RFM Analysis: A Strategy to Segment Your Customers

Segmenting your customers into like groups is a familiar mode of marketing, but what do you do when there's little difference among your customer base? No group of customers exists as a monolithic entity, but in high volume transaction businesses, particularly distributors, segmenting by firmographics likely doesn't feel as substantial as you'd hope. Instead, segment your customers based on their purchasing habits using a method like RFM analysis.

What is RFM Analysis?

RFM analysis is a simple but powerful way to understand your customers by looking at three key behaviors:

• how recently they've bought from you
• how frequently they buy
• how much money they spend

As you can probably guess, RFM stands for Recency, Frequency, and Monetary. The analysis bit comes into play by grouping your customers into buckets under each behavior, then assigning a score based on their placement. For example, someone who spends a lot of money with you would be in the fifth Monetary bucket and be assigned a score of “5.” Think of it like rating your customers on a report card. Instead of grades for math and science, you're grading them on their buying patterns. A distributor doesn't just want to know who bought something, but who's likely to keep buying and who might be drifting away.

Recency: When Was Their Last Purchase?

Recency answers the question: How fresh is this customer's connection to our business? A customer who bought steel sheets last week is obviously different from one who last purchased over two years ago.Imagine two fabrication shops. Shop A bought steel last month for a new project. Shop B's last order was in 2022. Recency tells you Shop A is more likely to need immediate attention, support, or might be ready for a new order, while Shop B is way past the danger zone and has likely lapsed as a customer.

Frequency: How Often Do They Buy?

Frequency tracks how regularly a customer makes purchases. This reveals loyalty and helps predict future buying behavior.

‍Consider two scenarios:

‍Scenario One: Machine Shop X orders steel plates every two months like clockwork.

‍Scenario Two: Manufacturer Y makes one massive annual purchase. Frequency helps you understand these different purchasing rhythms. The consistent buyer might be more valuable in the long run, even if their total spend per transaction is lower.

Monetary: How Much Do They Spend?

Monetary value looks at the total revenue a customer generates. It's not just about one big order, but the overall economic impact of a customer relationship.A $10,000 quarterly buyer isn't automatically more valuable than someone who consistently spends $2,000 monthly. Monetary value helps you see the full picture of a customer's worth.

How to Do RFM Analysis: A Step-by-Step Guide

Implementing RFM analysis isn't just about crunching numbers—it's about creating a meaningful framework for understanding your customers. Think of it like creating a customer map that shows not just where they are, but where they might be heading, and where you want them to go. You're not looking for a perfect mathematical formula, but a practical framework to understand customer behavior. Start by ensuring your data is clean and comprehensive. Missing or incomplete information can skew your results, so take the time to verify and validate your records. This might mean cross-referencing sales records, checking for duplicate entries, and ensuring you're working with a consistent time frame.

1. Gather Your Transactional Data

Most likely you’ll be exporting raw transactional data from your ERP and summarizing via pivot tables. You'llYou'll need:

• Customer names
• Purchase dates
• Order volumes
• Total transaction values

Pull this from your:

• Accounting software
• Ecommerce platform
• ERP system

2. Score Recency of Orders

Create a point system for how recently customers have purchased from you. Your point system can be fixed, or to add complexity, can be dynamic based on actual data. For example, the top 20% of customers typically purchase within X number of days, and so on. If you’re working with a larger dataset, you may want to divide into five groups, or quintiles.

‍Example Quintile:

• Bought in last 30 days: 5 points
• 31-60 days: 4 points
• 61-90 days: 3 points
• 91-120 days: 2 points
• Over 120 days: 1 point

3. Score Frequency of Orders

Count how many times each customer has purchased in your chosen time period (say, two years). Then divide your customers into five groups:

• Top 20% of buyers: 5 points
• Next 20%: 4 points
• Middle 20%: 3 points
• Next 20%: 2 points
• Bottom 20%: 1 point

4. Score Monetary Value

Total each customer's spending and create similar point brackets based on total revenue within the same time period.

• Top 20% of buyers: 5 points
• Next 20%: 4 points
• Middle 20%: 3 points
• Next 20%: 2 points
• Bottom 20%: 1 point

5. Create Two Customer Scores

Composite Score: Multiply the three scores to get a single number. A perfect customer would score 125 (5 x 5 x 5). A customer who rarely buys would score closer to 1. This metric is useful to simply rank your customers.Matrix Score: Combine the RFM scores into a customer group. The perfect customer that had a 125 composite score would have a matrix score of 555, their points from each bucket.

Customer Groups You'll Discover

Understanding your customer groups is like creating a strategic roadmap for your business. Each group represents a different relationship dynamic, and each requires a tailored approach. It's not about labeling customers, but about recognizing their unique value and potential.These groups aren't static—they're living, breathing categories that change over time, which is in fact the goal of RFM Analysis. You’re identifying your customers as they are today, then developing a strategy to move them into more valuable groups. A customer who's currently hibernating might become a champion with the right engagement strategy. Similarly, a top customer can slip away if not properly nurtured.This is why RFM analysis is an ongoing process, not a one-time exercise. You'll want to revisit these segments regularly, perhaps quarterly or bi-annually, to track how customer relationships evolve.

1. Champions (High R, High F, High M)

• Your best customers
• Prioritize keeping them happy

2. Potential Loyalists: (Medium R, High F, Medium-High M)

• Not your absolute top customers
• Great candidates for loyalty programs or other incentives

3. At Risk: (Declining R, Medium F, Variable M)

• Used to buy regularly, now buying less
• Need re-engagement strategies

4. Hibernating: (Low R, Low F, Low M)

• Rarely buy
• Decide whether to win them back or let them go

RFM analysis turns your transaction data into a roadmap for understanding and growing your customer relationships. It's not about complexity—it's about seeing your business through your customers' actual buying behaviors.

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