There comes a time when the marketing professional is at a loss.
Having tried everything – from personalized emails to loyalty programs – and things still not clicking, our marketer can’t help but wonder: what am I missing?
Here’s your missing piece of the puzzle: RFM metrics.
RFM Metrics take a holistic approach to customer behavior, combining Recency, Frequency, and monetary value to paint a complete picture of each customer’s value to your business.
In today’s post, we’ll show you how to implement RFM metrics in your business to drive customer loyalty and profits, acquire better customers, and even prevent customer churn.
RFM Metrics – Indicators of Customer Behaviour
The RFM Metric can be used as a powerful tool for uncovering insights into customer behavior:
- Who your customers are
- Why they buy
- What’s important to them
RFM Metrics are how you give meaning to what your data tells you; moving on from the simple understanding of your numbers, you can unlock a deeper understanding of customers’ motivations and user engagement.
Looking at how customers interact with your business over time unlocks valuable insights to improve your marketing strategies, attract customers similar to your ideal customers, and boost customer retention rates.
What Is an RFM Model
The RFM Model is a proven customer segmentation strategy marketers use to rank customers according to purchase behavior. The Model considers three variables – how recently, with what Frequency, and what monetary value customers bought.
To calculate RFM metrics, you need a 1 to 5 scale (where 1 is the lowest) to rank customers for each variable.
The RFM Model stood the test of time. It first became popular in 1995 when advertising heavily relied on direct mail. Advertisers will use RFM models to select customers who will receive flyers as a way to spend less on advertising.
While today advertisers and marketers alike aren’t so focused on saving on printing costs, RFM is still used to understand your customer base and predict future customer behavior.
The great thing about RFM models is that they allow you to be self-reliant with your data. For an RFM model to work, you only need your historical transactional data, so it becomes pretty easy to get started.
What Is RFM Segmentation
If the RFM Model is the strategy, RFM Segmentation refers to dividing customers into segments based on their RFM scores. The higher the scores for all three variables, the higher the value for that customer.
Similar to an engagement analysis, RFM Segmentation also allows you to identify different groups of customers based on user behavior, which you can then target with specific processes (retention, prevention, reactivation, etc.)
RFM can be a powerful segmentation method, as it provides a level of understanding of customer behavior, motivations, and product interactions that basic segmentation (such as demographic or other segmentation examples) can’t offer.
According to the scores, your RFM segments become groups of loyal and happy customers, dedicated but about to churn, newly acquired high-potential customers, former great customers that left, etc.
Consequently, segmenting your customer base using an RFM Model enables you to grow loyalty and retention, prevent your best customers from leaving, bring back great customers who churned, and even acquire customers who are a better fit for your business.
RFM Model vs. RFM Segmentation
While the RFM Model and the RFM Segmentation are similar concepts, they aren’t the same. For clarity, let’s look at the two and understand the main difference between them.
The RFM Model is the strategy, while the Segmentation is the act in itself.
So, the RFM model analyzes individual customer behavior based on the three RFM Metrics: Recency, Frequency, and Monetary value, while RFM segmentation groups customers into segments based on their RFM scores to enable targeted marketing strategies.
Let’s look even closer at the difference, using the example of a local grocery store (it can also be an online retailer, as RFM analysis isn’t limited to offline stores.)
So, the RFM model would analyze each customer’s behavior based on the three variables listed above.
For example, Customer A might have made a purchase three days ago, shops at the store twice a week, and spends an average of $50 per visit.
Meanwhile, Customer B might have purchased a month ago, shops at the store once a week, and spends an average of $20 per visit.
The RFM model analyzes these factors, then ranks customers according to the scores. In our example, Customer A would be considered a more valuable customer because their frequency and monetary values are higher.
On the other hand, RFM Segmentation will group all customers together based on their scores. For example, suppose the store creates three separate segments based on Recency:
- customers who have made a purchase in the last 7 days
- customers who have made a purchase in the previous 30 days
- customers who have not made a purchase in the last 90 days.
