# RFM analysis for improving conversion rates and customer insights

You are working as a digital marketer or digital marketing analyst in a company. The company has several thousand online customers, but beyond some top level metrics they don’t have any customer-focused insights. As such you might have the typical Google Analytics reports (e.g. which sources are customers coming from), know what products are purchased most often and what the average order value is. But what your stakeholders lack is a better understanding of the customers in order to drive marketing & content decisions and strategies for acquisition, growth and retention. That’s why they come to you asking to share some general “who are the customers?” insights with them.

Even though this probably one of THE typical asks for marketers and analysts it might lead to some inner stress levels rising due its vagueness. Above question can mean everything and anything from rather top level customer personas to in-depths customer and purchasing behavior reports.

So imagine you had an analysis that would not only group all customers into different clusters but also allow you to develop and present to your stakeholders targeted strategies for each cluster based on its characteristics. All leading to an optimized marketing & sales approach and in-depth customer insights for improved conversion rates. And the best of all is all you need is the transactions data of your company.

That’s where the RFM analysis comes into play. It’s a simple to understand and easy to apply data analysis model to segment your customers. The following is a step-by step tutorial on how to create such a model in Google Sheets. Furthermore it shows you specific strategy recommendations for each of the key customer clusters (and if you want to get started quickly you can plug-in your data into the provided workbook to use it as a template in order to segment your clients right away).

## The recency frequency monetary (RFM) analysis

The recency frequency monetary analysis (RFM analysis) is a classic analysis model for behavior based consumer segmentation. It segments customers by scoring them on a 1-5 scale in regards to how recently, how often, and how much they have bought (different scales may be used, however the 1-5 is usually the one used in a commercial context). Those three factors can then be used to predict how likely it is that a customer will purchase (or for some business models engage, e.g. apps) again.

Furthermore those segments can be grouped into clusters allowing you to develop targeted individualized content and promotion strategies which are more likely to convert with the customers in each cluster. Because each cluster is assigned a monetary value high value high-value customers can be identified easily and marketing spend can be allocated accordingly.

This guide gives a RFM Analysis example and shows step-by-step how to conduct a recency frequency monetary analysis with your data in Google Sheets. Afterwards it explains the content and promotion strategies, which can be applied to each individual cluster.

As usual I recommend working along in the above provided Google Sheet in order to understand everything. The workbook contains two sheets: *Sample Data *includes dummy data representing a transaction list (this could be anything from software sales to ecommerce store orders) and* RFM Model *which includes the solution to the RFM analysis example this guide is working towards.

If you are in a hurry: It is possible to replace to the dummy with your own data to use the *RFM Model* as a ready made template.

## Structure for this RFM analysis guide

This guide is structured into three stages. We’ll start by doing the actual analysis in Google Sheets, continue with messaging for the different segments in stage two and end with some strategies on how you can use the segments for your paid advertising campaigns. While the first part might seem a little bit technical I promise you it is not and all will be done in Google Sheets. It’s totally doable for everyone, even if you consider yourself not to be a math or technical person.

**Building the RFM analysis model:** How to use Google Sheets to segment your customer base including gaining insights on the attributes of each segment such as average lifetime value, frequency of orders and more.

**Define the messaging strategy: **How to address each segment with the most efficient messaging strategy.

**Improve your paid advertising:** How to use your high value segments to find similar customers via paid advertising – with better conversion rates and lower costs per acquisition (=incremental sales). And how to use your other segments to retain & grow existing customers via paid ads (=incremental revenue per customer).

## 1. Creating a RFM analysis example step by step

First step is to prepare the data and to calculate the following metrics for each customer:

- The most recent transaction
- The number of transactions per month for each customer
- The average amount purchased each month by each customer

In the following we will work in the sheet *Tutorial *of the provided workbook.

We’ll start by finding out the number of transactions each individual customer had. This is easily done by copying the formula =COUNTIF(‘Sample Data’!B:B,A2)from B2 down to B3403. It will count how often the value from the referenced cell occurs in column B from the sheet *Sample Data.*

Next step is to identify the most recent transaction for each customer. You can do so by copying the following formula from C2 down to C3403:

=MAXIFS(‘Sample Data’!C$2:C,‘Sample Data’!B$2:B,$A2)

The formula uses cell A2 as a reference to filter the corresponding rows in the sheet *Sample Data* with column B and returns the highest (=most recent) date from column C respectively.

Similar to this we use the formula =MINIFS(‘Sample Data’!C$2:C,‘Sample Data’!B$2:B,$A2) in in cells D2 to C3403 to get the date of the first transactions of each customer.

For the final RFM model you’ll need the amount of time the customer has been with the business. In our example we’ll use months for this. As such put =DATEDIF(D2,now(),“M”)into E2 and drag it down E3403.

Next we want to know how much each customer spends on average each month. Plug in =SUMIF(‘Sample Data’!B:B,A3,‘Sample Data’!D:D)/E3 from F2 to F3403.

The last step is to calculate the average number of transactions per month for each customer. So simply write =B2/E2 into G2 and copy it down.

