How to Sell Online

Learn to Run: The Power of the RFM Model in Personalization

/ 5 min read

The previous chapter explored the nitty gritty of implementing an ecommerce personalization strategy; which was broken in three distinct segments: using on-site targeting, email driven 1:1 product recommendations and on-site personalized product recommendations.

Using the crawl – walk – run approach to ecommerce personalization, this final chapter will cover the run aspect of personalization –– i.e. high value customer identification or influencer identification using RFM analysis to help you develop more targeted personalization campaigns.

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I will be covering the following facets of the RFM model:

  • Scoring: determining the value of customers based on
  • Recency: identifying the most recent purchases
  • Frequency: identifying number of repeat purchases
  • Monetary analysis: average order value analysis and total spend analysis over a period of time

As the saying goes in running a business:

Turnover is vanity. Profit is sanity. But cash is king.

In the context of marketing and scaling the growth of an ecommerce business, I’ll say that:

Sales are vanity. Retention/Loyalty is sanity. But advocacy is king.

Every e-tailer’s utopia would be to get as many customers actively and happily sharing their positive ecommerce brand experience with their friends, colleagues and family.

This is in effect, word of mouth marketing –– which is free and very profitable.

The key determinant of customer happiness is customer experience.

Personalization aims to optimize customer experience both on-site and on email messaging with relevancy. More relevant product recommendations, offers and messaging make a net positive experience.

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Breaking it to its bare essentials, here is what the customer journey process looks like:

  1. Discovery: potential customers become aware of your ecommerce brand
  2. Intent: browsing through your site when they are in purchase mode (top of mind brands stand to benefit)
  3. Choice reduction in a bid to make a final purchase decision
  4. Converting to finally purchasing
  5. Order fulfilment: same day, next day delivery or free shipping
  6. Issue resolution with your customer services team in the event of any problems arising (like hassle free returns)
  7. Returning customers and reactivating inactive customers through email if your products are replenishable or require a seasonal refresh (as in the case of fashion merchandising) and
  8. Advocacy: happy customers sharing their ‘unboxing’ experience, telling friends at dinner parties and happily recommending your brand due to their memorable experience. They are more or less sharing their positive experience.

Personalization really should be infused in all of the above steps, but the areas in which it has the highest impact are at the intent stage (when shoppers are browsing your website), choice reduction and nurturing returning customers.

Optimizing these phases of the customer journey as well as the more physical aspects –– i.e. order fulfillment and issue resolution –– drive up the proportion of customers that will eventually become advocates.

Advocacy is the one metric the most ambitious of ecommerce marketers watch very closely. It is exemplified in its rawest form as brand name searches and direct traffic.

Happy customers will share the name of your ecommerce brand or its website address –– meaning that their friends will simply search for your brand OR type your URL in their address bars. You can see this in your Google Analytics as an increase in direct traffic or branded search terms.

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Building an RFM Model

When you have optimized both the on-site and off-site experience for customers with a truly holistic and integrated personalization strategy, how do you discover your most valuable customers that bring in the highest conversion rate?

The RFM methodology is an acronym for the following three segments:

  • Recency is measured in days. You’d need to set a threshold meaningful to your business because the fewer the days from a customer’s last purchase the better.
  • Frequency is measured as the number of order per year from each customer. For some businesses, their best customers order monthly and for other replenishables-oriented businesses, their best customers order weekly.
  • Monetary analysis is the total order value over a period of time –– typically a over a year.

Marketers use the RFM model to filter out and score each customer by their most recent purchase by date (which is the ‘recency’ segment), by each customer’s number of orders (their purchase frequency) and then by their cumulative order value over a specified period of time (for the monetary analysis piece).

Each customer record should have the following fields in order to carry out RFM analysis for your store:

  • Total Order Value
  • Average Order Value
  • Total Number of Orders
  • Last Order Date
  • Value of Last Order
  • Date of First Order
  • Value of First Order
  • Average Number of Days Between Orders


BigCommerce Analytics offers this view out-of-the-box.

Then, the output of an RFM analysis would look something like this (this is from OroCRM):


As you can see from above, the analysis is split out into 3 segments for recency, frequency and monetary value with points awarded to each segment.


