RFM Model
This document provides instructions on building an RFM model and exploring customer data transitions within the model.
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This document provides instructions on building an RFM model and exploring customer data transitions within the model.
Last updated
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RFM segmentation categorizes customers based on their purchasing behavior using three elements:
Recency (R) measures the time since the last purchase.
Frequency (F) tracks the number of purchases.
Monetary (M) indicates the value of purchases.
These elements help businesses understand customer engagement, financial health, marketing effectiveness, and brand perception.
The RFM model creation enables you to
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To build an RFM model, access the Models menu section in Profiles.
Choose RFM Model from the list of available models and click Continue.
To complete the creation process, you must set up three sequential steps one after another.
Notes: Before advancing to Step 2, it is necessary to configure all the elements successfully.
The setting you are referring to determines the source of data inputs for the RFM model. The data can stream from either Event (1) or Data Object (2).
Event refers to the purchase event or the transaction event.
Data Object represents structured data entities in your Purchase Business Object or your Transaction Business Object.
By choosing between Events or Data Objects as the data source, you specify where the RFM model will gather the necessary information to perform the segmentation and analysis of customer behavior.
Note: If you choose Event as your Data Source, you must select the appropriate event source accordingly.
If you wish to build an RFM model while focusing on specific purchase behavior within a particular category, the WHERE clause can assist you in filtering and narrowing down the data accordingly.
When setting up an RFM model, you must select four attributes (1 to 4) and define the Time range (5).
Customer Identify: The attribute that defines the subjects who will receive the RFM scores.
Date: This attribute is used to calculate the time since the last purchase (Recency score).
Order: This attribute provides the necessary data to calculate the customer's purchase frequency (Frequency score).
Revenue: This attribute provides the data needed to calculate the customer's purchase value (Monetary score).
Time Range: This refers to the period during which the data attribute occurred.
In this step, you will pay attention to 3 parts:
Model matrix: Establishing the structure of the RFM model.
Adjust element scores: Fine-tuning the scores of each element as needed.
Granularity refers to arranging the Recency attribute according to different time periods, such as Day, Week, Month, or Year.
The bar chart allows for easy adjustment of the scoring.
The scorecard grades the scoring adjustments made in the bar chart by showing the number of customers (by percentage) in each category.
The pie chart illustrates the percentage of customers in each category as depicted in the scorecard.
Notes: Frequency Scoring and Monetary Scoring
The other elements have similar charts and functions as the Recency Scoring.
RFM Personas are automatically loaded based on the scores assigned to Recency, Frequency, and Monetary attributes.
If you wish to edit the RFM Personas, you can hover a Persona and click the EDIT button.
The RFM Personas Configuration has 3 parts
Personas: A list of labels for RFM Personas.
Score: A list of RFM scores for each Persona.
Customer Count: The number of customers belonging to each RFM Persona.
There are two parts in Step 3 - Schedule & Update that you should pay attention to
Data updated to Segments
Create New Segments: Customers belonging to RFM Personas will be added to a new segment.
Update Existing Segments: Customers belonging to RFM Personas will be added to an existing segment.
Delete: Customers belonging to RFM Personas will not be added to any segment in CDP 365.
Note: During the next computation time, the customer data will be added to the segments that have been selected in this settings.
Computation Schedule
In Step 3, you can:
Click the Back button (1) to review your previous settings.
Click Save (2) to initiate the computation process of your model as per the scheduled time.
Once you have created your RFM Model, go to the Predictive Models tab, you will find the list of newly created RFM models in that location.
To change the model status or make a copy of the model, please select the desired model by checking the box next to it and then click the Action button.
Once you select the desired model, you can access its settings (Configure tab), computation histories (Computation Histories), and version history.
The feature allows users to dive deeper, gaining more valuable insights after successfully creating a new RFM Model.
First and foremost, users have the ability to compare the five latest training versions (computation versions)
Secondly, the clear movement of customers between RFM personas is also displayed
Moreover, this feature empowers users to analyze vital metrics such as Recency, Frequency, and Monetary
Let's embark on an exciting exploration with the following steps:
(1): The interface consists of five tabs, each presenting unique insights through a variety of featured charts
(2): Access the settings to create customized charts
(3): To apply these settings in (2), users will utilize materials such as training versions, dimensions, and metrics
(4): The final result will be displayed based on the applied settings
Note: We created sample tabs, making it incredibly user-friendly for anyone unsure about selecting appropriate dimensions and metrics for creating meaningful charts.
In this tab, you can explore RFM personas, which represent the results obtained after creating a new RFM Model, presented in two visually engaging forms: treemap and bar chart. When you hover over these charts, you gain access to more detailed information.
Please note that the Available Fields area supports only the five latest computation versions and provides support to compare for just two training versions.
On the other hand, you have the flexibility to use distinct dimensions and metrics to create desired charts.
Unlike the first tab, the user transition tab showcases the movement of customers across RFM personas between two training versions. You can easily observe this movement by hovering over the lines in the diagram. Furthermore, for accessing more detailed data, you can utilize the data table.
The recency metric is visualized using both a tree map and a bar chart, making this tab quite similar to the RFM Model tab. However, there is a notable difference here: the Recency label dimension is employed to represent the five levels of the recency metric, ranging from very low to very high.
Similarly, we utilize the Frequency label and Monetary label for the Frequency and Monetary tabs, respectively.
In the Models tab, click the button.
During Step 1, which is the Prepare Data stage, you will configure the Data Inputs, which include the general information of the model such as its Name and Description. Additionally, you will set up the Data Inputs, specifying the and its .
: Assigning scores to Recency, Frequency, and Monetary. (RFM Personas) - Categorizing cstomers based on their RFM scores.
At the predictive model list, users hover over a predictive model and click to open the explore popup.
We allow users to create up to a maximum of 10 tabs, so you can completely customize your own set of tabs. Click , then choose one of the two options - Free form or User Transition - to create a new tab.
