Model vs. Transaction History: Which is Preferable for Customer Communications?

Modeling is useful for locating prospects. But, models only work if customer response is related to demographic factors which can be obtained about the prospects. In most cases, this is not true. For customer communications, using purchase history directly is preferable to modeling. Purchase history includes the products or services bought, and the transaction history. Product purchase history is used to make relevant offers to customers. Transaction history predicts which customers are most likely to respond to a promotion. The paper concludes with a detailed description of the information that can be learned from transaction history, and shows how its use produces profits far greater than could be produced by use of a model.

Many companies are using models successfully to improve their response rates and profits in marketing to outside prospect lists. To use a model successfully, the marketer has to have a marketing situation that meets both of two criteria:

• The customer response to a promotion must be significantly determined by factors that the marketer can append to a prospect list
• The lift in profits from using the model must more than pay for the cost of the appended data and the cost of building and running the model

How Modeling Helps Find Buyers

For example, if you are selling encyclopedias through the mail, you may rent a list of parents of high school students. Appended to this list you might be able to get the estimated household income, the PRIZM cluster code, the type of dwelling, home ownership, and a few other factors. It is possible that you could put these factors into a model and be able to prove that you can sell the encyclopedias profitably to people who:

• Have a child in high school
• Have a household income over $X
• Are homeowners of a home worth over $X
• Live in PRIZM clusters A, B, C, and D

You might find that if people have these four characteristics, your response rate is 2% or better. If they lack them, your response rate is below 1%. It could be that it cost you $Y per thousand to append this data to a rented list of parents of high school kids, and that your model costs you $35,000. If your mailing is big enough, the profits from using it to direct your mailing could pay for the cost of the model and the appended data.

Companies are doing this kind of analysis all the time. Banks use it to sell credit cards and home equity loans. Life insurance companies use it to sell life policies, annuities, health insurance and retirement plans. Brokers use it to sell mutual funds. In many situations, such data appending and modeling is a profitable solution to the marketing problem.

Where Modeling Does Not Work

On the other hand, for most products and services, modeling does not provide sufficient lift to justify the expenses. Thousands of companies have experimented with modeling, and come away dissatisfied, and somewhat poorer. Why is that? It is simply because in many cases, the factors that lead people to buy your product do not depend on data that you can buy to append to a prospect file. In many cases, modeling is not successful with the marketing of automobiles, packaged goods, non-profit fund raising, home sales, clothing, gasoline, furniture, or hardware. Why not? Because rented data factors are less important than other non-rentable factors in the buyers decision making process. Buyers can be classified by two characteristics:

• who they are – demographics
• what they do – behavior

Some people buy books and read them. Most people do not. Do the readers buy books because of their age, income, presence of children, or home value? Or do they do so because they bought books in the past and enjoyed them? In most cases, past behavior is a far better predictor of future behavior than any demographic factors that you can assemble about any group of prospects. That is why “mail responsive” lists are far more profitable for direct marketing than any other type of list that you can possibly buy.

Mail Responsive Lists

To sell its $15 allergy free air filters by mail, 3M purchased a large list of people who were known to suffer from various air-borne allergies. They asked a direct mail expert whether their project would be a success.

“No”, he replied.

“Why not? These people need this filter!”

“You should use a list of mail responsive people. People who have bought something – anything — by mail that costs $15 or more.”

“But these people may not need an allergy free filter.” The 3M experts argued.

“Trust me. I know what I am doing.” The expert said.

So they mailed one hundred thousand to each of two lists: the allergy sufferers and a mail responsive list. The mail responsive list blew the other list away. Responses were four to five times as great from the mail responsive prospects. Past behavior is the best predictor of future behavior.

Should You Use Models to Direct Customer Communications?

Companies today are beginning to shift from acquisition mode to retention mode. They want to acquire new customers, sure, but they find it more profitable to increase sales from the customers that they already have. So now we get to the big question: should you use modeling to channel the communications with your existing customer base? I say, “in most cases, no.” Why not? There are several reasons:

• Once you have gained a customer and built up a database file on her, you know so much specific information about her demographics, desires, needs, family composition and purchase behavior, that a model using appended data is really unnecessary
• Customers like to be treated as individuals, not as members of some data segment. It is more profitable to recognize them as people, than to treat them as data
• Use of simpler, less expensive techniques related to customer purchase history can result in higher profits than can possibly be gained through modeling.

We can base our customer communications on customer behavior that is stored in our marketing databases. You build a database that includes customer purchase history: a history of behavior. This behavior history is used directly (not using a model) to make relevant offers to your existing customers.