These three segments will be targeted with three distinct marketing strategies, each curated for customers inside the specific segments.
For example, the store can offer a 20% discount to recent customers in the “last 7 days” segment to encourage another purchase but provide product bundles to customers in the “last 30 days” segment to encourage more frequent purchases.
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RFM Metrics and Segments
The RFM Model considers only three metrics (or variables), known as the RFM Metrics: Recency, Frequency, and Monetary Value.
As the name suggests, this metric looks at how recently a customer has purchased from a store.
It’s generally agreed that recently purchased customers are more likely to respond positively to upsell or retention campaigns.
On the other hand, if the Recency score is low, the customer is at risk of churning.
This metric measures how often customers purchase from you and how loyal they are to your company.
Customers who make frequent purchases are typically more loyal and valuable to your business, providing steady revenue.
It’s not enough to know the cadence at which customers buy your products; you also need to understand how much they spend on them.
The monetary score looks at customers’ value from a financial point of view; this metric ranks customers according to how much money they spend in your shop.
Evidently, customers with high AOVs are more valuable to the business, so you need to take care of them and avoid giving them a reason to churn.
Conducting RFM Segmentation allows you to curate your campaigns for your customer segments and avoid blasting customers with spammy, irrelevant, and unwanted promotions.
For example, high-frequency customers with significant AOVs may be included in a loyalty program or a referral campaign. In contrast, a reactivation campaign will include high-spending customers with low Frequency.
Besides the metrics, another critical aspect of the RFM Model is represented by its four main customer segments.
- Power Customers
This customer segment represents your most valuable customers: they bought the most recently, placed recurring orders, and have the highest monetary value.
- Active Customers
These customers bought at least once over the past 12 months.
The issue with active clients is that they generate little money or make any purchases, so they tend to fall between the cracks.
However, it would help if you didn’t ignore them, as there’s still potential to turn them into power customers.
- At-Risk Customers
As the name suggests, at-risk segments are people who used to be regular customers. Yet, they last ordered a while ago (low recency and frequency scores.)
You can try reactivation campaigns for this segment. Still, we recommend you conduct a monetary analysis to inform your reactivation efforts.
There’s no point in bringing back customers with low monetary value, as you’d probably waste your budget. Instead, focus on efforts on customers who dropped from the power customer segment.
- Inactive Customers
This last segment refers to customers who have churned.
According to their previous segments, you can trigger win-back campaigns for inactive customers but follow the same logic you did with at-risk customers.
It’s best to leave certain folks alone because they don’t need your products anymore and focus your resources on winning back your most valuable clients.
How to Implement RFM Metrics
As days turn into weeks and weeks into months, you’ll realize that customers move from segment to segment as they progress.
The transition is natural, but you shouldn’t take it in stride.
In fact, your RFM Metrics can inform both acquisition and retention processes, and you can influence how customers interact with your business.
There are two critical points in the customer journey where RFM Metrics can (and will) influence your decisions: acquisition and retention.
- RFM in Acquisition
There’s the old way of acquiring customers:
- Developing a fictional buyer persona
- Creating messages and targeting ads around this persona
- Launch spray-and-pray campaigns.
With this old way, you cast a large net, not caring about what you catch (worried about quantity over quality).
RFM Metrics allow you to avoid “one-size fits all” acquisition campaigns. Instead, you can perform audience segmentation and focus your messages, audiences, and promotions around your best customers.
The RFM approach is far superior to the old approach, as it informs your decisions using accurate data captured from genuine customers.
When you use RFM Segmentation, you identify your most valued customers and uncover the purchasing reasons. Hence, updating your marketing efforts toward attracting and retaining more people like them becomes easy.
There are a couple of questions you need to ask (but your data holds the answers) to implement the RFM Metrics in Acquisition:
- Who are my best customers?
(age, gender, location, lifestyle, hobbies)
- What did they buy?
(best-selling products in your power customer category)
- How do they buy?
