> For the complete documentation index, see [llms.txt](https://docs.phocassoftware.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.phocassoftware.com/sales-insights/understand-calculations/understand-rfm-calculations.md).

# Understand RFM calculations

Learn how Phocas calculates RFM scores and uses them to determine customer segments.

The [Customer Segmentation feature](/sales-insights/explore-customer-insights/use-customer-segmentation.md) uses the Recency, Frequency, and Monetary (RFM) model to group your customers into meaningful segments and provide instant insights into customer behavior across their purchasing journey.

{% hint style="success" %}
Additional resources on our website:

* Read this blog to learn more about the RFM model and how it can transform your sales approach: [Customer segmentation for wholesalers that your customers will appreciate](https://www.phocassoftware.com/resources/blog/customer-segmentation-for-wholesalers)
* Read this guide to learn how you can maximize the potential of your data: [Smarter selling starts with segmentation](https://www.phocassoftware.com/resources/ebooks/the-power-of-customer-segmentation)
  {% endhint %}

The RFM model is an industry-standard analysis technique. It’s often used to manage customer accounts. As its name suggests, this model uses three key metrics to assess and rank customer behavior, as described in the table.

<table><thead><tr><th width="107">Metric</th><th>Definition</th><th>Why it's important</th></tr></thead><tbody><tr><td><strong>R</strong>ecency</td><td><p>How recently did the customer make a purchase?​</p><p>Higher threshold = more recent</p></td><td>Customers who purchased more recently are more likely to buy again, compared to those who haven’t purchased in a while. They're also more likely to react positively to marketing campaigns and promotions.</td></tr><tr><td><strong>F</strong>requency</td><td><p>How often does the customer make a purchase over the time period?​</p><p>Higher threshold = more frequent</p></td><td>Frequent buyers are generally more engaged and satisfied and, therefore, more loyal to the business.</td></tr><tr><td><strong>M</strong>onetary</td><td><p>How much did the customer spend over the time period (total spend)?​</p><p>Higher threshold = higher monetary value (more spent)</p></td><td>Customers who spend more are typically more profitable and worth nurturing. Combining monetary value with how recently and often a customer buys can help identify loyal, high-value customers.</td></tr></tbody></table>

**How segments are determined**

Segments are based on all three RFM scores working together, not just one score.

* **Recency (R)** determines the position on the X-axis
* **Frequency (F)** and **Monetary (M)** are combined to determine the position on the Y-axis

To calculate the combined Frequency and Monetary score:

* Add the Frequency score and Monetary score
* Divide by 2
* Round up to the nearest whole number

This means a lower Monetary score might be offset by a higher Frequency score.

**Example:**

Suppose you are looking at a specific customer in the Champion segment and are wondering how they got there, as they did not spend much.

Here are some sample numbers to show how the RFM scores work together:

* Monetary = $1,300 → score of 3
* Frequency = 19 → score of 4

Combined score:\
(3 + 4) / 2 = 3.5 → rounds up to 4

If this customer also has a high Recency score, they fall into the **Champion** segment.

This is expected behavior. The model is designed to identify highly engaged customers (recent and frequent), not just those with the highest spend.

**How Phocas applies the RFM model**

Phocas analyses the data in the selected database within the specified time period (the default is the last 12 months). It scores the customers on each of the three RFM metrics, on a scale from 1 to 5, where:

* 5 = highest recency, frequency, or monetary value.
* 1 = lowest recency, frequency, or monetary value.

It combines these scores into an overall RFM score from which it builds a model of your customers’ behavior, presented in a chart format.


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