You just received your quarterly sales report, and on it, you can clearly see your company’s top users, the most purchased membership, earnings, and profits. You’re almost reaching your sales quota, but you’re not quite there yet. Something catches your eye; printed in the behavioral charts, you can clearly see some users have been consistently buying your services, while others have gone down to the bottom of the report and are now dormant. The data is there, but it’s unorganized and you can’t seem to make a clear picture in your head of who else falls in those groups– because if you could just know who they are, you could easily reach out to them with a couple of enticing offers that will most definitely help you reach that sales quota you dream of so much. 

It occurs to you that, if you could segment your customers to know which ones are your top, average, and worst customers, you could make actionable recommendations to increase sales. You just hit the nail on the head! You ping your marketing department and rush them to get on this new project: an RFM analysis of all your customers. Surely, that will give you all the answers.

In case you, the reader, need a refresher, RFM is a customer behavior segmentation technique based on data. It stands for Recency, Frequency, and Monetary value where Recency identifies the last time a customer made a purchase. Frequency determines how many times a customer bought from you in a given time period. And Monetary value captures the amount a customer has spent on your brand so far.

The marketing team is hard at work running the analysis, and after some time, they find the culprit. A group of 10 segments, identifying all of your users based on their purchase behavior. Bingo! What now? Well, the answer lies in the data and the team’s analytical mind. The first thing to look at is the 10 segments an RFM analysis report provides.

An Actionable Guide for RFM Analysis

Years ago, RFM analysis was performed with Excel and it was a long process that included manually importing data from a company’s source such as an e-commerce platform like Shopify, Magento, Woocommerce, etc into an Excel table. Then the analysis called for sorting, scoring, filtering, and a number of actions to effectively organize customer segments. This process was tedious and required technical talent to do it right. Thankfully, new technology emerged with the capacity to run models with premade RFM algorithms to help you cut time in more than half. Not only that, but you can also send out the report directly to your most-used apps like Intercom, Facebook Ads, Google Ads, and so on, so your team can act on it on the spot. A few months back, we wrote an article explaining exactly how these models work.

Whether you perform the analysis on Excel, or through an automated tool, your result will be a segment report which will include 10 groups or scores within your customers based on their shopping behavior. The report is a result based on an “award” or a grade given to each customer. For example, a score could be labeled as customers who are ‘Champions’, or ones who ‘Need Attention’. By doing so, you're turning transaction absolute values into pieces of similar transactions using RFM. 

After you've assigned scores, you can group together clients who have the same or similar scores in each of the three categories (Recency, Frequency, Monetary).

The labels used will be determined by the differences between the three scores that consumers have received. There are a total of 1,000 distinct segments because we employ a 10 score segment and 3 criteria (Recency, Frequency, Monetary). Businesses may or may not require 1,000 separate segments, and based on the nature of the company, one can determine the number of scoring segments required and label them. Here is an example of those scoring segments.

The marketing team from the example above sends the report out but somehow overlooks it when planning their email marketing campaigns for this month. They are caught up in the daily bustle and hustle, prepping to send out a promo deal to all of their customers. Creative is done, the copy is done, social media is linked and they are confident this email will help the sales team reach their goal if they get at least a 14% rate. But reality seeps in, and their 14% seems really far off from the sad 1.5% rate they actually got. Why? Because they didn’t target the right customers (even though they were nicely organized in the RFM report).

The team followed their theoretical training, not a data-driven one. In a different scenario, the team laid out the report and created personalized emails for each segment. Because think about it, a loyal customer may jump on your promo offer because they tend to buy from you regularly. And, while a customer who’s at risk may not be faced by the offer, a new feature within their subscription may prompt them to upgrade their current subscription.

Targeting is key because every customer segment reacts differently to each messaging.

Rather than reaching out to the entire database, you should discover and target only the consumer groups that will prove to be the most profitable for your company. Beauty brand L’Occitane saw 25 times more revenue per email by implementing personalization. And they are not the only brand with positive outcomes thanks to RFM, thousands of organizations have been using it to deliver customized experiences to their customers and in hindsight, increasing sales profits.

Get to know your customers

Now that you have a clear diagnosis of where your customers fit, ask yourself these questions:

Who are my top customers?

Which customers could churn?

Who could potentially become a loyalist?

Which group of customers could be retained?

Which customers show the most probability of engagement?

For instance, if you have a good chunk of the pie as top customers, this means they are your most loyal consumers– they buy often and have high-priced tickets. In this case, send them rewards, stimulate them with new product offers, and build a relationship with them. 

On the other hand, customers who are hibernating may raise a red flag about your product. Ask why they have stopped buying from you with surveys, and send them incentives that can trigger the need to reconnect with you. Here’s a list of strategies you could use to act upon an RFM analysis.

  • Loyalty program
  • New product promotions
  • Surveys
  • Upselling/Cross-selling
  • Contests
  • Onboarding support
  • Discounts
  • Free trials
  • Brand awareness campaigns
  • Store credits
  • Time-limited promotions
  • Combo offerings
  • Share valuable information
  • Provide competitive advantage
  • Product updates
  • Brand updates
  • Upgrade offers
  • Custom-made services
  • Personalized campaigns

RFM analysis provides a targeted view into your customer data, and it is a great way to reach them on a more personal level, not only to increase sales, but build closer relationships, reduce churn, increase LTV, ROI, and more. Try one of our more advanced techniques to incorporate RFM into your business by centralizing multiple data sources and cross-referencing your customer data with inventory data for instance, and try finding relationships within your consumers’ behavior and your inventory. Datagran allows you to build and put ML models into production with little to no code so you can save your team time and start acting on your data fast.