In today’s highly competitive market where customers are spoilt for choice, as an entrepreneur, marketer, and /or business owner, it’s crucial you understand exactly who your customers are and the value they hold.
This will ensure you’re allocating the right resources, to the right audiences and therefore servicing their needs effectively.
Recency, Frequency, Monetary (RFM) is a statistical method that allows you to identify key customers and answer important questions such as:
RFM has been around for decades and is based on the Pareto formula that 80% of your revenue comes from 20% of your customers. This tried and true approach works, but it’s based on using past customer activity, as opposed to using algorithms to anticipate and influence future behavior.
Recency, Frequency, Monetary (RFM) is a statistical method that allows you to identify key customers. RFM analysis involves ranking customers based on how recently they have made a purchase (recency), how often they make purchases (frequency), and how much they spend per purchase (monetary).
How do you explain RFM in plain English to your boss or to other stakeholders? In plain English, RFM means finding your best clients or customers. An RFM is a detailed analysis of all of your customers, from one and done customers to your most loyal, repeat customers.
You can put your customers into quality buckets, for your most valuable to least valuable customers. With this information, you can predict future customer behavior, reward your most loyal customer, and understand the monetary value of new customers.
It's not enough to know how much a customer is worth because not all customers are created equal. Loyal customers making high-value transactions are worth far more to your business than infrequent customers who spend very little.
The RFM formula is Recency x Frequency x Monetary Value.
Recency is typically measured in months. If your customers have made purchases over a period of time ranging from 2 months to 44 months ago, you assign a recency value that correlated to many months ago the last purchase was made.
Frequency is how often a purchase is made. For example, you could assign a value based on how many purchases the customer made per year. You could assign a 12 for a customer who made 12 purchases a year, or a 0.5 for a customer who made 1 purchase every other year.
Monetary value refers to how much the customer group is worth to you or how much money they are spending. What is the total dollar amount that each customer has spent at your business?
RFM is a way to understand customer behavior and figure out who your best customers are. The old adage says any customer is a good customer. And that may be true. But with an RFM analysis, you can understand who your best customers are.
RFM divides customers into segments based on their transaction history:
For each of these components, Recency, Frequency, and Monetary, you'll want to assign a value. Based on the score, R*F*M, you would assign a value ranging from 1 to 5 with 1 for the lowest and 5 for the highest.
So the highest possible score would be a 5. That would be for a customer with a recent purchase, at high frequency, and for high purchase amounts.
On the opposite side, an RFM score of 1 would be your lowest value customers. These are customers who made one purchase only and for a low dollar amount quite a long time ago.
If you have limited resources for promotions and direct mail marketing, you may want to concentrate your efforts on your higher-value RFM customers. But this doesn't mean you should ignore your 1's or your low-value segment customers.
You can examine their customer purchase behavior for learnings. Did their order ship late? Was it for a product that was recalled? Was there a customer satisfaction miss that you can correct with a promotion? I can recall an instance where I thought I had lost a new customer only to find out that the purchasing agent had left that company and moved to a new business. That new business was a new lead. And guess what? They became a new customer again.
Using RFM segments to understand your customers should then form the backbone of ongoing marketing initiatives. RFM ensures:
With RFM, you take a deeper look at your existing customers and examine their purchase behaviors.
RFM in machine learning is using an automated or machine learning way (algorithms) to evaluate your customers and create customer segmentation from your best (highest value) customers to your worse. Businesses use RFM in machine learning for predicting future customer behavior.
Because RFM bases customer value on three parameters rather than only one, these segments are more meaningful to you and your marketing strategies.
Many people think of valuable customers as customers who have spent a lot of money. But if spend is the only customer behavior you're evaluating, you may miss out on other key things.
Let's look at hypothetical high value customer Henry Hogsworth. Henry Hogsworth has spent over $50,000 at your business. But he hasn't made a purchase in over 30 months. Because Henry is worth $50K, you may have him listed as a valuable customer to target on your next marketing campaign. But Henry is likely a lapsed customer who is unlikely to respond to your marketing efforts.
Similarly, you may think that Tom Jones is a low value customer because he has only spent $480. But what if Tom Jones had made 5 purchases within 4 weeks?
Common sense dictates that Tom Jones is going to be more responsive to your next sales promotion than a customer who hasn't spent money at your business in nearly 3 years.
Including recency and frequency in the mix means you can identify these customers and target specific conversations to them to encourage behavior change.
On the other hand, it could be Henry purchased 1 high-value item from your business like a boiler. In the past 30 months, Henry has had no issues with the boiler and when it needs replacing in several years out, he may reach out to you again.
