Content based filtering focuses telegram user database on the specific shopper. The product recommendation software tracks a users actions, such as web pages viewed, products clicked on, time spent on various categories, and items added to cart.
Based on this information, a customer profile is created. This profile is then compared to the product catalogue to identify which items to show.
3. Hybrid Recommendations
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The best recommendation software actually combines both techniques to give the most accurate prediction. This is how Barilliance works.
By combining both techniques, product recommendation engines are able to apply the "wisdom of the crowd" to prospects before they gather much data. As more information is learned about that particular user, recommendations become more and more personalized based on their session and use history.
Dynamic Product Recommendations: Don't use static product recommendations. Click Here to see how Barilliance personalizes recommendations on your home, category, and product pages.
Merchandizing Rules
In brick & mortar, stores are forced to choose a single merchandising strategy.
Retailers can use personalization technology to create specific merchandizing strategies for any segment of customers. One of the main tools retailers use to accomplish this are product recommendations.
How Merchandizing and Product Recommendations Interact
By default, product recommendation engines work algorithmically.
However, the best engines allow retailers to "overrule" the software's recommendations in lue of explicit merchandizing rules you set up.
Exmaples include: