The 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 our video on running RFM and CLV AI notebooks to learn more:
What does the RFM notebook do?
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.
- 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
- Select AI Workbench from the BlueConic navigation bar.
- Click Add notebook.
- A popup appears. Scroll down to RFM notebook and click it.
- The RFM notebook opens.
Configuring an RFM notebook
- Go to the Notebook editor page to initialize the parameters and to see the description of the notebook and its parameters.
- Go to the Parameters page.
- Configure the parameters:
- Select the RFM segment that defines the customers whose orders will be used for the analysis.
- (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. E.g. 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.
- Verify whether the profile properties where the RFM values and scores will be stored are the ones you prefer.
- (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).
- Enter a name for the notebook and click Save to save the connection.
Running the RFM Notebook
- Go to the Schedule and run history page.
- Click Run now to run the RFM analysis manually.
- To schedule the import and export for a future date, activate Enable scheduling. Click the Settings icon . 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.
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.