Implementing RFM analysis and segmentation in customer marketing

It’s one thing to collect insightful data but it’s another to transform that raw data into something actionable. We call it “big data” for a reason — there’s a lot of information at your disposal. Without the help of some method or algorithm to make sense of that information, it will remain useless.

An RFM analysis is one of the methods you can use to make big data actionable, and it’s indispensable for customer marketing. There’s good news, too: an RFM analysis can be simple. Even without a background in Python or SQL, you can conduct this analysis to segment your audience of customers and target them more effectively.

What is an RFM analysis, anyway?

RFM stands for recency, frequency, and monetary value. It’s a method of data analysis born from the prehistoric days of direct mail marketing. In their paper on predictive analytics, Jan Roelf and Tom Wansbeek devised a more effective way of targeting customers with direct mail by predicting which customers were most likely to make another purchase.

This probably sounds familiar to you, even if you don’t target customers with this exact method. Nowadays, you can round data up in handy martech software like CDPs to segment and study your audience. CDPs can do the heavy lifting for you and prepare your data for an RFM analysis, so you’ve likely been doing some type of this method without a formal name.

Let’s break the RFM analysis into its essential parts:

• Recency: When was the last time a customer interacted with your company? If a customer interacted with your brand recently, they’re more likely to have your product on their mind. “Interaction” includes website visits, purchases, app usage, and even social media engagement. When Roelf and Wansbeek devised the RFM analysis, they couldn’t have imagined the types of customer data we can gather to gauge how recently someone’s interacted with a brand. Most often, companies measure recency in days.
• Frequency: How often does a customer interact with your company? As you might imagine, customers who check your website every day or chain purchases are more likely to make another purchase.
• Monetary value: How much has a customer spent on your company’s products? It seems like common sense: big spenders have more money to freely spend, and that means more money to spend on your products.

You can calculate an RFM score this way: 

(Recency score x recency weight) + (Frequency score + frequency weight) + (Monetary value score + monetary value weight). 

Or you could have a CDP calculate it for you behind the scenes. If your RFM score is a larger number, you have a better result. Simple, right?

Who can use RFM analysis?

You used to need a background in statistics or data analysis to calculate RFM scores. Taking complex data sets and running Python or SQL scripts on them was your best chance to gather customer insights.

Thankfully, modern marketers can run an RFM analysis with many contemporary tools. We’ve mentioned CDPs as one helpful option because they gather all your customer data in one place, unify that data, and then surface insights from within it.

Benefits of RFM analysis

Going back to the roots of an RFM analysis, have you ever gotten a piece of promotional mail that felt so irrelevant to you that it went directly into the trash? Maybe you hadn’t bought from that brand for years, or you purchased once and didn’t expect to be bombarded with weekly mail. 

An RFM analysis mitigates the risk of annoying your customers — it also increases your chances of making more money. Here’s how.

Fast customer segmentation

Segmenting customers is essential for better marketing campaigns. You can put your customers in buckets and personalize your messaging for each group. These are some audience segments an RFM analysis can identify:

• Churn and unsubscribe risk customers: If customers’ RFM scores have dropped, particularly in the frequency or recency categories, this is your chance to re-engage them before they drop off for good.
• High-value accounts and customers: Calculating a monetary value for a customer means you can identify which ones pay you the most money. This is the segment of your audience you want to pay the most attention to and consider how to raise their purchase frequency. 
• Frequent shoppers: Customers with a high-frequency score are return shoppers, and, as such, they’re loyal to the brand even if they don’t pay top dollar. They’re an audience segment that can leave helpful reviews, and you can engage them with deals that expedite their purchases.
• Discount shoppers: You can target customers who purchase often for a low monetary value by offering promotional prices. This group may not be a priority one, but they’re an audience segment that could respond well to deals, especially if they’re personalized.
• Highest-ranking customers: Customers who have the best scores overall are your brand advocates. This is an audience segment you want to spread the word about your brand through reviews, social media callouts, surveys, and so on. They’re also a good audience to pitch new products and features to, or to ask for case studies. 

Easier remarketing

We identified some groups that are likely to purchase again in the segments above. But what do you say to them? 

