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How to Create a Best Customer Profile

It’s a simple question, really. “Who are my best customers”? Unfortunately, the answer can be frustratingly complex. This article discusses various techniques that will enable a small to mid-size company create a “best customer profile“.

What’s a Customer Profile?

A customer profile is an analysis undertaken to gain insight into a given customer base. It seeks to understand the key factors that determine who is and, conversely, who is not a purchaser of a company’s products and services. A customer profile can be based upon many different criteria.

Generally speaking, there are two main types of customer profiles:

  1. Demographically based profiles – age, income, marital status, etc.
  2. Behavioral profiles – which products were purchased, how much was spent, when was the last purchase, etc.

The first profile is demographic, a set of characteristics. The second profile is behavior-based, involving what the customer is actually doing. Both types of profiles are important and, ultimately, incorporating both sets of factors into your analysis will yield the best results.

A demographic profile can easily be accomplished with the assistance of a mailing list provider like Mailing Lists Direct. Your customer file is matched against a large, nationwide database of Consumers and demographic information is appended to each of your customer records. From there, a computer program tabulates the key findings into a Profile Report by comparing your customer file to the rest of the general population.

A behavioral profile may be accomplished internally or externally. The primary behavioral profile analysis is known as “RFM”, which stands for Recency, Frequency and Monetary Value.

Recency means the time since a customer last purchased from you. Frequency means the number of purchases over a given time period. Monetary Value relates to the value of customer purchases over a given time period or number of transactions.

RFM analysis works because:

  1. Customers who have purchased recently are more likely to buy again versus customers who have not purchased in a while
  2. Customers who purchase frequently are more likely to buy again versus customers who have made just one or two purchases
  3. Customers who have spent the most money in total were more likely to buy again. The most valuable customers tend to continue to become even more valuable over time.

How does one create a RFM profile?

Using RFM analysis, customers are assigned a ranking number of 1,2,3,4, or 5 (with 5 being highest) for each RFM parameter. We start with Recency because Recency has the highest “predictive value” relative to future purchases. In other words, the more recently a customer has purchased from you, the more likely they will purchase again from you again.

Sort all of your customers based on the time that they last purchased. Divide your entire customers into five equal groups (20% each), coding the most recent purchasers as “5”, the next group as “4” and so on. The bottom group would consist of those who have not purchased for some time and would be assigned a “1”.

Frequency is next because it has the second-highest predictive value. The Frequency analysis is done the same way as Recency. Sort your customers based on the number of times they’ve made a purchase (per day, per week, per year…whatever makes sense for your business). Once again, divide the list into five equal groups and assign “5” to the group that has purchased the most, “4” to the next group, etc.

Finally, create a ranking according to Monetary Value – the total amount spent with you in a given time period. Sort your customers based on that number and divide by five, assigning the top 20% of your customers (i.e.- the big spenders) a “5” and the bottom 20% a “1”.

Once this is completed, all of your customer records will have a three-digit code ranging from 555 to 111. Now you can sort your entire customer file based on this new number. First sort the entire file based on Recency, which divides it into five segments. Then sort each of those five by Frequency, resulting in twenty-five cells. Finally, divide those by Monetary Value, creating 125 equal RFM cells.

The records coded with “555” will respond the greatest to your promotions and allow you to make the most profit, while the “111”s will be least responsive and will probably result in net losses each time you market to them. Simply put, do not market to the unprofitable cells and your response rates and profit will soar.

How do you determine which cells are unprofitable? There is no formula for this because the number is different for every business. The best way to determine which cells to not market to is through testing. If testing isn’t an option due to timing, budget, etc., drop the bottom 50% for a “first cut” in order to significantly increase your results.

The power of RFM is its ability to show you which customers to not market to, thereby saving you money. By eliminating former customers who, chances are, will not respond to your offer in significant numbers, you increase your response rates and your total returns on investment.

Although RFM analysis is a useful tool, it does have its limitations. A company must be careful not to over-solicit customers with the highest rankings. Experts also caution marketers to remember that customers with low cell rankings should not be neglected, but instead should be cultivated to become better customers.