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Modeling RFM with AI Workbench

How to create and configure an AI marketing model to calculate recency frequency monetary value with BlueConicThe RFM (recency, frequency, monetary value) notebook is a marketing model for the BlueConic AI Workbench. You can use the model to analyze customer order data and score customers on three dimensions: Recency (how recent was the last purchase), Frequency (how frequent are purchases made), and Monetary value (how much was spent).

With RFM scores stored in customer profiles, you can create segments like "Frequent Buyers," "Recent Customers," or "Top Spenders in the Month before Holidays". This will allow you to market to specific types of customers based on their purchasing behavior. Note that in order to use the RFM notebook, you first need to have customer order data in BlueConic.

Watch the video: Configuring the RFM and CLV Notebooks in AI Workbench

Scoring RFM (Recency, Frequency, Monetary value) with AI Workbench

The RFM notebook examines the orders on the event timeline of customer profiles in a segment within a specific period of time. By default the notebook considers up to 500 timeline events (only order events with a "revenue" field are considered) per customer in all time, but you can configure a specific time frame. Per customer profile it determines the most recent order, the total number of orders, and the total amount spent over all orders. It stores these values on the profile. Next, the notebook scores customer profiles by bucketing these values in five equally sized score buckets (quintiles). Scores are values from 1 through 5, with 1 meaning the bottom 20% of the segment, and 5 meaning the top 20% of the segment.

For example:

  • A customer with an RFM Recency Score of 1 is in the bottom 20% of the segment when it comes to the most recent order. In other words: the most recent order is more towards the start date when compared to most recent orders of other customers in the segment.
  • A customer with an RFM Frequency Score of 3 is in the middle 20% of the segment when it comes to the total number of orders. In other words: the customer orders about as often as average customers in the segment do.
  • A customer with an RFM Monetary Score of 5 is in the top 20% of the segment when it comes to total amount spent of all orders. In other words: this customer is among the customers spending most in the segment. 

Adding an RFM notebook

  1. Select AI Workbench from the BlueConic navigation bar.
  2. Click Add notebook.
  3. A pop-up window appears. Scroll down to RFM notebook and click it.
  4. The RFM notebook opens.

How to use the BlueConic customer data platform to calculate recency frequency monetary value (RFM) using AI and machine learning for customer segments

Configuring an RFM notebook

  1. If you have the Notebook editor permissions, you can go to the Notebook editor page to initialize the parameters and to see the description of the notebook.
  2. Go to the Parameters page.
  3. Configure the parameters:
    1. Select the RFM segment that defines the customers whose orders will be used for the analysis.
    2. (Optional) By default the notebook will use "31 days ago" as start date and "today" as end date to limit the analysis to orders within this time frame. Select a Start date and End date to define a custom time frame. Note: Order dates will be interpreted in UTC. For example, a purchase made at 9 PM ET on April 21st 2020 will be interpreted as being made at 2 AM UTC on April 22nd 2020. 
    3. Verify whether the profile properties where the RFM values and scores will be stored are the ones you prefer.
    4. (Optional) If you'd like to store the average of the recency score, the frequency score, and the monetary value score on the profile as well, select a profile property to store the average RFM score property (optional).
    5. Enter a name for the notebook and click Save to save the connection. 

Running the RFM Notebook

  1. Go to the Schedule and run history page.
  2. In the metadata section at the top of the page, you can request email notifications each time the notebook runs or only for failed runs. For details, see: setting up email notifications for AI Workbench.
  3. Click Run now to run the RFM analysis manually.
  4. To schedule the import and export for a future date, activate Enable scheduling. Click the Settings icon How to run RFM AI models on customer profiles and customer segments in BlueConic. Select how to schedule the import by choosing an option from the drop-down list:
    • Every X minutes
    • Number of times per day
    • Days of the week
    • Days of the month
    • Weekday of the month

    Set a time for the import. Click OK

How to use AI marketing RFM calculations on customer profile segments using BlueConic


After running the notebook, you can view its output by clicking Preview. Scroll all the way to the bottom to see a graphical representation of the RFM analysis.

Tip: You can display the graph of the most recent run in an insight on a dashboard by adding a Notebook - Single cell insight, selecting your notebook and selecting cell number "16".

How to view customer RFM scoring by customer segment in the BlueConic customer data platform (CDP)

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