(recency and frequency, payment method, channel of acquisition)
The answers here should inform your future acquisition campaigns. Instead of replicating the same audiences repeatedly using random products, you can create audiences that resemble your best customers, then push those audiences on your acquisition channels.
It’s the smart way of acquiring new customers in eCommerce and Retail.
- RFM in Retention
Recency, Frequency, and Monetary metrics allow you to seize opportunities when they present themselves or do damage control when customers are disappointed and prevent instead of reacting to their complaints.
At the same time, you can set up nurturing campaigns for your customers to build stronger and more lucrative relationships with various customer types. In time, this empowers you to increase your Customers’ Lifetime Value and grow healthily and predictably.
For example, customers with high AOVs and frequent purchases need to be wined and dined, so it’s clear how much you value them.
While effective customer segments aren’t about neglecting lower-value customers, we advise you to prioritize your high-value customers in your retention campaigns.
Returning to RFM & Retention, a natural progression unfolds once you’ve completed the RFM segmentation process.
- Research on power customers
Once you see who are your best customers, you need to understand what makes them tick. Conduct customer interviews to uncover the jobs to be done for this segment and identify what triggered the customer to buy.
- Surprise & Delight
Don’t sit on all the valuable insights you get from understanding your RFM Segments.
Start molding your business to meet customers’ needs and create relevant and compelling products, services, and marketing campaigns.
This ensures high customer satisfaction levels, and you’ll keep generating revenue from current customers.
- Prevent customer churn
Look at how customers are transitioning through RFM groups. Are they moving on lower segments?
If so, those customers might be at risk of churning. It’s time you took action and created curated retention campaigns to keep them engaged and satisfied, reducing the churn rates in the process.
For example, you could offer discounts for their favorite products or create tailored loyalty programs that will remind them why they loved you in the first place.
- Create brand advocates
Word-of-mouth advertising (the Holy Grail of marketing) is something that most marketers are after. With the RFM model, you can identify those customer segments most likely to respond positively to your referral programs.
Send targeted messages asking happy and loyal customers to recommend you to their peers and enjoy the free acquisition of putting customers first.
And there you have it – our RFM Metrics story has come to an end.
It’s important to understand that, as powerful as they are, RFM segments and metrics aren’t a magic wand that will instantly solve all your problems.
You have to be intentional about prioritizing data analysis and customer research, as well as training your creative muscles to get all benefits that come with an RFM-related approach.
Use RFM metrics to identify your most valuable customers, develop targeted retention strategies, and ultimately, boost your profits.
And remember: your business is all about serving the customers better, and RFM is one of the ways you can make this job more manageable.
Good luck and happy segmenting!
Frequently Asked Questions about RFM Metrics
What are RFM metrics?
RFM metrics the tools used by marketing professionals to track and analyze customer behavior.
RFM stands for Recency, Frequency, and Monetary Value, which are the three key metrics used to evaluate a customer’s purchasing habits and engagement with a business.
What is a good RFM score?
RFM scores vary depending on the business and industry. However, the higher the score (4-5), the more engaged and loyal are your customers.
A high Recency score means the customer has made a purchase recently, a high Frequency score means they have made multiple purchases, and a high Monetary Value score means they have spent a significant amount of money with the business.
How is the RFM score calculated?
RFM scores are calculated by analyzing a customer’s transaction history to determine the Recency, Frequency, and Monetary Value of their purchases.
The scores are typically calculated on a scale of 1-5, with 5 being the highest score.
Is RFM a predictive model?
Typically, RFM is used to analyze past behavior and predicting future behavior based on historical data.
RFM scores can be used as inputs to predictive models to help identify which customers are most likely to make future purchases or even churn.
How many RFM segments are there?
The number of RFM segments can vary depending on the business and the specific analysis being performed.
However, a common approach is to divide customers into four segments based on their RFM scores: “Power Customers” (high scores in all three metrics), “Active Customers” (average scores), “At-Risk Customers” (low Recency and Frequency scores, usually dropped from higher-valued segments), and “Lost Customers” (low Recency and Frequency scores).