# Analyze RFM Customers

RFM is a common heuristic technique used to measure customer value. The abbreviation ‘RFM’ stands for ‘recency,’ ‘frequency,’ and ‘monetary value.’ Given the common business knowledge that most of a firm’s business comes from a relatively small number of clients (e.g. the 80/20 rule or the Pareto Principle), it is important not only to track RFM customers, but to see how they develop over time or after a launched Campaign.

{% hint style="info" %}
Tracking how your RFM Customers develop can be a valuable tool for understanding trends in your User Base. However, for launching Print Campaigns, the Model Builder can often choose Customers more effectively, while also offering post-Campaign tools for analysis and improvement.
{% endhint %}

### Identify your RFM customers

Click on the Insights Tab in the top right of your screen. You will be taken to the Insights page.

* On the Insights page, click the Edit Button in the header.
* Provide a new Selection Name.
* Choose any customers to Blacklist as needed.
* Click apply.

<figure><img src="https://985110910-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F0Hio8LJewN8lQY2Vlqy0%2Fuploads%2FTJv84VfpxMU5caJOef79%2FCPP011_Insights_Edit.png?alt=media&#x26;token=039de3c2-9d62-444d-b07a-164b9fbfac84" alt=""><figcaption></figcaption></figure>

#### Recency

Recency is crucial because the longer it takes for a customer to return, the less likely they are to return at all.

* In the customer\_recency graph, Filter out Customers with Recent Orders. In our example, we are using customers who have ordered within the last 8 weeks.

{% hint style="warning" %}
Please make sure you have recent transaction data available on the Platform, to be able to filter Recency Customers.
{% endhint %}

<figure><img src="https://985110910-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F0Hio8LJewN8lQY2Vlqy0%2Fuploads%2FenTYYzZ8guEXW0GXp505%2FCPP012_Insights_Recency.png?alt=media&#x26;token=524d8ffb-bf05-4b01-ba55-84479a89b28a" alt=""><figcaption></figcaption></figure>

#### Frequency

Frequency is also important; it measures the ‘intensity’ of a customer’s relationship with your business.

* In the customer\_frequency graph, Filter out Customers with high Frequency of Orders. In our example, we are using customers who have ordered 10 or more times.

<figure><img src="https://985110910-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F0Hio8LJewN8lQY2Vlqy0%2Fuploads%2FnuHs5XMKdxeSwjwceqjo%2FCPP013_Insights_Frequency.png?alt=media&#x26;token=b8bac37f-79b7-444c-ba9e-1cca94059ba2" alt=""><figcaption></figcaption></figure>

#### Monetary Value

Tracking the monetary value of previous purchases allows you to distinguish between heavy and light spenders, with heavy spenders being preferred.

* In the customer\_monetary graph, Filter out Customers with high Monetary Value. In our example, we are using customers with a value of 4000 Euro or more.

<figure><img src="https://985110910-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F0Hio8LJewN8lQY2Vlqy0%2Fuploads%2FllI4nJl9XRLEh7ySHg3a%2FCPP014_Insights_Monetary.png?alt=media&#x26;token=a35d98f2-caf1-41c0-8f0b-61ec8d3ecee5" alt=""><figcaption></figcaption></figure>

* Adjust the Sliding Filters as needed to increase or decrease the size of the selection. For example, here we adjusted Recency and Monetary Value to identify our top-10,000 RFM Customers.

<figure><img src="https://985110910-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F0Hio8LJewN8lQY2Vlqy0%2Fuploads%2FTGVrdydEQxAMdlIqlApq%2FCPP015_Insights_RFM10K.png?alt=media&#x26;token=d7ced56e-f71a-47e3-a37d-bf4737a1b30b" alt=""><figcaption></figcaption></figure>

* Click Export to create a Selection of your RFM customers.


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