Serve your customers’ better by understanding their needs and personalising their interactions

By using ‘unsupervised’ machine learning techniques, we can find hidden patterns and relationships in data which can be used to understand customers’ needs and target your offering in a highly personalised way.

  • Identify distinct groups of customers with similar needs or behaviours so you can devise appropriate product and marketing strategies for each group.

  • Automatically recommend products or content to customers based on the combination of (a) what we know about them (b) what we know about other customers who are similar to them

  • Boost cross-selling by identifying which products or content complement each other.


Clustering and Segmentation

Customer Segmentation identifies groups of customers where each group (segment) has different needs or behaviours.

This enables you to devise winning product and marketing strategies for your target segments.

As well as the standard k-means clustering algorithm, we also apply algorithms which can provide a more intelligent fit to the data, such as Gaussian Mixture Models and density-based spatial clustering (DB-SCAN).

Web Design

Recommendation Engines

Serving relevant, personalised content to customers improves their experience, strengthens their connection with your brand, and increases engagement with your services.

Depending on the situation, various approaches can be used (in combination) to surface relevant recommendations, based on:


  • Identifying what content similar customers have consumed/liked


  • Customers’ stated preferences


  • Text-based product or content descriptions

Online Shopping

Association Analysis

Association Analysis (a.k.a. market basket analysis) identifies associations between things such as:

  • What items are typically bought together?

  • What other items or content do users browse when looking at this item?

  • Or generally, people who do X are Y times more likely to do Z.

The technique can be used to boost cross-sell, by displaying relevant alternatives to customers or by helping you create bundles of complementary products.