Understanding RFM scores lets you tweak messaging for better retargeting. For instance, your most loyal customers in all aspects of RFM probably don’t need a discount to be incentivized to buy. On the other hand, customers at risk of churn might appreciate a discount on the upcoming renewal.

Personalized email campaigns

Just like using RFM for better snail mail, you can apply analysis to email for more personalized campaigns. With the knowledge you get from an RFM analysis, you can target people at risk of churn with reminder emails before their renewal, or you can ask loyal customers to leave your brand a review.

Less risk of spam

An RFM analysis reduces the risk of your marketing efforts going directly into the trash — whether by direct mail, email, or any other modern means. Segmenting your audience gives you the chance to get in front of your audience at the right time with the right message.

Better budget allocation

Marketing budgets never seem to have enough money. Less is always more. Use an RFM analysis to stretch that money further by only targeting audiences that are going to spend. After all, 80 percent of your sales will come from 20 percent of your customers.

A quick warning about RFM analysis

Never take one method to slice up your data and use it as your only course of action. An RFM analysis works best if you also study demographic data, product data, and so on. The more recent this data, the better, so prioritize software that gives you real-time updates.

In addition, RFM analysis is most powerful when paired with predictive analytics. The RFM analysis can tell you what customers historically do, and predictive analytics can tell you what to do about these trends. If you don’t have a plan of action after your RFM analysis, then the data is as good as useless.

Implementing RFM analysis for customer marketing

An RFM analysis doesn’t have to be complicated. First, you need the customer data to analyze. If you aren’t already aggregating your data in a Cloud Data Platform like Snowflake, this is an excellent option to pull disparate sources and reports into one place, and then activating that data with a CDP is a cinch.

1. Decide on a scoring system

Most RFM analyses use a scale of 1 to 5 (1 being the lowest and 5 being the highest) to score customers for each value. Let’s go through some examples of what these scores might look like.

Scoring by recency

Remember, a recency score is how often a customer uses or buys your product. Depending on your industry, you may scale every year, weekly, or in any other timeframe that makes sense for you. 

Let’s say your ideal customer uses your product daily. This would be your 5. A customer that uses your product once or twice a year gets a 1. A customer that uses your product a couple of times a week may be a 4. This scale will all be relatively dependent on other customers’ behavior.

Scoring by frequency

Frequency gauges how often a customer interacts with your product or brand, depending on your goal. If a customer purchases at your store once a week and that seems to be the most active type of customer, that customer is a 5. If another customer purchases at this same store less often, their score drops. 

Scoring by monetary value

Monetary value can have some of the biggest disparities by industry. If you target enterprise business accounts, monetary value may vary widely by individual plan. If you sell one product and a few accessories, you may find monetary value to have less variance.

Even if a customer purchases less frequently, their monetary value may be higher. Maybe they purchase every quarter for a total of $5,000. If another customer purchases every week for a total of $3,000, they’ll receive a lower score than the customer purchasing quarterly.

2. Compile customer data

To start, take raw data about your customer transactions and house it in one spreadsheet, database, or platform. Once you have your scoring system figured out, assign each customer their recency score, frequency score, and monetary value score. You’ll typically gauge these using your scorecard.

3. Calculate the RFM for each customer

A full RFM score has all three values. Your perfect customer has a 555, and your lowest-value customer has a 111. Based on the variance between these scores, you’ll find distinct audience segments.

4. Segment accordingly

Based on RFM score, you can segment your audience into groups similar to the ones we described under the benefits of RFM analysis. This ensures you’re speaking to the right customers at the right time.

5. Strategize for a segmented audience

Congrats, that’s all there is to it! Now, the advantages of RFM are yours. Use it to create personalized email marketing campaigns, relevant ad copy, and marketing content for each audience segment.

If you update an RFM analysis regularly, you can also identify changes in trends, such as customers who don’t shop with you as frequently or customers who are spending less. This kind of data is power.

RFMs and CDPs: Nail your customer marketing goals

Modern systems make RFMs easy. In fact, if you have a CDP like Simon, an RFM report is easy to run and maintain. Because CDPs help you activate your data, many of them have inbuilt RFM features since it’s such an essential report for successful marketing campaigns. If you haven’t already, take a look at your RFM metrics. You may be surprised how much it helps!

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