could tolerate higher prices. Finally, I built a dynamic pricing engine that adjusted prices in real time based on demand signals and inventory levels. Result: The new pricing strategy increased revenue by 12% over six months and reduced churn by 3%, while maintaining a positive user growth rate. This experience taught me the importance of combining data analysis, statistical modeling, and controlled experimentation to make data‑driven pricing decisions.