Driving Revenue with Personalization: Recommender Systems Part 3

We’ve all been semi-creeped out by the way some retailers know exactly what we’re thinking or have been looking for. According to McKinsey, in general, 80% of companies have seen an uplift since implementing personalization. On the other hand, bad personalization can do the exact opposite – 71% of customers feel frustrated when a shopping experience is impersonal. Worse still, 63% of consumers will stop buying from brands that use poor personalization tactics.

The third session of this four-part data science webinar series focuses on collaborative filtering and how it can be used to personalize product suggestions. We will use an in-house implementation of this algorithm that will be applied to a real-world e-commerce dataset. First, we will show participants how to work through analyzing the ratings given by users to various products. Then, we will establish a similarity metric between existing users so we can finally recommend products that are to a particular user’s liking.