For all above instead of “M” you could also use “Y” or “D” to set the time unit to years or days respectively. It doesn’t really matter what you choose as we we’ll be coding each data point into a 1-5 scale for the RFM analysis later on anyway. However to make your data more vividly choose a unit, which make sense to your business model, e.g. if you are selling cars it would make sense to choose years, while it might make more sense to choose days when you are selling coffee.

## Calculating R,F and M

Two steps are necessary to calculate the R, F and M scores:

- Determine how each customer ranks for recency, frequency and monetary
- Assign a score to each recency, frequency and monetary rank

Luckily Google Sheets has a handy formula for returning the rank of a specified value in a dataset.

As such plug in and copy down the following formulas.

For the recency rank: =RANK(C2,C$2:C$4296,1) in H2 to H3403

For the frequency rank: =RANK(G2,G$2:G$4296,1) in I2 to I3403

For the monetary rank: =RANK(F2,F$2:F$4296,1) in J2 to J3403

The last argument (1) in the formulas ensures that the highest values in the respective dataset gets a higher rank and vice versa. E.g. a customer with an average order value of 10$ would get a higher rank than a customer with an order value of 5$.

Next we’ll create a RFM rank matrix to convert a customer’s ranks on recency, frequency and monetary into the wanted 1-5 rating. For this you will use the formula PERCENTILE to get the minimum rank a customer has to have on each for the three factors to get a certain rating. Since we have ratingd from 1-5 we’ll need five percentiles which equal to 20% percent steps.

Put =PERCENTILE(H$2:H$4296,0.8) into Q2 to get the lowest possible rank for the highest R score, similar put =PERCENTILE(H$2:H$4296,0.6) into Q3 to get the lowest possible rank for a R score of 4 and so on. Do the same for the F score in column R and and for the M score in column S.

No we’ll do the actual conversion for each customer. Put the formula =if(H2>=Q$2,$T$2,if(H2>=Q$3,$T$3,if(H2>=Q$4,$T$4,if(H2>=Q$5,$T$5,$T$6)))) in K2 and copy it down to K3403 to determine the R score. The conditional statements will determine if the respektive rank is above one of the thresholds of the RFM rank matrix and then assign an according score. Do the same for the F score by copying =if(I2>=R$2,$T$2,if(I2>=R$3,$T$3,if(I2>=R$4,$T$4,if(I2>=R$5,$T$5,$T$6)))) from L2 to L3404 and for the M score by copying =if(J2>=S$2,$T$2,if(J2>=S$3,$T$3,if(J2>=S$4,$T$4,if(J2>=S$5,$T$5,$T$6)))) from M2 to M3404.

As a last step we want count how often each of the RFM combination (there are 5x5x5 = 125 combinations) occurred. Again we will use the COUNTIF formula for this. Write =COUNTIF(N:N,Q10)into R10 and copy it down to R134.

You are done! ….at least technically. You gave each of your customers a RFM score based on the recency, frequency and monetary value of past purchases. However in order to make your analysis actionable you should group your 125 segments into more meaningful clusters and define strategies for each of them.

## 2. Clustering the RFM analysis segments

Even though many others suggest having ten or more clusters I would recommend focusing on having only six key clusters (at least in the beginning). As such the implications for each cluster are still manageable and content and promotion strategies for each can be created.

The following describes content strategies for your email list and consequently customer segmentation for each of the defined clusters.

### High Value Customers

Segments: 555

Description: These customers are your most valuable customers. They buy frequently, are spending a high amount on each transaction and are still very active (=bought something recently).

Strategy: Obviously these customers have proven that they are willing to pay and to buy often from your. So don’t use price incentives (e.g. discounts) to generate incremental sales. They love engaging with you so rather reward them by testing new product (or feature) launches with them first. Being your most loyal customers the probability that they give some valuable feedback on the new products and recommend them to others will be highest with them. If possible try implementing loyalty programs (as well as advocacy and review programs) to reward and keep their loyalty. In addition these are the customers you should target your most expensive products at.

### Core Customers

Segments: 34X, 35X, 44X, 45X, 54X, 551, 552, 553, 554

Description: Your core group of loyal customers. While they might be spending less frequently or lower amounts than your High Value Customers, they are still very valuable as they are regular and recent purchasers of your products.

Strategy: Probably not as effective as your *High Value* customers you can still look into upsell opportunities. Aa such if you are selling several products (e.g. as an ecommerce business) you can add value for those clients by recommending products based on previous purchases. Advocacy and review programs can help you spread word of mouth for your business through this cluster.

### New Customers

Segments: 51X, 52X

Description: Your newest customers. They recently had their first transaction with you. As such they obviously will have low frequency score. Even though they can have high monetary scores already, if they are high spenders.

Strategy: Most first time buyers will never graduate to promising and finally loyal customers. It is important to have an optimized onboarding with clear strategies in place (such as a triggered welcome email sequence) to encourage repeat purchases.

### Promising Customers

Segments: 33X, 43X, 53X

Description: Customers, who finished the onboarding process but aren’t in the Loyal Customer cluster yet. They buy fairly often, but haven’t reached the frequency levels of the* Loyal Customers* or *High Value Customers* yet.

