Judging and segmenting customers based on one aspect won't give you an accurate understanding of them. This is why RFM model is important.
RFM gives a combination of three different customer attributes in order to rank your customers.
What is RFM?
RFM is an acronym for Recency, Frequency, and Monetary value.
It informs you about your most valuable customers, as it tells you:
- Recency: The last time since a customer has made a purchase.
- Frequency: How often a customer usually makes a purchase.
- Monetary value: The total amount of money a customer has spent.
RFM is based on Pareto's principle which states 80% of the effects come from only 20% of the causes.Similarly, 80% of your income is actually coming from 20% of your customers. RFM helps you understand your 20% better.
SO, what's exactly an RFM Analysis?
Put simple, an RFM analysis is one of data analysis techniques that helps organizations know more about their customers based on the data they have of their customers’ spending habits. Since it shows:
- Recency – customers who bought form you most recently, are more likely to repurchase than customers who bought in a long time.
- Frequency – the chance that a customer who buys from you on a regular basis will buy from you again is higher than the chance of a customer who comes to you infrequently.
- Monetary value – a customer who has spent a large sum of money on the products you sell, will willingly spend again more than a customer who has spent a much smaller amount.
With this set of knowledge you should be able to assign your points system. Each customer gets points based on how recent, how often, and how much money they have spent buying your products. E.g. you can start assigning scores 1 to 5 (where 5 is the highest score).
If a customer buys most recently, they get higher points. If they buy most frequently, they get higher points, and if they’ve spent large amounts of money they, also, get higher points.
Then, you can concatenate these three numbers together and therefore you get the overall customer’s score.
Subsequently, you can group your customers based on their spending habits.
- Loyal customers
- Best customers
- New customers
- lost customers
After the segmentation of your customers, marketers will be able to use the output in order to target specific clusters of customers that they see fit their behavior. Thus, generating higher conversion rates.
RFM Analysis assists businesses to answer a number of important questions that are being asked in all nearly all of them:
- Who are the best customers?
- Who are the customers that are on the verge of churning?
- Who are the potential customers that can be used to gain profit?
- Who are the lost-case customers?
- What are the groups of customers who are most likely to interact with different company’s campaigns?
This leads to the ability of sending tailor-made campaigns to customer segments that will increase the probability of conversion.
An RFM analysis helps companies segment their customers and give them points according to their Recency, Frequency, and Monetary values. This allows you to understand their customers better and be able to target them with different marketing campaigns.