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A balancing act: using RFM analysis to Identify which customers you are likely to lose

Bill Mekie

A Balancing Act: Using RFM Analysis to Identify the Attention Each Customer Needs

If you've been following our recent series of blog posts, you're already aware of the importance of peering beneath the surface in the world of wholesales. In "The Customer Churn Conundrum: Seeing the Forest for the Trees," we shed light on understanding revenue impacts, evaluating client growth or decline, and in "The Tip of the Iceberg: Unveiling the Hidden Margins in Your Business," we uncovered how those seemingly smaller clients can have a huge influence in a wholesaler´s gross margin.

Now, the (sometimes literal) million-dollar question is this: how do you identify customers at risk of churning? How can you provide the attention each customer deserves, no matter their size?

To tackle these questions, we're diving deep into customer segmentation with a Recency Frequency Monetary (RFM) analysis. RFM analysis is a customer segmentation technique that evaluates Recency (how recently a customer made a purchase), Frequency (how often they make purchases), and Monetary (how much they spend), helping businesses understand and target their customer base effectively. This powerful tool will help you make sense of your customer base and unearth hidden opportunities.

As Moby Analytics´ CEO, Dimitri Kassubeck, explains in the accompanying video, RFM Analysis is about crafting matrices to segment your customer base efficiently. In the example provided, derived from actual randomized wholesaler data, we focus on two pivotal factors:

1-Number of Orders in the Last 12 Months; and

2-Number of Days Since Their Last Order.

Deciphering Customer Segments

Using these variables, we carve out distinct groups within your customer base. On one axis, we have customers who have made only one order in the past year, while others fall into segments with two, three, five, up to six or more orders. On the other axis, we examine latest purchases per customer, ranging from those active within the prior month to those who haven't made a single order in the last nine months.

As we traverse the matrix, we encounter different customer groups: champions, loyal customers, potential loyal customers, and new customers. This categorization enables us to pinpoint customers in need of attention and those on the edge of churning.

1. Your Champions

Your champions are those customers who exhibit high activity levels and have recently placed orders. These are the customers who've made six or more orders in the last three months, meaning they're also the ones with the lowest risk of slipping through the cracks.

2. Customers Needing Attention

A quick glance at our dashboard reveals that customers requiring attention often fall along the diagonal line. For instance, if a customer was very active in the past (making more than six purchases in the last year) but has mysteriously dropped their orders in the last month, it's a clear sign that they're in need for reengagement. These are opportunities waiting to be seized. Give them a call!

3. Customers at Risk

The most important group, however, are the customers whose purchasing frequency has dramatically dropped compared to their historical behavior. These customers demand special and immediate attention to prevent them from dropping out of your customer list forever. As we have seen in The Tip of the Iceberg, losing any customer, no matter their size, can be hurtful for your gross margin. By identifying customers at risk of churning and reaching out to them to understand this change in behavior before it is too late, a wholesaler will gain an important competitive advantage, retaining a valuable customer and avoiding losing them to the competition.

Leveraging Predictive Models

Usually, being able to assess who the customers in need of attention are, and which customers are in risk of churning is the type of analysis most wholesalers do not have access to, although they already possess all the data they need to come to these conclusions. Our analysis, however, goes deeper: we also harness the power of probabilistic models to estimate the likelihood of losing specific customer segments. Combined, these models offer insight into where wholesalers should focus their retention efforts. Not all customers who buy every six months are at risk of churning, but those whose order patterns deviate significantly from their historical behavior are, with an impressive level of confidence (around 90%), in need of attention.

In Conclusion

In a nutshell, RFM analysis is the wholesaler´s secret weapon, allowing wholesalers to dissect their customer base, identify champions, and spot potential churn risks. Just like in a balancing act, it's all about keeping an eye on what´s going on in front of our eyes and acting at the right time not to drop the ball. Mastering the dynamics of your customer relationships is key to maximizing retention and growth.

Curious to see how Moby Analytics can utilize your data to predict which customers need attention and those at risk of churning? Request a free demo by clicking the link below.

Bill Mekie
Bill Mekie
Data scientist

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