The business world – and digital marketing in particular – is always evolving and developing new concepts. This is the case with the acronym RFM: Recency, Frequency, and Monetary. This concept originated in e-mail marketing in 1995 and was revived with the development of e-commerce.
It is a very effective formula for identifying the customers who spend the most and increasing the conversion rate. By analysing the three variants, each customer is assigned a score (typically between 1 and 5 points), from which segments are created according to their strategic value, with marketing campaigns specifically targeted at them.
Rather than grouping customers by demographic and psychographic data, this technique makes it possible to create segments based on actual consumer behaviour, including purchase history, channels used, online searches, responses to previous campaigns, etc.
80% of business comes from 20% of customers.
– Pareto Principle
What RFM Means
A customer who has made a purchase recently, who buys frequently and spends more money on your brand has a better rating than an occasional customer who spends little.
To target marketing resources effectively, you need to know the customer and their spending habits well. Take these three questions into account:
Recency: This refers to how recent the customer’s last purchase was. How many days have passed since the last purchase or contact?
Frequency: The number of times the customer buys your products or services, on average, in a given period of time.
Monetary: How much the customer has already spent on interactions with your brand during that period.
How to use RFM in marketing campaigns
The aim is to give each customer a score according to the three parameters of the RFM model, so that the best customers will have a score of 555 and the worst customers 111.
Let’s imagine that Andrew is an excellent customer, who shops frequently (F=5) and has bought several high-value goods (M=5) in the last month (R=5).
Andrew scores 5 in each of the RFM metrics, with the highest total score: 555.
Beatrice, on the other hand, is an occasional consumer (R=1), who rarely shops (F=1) and prefers cheaper products (M=1). As a customer, she has the lowest score: 111.
In Charles’ case, his last purchase was a few weeks ago (R=3), but he is not a frequent consumer (R=2), despite having bought a premium product (M=5). Charles’ RFM score is 325.
Try putting together several customers with similar ratings and defining the strategy to be followed in each segment. With the data you’ve obtained, all that’s left to do is construct a graph to easily visualise the marketing actions to be carried out.
| Customer segment | RFM | Behaviour | Strategies |
| Champions | 555, 554, 544, 545, 454, 455, 445. | They bought recently, tend to buy frequently and spend more money. | Reward these customers. They can be early adopters of new products and are true brand ambassadors. |
| Loyal customers | 543, 444, 435, 355, 354, 345, 344, 335. | They spend a lot of money and quite often on e-commerce. They are sensitive to promotions. | Do some upselling. Offer higher-value products and ask them to leave comments. Create loyalty. |
| Potential loyal customers | 553, 551, 552, 541, 542, 533, 532, 531, 452, 451, 442, 441, 431, 453, 433, 432, 423, 353, 352, 351, 342, 341, 333, 323. | Recent customers who have spent a lot and bought more than once. | Offer a membership or loyalty programme. Recommend other products. |
| New Customers | 512, 511, 422, 421 412, 411, 311. | Have bought recently but don’t buy often. | Make them feel welcome. Make sure they get what they want and start building a relationship of trust. |
| Promising customers | 525, 524, 523, 522, 521, 515, 514, 513, 425,424, 413,414,415, 315, 314, 313. | They’ve bought recently but haven’t spent much. | Create brand awareness, offer discounts, gifts or free trials. |
| Customers in need of attention. | 535, 534, 443, 434, 343, 334, 325, 324. | They are above average in terms of recency, frequency and monetary value, but have not bought recently. | Launch limited-time offers and recommendations based on previous purchases. |
| Customers not to be missed | 155, 154, 144, 214,215,115, 114, 113. | They’ve made the biggest purchases and frequently but haven’t returned to the online shop. | Bring them back with new product launches. Talk to them and don’t let the competition take them away. |
| Almost dormant customers | 331, 321, 312, 221, 213. | Below average in terms of recency, frequency and monetary value. | You’re in danger of losing them as customers Share valuable resources, recommend popular products, new discounts, etc. Reconnect with them. |
| Customers at risk | 255, 254, 245, 244, 253, 252, 243, 242, 235, 234, 225, 224, 153, 152, 145, 143, 142, 135, 134, 133, 125, 124. | They have spent a lot of money and bought many times, a long time ago. | Send personalised emails to reconnect. Offer promotions and valuable resources. |
| Hibernating customers | 332, 322, 231, 241, 251, 233, 232, 223, 222, 132, 123, 122, 212, 211. | The last purchase was made a long time ago, with little spending and a low number of orders. | Offer relevant products and special discounts. Recreate brand value. |
| Lost customers | 111, 112, 121, 131, 141, 151. | They have the lowest scores in Recency, Frequency and Amount. | Try to win them back with a personalised campaign. If it doesn’t work, ignore them
. |
Source: Connetif.ai (Adapted)
The benefits of RFM
RFM analysis generates several benefits for any business. Knowing more about customer behaviour allows you to
More revenue
Providing a good service or offering a certain product to a specific group of customers increases the chance of selling more, whether in quantity, cross-selling or upselling.
More efficient marketing
Segmenting customers into small groups allows you to better identify your target audience and develop more assertive marketing campaigns. You’ll be able to see, for example, which segments respond more favourably to a given campaign or, conversely, which ones don’t deserve attention.
Better distribution of resources
Visualise which customers need to be rescued, which are dormant and which need an incentive to make new purchases. Who are the customers at risk of abandoning the brand? Why don’t they buy online? Find out the causes and calculate whether it’s worth investing some effort in recovering these customers.
Greater customer retention
Building customer loyalty has an impact on the company’s turnover. By knowing who your most loyal customers are and who consume the most, you can personalise your communication and customer service.
Better user experience
Anticipating and understanding the needs and desires of each customer provides a higher level of shopping experience.
References:
