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Marketing strategy

RFM a Great Way to Start With Customer Segmentation

Are you looking to communicate in a more personalized way with your customers and want to move beyond basic demographic segmentation? Then the RFM model might be for you. You might be wondering what RFM is right? RFM stands for Recency, Frequency, and Monetary value.

This definition from Optimove I believe explains RFM perfectly, “RFM segmentation allows marketers to target specific clusters of customers with communications that are much more relevant for their particular behavior – and thus generate much higher rates of response, plus increased loyalty and customer lifetime value. Like other segmentation methods, RFM segmentation is a powerful way to identify groups of customers for special treatment. RFM stands for recency, frequency and monetary”.

I’ve had a vast experience working with a loyalty program that started segmenting based on the RFM model (we later incorporated other variables, but I will talk about this at the end), and we also build from scratch an RFM model for a side business a few years ago, so I have some hands-on experience that I know can be of use to you. I will start going a little bit more in-depth about the three components of RFM, what they are and why they are important to have a better segmentation based on purchase patterns.

Let’s start with R, Recency, represents how many days go by between one purchase and the next one. To measure your customer’s recency helps you know how often your most loyal customers buy from you, the more loyal your customer is the fewer days go by between one purchase and the next, and that means they score high in Recency. Recency also lets you know if you are top of mind with your customers, telling you that they think of your brand often. The recency metric varies from type of business, for example, if you are a supermarket or a store that sells convenience goods, it is very likely your customers will have low recency, but if you are someone that sells seasonal products, the number of days that go by between one purchase and the next will be higher. Take this into consideration when starting to measure your customer recency. Your highest number will start with your most loyal customer, no matter your industry.

The next variable is Frequency. This is how many times your customer buys from you. It is usually calculated in a calendar year or twelve months. With frequency, you can see if there are any patterns in your customer behavior. Maybe they think of your business more often for the holiday season or summer, or maybe the frequency stays the same during the entire year. The more frequent the higher the loyalty.

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And the last variable is Monetary value or amount purchased. This is how much money your customer spends with you. What I like particularly about the RFM model is that it doesn’t put all the weight on how much a customer spends, but it also values other loyalty factors that are equally or even more important than spending.

I won’t go into much detail on how you should calculate the value for each of the 3 metrics, but it is something very simple. Here you can learn how to do it.

What I will discuss is a few examples of how you can use the segmentation that comes from your RFM results.

In my experience, the best way to use the RFM segmentation is with direct marketing, especially with emails. Dividing your customer database into segments based on RFM will allow you to talk to your customers in a more personalized way. Customers in the higher tiers are your most loyal customers, they already shop from you very often, so the message for them should be focused on keeping the relationship strong, then customers in the lowest tears might need a little bit more of an incentive to either increase their spending or buy more frequently

This also translates into social media. You can use the same segments and databases to create custom audiences within Facebook and target your message the same way you would do your emails. Remember that these customers have bought from you in the past, so you should aim your objectives towards consideration and conversion, rather than awareness. If you are looking into getting new customers, you could use the same databases to create lookalike audiences, so you can find new customers that could have the same characteristics as your current customers, and become loyal to your brand.

I am very passionate about data and customer segmentation and I have seen great communication coming from a good RFM model. If you implement an RFM model and make it work for your marketing communication, with time you might feel like you need something more predictive, and then you can start working with a data team to build from your RFM model a new model that can incorporate predictive analytics for each of your customers. But I hope this last comment doesn’t discourage you, because like I said at the beginning, if you want to move away from demographic segmentation, then RFM is perfect and a simple way to start.


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