Wednesday, December 3, 2008

Customer Loyalty Mathematics

Dr. Mark Klein is a physicist who is using his mathematical skills to help companies tune their marketing programs. He has developed some mathematical models which, according to Dr. Klein, will improve the effectiveness of marketing campaigns. The reason I have chosen to mention this is that it is rare to find someone who is specifically mentioning the use of mathematics to understand customer loyalty. What appears most often in the market is different companies and consultants saying they do customer loyalty surveys better than others. In most cases the difference between the various groups (companies or consultants) is negligible.

The good news is that Dr. Klein mentions some mathematical techniques that he uses. The bad news is he doesn't say enough for someone like me to understand those techniques and be able to validate them.

He has a "Field Guide to Mathematical Marketing" available from his web site (55 pages). The book, which can be downloaded from his website at no cost, does an excellent job of making the case for using mathematics to better understand your customers. For that reason alone, the book is worth the time to read, especially if you think that mathematics has no place in analyzing customer loyalty.

Unfortunately, there are no mathematics in the document. The document does contain vary good descriptions of the statistical applications that appear to be one of the key tools he uses (factor analysis, factorial designs and fractional factorial designs). I am surprised he doesn't mention some of the other experimental statistical designs such as Latin Squares and Graeco-Latin Squares as long as he is using experimental designs. In any case, as I learned in school, it is always a good idea to know what is inside a black box (for those not used to the term, a black box is something that performs but you don't know how or why) before you buy it and thus until I know more about his application of the various methodologies noted, I am limited in the amount of support I can give. One of my concerns is what assumptions are required.

Some interesting observations made by Dr. Klein:
1. He believes that customer loyalty to be a more predictive measure of customer purchasing than customer satisfaction.
2. He also believes that customer satisfaction is a backwards-looking measure whereas customer loyalty, as noted above is more predictive of the future behavior of your customers.
3. He believes that most people in marketing (and probably in customer service/support) avoid using mathematics even though it would be beneficial.
4. He is a strong proponent of selling to existing customers and believes that the more information that you have about you're customer, the better job you can do in developing marketing programs to encourage them to buy.

Finally, I reviewed the software program on his site that allows a demonstration. Here is where I am a little concerned about the "black box." I am reminded of the phrase from my programming days that said "garbage in, garbage out" was corrected to say "garbage in, gospel out." The demonstration is excellent without being too complex for the non-mathematical observer, but it is not clear what is happening and why. Of course, that may be the idea, to sell the concept without giving away the company secrets.

The bottom line is that I am not recommending Dr. Klein's mathematical modeling because I do not have sufficient information to evaluate how he is using the techniques. I am, however, pointing out that there are a few people with enough education, such as Dr. Klein, to use the power of mathematics to help companies improve their understanding of their customers and customer loyalty.

1 comment:

Mark Klein said...

Hey, thanks for the nice words about the book. I was trying to reach marketers who have not been using mathematical techniques, so yes, I kept it simple. While we are not going to share all of our hard won secrets, we will certainly talk about what goes into our modeling, how accurate it is, and what some real client data looks like. Reach me at


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