E-commerce Product Recommendation Engine

Bigger baskets from every portal visit

Personalised offers: cross-sell and up-sell based on customer micro-segmentation, using internal and external data.
Smarter substitution: higher-margin substitutes recommended automatically when they are available.
Dynamic pricing: prices based on customer profiling and real-time buying and browsing behaviour.

Complexica's E-commerce Recommendation Engine is used within portals and other digital environments to generate dynamic and personalised cross-sell and up-sell offers. Based on self-learning algorithms that automatically update in real-time, our E-commerce Recommendation Engine "self-tunes" to create improvements in the conversion rate and average order value, as well as the customer shopping experience.

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To maximise relevance, conversion, and margin, our E-commerce Recommendation Engine goes beyond simple affinity analysis of market baskets to provide your online portal with:

Optimised cross-sell and up-sell offers based on customer micro-segmentation analysis that draws on both internal and external data
Automated product substitution recommendations where a higher-margin substitute is available. Product descriptions are read by Larry, the Digital Analyst® in natural language to generate a product substitution matrix, which improves over time through machine learning
Dynamic pricing based on customer profiling and micro-segmentation analysis, as well as real-time buying and browsing analysis.

For products and services limited by capacity or supply, yield can be maximised by factoring in demand, supply, customer history and behaviour, as well external factors such as time of day and device.