Until recently, visitors to the webshop received the same offerings, without any personal recommendations based on their previous purchases or interests. As a result, many fans overlooked relevant products, and PSV missed out on opportunities to enhance the customer experience and increase sales. PSV aimed to create a webshop that caters to the preferences of individual supporters. For instance, the site could automatically display the latest away shirt to fans who traditionally buy it every season, or suggest popular combinations of items.

Datacation has developed a hybrid recommendation system that considers purchase history, similar products, and demographic data. This system provides fans with personalized product recommendations based on their individual preferences and previous buying behavior.

The algorithm is designed to automatically learn from new interactions, allowing the recommendations to continuously align with the fans' behavior. PSV conducts A/B tests to evaluate the impact of the system and to implement further optimizations.

The recommendation system enhances the user experience of the PSV webshop and boosts conversion rates.

• Fans receive personalized recommendations tailored to their purchasing behavior. 
• The webshop and email campaigns lead to increased conversions by offering more relevant product suggestions. 
• The model continuously improves by learning from new interactions.