Recommend the perfect look to your customers thanks to innovative AI technologies.
Imagine you’re going to the mall to buy some summer pants. In the window you see the type of pants you had in mind: Light blue linen pants in your cut. The mannequin is also wearing a top that matches the color of the pants. You decide to try on the two items in the store. You end up buying both – the pants and the top. Does it sound familiar?
This is known as the Diderot effect: After people have bought or want to buy an item, they feel the urge to buy further items to create a coherent, complete image.
You, the retailer, can apply this principle very effectively at every customer touchpoint. For example, in your online shop, through personalized recommendations: The customer’s individual preferences are tracked from the very first click by a recommendation engine using AI technologies (e.g. reinforcement learning). In the fashion industry in particular, personalized customer service offers huge potential for inspiring customers and encouraging them to buy.
prudsys is a leading provider of real-time personalization in omnichannel business, increasing customer loyalty and turnover for many major fashion retailers in Germany.
This white paper sets out the advantages of a digitally-guided customer journey, describes successful application scenarios and explains how you can use the Diderot effect in your sales strategy. You will also learn about the technological basis of real-time personalization.
We offer this white paper available to you free of charge in exchange for some personal information.
How to download the white paper
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Use our recommendation engine to offer your customers product and content recommendations at every touch point as well as highly relevant personalized search results. Score points with a unique customer experience and maximize customer loyalty and your earnings.
If you wish to dive deeper into the topic of dynamic prizing, we recommend our free white paper to you. Find out what you as a retailer may gain by using dynamic prize optimization and which retail use cases are promising. The white paper also explains the technical basics of dynamic pricing and addresses central issues of customer protection.