Complexica's E-commerce Recommendation Engine is powered by Larry, the Digital Analyst® and provides dynamic and personalised cross-sell and up-sell recommendations for B2B and B2C selling environments. 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.
To maximise relevance, conversion, and margin, our E-commerce Recommendation Engine goes beyond simple affinity analysis for analysing market baskets to provide your e-commerce site with:
- Cross-sell and up-sell recommendations based on customer micro-segmentation analysis that draws on both internal and external data
- Product substitution recommendations for out-of-stock items, or items where a higher-margin substitute is available. Product descriptions are read 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
To learn more about our E-commerce Recommendation Engine, please contact us