4 Good Reasons to work with RFM in Online Marketing

In today’s world personalization is thought to be the key to successful marketing campaigns as well as to retaining customers, and with so much competition in today’s B2B and B2C markets, companies are willing to invest heavily in campaign optimization and tools which will allow them to send customers highly targeted messages. But remaining questions are, “What is the best way to segment my customers and what do I say to them?”

There are several methods a person can use to steer the conversation with their customers, but for returning customers, my hands-down favourite is to use RFM.  For those less familiar with RFM, it is a statistical model that results in scoring mechanism made up of three columns that focus on three areas of your customers’ behaviour:

Recency: the last time a customer made a purchase,

Frequency: How often a customer makes a purchase

Monetary Value: The value of the purchases (aggregate or average) made by the customer

Keep reading to for Bool’s 4 good reasons to work with RFM in online marketing.


1. Automatic Customer Segments and Subsegments:

Customers with similar behavioural patterns are automatically linked together regardless of lifetime based on their RFM score.

The person analyzing the customer scores has three main options when creating customer segments:

  1. Group customers based on their total R, F, M, column scores.
  2. Create more targeted groups by examining each of the three columns singularly
  3. Combine the scores from just two columns

Granted, knowing which of the three above options to use in a given scenario is a bit of an art, but after a few tries, targeting customers will start to become second nature.


2. Fine Tuning Messages:

When comedians want to test new material, they will consider who their audience or fanbase is, write the material and try it out on stage.  Immediate success is determined by a laugh response. RFM scoring works much in the same way.

If a customer has a low Frequency score, meaning they have been gone a while, then it wouldn’t make sense to send them a message insinuating they are a regular customer.  While a customer with a high Recency score should not receive a “haven’t seen you in a while” message.

By understanding the meaning of the customer scores, messaging becomes highly targeted to relevant audiences intuitively.


3. Customer Behavioural Predictions:

RFM models are built by sorting clients into clusters based on their behaviour and applying a score. For example: if Bool were an ecommerce site selling socks, and the average person purchases 3 pairs of socks at a total value of $30 on their first purchase, new customer behaviour can be predicted based on whether a new customer completed a total purchase valued above or below $30.

This understanding of customer behaviour allows for companies to create more targeted messages and offers based on the existing customer patterns. Of course, with more customers and purchases the model becomes more accurate.


4. Company Strength and Growth Opportunities:

By reviewing the RMF Segmentation results it is possible to understand the overall strength of the company’s customer base as well as pin-point growth opportunities. This is made possible by examining the group sizes per customer score segments. Ideally every customer should have a maximum score, however, healthy companies will see customer scores spread out fairly evenly across all RFM segments.

Once the company segments are mapped out and understood, growth opportunities become more apparent, and it is possible to focus on building wholistic strategies where several departments work together as a cohesive unit.

The above are the main reasons why Bool loves to work with RFM.  If you would like to learn more about RFM including, how it can help your company please contact me at: dara@boolbiz.com, otherwise, please feel free to leave a comment and we can keep the conversation going.


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