More Effective Customer Segmentation with RFM Analysis

 

What is RFM analysis?

 

The RFM in RFM analysis stands for recency, frequency, and monetary value. RFM analysis is a way of segmenting and ranking clients based on how they’re likely to act in the future. The criteria for this analysis are as follows: how recently a customer made a purchase, how often they make a purchase, and how much money they spend. Through RFM analysis, companies are able to determine which customers are the most loyal and deserve special treatment. 

Recency

 

Recency refers to the date of the customer’s last purchase. Note that it does not matter what was purchased. If a purchase was made recently (such as in the last few weeks), then the customer still has the brand fresh in their mind and is more likely to choose the same brand for their next transaction. This is why it’s a common practice for B2C businesses to single out new customers and target them in their advertising.

 

Frequency

 

The next factor is the frequency of purchases within a given period of time. This could vary widely depending on the nature of your product/service and the price point.

 

The more frequently a customer buys your products, the stronger their engagement with your brand and sense of loyalty. This is a key metric that determines the lifetime value of a customer. 

 

Monetary

 

The monetary factor is the total amount of purchases; if we consider monetary alone, a customer is considered a good customer if the total amount is large even if the purchase frequency is small. However, if the perspective of frequency is included, the positioning of the customer will change.

 

For example, if a customer used to purchase frequently and the total amount was large, but there is no recent purchase history, the customer is considered to have moved on to a competitor’s product.

 

In RFM analysis, customers are analyzed not from a single viewpoint, but by combining the three elements of recency, frequency, and monetary altogether.

 

The benefit of RFM analysis is that customers that existed vaguely can be grouped and organized. Since they can be ranked as good customers, new customers, etc., it becomes possible to take actions that are likely to stick with each of them.

 

 

Pros and cons of RFM analysis

 

Pros

 

By conducting an RFM analysis, you can pinpoint the customers with less value in the long-term - as in, those that are less likely to make purchases in the future. Then you can spend less time approaching this group and focus your efforts on the customers who are more likely to lead to sales. This enables both your marketing and sales teams to work more efficiently and get results faster.

 

Cons

 

RFM analysis can get better results the more detailed your data is - however, at the same time, the actual analysis can be quite time-consuming. 

 

Also, RFM analysis prioritizes the most recently purchased customers as the best ones, so there is a possibility of overlooking good customers that only make a purchase once in a year or so.

 

There also may be customers who did not purchase during the time period measured, but are actually highly motivated to buy from your company. If overlooked, it may lead to customer churn due to lack of follow-up.

 

To ensure that you evaluate your customers correctly, it is a good idea to mix several different analysis methods instead of relying on RFM analysis alone.

 

 

How to conduct an RFM analysis

 

RFM analysis is conducted in three main steps: collecting data, deciding on base values, and aggregating the information.


1. Gather customer data

Collect all the data you can, from both online and offline purchases. Next, sort the data separately by date of purchase, number of purchases within a set period of time, and total number of purchases.

 

RFM analysis does not take into account what product(s) they bought. If you wish to analyze by product as well, you can just add a separate column in your spreadsheet to include this information.


2. Set the base values to use as a point of reference

Determine the base values for R, F, and M. For example, for recency, set the time period for what you define as a recent purchase, such as within one week, within two weeks, within half a month, and so on. In addition, define the values for the frequency and monetary categories.

 

It’s not a problem if you don’t set these values perfectly the first time. Just try to choose these values to serve as a point of reference and set them logically.


3. Group the data and rank your customers

 

Finally, you can then group the data according to the values set in step 2. For example, one customer might have high frequency because they purchased thrice in the past year, but low monetary because they were all inexpensive products. 

 

Then, based on how well they do in each of the categories, rank them as VIP customers, repeat customers, new customers, inactive customers, etc.

 

Once the data is fully understood and analyzed, you can then discuss and decide on the best approach for each type of customer.

 

RFM analysis examples

 

Some may find it difficult to picture how to use RFM analysis. Here are two examples of how to actually use RFM analysis.

 

Put yourself in the customer's mindset

 

VIP customers, or those who have high marks for all 3 RFM categories, often have a strong attachment to the brand and want to feel that they are valued by the business. Therefore, it’s smart to offer special gifts and services to these important customers, such as giving them access to new products before ordinary customers.

 

These VIP customers don’t need a flash sale to convince them to make a purchase- they are already loyal buyers and instead respond better to special, personalized  treatment directly from the brand to them.

 

Low monetary means less purchasing power

 

Generally, customers who only buy the cheaper products are those with less purchasing power. They are unlikely to contribute significantly to your company’s profit. However, if they have a high frequency, then they are still willing to buy, so you can approach this group with discounts and special offers.

 

On the other hand, if the monetary value is high but frequency is low, the customer can be assumed to have high purchasing power, so it’s important to consider why the frequency isn’t growing and come up with a strategy to work on this metric.

 

 

Points of caution with RFM analysis 

 

There are a couple of limitations to RFM analysis that you should be aware of before jumping in.

 

It is not suitable for products that are used infrequently

 

This method of analysis doesn’t work with services or products that are purchased only a few times in a customer’s lifetime. The frequency is so low that accurate data cannot be obtained.

 

The target of RFM analysis should be a product that is purchased frequently and when they can easily switch over to a competitor’s product.

 

Only surface-level analysis is possible

 

It is not possible to analyze the background of why each customer made a purchase. If you want to factor this in as well, you should combine RFM with another form of customer segmentation.

 

Don’t forget about inactive customers

 

In RFM analysis, customers who have made a purchase recently or frequently are considered VIPs. Therefore, customers who just happened to not make a purchase during the time period measured, or the long-term customers who have a low frequency may get evaluated as not deserving much attention.

 

Be aware of this room for error, and prepare a different approach for the older, low frequency customers so you can keep them engaged and familiar with your brands.

 
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