RFM is a measurement that refers to recency, frequency, and monetary value. Recency is how recently a customer has made a purchase, frequency is how often they make a purchase, and monetary value is how much they spend per transaction.
RFM can be used to identify your best customers, as well as those who may be at risk of churning. By segmenting your customers based on RFM, you can target them with personalized messages and offers that will encourage them to keep buying from you.
To calculate RFM, you'll first need to gather data on your customers' purchase history. This can be done through your point of sale system, e-commerce platform, or customer relationship management (CRM) software. Once you have this data, you can start to calculate RFM scores. By using quartiles, you can split your customer base into four equal groups, based on how they rank on each of the three RFM factors.
Overall, RFM scores are highly beneficial to managing your consumers and predicting their behavior. Utilize RFM in your company or business to better support and serve your customers.
How does RFM work?
RFM works by analyzing the recency of customers' purchases, how often they purchase, and the amount of money they spend each time. This data can then be used to segment customers into different groups based on their purchasing habits and target them with relevant marketing messages.
What are the benefits of using RFM?
The main benefit of using RFM is that it allows marketers to identify their most valuable customers and tailor their marketing efforts accordingly. By targeting these high-value customers, marketers can maximize ROI from their campaigns and increase sales revenue.
Fun Fact:
"RFM analysis is a marketing technique used to determine quantitatively which customers are the best ones by examining how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary). RFM has been found to be an effective way of segmenting customers into groups for further analysis and targeting" (Luo, 2018).