Image source: OroCRM

  • In the recency segment, this company designated the score of 5 to customers that have not ordered over the last two months.
  • In the frequency segment, customers that have made less than 5 purchases in the past year are also designated the score of 5.
  • And in their monetary value segment, customers’ whose cumulative spend over the past year has been less than $5,000 are designated the ‘worst’ score of 5.

Their highest rated customers are graded a top mark of 1 if they have made a purchase in the last 7 days, have had 50 or more orders in the past year and spent more that $20,000 over the past year.

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The spreadsheet above on the other hand gives lower numeric scores to top performing customers by recency (0-30 days), frequency (16+ orders in the last 12 months) and monetary value (total orders over $500).

How Should Marketers Use RFM Scores?

Here are a few ways to use RFM scores to improve your marketing decisions and rules for 1:1 personalized email campaigns:

RFM scores should be used to create segments for personalization

Your RFM analysis will help you establish segments for existing customers such as high value customers, most active customers and newest customers.

By doing this, you are able to merge your revenue goals with better targeted unique messaging and personalized offers. RFM analysis can also help you create segments that identify inactive customers.

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RFM scores should be calculated by channel

For multichannel retailers, RFM scores should be calculated by channel in order to better understand the quality of customers per channel.

BigCommerce analytics offers a per customer view of last touch channel before conversion for all repeat customers. This data can be exported to give you a solid view of repeat customer purchase value by channel.



Integrate RFM scoring into your shopping cart abandonment strategy

RFM scores could be used to determine the incentive value threshold you are willing to offer customers that have recently abandoned their shopping carts.

As an example, higher value customers with order totals above a set threshold could be given steeper discounts to complete their purchase.

Your Three Types of Customers According to the RFM Model

High recency, high frequency and high monetary

Customers scoring highest for recency, frequency and monetary value will be in your most loyal customer segment and should be rewarded with exclusive offers and special privileges.

For example, shipping could be free to your best customers.

Here is the BigCommerce Insights view of this cohort.


High recency, low frequency and low monetary

Customers in this segment will most likely be your newest customers. Make sure you put your best foot forward, by sending them welcome offers, product-guides or relevant information to get them accustomed to your brand and store.

Here is how BigCommerce Insights depicts this cohort.


Low recency, low frequency and low monetary

These will be deemed your inactive or least engaged customers. You would either want to attempt to re-activate them or double-check to see if they should remain on your list as customers.

Wrapping Up

I’ll wrap up by saying that RFM analysis should be at the heart of your personalization strategy. Use RFM to send better personalized emails, for more targeted product recommendations and in your on-site targeting.

Once RFM segmentation is integrated into your customer list and ecommerce site, it is a relatively simple way of delivering more tailored messaging to your customers based on their past behaviors.

Use it as a strategic base for all your segmentation activity in order to help increase conversions and response rates. Depending on the nature of your online retail business, you will most likely find that 10-25% of your customers account for 60-80% of sales (the 80/20 rule).

Stats on customers that have only purchased once (whom I refer to as one-night standers) will be startling –– and you really can’t argue with data!

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Kunle Campbell

Kunle is a trusted advisor to ambitious, agile ecommerce brands. His core strengths lies in growing revenue by developing and executing scalable customer acquisition and search marketing strategies for online retailers.  He blogs, runs webinars and teach courses about ecommerce growth on He also hosts an ecommerce podcast dedicated to growing and scaling online retail businesses.

View all posts by Kunle Campbell
Leave a Comment

6 comments on “Learn to Run: The Power of the RFM Model in Personalization

  1. Lucía Vargas on

    Hi, very interesting article. I only have one doubt left, how do we know that we have enough data to make an analysis that really represents our consumers?

  2. Ilke Karabogali on

    Great article Kunle..!

    I’d also be happy to learn about your approach to real-time segmentation models which in itself uses in-memory databases to keep track of every single visitor behavior on an e-commerce site. These models (in-fact engines) analyse the visitor behaviour from a broad perspective and include recency, frequency, monetary value, pages visited (clickstream behaviour, add to cart and purchase actions), reaction to discounts etc. And all happen in real time, thanks to predictive algorithms :)

    I’ve been working on these kind of models for a few years now and I’d love to get your opinion. I’ve tried to elaborate on this point here:

    Please let me know what you think



  3. isaac on

    Hello Kunle, pls how will u define RFM for a Utility company ( gas and electric), bearing in mind that this a contractual relationship.

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