A global beauty company's analytics and innovation teams faced persistently high new product launch failure rates, driven by inaccurate forecasting and over-optimistic assumptions. Innovation teams consistently overestimated execution plans, misaligning forecasts with reality. There was no systematic capture of launch learnings to improve forecast accuracy for future innovation.
Cloverpop built a new product launch forecast on product attributes, planned execution, and market trends with a comprehensive scenario simulator. Statistical models incorporated quantitative execution variables and categorical product attributes to project execution parameters and forecast sales.
Results
30+ new product concepts forecasted
30% improved forecast accuracy
Fact-based execution assumptions replaced over-optimistic planning, reducing launch failure rates and grounding forecasts in data. A scenario simulator gave innovation teams a structured way to plan and pressure-test launch scenarios. A decision flow connected ideation through to launch, and a system of record captured decisions and learnings for continuous improvement across future innovation cycles.