RFM Analysis and how companies can use it for better efficiency:
What is RFM Analysis?
RFM Analysis is a marketing methodology/technique to group customers and rank them based on the metrics of Recency, Frequency and Monetary from their past transactions to segment customers and perform targeted marketing campaigns for a better throughput.
RFM Analysis quantitatively ranks customers on the basis of
Recency (R): When was the last transaction made? Companies usually try to measure recency in days. But this may vary depending on the product or the industry. If it’s an FMCG product like milk, then recency would be on a daily basis, but if it was a manufacturing equipment, the recency might have to calculate in years.
Frequency (F): Frequency of the purchase the customer has made over a given period of time. First time buyers might be good target for targeted follow-up advertising to convert them into more frequent customers.
Monetary (M): Amount spent by a customer over a period of time. Customer who have a high transaction value have a better probability of spending more and could potentially add more value to the business.
How to compute RFM analysis:
RFM analysis scores customers on each of the three main factors. In general, each customer is given a score from 1 to 5 is given, with 1 being the least and 5 being the highest. However, depending on the industry, various implementations of an RFM analysis system may use slightly different values.
Organizations average these values together, then sort customers from highest to lowest to find the most valuable customers, while some businesses weigh the values differently while calculating the final scores.
Analytics for enabling RFM Analysis:
The tool I would be using today is Alteryx, a self-service analytics tool that enables citizen data scientists and business analysts achieve complex tasks without the burden of writing code. An analyst can create an automated workflow in Alteryx with different rules such that, Alteryx can directly source data from the database (SAP, Oracle etc), apply complex logics, and output the results to the corresponding stake holders without any manual intervention required enabling the analyst to focus more on deriving insights.
How to analyze these results?
Customers with the top scores across all the segments are our best customers with high spend, frequent visits. These are our most loyal customers who should be safeguarded.
Customers with good frequent visits but low monetary score can be target with some discounts to increase their purchasing power to transition them into customers with high spending.
Multiple such combinations can be put in place to target customers for the best revenue generation by a company.
