Client Snapshot:
A leading fashion retailer whose product recommendations weren’t keeping up with what customers actually wanted.
Challenge:
The retailer’s existing recommendation engine was generic, showing the same popular items to nearly every visitor rather than adapting to individual browsing history, preferences, or past purchases. Customers were receiving recommendations that didn’t match their taste, and the retailer was leaving conversion and repeat-purchase revenue on the table as a result. The team wanted personalization that felt genuinely relevant, not just a “customers also bought” widget bolted onto the product page.
Approach:
Scadea’s data science team started by auditing the signals already available in the retailer’s data, browsing behavior, purchase history, and product attributes, to understand what a real personalization model could learn from. Rather than replacing the existing recommendation widget with another generic system, the team designed a machine learning approach built specifically around the retailer’s own customer behavior patterns.
Solution:
Scadea built a machine learning-based recommendation engine that learns from individual browsing behavior, purchase history, and stated preferences to surface products tailored to each customer, rather than showing the same catalog highlights to everyone. The model was integrated across key touchpoints, including the homepage, product pages, and post-purchase emails, and designed to keep learning from new interactions so recommendations improved the more a customer engaged with the site.
Results:
Customers now see recommendations that reflect their own browsing and purchase history rather than generic bestsellers, making the shopping experience feel more relevant from the first visit onward. The retailer has a recommendation system that improves continuously as it learns from customer behavior, giving the business a personalization capability it didn’t have before and a foundation to extend across additional touchpoints over time.
