Don’t be a stranger, make every interaction personal

Dynamic personalisation based on real-time data

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Every brand and ecommerce site is different, requiring tailored personalisation strategies. Build highly relevant search & discovery experiences based on these. Remain in full control and determine the optimal personalisation strategies for your shoppers by applying and testing different personalisation algorithms, triggers and weights.

Build highly personalised search and discovery experiences

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Search results

Tailor search results to each shopper’s interests and intent, based on real-time behaviours.

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Product Listing Pages

Optimise conversion by displaying the products your shoppers are most likely to buy.

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Personalise at Scale

Deliver personalised rankings at each step of the journey.

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One-to-one personalisation

Individualised experiences are the future! Every shopper likes to be an audience of one. Delight customers with experiences tailored to their particular interests and intent.

One-to-many personalisation

One-to-one personalisation isn’t always necessary or possible. Overcome data limitations by providing relevant recommendations on a segment basis.

Segmentation for one-to-one and one-to-many can be based on:

  • Filters & facets (icons)
  • Category and product listing pages
  • Recommendations
  • Content
  • Omnichannel
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Our customers have experienced incredible results from applying our personalisation algorithms

  • 80% of the turnover now generated via 20% of the products that have personalised recommendations applied
  • 21.1% increase in adds-to-basket from 1:1 personalisation
Read the Cdiscount case study

“Attraqt makes it possible to really act on the data that we get from different platforms, and use it in different campaigns, facets, synonyms, as well as redirects, and be able to, on a continuous basis, improve the experience for our customers.”

Stein Berens Online Merchandiser, De Bijenkorf

For inspiration on how to personalise the shopper journey for memorable omnichannel experiences, please consult our use case library