As we continue our choosing the right analytics for the job series, today we explore the topic of clustering algorithms. Clustering algorithms enable businesses to differentiate between customers in broad ways such as “Customers who like leading-edge technology” and “Customers who are value conscious.”
One of the benefits of painting customers with this kind of broad brush is that it can help direct and justify large-scale expenditures on store design, new merchandising schemes and promotional programs.
Using analytics in this way enables retailers to make far more accurate decisions than can be achieved through traditional methods of database querying on customer attributes such as recency, frequency and monetary value of past purchases. Often called “behavioral segmentation,” clustering algorithms are more accurate partly because they can handle greater data complexity. While query-based segmentation generally involves no more than three to six customer attributes, analytic-based segmentation can encompass dozens or even hundreds of attributes, greatly expanding the range of possible segmentation schemes.
With many more ways to group customers, and the ability to try lots of alternative groupings quickly, businesses can make better strategic and resource allocation decisions. One large U.S. retailer, for example, has used analytics-driven segmentation to better understand and serve those customers who account for the bulk of its revenues. The retailer used the characteristics of its best customers to define population segments, and used these segments to guide decisions such as how its stores are laid out and how its staff interacts with customers. By using clustering algorithms in this way the retailer was able to increase same-store sales in the first quarter of implementation by 8.4 percent, resulting in a 15 percent increase in total revenue.
Next time we’ll continue the series with an overview of regression models. 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).
