Your recommender model shows 85% precision in back-testing. Your attribution dashboard claims 40% of revenue. But how much of that is truly incremental? In the rapidly evolving landscape of AI-driven retail, the distance between a good model and a good personalization...
In earlier articles, we have discussed the business of building recommendations: see Horses for Courses, and Hello Rubber, Meet Road. These have focused largely on offline recommendations that are generated and sent in batch mode; here, we shall focus on the real-time...
Our Journey: Why We Shifted to Rust When we first decided to look beyond R and Python for data science, it wasn’t because we were chasing performance benchmarks or intrigued by language design. It was something more mundane — deployment pain. Our product had to...
“No one told you when to run, you missed the starting gun” Pink Floyd, Time Understanding Time-Based Customer Behaviour Models In our previous discussions about predictive modelling for customer retention, we explored models that predict a customer’s...
On the importance of connective tissue between technical solutions and business problems. Also on Indiana Jones, Monty Python and the Holy Grail, and such other important matters. Sometimes, enterprise data science can feel like one of those scenes in the Indiana...
