AI for Product Recommendations on E-commerce
E-Commerce
Industry
147.000
Products
4 M
Active Registered Customers
A major player in the e-commerce sector, with more than 4 million active registered customers and a catalog of approximately 147,000 products, wanted to improve the effectiveness of its promotional campaigns by making Client Product Recommendations more relevant to each individual customer.
Improving Campaign Performance
The company’s existing recommendation strategy was based primarily on best-selling products, showing essentially the same products to every customer.
The objective was to move beyond this static approach and introduce a data-driven recommendation strategy capable of:
Personalize
Personalizing product recommendations to identify what each customer is most likely to purchase next;
Engage
Increasing engagement with promotional communications;
Convert
Improving conversion performance across email campaigns and website pages.
Predicting the Next Best Product
Using Elai, the company developed a Next Best Product predictive model capable of analyzing historical purchasing behavior and predicting the products most relevant to each customer.
The Elai platform was connected to the company’s CRM and purchase tracking systems, allowing the model to learn directly from historical customer transactions and continuously improve its recommendations.
From Purchase Data to Activation
01- Connect & Prepare Data
Historical purchases were integrated into Elai, using data from approximately 250,000 users, 147,000 products and 8 million purchases.
02 – Train & Predict
The model analyzed purchasing sequences to predict the most relevant next products for each customer, with iterative validation and refinement.
03 – Activate Recommendations
Personalized product recommendations were deployed across promotional emails and website pages.
Personalized Product Strategy
Elai enabled the company to replace its static recommendation approach with a more personalized strategy.
Before
5
Best-selling products
The same five popular products were shown to every customer, regardless of individual preferences.
After
5 + 5
Trending + Personalized
Five trending products followed by five AI-predicted recommendations tailored to each customer’s purchasing behavior.
Seamless Integration
The recommendation system was connected via API to marketing automation and content personalization platforms, enabling continuous and automated model management.
The Impact of Personalization
Thanks to Elai, the e-commerce company was able to move from a static, one-size-fits-all recommendation strategy to a predictive approach based on individual customer behavior.
By transforming historical purchasing data into personalized Next Best Product predictions, the company increased the relevance of its recommendations and improved conversion performance across its digital customer touchpoints.