You have probably read all the hype about Machine Learning (ML) for e-commerce. Supposedly, it can read the minds of customers and predict what they are about to do. Maybe it will someday, but the fact is that we’re not there yet.
The problem is ML is so riddled with misconceptions and marketing lingo that it's easy for retailers to become blind to the hype.
Machine Learning is destined to become a huge asset to retailers looking to automate many of their most resource-intensive and costly manual processes in order to gain meaningful insights on their customers and, most importantly, to generate clicks, purchases, repeat visits, positive ratings and operational efficiency.
But what is ML really? Why is it important? How do you know if you need it? If yes, how do you evaluate ML vendors? And why hasn’t the industry come further in ML adoption?
One aspect that confuses the discussion is that there are varied definitions and expectations for Machine Learning (not all ML software is created equal). As well as the insufficient knowledge about the inner workings of ML models that make them seem magical.
Case studies will be used to demonstrate how different levels of ML can be applied to on-site search and category merchandising.
Key takeaways:
