Multiple survey participants
Predictive models
Key actionable features
>High score in metrics
A leading beverage company struggled to decode consumer behavior across categories, with siloed analytics limiting insight into awareness, perception, and consumption. To move from assumptions to evidence, we applied machine learning to survey data from multiple consumers, classifying segments, uncovering key drivers, and empowering teams to make targeted, data-driven marketing and product decisions.
Key challenges
Manual handling limiting scalability and insight generation
Difficulty linking consumer behavior to marketing strategy
Unclear understanding of consumer motivation
Data complexity and non-actionable variables
No predictive insight into future trends
The solution
Data engineering and modeling
Built different category models
Grouped and refined features
Applied PCA to key variables
Insight generation
Linked behaviors to levers
Used SHAP and dependency analysis
Revealed influence and quantified feature contribution
1
Data preparation
Cleaned multiple surveys
Grouped features and filtered
2
Modeling
Built category models
Snowflake deployment
3
Activation
Used explainable AI
Shared insights enterprise-wide
Marketing and digital Initiatives
Optimized digital and sponsorship campaigns
Enhanced consumer reach and engagement
Product strategy
Guided product improvements based on quality perception
Aligned packaging and pricing with consumer preferences
Sales and inventory
Improved product availability and sale alignment
Refined inventory management via facilitator insights
Sales and inventory
Improved product availability and sale alignment
Refined inventory management via facilitator insights
Transform your enterprise with AI that delivers
