Skip to content

RFM analysis

Every customer you acquire is worth its weight in gold. However, not all customers are equally valuable, especially when their responses to your marketing efforts differ.

It’s important to track customer potential and convert it into measurable indicators. This is where RFM analysis comes in. RFM is a model used to determine customer value based on three dimensions:

  • Recency – reveals how much time has passed since the customer’s last order,
  • Frequency – indicates how often the customer places the order,
  • Monetary – reflects the amount of money the customer has spent.

RFM analysis requires existing orders stored in the platform. You can track orders using our tracking code on your website or import them through a file. Learn more about importing order data.

By using the RFM model, you can easily label your customers based on their purchase behavior. This labeling can help you design effective marketing campaigns and improve communication. The RFM model is also useful for maintaining a healthy customer database by identifying inactive or potential buyers.

Using the RFM model is simple. You just need to assign a score to each of the three dimensions. However, scoring requires careful consideration and extensive knowledge about your customers.

Let’s proceed to setting up the RFM model.

RFM model settings

Before you begin, make sure the Web tracking channel is correctly configured.

Here is how you can access the RFM model settings:

  1. Go to Settings > RFM > Model settings.
  2. In the General settings section, set the size of RFM matrix. The matrix illustrates your customers’ purchase behaviors graphically. By default, the matrix size is set to 5 x 5 x 5.
  3. In the next field, select a time period you want to analyze in the model: 4 months, 5 months, 6 months, 1 year, 2 years. You can select only one time period.
  4. In the score ranges section, determine how you want the scoring to be calculated. Scoring refers to a customer rating system based on the three dimensions: recency, frequency and monetary:
    • Calculate ranges automatically using quintiles – this is a default option. Choose it if you are using the RFM analysis for the first time, or you want to test it. The score ranges are calculated using statistical parameters.
    • Specify ranges manually – set scores from 1 to 5 for each range based on customer purchase behavior. The worst score is 1, while the best score is 5.
      • for the recency dimension, set the range depending on the time that has passed since the last customer’s purchase, e.g., if 70 days has passed, the customer receives 3 points.
      • for the frequency dimension, set the range based on the number of orders within a given time period. For example, if a customer has placed more than 16 orders in one year, they receive 5 points.
      • for the monetary dimension, set the range based on the total value of orders in a given time period. For example, if a customer has spent more than 800 USD in one year, they receive 5 points. The monetary values are in the currency declared during account registration with ExpertSender CDP.

Make sure the score ranges reflect actual events and customer behavior in your store rather than an ideal situation. Only then will RFM analysis be useful for your business.

RFM segments

  1. Use the table in the RMF segments section to understand the scoring. The Segment column contains 11 labels that represent customer purchase behaviors, e.g., champions, promising or at risk.  
  2. The Score column contains the 3-digit scores you just specified. If you find the score irrelevant for a particular label, you can easily drag and drop them to a more suitable row. For example, a customer with a score of 355 would be attributed to the ‘Loyal’ segment. To decipher the scores, consider the following example for customers labeled as ‘Champions’ with a score of 454:
    • 4 = it has been between 45 and 60 days since their last purchase, and the score for this rage is 4.
    • 5 = the customers have placed over 16 orders, and the score for this rage is 5.
    • 4 = they have spent between 500 and 800 USD total, and the score for this rage is 4.
  3. To reset the scores and start again, click the Reset scores to default values button.
  4.  Once you are finished, Save your work.

RFM analysis – interpreting statistics

To view the results of your RFM model settings, go to Customers > RFM. After some time and a few email campaigns, you will find statistics for the following customer RFM segments:

  • Champions
  • Loyal
  • Potential loyalists
  • New customers
  • Promising
  • Need attention
  • About to sleep
  • At risk
  • Cannot lose them
  • Hibernating customers
  • Lost customers

The statistics are based on RFM calculation logs, which we perform every day at a fixed time.

The RFM panel is divided into two parts. On the left side, you will find the RFM results presented in a graph. Use the toggle to switch between two graph forms:

  • The matrix – the 5 × 5 × 5 grid displays the layout of customer segment based on the recency dimension on axis X and the monetary dimension on axis Y. You can apply additional filters, such as average order value, revenue, and customers.
  • The bar graph – it shows the number of customers attributed to each segment, based on their purchase behavior. Compare the last RFM calculation log with another by choosing the time period in the calendar above the graph. The current statistics are presented in gray. You can also apply average order value, revenue, and customers filters for this graph.

The grid table below complements the matrix and bar graph statistics. The rows represent the RFM dimensions: recency, frequency and monetary. The columns represent the scores ranges you set in Settings > RFM > Model settings.

On the right side of the RFM panel, you will find a brief description of each customer segment. You can either choose the segment from the dropdown menu or click the segment on the segment in the matrix or the bar graph to display the following information:

  • The number of customers currently in the selected segment,
  • The average order value for this segment,
  • The revenue generated by customers in this segment.

Each value has a trend indicator that informs you about value growth (green arrow) or decrease (red arrow) compared to the previous month.

RFM analysis is a powerful tool for creating customer segments. Click the Create segment button in the top right corner to instantly build a group based on similar purchase behavior.

On this page
To top