> For the complete documentation index, see [llms.txt](https://docs.chord.co/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.chord.co/model-your-data/predictive-models/customer-lifetime-revenue-clr/clr-and-rfm-on-the-chord-commerce-data-platform.md). # CLR and RFM on the Chord Commerce data platform ### Introduction We can examine the customer lifetime value (CLV) information within the Chord 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. {% embed url="" %} 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. {% embed url="" %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.chord.co/model-your-data/predictive-models/customer-lifetime-revenue-clr/clr-and-rfm-on-the-chord-commerce-data-platform.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.