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RFM (Recency, Frequency, Monetary value) segmentation is one of the most tried and tested methods of segmentation used in direct marketing. It is based on the presumption that someone’s future actions are best predicted by their past ones.
So how can this popular method of segmentation be used in today’s data rich world, can it help answer the 64 million dollar question in email marketing: who do I send what to and when do I send it?
In the early days of offline direct marketing, RFM was often used to target consumers with expensive direct mail. It saved money and by targeting those most likely to buy again whilst removing those least likely to purchase; you would achieve a far higher ROI than if you mailed everyone. Its other benefit is that it is easy to apply and easy to understand.
It lost favour in the early days of email marketing; because email was cheap to send, so therefore the segmentation was easy, “there’s one list, let’s send it to everyone, isn’t email marketing great!”
This approach worked for a while, but as time has passed this strategy has gradually delivered diminishing returns for email. This is due to the wave of change that developed within the modern online society, making the use of the media extremely difficult without proper targeting.
The consumer now expects to see only what they want and is becoming increasingly intolerant when that doesn’t happen.
This means that modern segmentation is not just about saving money, we segment now for relevance, and to increase the consumer’s engagement with the brand. This means that a marketing communication should be seen as a key customer touch point, and should be a positive experience not only for those that buy now, but also those that don’t (but might in the future).
I’m not saying that each email is a “moment of truth” situation, but if we cause subscribers to complain or unsubscribe we lose the ability to influence them in the future.
So we need to segment, and it’s more important now in the online world, than it has ever been before.
In the original development of RFM segmentation, marketers used the data they had, not only the RFM but also category data as well. With this you could get to the who and what in a reasonably straight forward way.
RFM was one of the first “engagement” segmentation models, if you were in the top segments for Recency, Frequency and Monetary Value you were a highly engaged customer, and the other side of the segments, meant you were the most disengaged of the whole customer list!
So how does RFM cut it in the online world, and do the same rules need to apply?
I think the answer is yes, but let’s do a bit of redefining first. We need to accept that engagement data now goes beyond the transactional. So, by dipping into the rich sauce of online behavioural data, maybe we need to call it what it is; Engagement RFM (e-RFM).
This means that we look at what people have done in the past and what they are doing now in real time. Are they interacting with email (Recency, Frequency)? When did they last visit the website (Recency, Frequency, Duration)? What are they like as a customer (RFM)? The element we are adding to RFM (showing us likelihood to purchase) is the web behavioural data (which can show us “intent” to purchase).
Online RFM allows you to be more accurate with your predictions, by combining all the key behavioural data. This gives the marketer the control they need to make informed decisions on segmentation and targeting.
Developing this type of segmentation is a bit like eating an Elephant (apologies to the WWF), it’s only possible in small chunks. You should start by looking at someone’s RFM score or how recently they have interacted with you online when choosing your most engaged segments for example. But the important thing to remember is that it doesn’t stop there and by combining more of the key data points, your targeting can become increasingly accurate and meaningful.
Engagement RFM provides a mechanism to successfully target people based on their past behaviour and their current position in the Customer Lifecycle.
One example of this would be the use of RFM in the management of sending frequency. Those who are the most engaged, would be sent emails at the greatest frequency (to influence conversion), whereas those who are less engaged, would be sent emails at a lesser frequency (to maintain engagement with the brand).
The objectives for the different types of communication would not only affect frequency, but define the content and the message of each of the emails too.
Ultimately, the goal of sending emails to people who want them, making them happy (rather than upsetting them) and influencing them to buy more, becomes a little closer to reality