Dynamic Pricing Decision Support for a Boutique Fitness Class Marketplace
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Problem
Flat pricing, empty slots
A boutique fitness marketplace aggregating around 50 partner studios priced every class credit at a flat rate regardless of demand. Peak evening sessions sold out in minutes while mid-morning slots ran 40% empty, partner studios questioned their payout economics, and the operations team spent roughly 14 hours a week hand-adjusting prices in spreadsheets with no forecasting to guide them.
Solution
Forecast-driven pricing recommendations
We built a demand-forecasting and pricing decision-support system rather than a black box. Slot-level models forecast fill probability for every upcoming class, a recommendation engine proposes credit prices within agreed floor and ceiling guardrails, and the operations team reviews and approves changes from a single dashboard. Partner studios receive transparent monthly reports explaining how their pricing moved and why.
Measurable Impact
What changed after launch
Off-peak class fill rates improved from 46% to 68% within 4 months of rollout
Revenue per available class slot up 19% across the 50-studio network
Weekly manual pricing effort cut from roughly 14 hours to under 2
Partner studio monthly churn reduced from 3.1% to 1.9% following transparent pricing reports
Tech & Tools Used
What powered the build
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