A dynamic pricing model can raise prices whenever demand signals look strong, capturing more revenue per unit. Push that too far, though, and customers notice steep, frequent price swings, and trust in the fairness of the pricing erodes in ways that outlast any single transaction.
The AI Price Optimization Lab models 200 products, each with its own demand strength and price elasticity. Sliding dynamic-pricing aggressiveness up lifts modeled revenue on high-demand products while a customer-trust index falls as price swings grow more frequent and steep.
The interesting part of this case study is that the revenue gain and the trust loss show up on completely different timescales — the extra revenue lands this month, while the trust erosion compounds quietly and shows up as lower demand much later.
🧪 Try it yourself: the AI Price Optimization Lab simulation lets you slide pricing aggressiveness and watch the monthly outcome update live.
🧪 Try it yourself: the AI Price Optimization Lab simulation lets you experiment with everything described above directly in your browser.