Galadrim applied AI-powered video analysis to objectively measure the cosmetic gesture and enrich Chanel’s understanding of the customer experience.
Chanel’s Neuroscience team wanted a scientific, repeatable way to study how customers use skincare and make-up products during video-based tests. The core goal was to transform the inherently subjective notion of “ease of use” into quantifiable data about how a person applies a product.
A working hypothesis guided the study: fewer application movements generally correlate with greater ease of use.
Capture the way a product is applied (who, where, how).
Turn raw video into structured metrics—notably counts and categories of movements—that can be compared across products, protocols, and participants.
We implemented video recognition algorithms in Python using MediaPipe to analyse application sequences.
Body & hand landmarking: The system detects and tracks the head, hand, and fingers of the participant during application.
Motion capture: It records trajectories and movement patterns throughout the routine.
Semantic classification: Using AI, movements are counted and categorised into meaningful gesture types (e.g., half-circle massage, tapping), enabling like-for-like comparisons.
This pipeline turns each test video into objective measurements that can be aggregated or reviewed at the level of a single participant, a specific product, or an entire study.
Objective ease-of-use indicators: Movement counts and types provide a concrete basis for comparing application experiences.
Consistent evaluation across tests: A standardised, automated approach reduces variability inherent in manual scoring.
Actionable insight for product teams: Teams can spot patterns (e.g., excessive tapping or complex sequences) and optimise formulas, textures, or applicators accordingly.
