RFM Analysis For Successful Customer Segmentation

Congratulations! You’ve reached the ultimate resource about RFM analysis that exists on the internet.

Most other articles you’d find on Google about RFM analysis are either too shallow or too complex.

But it’s not the case here. On this page you will learn everything about RFM

Along with the basics, you would also learn how you can apply RFM model in your own business.

So are you ready to learn something lucrative? Then let’s begin…

Table of Contents

RFM Analysis is Based on a Simple Technique

RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behaviour based customer segmentation. It groups customers based on their transaction history – how recently, how often and how much did they buy.

RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalisation services.

Combination of three parameters

Valuing customers based on a single parameter is insufficient.

For example, you can say that people who spend the most are your best customers. Most of us agree and think the same.

But wait! What if they purchased only once? Or a very long time ago? What if they are no longer using your product?

So..can they still be considered your best customers? Probably not.

Judging customer value on just one aspect will give you an inaccurate report of your customer base and their lifetime value.

That’s why, RFM model combines three different customer attributes to rank customers.

If they bought in recent past, they get higher points. If they bought many times, they get higher score. And if they spent bigger, they get more points. Combine these three scores to create the RFM score.

Finally you can segment your customer database into different groups based on this Recency – Frequency – Monetary score.

Customer Segments with RFM Model

You can create different types of customer segments with RFM analysis, but here are 11 segments we recommend.

Think about what percentage of your existing customers would be in each of these segments. And evaluate how effective the recommended marketing action can be for your business.

Customer Segment Activity Actionable Tip
Champions Bought recently, buy often and spend the most! Reward them. Can be early adopters for new products. Will promote your brand.
Loyal Customers Spend good money with us often. Responsive to promotions. Upsell higher value products. Ask for reviews. Engage them.
Potential Loyalist Recent customers, but spent a good amount and bought more than once. Offer membership / loyalty program, recommend other products.
New Customers Bought most recently, but not often. Provide onboarding support, give them early success, start building relationship.
Promising Recent shoppers, but haven’t spent much. Create brand awareness, offer free trials
Customers Needing Attention Above average recency, frequency and monetary values. May not have bought very recently though. Make limited time offers, Recommend based on past purchases. Reactivate them.
About To Sleep Below average recency, frequency and monetary values. Will lose them if not reactivated. Share valuable resources, recommend popular products / renewals at discount, reconnect with them.
At Risk Spent big money and purchased often. But long time ago. Need to bring them back! Send personalized emails to reconnect, offer renewals, provide helpful resources.
Can’t Lose Them Made biggest purchases, and often. But haven’t returned for a long time. Win them back via renewals or newer products, don’t lose them to competition, talk to them.
Hibernating Last purchase was long back, low spenders and bought seldomly. Offer other relevant products and special discounts. Recreate brand value.
Lost Lowest recency, frequency and monetary scores. Revive interest with reach out campaign, ignore otherwise.

As you can gauge, RFM analysis is a handy method to find your best customers, understand their behavior and then run targeted email / marketing campaigns to increase sales, satisfaction and customer lifetime value.

On the Other Side: Recurring Sad Tale of Email Marketing

Consider this case…

Carol has put up the perfect email newsletter – content, design, subject line, call to action, social media links… She sends out the newsletter expecting stellar conversion rates. Her mental math reasons that even if it converts at a “low” 10% rate on her 3500 customers, she’d be richer by a few thousand dollars within hours.

Ten minutes.. half hour.. two hours..8 hours pass. But at the end of the day, it’s only 1.5% people who clicked the link and a single sale.

Very much disappointing, isn’t it?

What did she miss out?

Carol did everything perfectly, except one – targeting.

She sent the same email to everyone.

Different customers react to different messaging.

A price sensitive customer would grab a discount offer, but someone who regularly buys from you may get excited about a new product launch more.

That’s the catch here.

Are we leaving gold on the table?

Most of us are not even close to Carol.

Whether you are in online commerce, retail, direct marketing or B2B – most of us are so busy with daily chores that we don’t spend enough time on marketing. Our marketing campaigns are hurried, fall short on copywriting, lack professional design, and we don’t pay enough attention to tracking or improving conversions.

Of course, we wish to do all of that. But we don’t.

By not understanding our customers enough, we don't leave money but a huge pot of gold on the table. Click To Tweet

What if we understood our customers a little better and sent them more relevant campaigns?

I bet our success rate will be much higher.

Not only will we make more money, but our customers will also be happier and loyal.

Still not convinced yet? You will be in a few minutes.

Here’s Where RFM Analysis Becomes Super Useful…

Sending a message tailored to the customer group will generate much higher conversions.

Isn’t it obvious?

All marketing campaigns should pick up a target segment first, then create promotional material that will resonate with that audience, and then put pedal to the metal.

Unfortunately, most of us don’t do that.

Here’s where RFM Analysis is super useful.

RFM makes identifying customer groups easy.

RFM analysis readily answers these questions for your business…

  • Who are my best customers?
  • Which customers are at the verge of churning?
  • Who has the potential to be converted in more profitable customers?
  • Who are lost customers that you don’t need to pay much attention to?
  • Which customers you must retain?
  • Who are your loyal customers?
  • Which group of customers is most likely to respond to your current campaign?

Proven Effectiveness – Decades of Academic and Industrial Research

RFM has a track record of many decades.

First of all, it’s based on the Pareto Principle – commonly referred to as the 80-20 rule.

Pareto’s rule says 80% of the results come from 20% of the causes.

Similarly, 20% customers contribute to 80% of your total revenue.

People who spent once are more likely to spend again. People who make big ticket purchases are more likely to repeat them.

Pareto Principle is at the core of RFM model. Focusing your efforts on critical segments of customers is likely to give you much higher return on investment!

Roots in Direct Marketing, Database / Catalog Business

The concept of RFM was originally introduced by Bult and Wansbeek in 1995. It was used effectively by catalog marketers to minimize their printing and shipping costs while maximizing returns.

Rising popularity of computerization made it even easier to perform RFM studies because customer and purchase records were digitized. An extensive study by Blattberg et al. in 2008 proved RFM’s effectiveness when applied to marketing databases. Numerous other academic studies have also approved that RFM reduces marketing costs and increases returns.

Windsor circle reported significant success using RFM

for their retail customers:

  • Eastwood increased their email marketing profits by 21%
  • L’Occitane saw 25 times more revenue per email. 25 times, not 25%…
  • Frederick’s of Hollywood recorded conversion rates as high as 6-9% in their campaigns

I hope you are now convinced about the usefulness of RFM analysis for your own business.

Now let’s get on the math behind all those results.

RFM Score Calculations Simplified

Wondering how to calculate RFM scores for your customer database? Here’s how…

We need a few details of each customer:

  • Customer ID / Email / Name etc – to identify them
  • Recency (R) as days since last purchase: How many days ago was their last purchase? Deduct most recent purchase date from today to calculate the recency value. 1 day ago? 14 days ago? 500 days ago?
  • Frequency (F) as total number of transactions: How many times has the customer purchased from our store? For example, if someone placed 10 orders over a period of time, their frequency is 10.
  • Monetary (M) as total money spent: How many $$ (or whatever is your currency of calculation) has this customer spent? Again limit to last two years – or take all time. Simply total up the money from all transactions to get the M value.

Here’s an RFM analysis example:

Customer ID Name Recency (days) Frequency (times) Monetary (CLV)
1 Robert Johnson 3 6 540
2 Serena Watson 6 10 940
3 Andy Smith 45 1 30
4 Tom West 21 2 64
5 Andrea Juliao 14 4 169
6 Paul Owens 32 2 55
7 Sandhya Mhaskar 5 3 130
8 Joe Woods 50 1 950
9 Ammar Fahad 33 15 2430
10 José Barbosa 10 5 190
11 Salman Desheriyev 5 8 840
12 Alexander Diesel 1 9 1410
13 Cheng Liao 24 3 54
14 Anton Sundberg 17 2 44
15 Tarun Parswani 4 1 32

Consider customer Robert Johnson – he last ordered 3 days ago and placed a total of 6 orders worth $540 till date.

Applying RFM Score Formula

Once we have RFM values from the purchase history, we assign a score from one to five to recency, frequency and monetary values individually for each customer. Five is the best/highest value, and one is the lowest/worst value. A final RFM score is calculated simply by combining individual RFM score numbers.

Look at the table below. To calculate score, we first sort values in descending order (from highest to lowest). Since we have 15 customers and five scores, we assign a score of five to first three records, four to next three and so on. For overall RFM score, we simply combine R, F and M score of the customer to create a three digit number.

Note: The most recent purchases are considered better and hence assigned higher score.

CID R Value R Score   CID F Value F Score   CID M Value M Score   CID RFM Score
12 1 5 9 15 5 9 2430 5 1 544
1 3 5 2 10 5 12 1410 5 2 454
15 4 5 12 9 5 8 950 5 3 111
7 5 4 11 8 4 2 940 4 4 222
11 5 4 1 6 4 11 840 4 5 333
2 6 4 10 5 4 1 540 4 6 222
10 10 3 5 4 3 10 190 3 7 433
5 14 3 7 3 3 5 169 3 8 115
14 17 3 13 3 3 7 130 3 9 155
4 21 2 14 2 2 4 64 2 10 343
13 24 2 4 2 2 6 55 2 11 444
6 32 2 6 2 2 13 54 2 12 555
9 33 1 15 1 1 14 44 1 13 232
3 45 1 3 1 1 15 32 1 14 321
8 50 1 8 1 1 3 30 1 15 511

Thus, customers who purchased recently, are frequent buyers and spend a lot are assigned score of 555 – Recency(R) – 5, Frequency(F) – 5, Monetary(M) – 5. They are your best customers. Alexander Diesel in this case, not Ammar Fahad – the highest spender.

On the other extreme are customers spending the lowest, making hardly any purchase and that too a long time ago – a score of 111. Recency(R) – 1, Frequency(F) – 1, Monetary(M) – 1. Andy Smith in this case.

Simple?

Let me quickly explain why we made groups of three for each score.

How to calculate RFM score on scale of 1-5?

Different businesses may use different methods of rfm formulas for ranking the RFM values on the scale of 1 to 5. But here are two most common methods.

Method 1: Simple Fixed Ranges

An example can be:
If someone bought within last 24 hours, assign them 5. In last 3 days, score them 4. Assign 3 if they bought within current month, 2 for last six months and 1 for everyone else.

As you can see, we’ve defined a range for each score ourselves. Range thresholds are based on the nature of business. You’d define ranges for frequency and monetary values like this too.

This scoring method depends on the individual businesses – since they decide what range they consider ideal for recency, frequency and monetary values.

But there are challenges with such fixed period / range calculation for RFM scores.

As the business grows, score ranges may need frequent adjustments.

If you have a recurring payment business, but with different payment terms – monthly, annual etc – the calculations go wrong.

Method 2: Quintiles – Make five equal parts based on available values

Recall your school days. There was a term – Percentile in maths. Percentile is simply the percentage of values that fall at or below a certain observation.

Here’s a graphic from MathIsFun.com that explains this clearly:

Quintiles are like percentile, but instead of dividing the data in 100 parts, we divide it in 5 equal parts.

If you understand percentiles, it’s easier to understand quintiles. If we make five equal ranges of percentile, a percentile score of 18 will fall in the 0-20 range, which would be 1st quintile. A percentile value 81 will fall in the 80-100 range, and hence 5th quintile.

This method involves slightly complicated math, but solves a lot of problems in fixed range method. Quintiles work with any industry since ranges are picked from data itself, they distribute customers evenly and does not have cross overs.

Quintiles is our recommended method to calculate RFM score. We use quintile based RFM calculations in Putler – our business analytics and marketing insight solution for online merchants.

Visualizing RFM Data

A graphical representation of RFM will help you and other decision makers understand your organization’s RFM analysis better.

R, F and M have scores from 1-5, there are a total of 5x5x5 = 125 combinations of RFM value. Three dimensions of R, F and M can be best plotted on a 3D chart. If we were to look at how many customers do we have for each RFM value, we’d have to look at 125 points of data.

But working with 3D charts on paper or a computer screen is not going to work. We need something in two dimensions, something easier to depict and understand.

Simpler representation of RFM analysis

In this approach, we plot frequency + monetary score on X-axis (range of 0 to 10) and recency (range of 0 to 5) on Y-axis. This reduced possible combinations from 125 to 50. Combining F and M into one makes sense because both are related to how much the customer is buying. R on the other axis gives us quick peek into re-engagement levels with customer.

Consider a subscription business for example. For a customer with monthly subscription of $100, their monetary value will be $1200 for the full year, but frequency will be 12 owing to monthly billing.

On the other hand, a non-recurring business, or annual subscription at $1200 indicates good monetary value but frequency is only 1 due to single purchase.

The customer is equally important in both cases. And our approach of combining frequency and monetary scores gives them equal importance in our RFM analysis.

Making it more effective – creating segments

Understanding 50 elements can still be tedious. So we can summarize our analysis into 11 segments to understand our customers better.

If you recall, we discussed these segments at the beginning of this article.

Here’s a table that explains how you can create 11 customer segments based on RFM scores.

Customer Segment Recency Score Range Frequency & Monetary Combined Score Range
Champions 4-5 4-5
Loyal Customers 2-5 3-5
Potential Loyalist 3-5 1-3
New Customers 4-5 0-1
Promising 3-4 0-1
Customers Needing Attention 2-3 2-3
About To Sleep 2-3 0-2
At Risk 0-2 2-5
Can’t Lose Them 0-1 4-5
Hibernating 1-2 1-2
Lost 0-2 0-2

Easier interpretation with color codes and color intensity

If we give a distinct color to each segment, it would allow easier recall. Additionally, if we determine color intensity based on the number of customers in that segment, our pictorial representation of RFM will be much easier to share and understand.

Our ultimate RFM analysis presentation

So here’s our final RFM summary report!

Software for RFM Analysis

With focus more on customer relationship management (crm), rfm has become an integral part of business. If you are doing one-off evaluation of your customers’ shopping behavior, you can get away with performing a manual or semi-automated RFM analysis. But if you have a slightly large database, you don’t want to do all the complex calculations yourself.

RFM calculations using Excel

Bruce Hardie and Peter Fader wrote a detailed note about using Excel to calculate RFM scores. They also have a sample Excel file that you can use. But this note is from 2008 and may need updates.

There is also an Excel template from UMacs Business Solutions that sells for $3.99.

There is a walkthrough for setting up RFM calculations using Excel on CogniView’s site as well.

CRM tools do RFM

There are plenty of CRM software that can automatically calculate RFM scores and segment your customers. Check with the CRM of your choice if they already have RFM support.

RFM using Python / R and other analytics tools

R and Python are popular for statistical and business analytics. If you have your own data science team, it would be best to create a custom RFM model for your business using your existing tools.

RFM for Shopify, BigCommerce and TicTail

RetentionGrid is a software service specialized in RFM analysis. It can bring in data from your Shopify, BigCommerce or TicTail store and show beautiful visualization of RFM segments.

RFM analysis and much more for all online stores

Putler is a great suite of business analytics and reporting tools for e-commerce. It supports all major payment gateways and e-commerce systems and generates RFM segments automatically. Putler also gives you detailed reports on a whole lot of other things – sales, products and visitors.

RFM analysis in Putler is available in the customer dashboard. Here’s how it looks.

Variations of RFM Model

RFM is a simple framework to quantify customer behaviour. Many people have extended the RFM segmentation model and created variations.

Two notable versions are:

  • RFD (Recency, Frequency, Duration) – Duration here is time spent. Particularly useful while analyzing consumer behaviour of viewership/readership/surfing oriented products.
  • RFE (Recency, Frequency, Engagement) – Engagement can be a composite value based on time spent on page, pages per visit, bounce rate, social media engagement etc. Particularly useful for online businesses.

You can perform RFM for your entire customer base, or just a subset. For example, you may first segment customers based on geographical area or other demographics, and then by RFM for historical, transaction based behaviour segments.

Our recommendation: start with something simple, experiment, and build on.

Applying RFM Segmentation to Your Business

“Customers / User segmentation isn’t something that is alien in the marketing world. The big brands have this down to a T, and the little guys are just waking up to the power behind having a laser-focused strategy – laser-focused on user segmentation”.

These words are from Neil Patel in his blog post about how user segmentation works in content marketing.

Marketers have used RFM based segmentation to optimize their return on investment on marketing campaigns for years. This is typically done by sending targeted messages to those 11 segments we discussed earlier – or any other custom segmentation that situation demands.

RFM for better email marketing

Create segmented lists in your email marketing software (MailChimp, Campaign Monitor etc) from RFM analysis. Then run an automatic drip campaign on each segment. If possible, automate moving people between segmented lists as they move from one RFM segment to another.

You can further segment based on open and click rates, and products purchased. This gives you laser focused, highly relevant market segments. This strategy drastically improves results.

RFM to improve customer lifetime value

How much a customer spends with you during her lifetime is based on a number of factors. RFM can assist in many of those aspects – reducing churn, offering upsells and cross-sells to segments that are more likely to respond, increasing loyalty and referrals, selling high ticket items and more.

One word of caution though. Do not go overboard. If you keep sending marketing campaigns to one segment of your customers, they may get irritated and stop buying.

RFM for new product launches

Promoting new products to loyal customers is a great way for getting initial traction and feedback. You can contact your Champions and Loyal Customers even before building a product. They can provide you great insights into what to build and how to promote it. This group of people will also happily refer your product to their circles of influence.

RFM to increase loyalty and user engagement

If you run a loyalty program, Potential Loyalist is the first segment you may target. You want to make sure their initial experience with your product and service is pleasant and memorable. Follow up with a few timely promotions and they are highly likely to buy again. Sending educational content to these customers will also increase their engagement with your brand.

RFM to reduce customer churn

At Risk and Hibernating are two segments that you need to pay special attention to. Send personalized emails or call to reconnect with these customers. You may even offer repeat purchases at a discount or run surveys to address their concerns before you lose them to competitors/alternatives.

RFM to minimize marketing costs and improve RoI

Untargeted marketing campaigns can be expensive. Focusing on a smaller segment of customers will significantly reduce costs, allow you to do more experimentation, and make decisions based on data.

As a matter of fact, the roots of RFM are in direct marketing. Where they reduced costs of printing and shipping catalogs by targeting only those customers that were more likely to respond to these campaigns. So whether you are doing digital marketing, print or media, segmentation will reduce your costs and improve return on investment.

RFM for remarketing / retargeting campaigns

Remarketing is a smart technique where you show your ads / promotions to people who’ve been to your site at least once – but are now on some other site. They will see your ads on the other sites they visit – this improves click rates and overall effectiveness.

A simple way to use remarketing with RFM can be to export a segment of your customers – especially the New Customers or Promising ones – to the campaign management solution you are using. Then show promotions to that group of people.

RFM to understand your business better

Most small businesses do not fully understand their customers. They may not know their customer demographics or firmographics. Collecting and understanding this information can also be time consuming and costly.

RFM analysis becomes a quick method to understand your customers’ behavior. And since it is based on actual transaction history, it’s much higher quality. Looking at different RFM segments can reveal insights about your own business. Asking questions about how your segments compare to each other can open up huge opportunities of growth.

How would you use RFM model?

So, think about how your business has run so far, and what can you improve if you knew your customers better.

  • Would you send handwritten thank you notes to your best customers?
  • Would you send discount to people who are not spending enough?
  • Can you afford to disregard lost customers?
  • How can you tie this back to your own systems?
  • What else would you do?

Summary – Pros, Cons, Recommendations

RFM technique is a proven marketing model that helps retailers and e-commerce businesses maximize the return on their marketing investments.

Some advantages of RFM:

  • RFM is useful for different types of businesses – online, retail, direct marketing, subscriptions, non-profits…
  • You get to know different customer segments and can identify your best customers
  • RFM helps craft highly targeted marketing campaigns
  • It aids customer relationship marketing and customer loyalty
  • Combine it with other tools to get detailed customer analytics and customer insights
  • RFM reduces marketing costs due to optimize targeting
  • It decreases negative reactions from customers due to controlled targeting

Some disadvantages of RFM:

  • It may not be useful when most customers are just one-time purchasers.
  • When you sell just one product and that too only once.
  • RFM is a historical analysis. It is not for prospects
  • Without a software / tool, calculating RFM scores and segments can be confusing
  • Sending too many campaigns to one particular segment can upset customers

Our recommendations:

  • Definitely use RFM model – first understand your customers, then run targeted campaigns
  • Use Putler for comprehensive reporting, including RFM, if you sell online
  • Run automated email / outreach campaigns based on RFM

Additional Resources

Leave a Reply

Your email address will not be published. Required fields are marked *