Strategy: You have already accomplished an initial relationship with the customers. Now you should focus on increasing monetization and frequency depending on what they are currently lacking. You can test personalized product recommendations based on past purchases and special offers based on spending thresholds. Increase brand awareness to stay top of mind and to increase frequency.

### Need Attention Customers

Segments: 24X, 25X

Description: Customer, who once purchased from you with a medium to high frequency but stopped for some reason a while ago.

Strategy: Goal for this customer cluster is to reactivate them before they get lost at all. Part of this can be to try to find out, why they left through analyzing their behavior or surveying them. Try limited-time offers as well as individualized recommendations based on past transactions. Price incentives can also be tested.

### Lost Customers

Segments: 1XX

Description: These customers have not purchased from you in a long while. Some of them might have been high frequency and big spenders, but stopped buying at some point.

Strategy: As with the “Need Attention” segment you should try to reconnect with these customers. By having even more aggressive offers and price incentives you can try to reactivate them. However it can make sense here to segment this cluster even further by F and M in order to identify low value customers

Obviously there are still some white spots left for some of the customer segments. Above are only key clusters you should focus on in the beginning. You can cluster all the left out segments into a* General Population *group sending those more generalized and less segment specific content. Once you have mastered above key groups you can start clustering the rest of your segments.

In addition the thresholds for above clusters aren’t set in stone. As soon as you fully understand the implications of the model and the clusters for you business you can start moving the segments around to form your own clusters, fitting best to your business.

You can add the following formula to cell O2 and copy to O3403 to group the segments with above clusters in the Google Sheets workbook:

=IF(AND(K2=5,L2=5,M2=5),“High Value”,

IF(AND(K2=1),“Lost”,

IF(AND(K2>2,L2>3),“Core”,

IF(AND(K2>2,L2=3),“Promising”,

IF(AND(K2=5,L2<3),“New”,

IF(AND(K2=2,L2>3),“Need Attention”, “General”))))))

## 3. Using the clusters for paid advertising

The last stage is to use your clusters for paid advertising. Obviously this is only optional as above messaging strategies will already be useful for other marketing channels such as email marketing. We won’t go into detail here on the actual implementation on the different advertising platforms (if you need some instruction feel free to sign-up below and I’ll send them to you as a follow-up) but rather talk about the overall agnostic strategies you can use for advertising to your clusters. Using different targeted strategies for your clusters should overall significantly improve your ROI for your advertising spend.

A prerequisite to use the clusters for paid advertising is that you included email addresses in the RFM model (rather than e.g. customer ids). As such you classified each individual email address with a cluster.

It is now possible to upload the email addresses from each cluster as individual lists into your advertising platform. E.g. this is called “Customer Match” in Google Ads, “Custom Audiences” in Facebook Ads and “Tailored Audiences in Twitter Ads. Depending on the platform please be aware that there might be a minimum number of contacts you need for each list.

After uploading your six clusters you’ll have three strategy options on how to use them:

### Acquire new customers

Clusters: High Value, Core

Each of the platforms have features to find similar users to the ones you uploaded with your email lists (e.g. “Similar Audiences” in Google Ads, “Lookalike Audiences” in Facebook Ads and “Look-alikes” in Twitter Ads). As such you can use those features to target customers which are similar to your “High Value” and “Core” customers promising the most return on your advertising spend. However don’t use the messaging strategy for “High Value” and “Core” users as we defined above. Obviously rather treat the similar users as potential new acquisitions and such use your regular messaging and creatives for those.

### Retain and grow existing customers

Clusters: “Promising”, “Need Attention”

As defined above in the messaging strategy we want to improve the frequency and the touchpoints we have with those customers. As such they are prime candidates for remarketing campaigns. In the remarketing ads you want to show product recommendations as well as increase overall brand awareness. If you are doing some promotions or sales these are some prime candidates to advertise to (especially on dates such as Black Friday) exclusively as your “High Value” and “Core” might have bought anyway.

### Don’t advertise to

Clusters: “New”, “Lost”

The last option is to not advertise at all to some customer. As such it might make sense to exclude the “New” cluster in order to not spam new customers who are currently going through the onboarding sequences and who have just been acquired. Second, you might want to exclude the “Lost” clusters. They’ll have the lowest ROI for your advertising spend. However you might want to test here and another strategy could be to try to reactivate them with the strategies outlined above.

## End Note

We went through a lot with above RFM analysis example and it might seem intimidating to implement all of it. But I promise it will be worth it. You will improve your customer relationships vastly by having a more personalized approach in messaging which will lead to improved conversion rates in return. You’ll also see a significantly improved ROI for your advertising spends should you choose to implement above paid advertising strategies.

To implement all of above for your own business or job I recommend to go through the guide step-by-step: Build the RFM model in Google Sheets first. Afterwards define your messaging and advertising strategies. Start here with your most valuable customers and work your way done.

I hope you enjoyed the RFM analysis guide!

Again, reading is one thing but it is essential that you work through it at least once to truly understand everything. Subscribe below to get the worksheet and I’ll also send you some follow-up resources with some more in-depth instructions on how to create email lists for your paid advertising platform to target your clusters individually.