> 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/recency-frequency-monetary-value-rfm.md). # Recency, frequency, monetary value (RFM) ### Introduction We are thrilled to present our latest revenue prediction tools for customer segmentation. In this discussion, we will delve into the RFM framework, which stands for recency, frequency, and monetary value. We will also explore customer lifetime revenue and forecast customer re-engagement. {% embed url="" %} RFM is a proven approach that evaluates a customer's recency, frequency, and monetary value. We examine the time since the customer's last purchase to determine recency. On the other hand, frequency is the total number of purchases the customer makes. Finally, monetary value refers to the total amount the customer spends in all company interactions. We can effectively segment our customer population by considering all three factors together. ### Interpreting RFM scores One approach is to assign each customer in your base a ranking of one to five for each of the three measures - monetary, frequency, and recency. A rank of one for monetary represents customers who have spent the highest amount, whereas a rank of five corresponds to those in the lower 20th percentile of spending in the customer base. Similarly, for frequency, a rank of five would indicate customers who have only made a single purchase. In contrast, a rank of one would tell those who have completed four or more purchases, although this may vary for different stores. Once we have ranked the customers based on these measures, we can utilize these scores to indicate our progress over time. We can start with a static snapshot of the average RFM score of the company and monitor the progress over time. The goal is to encourage customers to engage more frequently and spend more money. By achieving this, we can move customers towards higher frequency and greater spending, increasing customer engagement. {% 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/recency-frequency-monetary-value-rfm.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.