Understanding customer behavior with RFM

business analytics

slug: recipe-business-analytics-understanding-customer-behavior-with-rfm

Recipe: How Are Customers Behaving?

category: business analytics

Problem

You want to segment customers based on purchasing behavior:

• identify high-value or loyal customers
• detect at-risk or inactive customers
• create targeted campaigns for retention or upsell

Solution

Determine these metrics:

• how recently did each customer make a purchase (or interact in some way)
• how frequently has each customer purchased
• how much money has each customer spent

Follow these steps to perform RFM segmentation:

• load transaction data
• calculate Recency, Frequency, and Monetary metrics
• assign RFM scores for each customer
• group customers into segments based on RFM patterns
• optionally visualize segment distribution

Step Sequence

load step -> rfm step -> calculate step -> chart step

Input Datasets

• transactions_clean — cleaned transactional data
• customers_standardized — standardized customer dataset
• Notes: transaction date, amount, and customer identifier are required

Output Dataset

• rfm_segments — dataset with customer IDs, RFM scores, and assigned segments
• Notes: can be used for targeting campaigns or analytics dashboards

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load datasets	Transactions and standardized customer info
rfm step	Compute Recency, Frequency, Monetary metrics	Example: Recency = days since last purchase
calculate step	Assign RFM scores and segment labels	Example: Top 20% in monetary = “High Value”
chart step	Visualize segment distribution	Optional bar chart or heatmap of segments

Variations & Extensions

• Combine with classify step to predict future behavior for each segment
• Use dashboard step to monitor segment performance over time
• Integrate with cube step to summarize metrics by region or product category

Concepts Demonstrated

• RFM customer segmentation
• Scoring and categorization of behavioral metrics
• Integration of segmentation with visualization
• Sequencing analytics for actionable insights

Related Recipes

• Customer lifetime value modeling
• Clustering analysis using k-means

Notes & Best Practices

• Ensure transaction data is clean and standardized before computing RFM
• Consider adjusting recency, frequency, and monetary thresholds based on business context
• Document segment definitions for reproducibility