Collaborative filtering enables merchants to take a degree of targeted action even for first-time customers. This type of analytics is often behind the product recommendations offered on e-commerce sites and the printed coupons generated at in-store checkout.
The form of collaborative filtering most often used in retail is sometimes referred to as an “affinity model” or “lookalike model.” It infers how an individual will behave based on how other individuals who look similar (share one or more characteristics) have behaved in the past: “People who buy/view product X often buy product Y.”
Collaborative filtering doesn’t have to be triggered by a current transaction. It can be used to target subsequent outgoing campaigns and other kinds of promotions. Still, this analytic technique is fundamentally transaction-oriented. The algorithms used are best suited to modeling data about items purchased or viewed. They’re not effective for modeling purchase and view data with the wide range of other information retailers have in their databases (e.g., attitudinal data, seasonal purchase patterns, natural product adjacencies, basket builders) or can access from external sources (e.g., demographics, public records, third-party marketing information).
For known customers, therefore, other types of analytics provide far more powerful and accurate insights.
Over the next several weeks we’ll provide an overview of a variety of analytic techniques. If you can’t wait for the next post on this topic; check out our Insights white paper: "Which Retail Analytics Do You Need?" (registration required).
