Principal Data Scientist
Recommender Systems Series
Part 3: Personalized Recommendations
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.
Join this webinar to give your online platform a leg up on your competitors and to learn how to get personalization RIGHT.
Missed out on Part 1 or 2? Catch up here: