ML Customer Intelligence Patterns, Purchase & Behaviour

Built a predictive ML solution for e-commerce transforming how shoppers are understood. The system draws on purchase histories, browsing behaviour, and product rating trends to build multidimensional customer profiles inferring preferences not obvious from any single interaction.

The platform integrates collaborative filtering with deep learning at enterprise scale architectures capable of modelling complex user-product relationships detecting subtle affinities among customers who appear unrelated on the surface. Customers frequently purchasing thermal imaging equipment are guided toward complementary accessories enhancing their workflow automation patterns. Intelligent cross-selling increases conversion rate optimisation while enriching the customer journey. Sentiment analysis from product reviews interprets customer opinions to adjust recommendations reflecting not just what users buy, but how they feel about those purchases.

The cold start problem was the first technical hurdle. New users with no purchase history can't benefit from collaborative filtering - there's nothing to collaborate on. We built a three-tier fallback system: for users with 10+ purchases, collaborative filtering; for users with 1-9 browsing sessions, content-based filtering using product attributes; for brand new users, editorial curations from merchandising. As users accumulated signals, they graduated automatically to more personalized tiers. Measuring the quality of recommendations at each tier guided investment in improving the weakest tier.

The exploration vs. exploitation trade-off required careful calibration. A pure exploitation strategy (always show the highest-probability recommendation) creates filter bubbles - users see the same product types repeatedly, never discovering new categories. We added an exploration budget: 15% of recommendation slots show "serendipitous" recommendations from categories adjacent to the user's history. This exploration generated some of the highest-margin cross-category sales while preventing the monotony that causes engagement decay.

Measuring recommendation quality proved harder than building the model. Offline metrics and alerting (precision@K, NDCG) measured whether our predictions matched held-out interactions. But the business cared about incremental revenue - revenue we wouldn't have earned without the recommendation system. We built incrementality tests: randomly suppressing recommendations for a holdout group and comparing their purchase rates to the recommended group. True incremental lift was 23% lower than naive attribution suggested, which shaped how we presented ROI to stakeholders.