The 80/20 Rule
Based on the Pareto Principal, better known as the 80/20 rule:
- 80% of our business success comes from 20% of our marketing efforts;
- 80% of our website conversions are done by 20% of our website visitors;
- 80% of our revenue and sales come from 20% of our customers.
We’re basically still in business largely because of the support of a fraction of our customer base – our best customers. So, from a marketing perspective, it would make a lot of sense to understand the characteristics and preferences of these customers to continue providing this group with what they’re looking for and keep them as customers. Moreover, by targeting our overall acquisition marketing using insights into behavior of our best customers, we’ll start focusing and attracting the right customers, who will be loyal from the start, instead of moving random customers up loyalty ladders.
But before we can start to understand our best customers, we need to identify them first. And that’s where a simple database marketing tool called recency, frequency, monetary analysis (or RFM) comes in handy.
RFM in a Nutshell
RFM uses sales data to segment a pool of customers based on their purchasing behavior, and the order of the attributes in RFM corresponds to the order of their importance in ranking customers. The resulting segments are neatly ordered from most to least valuable, which makes it very easy to identify the best customers.
Customers who have purchased from you recently are more likely to buy from you again than customers who you haven’t seen for a while. Recency is the most important factor, because the longer it takes for a customer to return to your business, the less likely he or she is to return at all. You can fix problems with good customers not coming in as often or spending as much, mostly because they’re still coming in. But when good customers stop coming in altogether – that problem is much harder to fix.
Customers who buy more often are more likely to buy again than customers who buy infrequently. Recency alone won’t sort out good customers from new ones. For that we need frequency, which measures the intensity of a customer’s relationship with your brand. And good customers, by definition, do business with you more often. You’re part of their habit.
Customers who spend more are more likely to buy again than customers who spend less. How much a customer spends on average or in total is the final measure of his or her value. It adds another level of detail to the customer picture, helping to distinguish between relatively light and heavy spenders. Its effect is often, but not always, highly correlated with frequency.
Calculating RFM Scores
To calculate RFM scores, we first need the values of three attributes for each customer: a) most recent purchase date, b) number of transactions within the period (often a year), and c) total or average sales attributed to the customer. Then we have to decide the number of categories for each RFM attribute (typically 3) and assign customers to categories (by applying rules specific to our business). Finally, we have to sort our customers on recency first, then sort on frequency in each recency category, and, finally, sort on monetary value in each combination of recency and frequency categories.
Using RFM Scores
Once we’ve calculated RFM scores, it’s easy to identify our best customers – they have the highest score. We can now start analyzing the characteristics and purchasing behavior of this group and try to understand what distinguishes them from the rest of our customers.
Do they tend to buy a subset of your products or services?
Do they live in demographically similar neighborhoods?
Are their lifestyles and/or life stages similar?
Why do they perceive more value in your business than the folks who you see once or twice?
Finally, we can start tailoring effective marketing messages to all our prospects and customers based on the RFM-predicted behavior of the most responsive segments in our customer database.