Before we get into the details of RFM, I would just like to inform you that you can have someone do customer segmentation through rfm analysis Fiver for less than 10$.
RFM segmentation allows users to group different customers according to the different procedures and methods that they will be exposed to. Basically, it sorts out what kind of treatment is allotted to whom, based on your segmentation, including identifying groups of customers that require special treatment.
RFM Analysis for Customer Segmentation
RFM stands for Recency, Frequency and Monetary. By the use of RFM, we can acquire segmentation of customers based on their behaviors and interactions relevant to the firm or business. By the help of this process, we can sort out loyal customers, average or bad-debt leading customers, and so on. Based on our requirements of segmentation and the nature and type of sorting we want to make within specific groups, this method will help us achieve our goal. Usually the marketers will target those customers and induvial that are the most relevant for them in terms of investing time and money on them for future growth.
Marketers will always have data that provides them with customer trends, purchases and interactions. The analytics and trends based on their history are recorded and the demographics and analysis are thus easily available to segregate and apply RFM segmentation on the data and customers.
RFM segmentation analysis is widely popular for 3 things majorly, which are:
- It is simple to use. There is no need for outside help from any software or any data analysts.
- The results gathered by the RFM method are clear and easily interpretable, so you can easily understand and know how to make your decisions.
- It uses up high level of clustered data and will still provide you with your required objective.
What is Recency, Frequency and Monetary?
The three factors involving RFM are Recency, Frequency and Monetary. They have been explained as follows:
Recency: This is basically the amount of time that has passed since a client has last made any activity/ transaction/ interaction. Time is an important factor as it represents how much the customer or client is engaged with the business and how long would it take for future interactions or how responsive they will be.
Frequency: This is the number of times a customer has interacted in any way, or any specific way, such as a transaction activity. The frequency of engagement between the customer and business represents who is more related, who is a loyal customer or who have been just a one-time user, and so on.
Monetary: This is the amount of money that has flowed between the customer and the business. This flow of money or this amount of transaction is recorded based on a particular period of time between any specific dates and represents averages, sums, lows and highs, and you can compare the monetary transaction of different customers within a specific period of time.
A Step-wise guide to RFM Segmentation and Analysis
The RFM segmentation has been explained step by step, for your convenience. You can also use the aid of an additional software which can operate on huge data clusters and give you results more accurately.
The first step is to allot the Recency, Frequency and Monetary values to customers according to the data base of the clients and make a spreadsheet of the data on excel or any other software you use.
The data needed for RFM to work can be simply stated as;
Recency: the amount of time elapsed since the last interaction of a customer, and is recorded according to the nature of your business, which could be hours, days, weeks or so on.
Frequency: the number of interactions or transactions within a time period.
Monetary: the total amount spent in the frequency range
The second step involves sorting of customers into groups based on highest to lowest levels of recency, frequency and monetary value. For this purpose, 3 or 4 groups are made of each category, 1 being the highest and 3 (or 4) being the lowest. From this method, we obtain 3 (or 4) different classes of each category of R, F &M.
Recency Frequency Monetary
R-group-1 (most recent) F-Group-1 (most frequent) M-Group-1 (highest spend)
R-GRP-2 F-GRP-2 M-GRP-2
R-GRP-3 F-GRP-3 M-GRP-3
R-GRP-4 (least recent) F-GRP-4 (only one transaction) M-GRP-4 (lowest spend)
This results in 64 different customer segments (4R x 4F x 4M) or 27 different segments (3R x 3F x 3M) based on if you used 4 or 3 classifications. Every customer can then be placed in one of these 64 (or 27) segments, which is further explained in Step 3.
The third step is allotting customers in their specific groups and recognizing which groups are beneficial, which are not, and which are to be addressed or worked for improvement upon.
To explain how it works you can understand this by an example. For instance, a customer who comes under the category with the most recent transaction ( R-group-1 – most recent), with a high transaction frequency (F-Group-1 -most frequent) but have a low monetary value of all those transactions (M-GRP-4 -lowest spend) will come under the category of “Lowest-Spending Active Loyal Customers”. Hence Customers with grouping 1-1-4 or 1-1-3 both will be sorted in this category. If it were 1-1-1 or 1-1-2 even, they would be seen as the “Best customers” for the business as they are most recent, most frequent and most spending.
Well, it really depends on organization to organization who is classified as what to them, some as important, some as less important, some as area of improvements and so on. According to the nature of business you can sort them and find the ones you are looking for through this technique.
To name a few different kinds of customers that businesses are looking for, they could be;
Best Customers – as discussed, those customers with the highest cash flows flowing through them and the business by having highest levels of recency, frequency and monetary. (1-1-1 and possibly even 1-1-2)
High spending new customers – customers that are new and are contributing with a low level of frequency but are spending a lot, and since they are new, their recency would be higher/most recent hence making them as 1-4-1 or 1-4-2 grouped customers.
Lowest-Spending Active Loyal Customers – These customers are belonging to a low spending, frequent transacting and most recent transacting customer group, as discussed earlier. They are hence grouped by 1-1-4 or 1-1-3.
Departed Best customers – these are the customers that have frequently transacted with high monetary amounts but it’s been a long time since they last transacted, hence making them as those who have departed or will have no further involvement with the organization/ business. They are grouped as 4-1-1, 4-1-2 or 4-2-1, 4-2-2.
The fourth step is actually the implementation of RFM and the primary reason of doing the whole analysis. It involves the decisions that are to be taken according to the analysis results, that show which customer falls in which class and how should every class be dealt with for improvement and better Returns, or any other reason you underwent the RFM analysis for.
The customers can also be contacted and can be interacted with, to give them awareness, or their behaviors and interactions can be implemented for improvements.
For example, the “Best Customers” can be communicated and can be provided with the awareness of them being the most loyal and important, making them feel appreciated and valued, forming even more stronger bonds between them and your business.
Lowest-Spending Active Loyal Customers can be given special offers and ore awareness of the business and incentives to make them more spending.
Churned Best Customers can be contacted with so to make them your loyal valued customers, and could be asked as to why they have stopped their interactions, possibly giving you reasons they left and opening areas of improvement for your business.
The Drawbacks of RFM Segmentation Model
RFM segmentation is a powerful tool, but keep I mind it does not take into account more than 3 factors (Recency, Frequency and Monetary value). Other factors could be important to your business such as products purchased, campaign feedback’s, and many more, depending on the nature of your business. The RFM Model also does not take into account future customer growths and behaviors/trends.