Building a Customer 360 for Personalization at Scale
Case Study
How a European Beauty Retailer Turned Transactional and Behavioral Data into Personalized Customer Experiences
A European beauty retailer had millions of customer interactions across its website, mobile applications, CRM, loyalty programme and physical stores. Although the company had extensive customer data, it was fragmented across systems and difficult to use consistently for personalization.
Dateonic implemented a Databricks-based Customer 360 foundation combining transactional, behavioral and product data.
The platform became the basis for recommendation engines, customer segmentation and propensity-to-buy models.
Turnover
8+ Months
Industry
E-commerce platform
Technology:
Azure Databricks, Delta Lake, Unity Catalog
Creating a Unified Customer View
Customer transactions, clickstream events, product interactions, campaign responses and loyalty information were integrated into a common analytical model. This allowed data scientists to work with a consistent representation of customer behavior rather than stitching together multiple systems for every model.
Personalizing the Digital Experience
The platform supported recommendation models using purchase history, browsing behavior, product attributes, promotions and availability. Models could be retrained more frequently as new customer behavior entered the platform.
From Segmentation to Propensity
The retailer also introduced propensity-to-buy models to identify customers showing signals of purchase intent. Predictions could be consumed by marketing and digital channels to determine which customers should receive a specific offer or recommendation.
Business Impact
- Unified Customer 360
- More frequent recommendation model refreshes
- Propensity-to-buy scoring
- Personalized product recommendations
- Reusable ML feature foundation
- Lower dependency on fragmented customer data