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    Picture this. Recommendations that show your customers what they want, right when they want it. Wouldn’t that be great? 

    In the past, recommendations was a “bolt-on” feature that lived separately from the rest of a site, which created a disjointed experience with limited impact. 

    In some cases, these recommendation widgets made up the entire personalization strategy for companies. And conversion improvements were limited because they weren’t personalized to an individual at all but rather they were based on the behaviors of large groups and applied to you as an individual. 

    Overall, personalization powered by a separate recommendations vendor resulted in little business impact.

    To deliver relevant customer experiences through personalized recommendations across the entire customer journey, you need to deeply understand the products and services you’re recommending. 

    To achieve this, Bloomreach understands which facets/filters perform best for each query, which product attributes an individual and a segment cares most about and which products perform best for each query.

    Recommendations need to be unified with search data to be most effective. The ability to personalize and to understand the user shopping intent by analyzing the search and browsing behavior in real-time is a unique Bloomreach offering. Adding search intelligence to the recommendations engine dramatically improves the quality of recommendations and therefore leads to higher revenue.

    Today, Bloomreach adds 3 new exciting features to the Recommendations Module of the Bloomreach Experience Platform (brX).

     

    Trending Products Algorithm

    Shoppers often tend to buy products that are in fashion and popular. To engage shoppers and improve the product discovery experience, especially on the home page, merchandisers are inclined to display products that are currently popular among site visitors.

    Bloomreach’s new Trending Products Algorithm helps to recommend the products that are popular on the site based on the visitors’ interactions and browsing behaviors. The Algorithm helps customers to improve the product discovery experience in a global context such as on the home page, null-search result page, etc.

    This helps to encourage customers to explore trends and products that are currently in fashion and popular and therefore helps merchants to achieve higher site engagements and ultimately increase revenue.

     

    Merchandising for Recommendations

    Recommendations are a powerful merchandising tool, but they reflect what customers are actually doing - not what the merchandisers want them to do. 

    With the new Recommendations release, merchandising rules can be created for all the recommendations algorithms. Merchandisers and marketers can now add manual curation to any recommendation algorithm to tailor the results according to their business requirements. Merchandisers can take a more active role in guiding customers to results that help the business achieve goals (and help the customer find the best product, not just the most popular).

     

    Top Performing Products Report

    With merchandisers now empowered to optimize the results of the recommendation, it is important to also have data about how the recommended products perform. Without this capability, it is difficult to assess whether the business goals being targeted are actually achieved.

    Available with Bloomreach Recommendations, the Top Performing Products report provides insights regarding the performance of products within the recommendation widgets to help merchandisers optimize and curate the recommendation results accordingly. Using product-level data such as clicks, revenue, conversions, and so on, with respect to the widget, the merchandiser can perform various merchandising actions to re-rank the product placements within the widgets, which improves the conversions and revenue of the products and overall widgets.

     

     

    Recommendations in the past have been a “bolt-on” feature. But adding search intelligence to the recommendations engine dramatically improves the quality of Recommendations and therefore leads to higher revenue. With the ability to recommend products that are popular on a site, add manual curation to any recommendation algorithm and have insights into the performance of these recommendations, teams can feel empowered to deliver great commerce experiences and increase overall revenue.

    Ready to give it a try? Reach out for a demo of Bloomreach Recommendations today.

    Niklas Winkels

    Niklas Winkels

    Niklas is a Product Marketing Manager and Developer Relations Expert with a background in Demand Generation.

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