Marketplaces · Revenue & Lifecycle Intelligence, Experimentation, Forecasting & Applied ML
Price optimisation for marketplace growth
Global Dating Marketplace
Delivered dynamic pricing and forecasting models that lifted daily revenue by 10%.
Problem
Pricing decisions relied on manual spreadsheets, producing volatile margins and limiting the ability to react to competitive changes.
Solution
- Defined the pricing hypotheses, guardrails, and success metrics with product and finance.
- Built demand elasticity models in Python with dbt powered feature pipelines feeding Superset dashboards.
- Ran controlled experiments to validate the models before automating weekly recommendations.
- Hired and trained analysts to own pricing playbooks, documenting workflows in Confluence for iterative improvements.
Outcome
- Revenue teams received trusted forecasts and recommendations via scheduled reporting and ThoughtSpot search.
- Marketing and product aligned on a prioritised experimentation roadmap with quantified impact.
- Leadership gained forward looking visibility for planning hiring, supply, and promotional spend.