28% higher conversion with AI-driven personalization
A major Dutch retailer increased online conversion by 28% through personalized product recommendations and dynamic content.
0%
Higher conversion
0%
More repeat purchases
0.0x
ROI on implementation
0.0M
Customer profiles
- Client
- National Retailer
- Sector
- E-commerce
- Result
- 28%Higher conversion
Background
The challenge
With over 2 million customers and 50,000 products, it was impossible to manually make relevant recommendations. The generic 'bestseller' approach resulted in low conversion and high bounce rates.
Key problems
- Generic product recommendations for all visitors
- Low click-through rates on email campaigns
- High cart abandonment due to irrelevant upsells
- No real-time personalization possible
Methodologie
Our approach
We implemented a real-time recommendation engine that combines customer behavior, product characteristics, and context data for personalized experiences.
01
Customer 360
Creation of unified customer profiles by integrating website behavior, purchase history, and email interactions.
02
Recommendation Models
Development of collaborative filtering and content-based models for different touchpoints.
03
Real-time Serving
Implementation of low-latency inference API for real-time personalization on website and in emails.
04
A/B Testing Framework
Setup of experimentation platform for continuous optimization of recommendation strategies.
Impact
Results
The personalization engine was rolled out across all digital channels and delivered significant improvements within 3 months.
- 0%
Higher conversion
Through relevant product recommendations at key moments
- 0%
More repeat purchases
Customers return more often due to better experience
- 0%
Higher email CTR
Personalized product blocks in newsletters
- -0%
Lower bounce rate
Visitors find what they're looking for faster
“The impact was immediately visible in our KPIs. Within a month we saw significant improvements in conversion and customer engagement.”
Director E-commerce
National Retailer
Technologies
Tools & platforms used
- Python
- Apache Spark
- Redis
- Google Cloud
- BigQuery
- Looker
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