RFM and CLR in the Hub



Introduction

We can examine the customer lifetime value (CLV) information within the core hub. Upon logging in, we can access our example plant store and navigate to the business overview and customer segmentation pages to view our revenue segmentation data.



On the splash page, we can include any relevant updates we wish to showcase. Using the explore function, we can navigate to the user table, which contains all the data we have been discussing, including the RFM measures of recency, frequency, and monetary value for each user.

Additionally, we can select a specific user data point to view all the relevant scores for that user. We can also match these scores to specific RFM groups, such as the 111 group.

Furthermore, we can mix this data with other user attributes, such as predicted lifetime net revenue or CLV. By running these analyses, we can determine which groups have the highest average CLV and explore further as needed.

Hub Example 2: Geo Analysis 

We may want to consider examining another measure by delving into user attributes and segmenting our data accordingly. One possible way to segment our data is by geography. Here, we will examine the average probability alive per geography for our example plant store, along with the average lifetime revenue. It's worth noting that results may differ from store to store.

Nevertheless, this demonstrates the numerous attributes that we can use to segment our customers and the various ways in which we can analyze our data over time. For instance, we can filter by cohort and explore how different attributes are distributed among our customers.