Are traditional KPIs and benchmarks really predicting?

Are traditional KPIs and benchmarks really predicting beauty product concept strength?

The high failure rate of beauty product launches has long been accepted as part of innovation but advances in AI, data science and meta-analysis research are challenging that assumption. In this session, Cambri and Revlon explore why traditional KPIs, purchase intent metrics and category benchmarks often fail to accurately predict in-market success.

Drawing on large-scale meta-analysis research, AI-powered predictive modeling, machine learning, and analysis of point-of-sale (POS) data, organizations can now combine concept testing, consumer feedback and real sales performance to achieve a more accurate understanding of concept strength and launch potential.

The session will examine how leading companies are moving beyond benchmark-led decision-making toward more predictive, evidence-based innovation systems.

Louise Duvall, director of insights and analytics at Revlon, will share perspectives on combining consumer understanding, creativity and AI-driven learning to improve innovation outcomes and reduce launch risk.

Key takeaways:

1. From data to decisions: Learn how AI uncovers patterns in concept and POS data that traditional KPIs and benchmarks often miss.
2. Beyond purchase intent: Explore how qualitative feedback, behavioral signals and contextual data can provide a more accurate view of concept strength and consumer response.
3. Real-world impact: Discover how leading organizations are using AI, machine learning and meta-analysis research to improve launch decisions, strengthen innovation pipelines and reduce product failure rates.