Two Types of Purchase Behavior

We can classify the customer behavioral data that is stored in a customer marketing database as being of two types:

1. Product history – what specific products did she buy?
2. Transaction history – how much did she buy, how often and how much did she spend?

Product history is easier to understand, and widely used. The most typical use is for affinity analysis. You look at customer purchases of some products, and draw conclusions from these product purchases as to the customer’s likelihood to buy other products. For example:

• Banks use checking and savings account behavior to find the best customers for credit cards and home equity loans
• Insurance companies use behavior on life and health policies to offer mutual funds and annuities
• Airlines study customer travel patterns to make vacation offers
• Booksellers learn what books interest their customers from their existing purchases

Transaction history is also very predictive, but less well understood. Most marketers today break transaction history into three related behaviors: recency, frequency and monetary amount (RFM). Over the last fifty years, marketers have learned that:

• Recent buyers are more likely to respond to new promotions than less recent buyers.
• Frequent buyers respond better than less frequent buyers.
• Big spenders respond better than small spenders.

Paul Wang and I give seminars several times a year for The Database Marketing Institute. Over the years, we have worked with many companies who have been experimenting with intelligent use of the RFM data contained in their customer databases. They have discovered some fascinating information through the simple graphing of customer RFM behavior. Let me give you a concrete example.

I do consulting work with an art gallery in Amsterdam, Holland that is using RFM software developed at the Institute. They began their database in 1993, and have sold art works to 267,028 customers since that time. They recently did a promotion to an Nth of their database, consisting of 56,239 customers. From the data in their file they are able to determine the most recent purchase date, the frequency of purchase and the size of the average order. Using this information, they coded the file for RFM. They created 75 RFM “cells”, each with 3,560 customers. There are five recency divisions, five frequency divisions and three monetary divisions.

The recently did a test mailing to 56,239 customers. In the test, they got 2,463 sales averaging $104.20 each for total sales of $261,560. Their outgoing promotion cost them about $0.84 each, for a total cost of $47,240. Their average profit margin on a sale was about 57%, so their gross profit from the test was $149,089. If we subtract their mailing costs of $47,240, they made a net profit of $101,848

They studied the RFM graphics that their RFM software created from the test results. In previous years, they had always mailed their entire customer base for promotions. Using the RFM test results, this year, they decided to do a selective rollout to determine whether they could increase their profits. The RFM graphics showed that if they used this test as a guide for a selective rollout to their customer base, they could increase their total profits by a whopping $93,487. Lets look in some detail about how they went about creating this additional profit.

Recency was very important in the response rates of their customers. They had five recency divisions, from most recent to most ancient. Each division was of exactly equal size – 11,248 customers. Here is how they responded on the test promotion:

Recency of purchase certainly determined the customer response to the offer. How profitable were the lower recency divisions? When looked at from the standpoint of break even, the lower divisions should not have been mailed at all:

All the lowest three quintiles lost money.

But frequency of purchase was even more dramatic in its effect on the success of the promotion. In terms of break even, only the first quintile (the most frequent buyers) made a significant contribution to profit. Here are the same 2,463 buyers looked at in terms of their frequency of purchase prior to the promotion. All of the frequency divisions were profitable, but the first one was far more profitable than the others were:

Perhaps the most interesting view of customer behavior came when these same buyers were compared in terms of their prior-spending behavior. The biggest spenders definitely responded better and brought in more profit, from three standpoints. First, let’s look at total sales:

As we said, the gallery had only three monetary divisions. In our discussion we have converted Dutch Guilders to US Dollars at the rate of 1 Guilder equals $0.48. The above chart is calibrated in Guilders. Why did monetary amount prove to be so decisive in terms of predicting sales? There were two reasons:

High dollar spenders tended to respond better

High dollar spenders had a higher average order size

These reasons are shown on the following two charts:

This shows response rate by monetary division.

This shows the average order size by monetary division. Both factors indicate that monetary spending, for this product, is a good predictor of purchase behavior.

Finally, the gallery looked at total spending by RFM Cell. Here, we look at the break-even index: which cells were profitable, and which were unprofitable. The contrast is striking:

Arthur Middleton Hughes is Vice President of The Database Marketing Institute that does research and consulting for e-mail and database marketing companies. He would love to hear about your problems. Perhaps he could help.  He can be reached at Arthur.hughes@dbmarketing.com or 954 767 4558. His new book Strategic Database Marketing 4^th Edition is due out from McGraw-Hill in 2011.

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Model vs. Transaction History: Which is Preferable for Customer Communications?1.051