An ABC Introduction to Data Mining for Dollars: Slicing and Dicing Your In-House List for Profit (Part 2 of 2)

In my last post, I introduced the RFM method, an effective direct response strategy to slice and dice your list for better conversion rates. The “R” represented recency—how long your customers have been with you. Today, I’m going to talk about the other components of frequency and monetary.

In my last post, I introduced the RFM method, an effective direct response strategy to slice and dice your list for better conversion rates.

The “R” represented recency—how long your customers have been with you.

Today, I’m going to talk about the other components of frequency and monetary:

Frequency
This segmentation tactic is another way to break down your house list: by how frequently customers have bought from you. So once you’ve divided your list based on recency, you look at it in terms of your customers’ purchase behavior. First, you identify your multi-buyers—customers who’ve purchased more than one product from you. You then split this list further, segmenting out two-time, three-time, four-time (and more) buyers. Those who have bought from you most often have proven their loyalty and obviously like the products and services they’ve been getting from you.

So if, for example, you’re considering launching a new product with a high price point, these would be your best prospects.

Monetary
Finally, you look at your list in terms of money. One way to do this is to divide your list by the amount of money each customer has spent with you. You might, for example, assign a benchmark dollar amount, such as $5,000, $10,000 or more. Customers at that level make up your “premium buyers.” This is the group that has the most favorable LTV for your company. These are your “VIPs.” Once you discover who your VIPs are, you can design products or offers specifically for them. Let’s say you have some kind of exclusive—and expensive—lifetime membership club. You would market this to multi-buyers who also fall into your “premium buyer” category.

If you offer payment options to your customers, another monetary way to divide your list is according to the payment options they have chosen: monthly, quarterly, yearly, etc. This will help you determine the initial purchase tolerance of each group of customers and which ones may respond best to future price points. As you can see, by looking at your customers’ purchasing habits—recency, frequency and monetary—you can identify the best customers for certain products. And by offering a product to customers who are likely to want it, you can improve your conversion rates.

By using the proven RFM method and other data-mining techniques, I’ve seen conversion rates double and triple. I’ve also seen inactive subscribers’ open rates surge from 0 percent to more than 30 percent.

However, many companies that send emails don’t have the capacity for data mining.
Unfortunately, some smaller businesses or start-up companies typically cut robust email features and analytics for cost savings. Oftentimes, these companies save money using online email service providers that can certainly get the job done, but don’t offer segmentation tools that allow for list analysis, where you can dissect your database into subgroups or “buckets.”

So when you’re searching for an email service provider, try to project what your segmentation needs may be going forward and if data mining is a strategy that you’ll want to deploy.

Hot Tip! When looking at email marketing companies, make sure you ask if there’s a list segmentation or data mining feature that can easily be done through their email platform. Find out the level of segmentation capacity (how far the segmentation of data can be drilled down to); if certain segmentation features are a standard feature or an upgrade; and what those costs may be on a monthly basis. Sometimes it may be an additional fee, but will certainly pay for itself over time.