Predictive Marketing

Chapter 10Play Six: Predict Individual Recommendations for Each Customer

A North American beauty and cosmetics company with hundreds of stores was looking to personalize its communications to hundreds of thousands of customers and ensure that every online interaction between the brand and its customers was consistent with its messaging. It wanted to shift the mind-set from being discount-driven to improving customer service and satisfaction. The company chose to combine cluster-based targeting with personalized recommendations to send customers more strategic, personalized offers. The company first used predictive analytics to organize its customers in product-based clusters such as “bath and beauty” and “face cream.” Then it emailed each of these customers content and recommendations that were based on their cluster. Clearly customers liked the emails and the company was able to increase revenues per email by six times.

In this chapter we learn all about making recommendations to customers. Recommender systems have been around for almost 20 years, Amazon being the primary example that started using this early on. There are three parts to making personalized recommendations: sending recommendations to customers at the right time, understanding the context, and sending the right content.

First generation recommender systems used simple rules configured by human beings based on things like keywords or titles. In other words, a merchandiser or content marketer set up a rule so ...