Our Customer Segmentation Approach was Based on RFM (Recency, Frequency, Monetary)

In another use case, we applied the RFM (Recency, Frequency, Monetary) approach to customer segmentation and showed how to interactively visualize and explore customer segments as well as use Guided Insights to identify customer segments of high value.

We will use an unsupervised machine learning clustering model that analyzes and groups a set of points in such a way that the distance between the points in a cluster is small (within the cluster distance) and the distance between points from other clusters is large (inter-cluster distance). There are multiple types of unsupervised algorithms (E.g.: hierarchical, probabilistic, overlapping) of which K-Means clustering is the most popular approach. Using Tellius, we are going to train a Bisecting K-Means model, which is a modification to the traditional K-Means algorithm where a number of clusters is defined apriori and the regular K-Means algorithm with k=2 runs to bisect the data until the desired number of segments is reached.

Tellius offers a robust machine learning layer that is built on Apache spark using Spark ML open-source library, where users can train, assess, and apply predictive models. The platform offers two approaches for training a model. One is called AutoML, where the user selects a target variable and relies on Tellius to select the appropriate algorithm, perform feature transformation, and fine-tune the parameters. The other is called Point-n-Click, which offers users more control over the model selection and the hyperparameter tuning approach. We are going to utilize Point & Click approach to building our model.

After the clustering model is trained and is ready to be implemented in production, we need to be able to apply the model to new data (i.e. scoring) and assign a segment label to each customer record unseen by the model. Tellius offers a few ways of applying the model to the new data. One way is through the Tellius interface using point-and-click functionality. More technical users may prefer to utilize Tellius’ prediction API to access a trained model using Python or CURL script. Let’s take a closer look at how to access the Bisecting K-Means model described in the previous section via API and score a dataset containing new customer data.