But Henry may need annual tune-ups, cleanings, service visits, and other ancillary products specifically for his boiler purchase.
With an RFM analysis, you can offer relevant products to your customers at the right moments and continually offer brand value. Tom will likely respond to frequent email drops promoting sales for smaller tools and parts, while Henry may respond to promotions for tune-ups and ancillary products for a boiler. And near the end of the boiler's warranty, he may be in the market to purchase a warranty extension or a new boiler.
“VIPs” (555): These are your most valuable customers – we know they spend a lot and often. Make them feel valued, surprise them with special experiences and rewards, and engage with them both on and offline. These are your brand advocates – ensure your marketing efforts are solidifying and strengthening your relationship with them, rather than just driving sales.
“Loyalists” (n5n): These customers shop with you frequently. They may not spend much, but they’ll always return to you when they’re ready to buy. Make sure they’re enrolled in your loyalty program and derive value from it; you can then personalize offers that encourage them to come in-store if it’s been a while and to spend a little more when they do.
“At risk” (2nn): These customers haven’t completely disconnected from you, but are at risk of doing so. The higher the n, the more important it is to get these customers back on board. Try targeted marketing with updates or upgrades for previous purchases – relevant, new product launches and one-off high-value offers to encourage a sale and renew the relationship.
“Low value churned” (111): Consider how much of your available marketing resources you want to dedicate to recapturing these lost customers. In an ideal world, they’ll come back under their own steam without any active marketing or cost on your part. Include them in email blasts and ad audiences, but think carefully before you spend too much time targeting an audience with marketing that may not resonate.
To establish yourself as a market leader, remain competitive, or stand out in today’s market, it’s crucial you have a clear understanding of your key audiences, their needs, and also their values. You can then use this understanding to drive targeted marketing campaigns that resonate with your customers, increasing loyalty and reducing market costs.
Small business owners can create their own RFM analyses using spreadsheets. But as your business grows, that will become more challenging. Over time, there will be more and more data points to capture: future purchases (dates, times, dollar amounts), returns, referrals, and data from Google Analytics like browser, session times, URL clicks, cart adds, and other conversion events.
Spreadsheets can be limited to 10,000 rows and it's easy to reach capacity. One high-value customer alone could max out your sheet's limit.
You'll need an enterprise-grade customer data platform or CDP.
A CDP is an amazing tool that will simplify your life and deliver powerful and actionable insights into your customers. A CDP can gather, store, and analyze billions of data points for all of your new and existing customers.
And a CDP will assign more attributes than any group of humans could possibly think of, and use these attributes to create and grade endless audience segments for you to target in the best way. For example, maybe a CDP will reveal that when there's cloudy weather in Scottsdale, AZ, you have a 550% increase in tie rods. Or that business owners are 150% more likely to click on your emails when there is a blue CTA button with 14-point font, but only on Wednesday mornings.
The most advanced enterprise-class CDPs serve as an engine for creating these types of RFM-driven experiences. They empower business users to orchestrate campaigns and build customer journeys quickly and seamlessly, using the full breadth of all your customer data across any and all channels.
While RFM is an amazing marketing tool to identify your best customers, RFM does have some limitations. Notably, RFM customer segmentation is often a manual process and prone to human error.
These are just a handful of potential issues. They seem minor, but can and do result in businesses missing their best and worst customers. A one-time customer may be assigned poor grades and considered a weak customer when in fact they are a high-value customer likely to make frequent related purchases.
Also, manual RFMs often do not account for seasonality (i.e. Black Friday or other holidays) or the impact of a promotion and the kind of promotion deployed.
As a result, it is important to use RFM analysis carefully, in order to avoid damaging your relationships with your most valuable customers. Using a CDP for machine learning RFM is best. However, manually doing RFM segmentation can still be a valuable tool for businesses if used correctly.
You can use RFM to effectively market to your customers.
Craft a unique marketing strategy designed for each RFM segment focused on their behavioral patterns.
There are a number of different buckets or segments in which you can place your customers. Here's one scenario.
Utilizing the RFM Analysis, you are able to effectively communicate your messaging to customers in a way aligned with their needs and their behavior.
This means a more efficient use of time and money.
You won't waste your time pursuing the wrong customers. And you won't waste your marketing budget either.
Instead, dedicate that time and money to your best customers with the highest customer lifetime value.
By understanding the different segments identified in an RFM analysis, your business can create marketing strategies that are better aligned with your budget and your customer's behavior. You'll build better relationships, run more effective marketing campaigns, and stop wasting time and money.
