Data analytics and machine learning in retail and consumer goods

Winning market share and reducing costs by better understanding retail consumers

How are you responding to today’s unprecedented macro volatility, fast-moving competitive landscape and constantly shifting customer expectations? The Retail and CPG brands that survive this tsunami of disruption will be those that serve up personalized, engaging customer experiences at every turn. But to do that, they’ll have to adopt platforms and practices that leverage advanced analytics and AI — all while supporting exponential scalability and ever-changing scope

Learn how data-driven companies adapt to unprecedented macro volatility

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Latest blog posts, webinars, and case studies

Why Databricks for Retail & CPG

Deliver high-impact analytics in tight SLAs

Databricks enables customers to deliver the most demanding of analytics to the front-line in your service windows. Need to allocate inventory or predict on-shelf availability for every store and SKU today? It’s not a problem.

Power personalized experiences with real-time data

Real-time awareness drives higher relevancy in recommendations, leading to higher incrementality and stronger customer satisfaction. Databricks enables companies to incorporate batch and real-time data of all types to power your e-commerce and mobile experiences.

Greater agility leads to improved resiliency

In this era of volatility, retailers and consumer goods companies need a platform that enables them to respond to changes in real-time. Deliver new insights and analytics in days and weeks, not months with your traditional warehouse.

Use cases

From in-store to mobile, consumers are expecting personalized omnichannel experiences. With Databricks, retailers and consumer brands can truly support a 360 understanding of their customers and have a real-time view of their supply chain.

Supply Chain Inventory

Improve accuracy in inventory predictions by understanding customer demand, enabling you to reduce excess inventory while avoiding lost sales.
Supply Chain Control Tower
Time series forecasting
Causal forecasting
Safety stock analysis
On-shelf availability (OSA)
SKU rationalization


Drive incremental revenue through enhanced segmentation based on deeper behavioural insights
Consumer segmentation
Customer lifetime value
Survival analysis & churn
Propensity to buy
A/B testing
Personalized recommendations

Pricing & Promotion Optimization

Optimize pricing during key moments of the customer lifecycle or season
Dynamic pricing
Price optimization
Promotion optimization
Promotion effectiveness

Additional Resources

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