At the core of customer segmentation is being able to identify different types of customers and then figure out ways to find more of those individuals so we can... you guessed it, get more customers! We'll detail how we can use K-Means clustering to help with some of the exploratory aspects of customer segmentation.
The data we're using comes from John Foreman's book Data Smart. The dataset contains both information on marketing newsletters/e-mail campaigns (e-mail offers sent) and transaction level data from customers (which offer customers responded to and what they bought).
In order to segment our customers, we need a way to compare them. To do this we're going to use K-Means clustering. K-means is a way of taking a dataset and finding groups (or clusters) of points that have similar properties. K-means works by grouping the points together in such a way that the distance between all the points and the midpoint of the cluster they belong to is minimized.
Okay, so how does clustering apply to our customers? Well since we're trying to learn more about how our customers behave, we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.