RFM is a tried and true segmentation method known for its effectiveness for increasing revenue from your existing customers.
You said no jargon! What is RFM segmentation?
The recency, frequency, and monetary model is a powerful segmentation method. The popular three-variable formula is highly valuable for identifying groups of customers with a view to driving increased revenue.
|How recently customers buy||How many times customers buy||How much customers spend|
Using these three variables you can segment your customer behaviours and patterns that can easily inform marketing decision making. They act as markers to your overall business health. RFM segmentation is a tried and true method that will help to increase the lifetime value of your customer base. It is still proving its worth in the ever-changing omnichannel landscape.
RFM segmentation enables marketers to target specific groups of customers with messaging that is more relevant for their particular behaviours. This form of powerful targeting significantly increases rates of response, loyalty and customer lifetime value.
RFM was first developed several years ago for the direct mailing industry, where marketers would use it to decide the sort of mail catalogues that different families would receive. For example, some families would receive catalogues that showcased high-priced items, while others received inexpensive ones, and some wouldn’t even get a catalogue – all based on how they scored on a set of 3 variables as defined by the RFM. Some 30 years later, the RFM model remains a useful method for optimizing your sales and marketing spend, as well as thinking strategically about how to engage your customers.
RFM segmentation enables marketers to target specific groups of customers with messaging that is more relevant for their particular behaviors.
RFM analysis is valuable for three reasons:
- It is simple – marketers can use it effectively without the call for data scientists.
- It is intuitive – segments lend themselves to messaging and interpretation.
- It is easily activated – with a CDP marketers can implement campaigns based on this data almost immediately.
What can you do with your RFM segmentation?
- Identify specific groups of customers
- Target them for marketing campaigns
- Promote repeat purchase and loyalty
- Defend against attrition/defection
- Acquire new customers who resemble your very best customers
How achievable is it? (clue: very achievable)
RFM segmentation is designed to increase the monetary value of your customers by stimulating recency and frequency.
Using the high-value recent segment for acquisition is a modern adaptation of RFM that is made possible by using a tool like Lexer. Once you identify your best customers you can create lookalike audiences on channels that resonate most to them, about products that they care about, at a time that will appeal to them.
On the flipside, finding out who your lapsed customers are can lead to the reactivation of once loyal brand advocates.
Each brand will take a different approach to identify the key points for each variable. It is favourable to choose as few variables as possible to ensure that you have the ability to act on the segments.
How to segment by recency
Recency of purchase can indicate how engaged a customer is with your brand.
Start by asking the question: How often do you want your customer to purchase from you? Let’s say you are a fast fashion brand, selling at a low price per item so, you want your customers to purchase at least once per season. For you, a customer that has bought in the last 3 months is an active customer and a customer who hasn’t purchased in over 3 months is considered lapsed.
How to segment by frequency
Frequency describes how many times a customer has purchased your products.
Start by grouping your customers by the number of orders. Do you have lots of customers that have only purchased once? Our recommendation is to define two groups – those who have not ordered often vs those who have.
How to segment by monetary value
Monetary value describes how much a customer has spent with you.
This can be influenced by how many times they have purchased, the number of products they purchase per order and the value of the items they purchase. An easy starting point is to find the average monetary value and create two segments of above and below average.
Identifying behavioural segments
In our mock dashboard below, we have set up a search for customers we identify as high value recent. We uncovered 55,657 individuals have made transactions of more than $393-$1000 online this year.
The customer profile here shows a highly engaged customer, who will most likely be willing to further engage and is likely to be an advocate for the brand.
In our Customer Data Platform, your data is updated in real time, ensuring that as customers move through into different segments they are captured and their journey is altered. Your best bet for continued loyalty is to ensure that customers moving down into a low spend band are immediately led into a retention strategy, and on the flip side, low>high spenders are rewarded for their positive behaviours.
Choosing a CDP in which you can capture other data points like an interaction with advertising, email, social will level-up your ability to predict how to convert the maximum value from each individual customer.
Four examples of behavioural segments
Highly engaged customers who have purchased the most recent, the most often, and generated the most revenue.
Action: These customers have shown they have a higher willingness to pay, so avoid discount pricing to create incremental sales. Instead, focus on value-added offers through product recommendations based on previous purchases. Focus on VIP offers, loyalty and new, exclusive products.
Re-engaging high value lapsed customers can be made easier by cross-analyzing with other data to identify products purchased/time of year purchased etc.
Action: Customers in this segment may have opted out of email if you have their PII data and can continue to re-market across other channels.
Customer surveys may help to provide an opportunity to correct poor experiences and better understand your customers. Did they lapse because of a poor experience, seasonal products or a ‘one-time’ thing?
Customers who return often, but are low transactors.
Action: You’ve already succeeded in creating loyalty. Concentrate on increasing monetization through product suggestions based on past purchases. Consider incentives tied to spending thresholds linked to your store average order value.
Action: Revive their interest with promotional incentives of more aggressive discounts off a range of products based on past purchases. Don’t lose these customers to low-cost rivals. Recreate brand value and win them back!
Increasing the value of RFM segmentation
In summary, RFM segmentation is a straightforward and powerful method of customer segmentation. For the best results possible combine RFM segments with other segmentation modeling methods.
By including additional data points like products purchased, prior campaign responses, demography, and social, you can quickly and easily create rich audience segments.
To super-charge RFM data, combine it with modern predictive analytics technologies to more accurately predict future customer behaviour.
A CDP makes RFM segmentation easier than ever
Implementing a Customer Data Platform can drastically reduce the time it takes to effectively implement your RFM segments and activate them instantly across all channels.
It’s the modern way to activate RFM segments.
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Our Customer Data Platform brings data together to help you find high-value customers, create customer personas and cost-effectively acquire more of them.
Images by Pikisuperstar – Freepik.com