Machine Learning: Predictive Personalization for E-Commerce

Built a predictive learning solution for e-commerce that identifies customer behavior patterns using purchase histories, browsing activity, and product ratings. By applying collaborative filtering and deep learning at enterprise scale techniques, the system uncovers subtle connections between users and products to deliver highly personalized recommendations.

For example, a shopper who frequently purchases FLIR thermal products might be prompted with complementary items such as tripod mounts for thermal cameras, protective carrying cases, or advanced lens attachments that enhance imaging precision. The recommendation engine can also surface related categories like rugged outdoor gear, inspection drones equipped with thermal sensors, or specialized software for analyzing infrared data.

By intelligently connecting these product ecosystems, the solution not only boosts cross-selling opportunities but also enriches the customer's shopping journey with items they might not have discovered on their own. To further enhance accuracy, the system incorporated sentiment analysis from product reviews, allowing recommendations to adapt not only to buying habits but also to customer opinions and preferences.

The cold start problem was addressed with a three-tier fallback: collaborative filtering for users with 10+ purchases, content-based filtering for users with browsing history, and editorial curation for brand-new visitors. An exploration budget (15% of recommendation slots) prevented filter bubbles by surfacing serendipitous cross-category suggestions which generated some of the highest-margin sales. Measuring true incremental lift via holdout tests revealed that naive attribution overstated ROI by 23%, shaping how results were presented to stakeholders.