Recommendations

Recommendations

Open the black box and take control of what your customers see

Recommendations built around your merchandising needs

  • Collaborative filtering
  • Product matching upsell and cross-sell
  • Custom models

Apply ‘other people looked at this’ logic to your recommendations and help people explore more of your product portfolio

Add recommendations further down the funnel to help customers find additional products and increase average order values

Build your own algorithms that take into account your specific nuances ensuring that recommendations are relevant for your business rules

More visitor knowledge, better results

  • Product interest
  • Affinity scoring
  • Truly incremental

Evolve your recommendations on the fly through knowledge of your visitors’ last viewed products

Automatically score your products based on relevance to the visitor and ensure they get airtime they deserve

AB test our product recommendations to understand the true impact they are having on your visitors

Make recommendations throughout the customer journey

  • Homepage
  • Product
  • Cart
  • Auto filters

Take over hero blocks with recommendations based on recent browsing history or your own custom model to boost engagement at vital times

Deploy our recommendations ‘up high’ or ‘down low’, ensuring that you get the best visibility and cross-sell opportunities

Take advantage of that ‘last chance upsell’ by delivering recommendations in the cart, based on what your users have seen

Increase average order and speed to purchase by showing only products that are currently in stock

Additional features

Seamless integration

Quickly deploy with no extra code

Performance metrics

See impact on conversions and revenue

Model optimization

Get more relevant as time goes on

How Adaptive targeting enhances Recommendations

Did you know that 10-30% of typical ecommerce site revenues are generated from product recommendations?

Thanks to our Adaptive targeting technology you can promote personalized product recommendations to your “thirty-something” high value returning visitors living in the New York area. Our targeting and segmentation technology automatically take into account new insights as they are observed or gathered from a variety of digital sources and adjusts to these segments.

TeeTurtle case study

Ramy Badie, CEO TeeTurtle, shines light on the weird and wonderful world of anime and how Qubit has helped gamify the ecommerce shopping experience. On TeeTurtle’s homepage is a custom recommendation engine build by Qubit that takes into account referrer, previous purchases and current trends

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