Category Archives: Introductory / Overview



Greetings.  Thanks for stopping by.  The below is a quick introduction so you’ll know whether or not this is for you.

We’ll start by trying to get a few things straight. is not meant as a replacement for a textbook in marketing analytics / econometrics, etc.  I’ll mention some textbooks down the line that might be helpful in some areas but this meant to be like a textbook.  This is meant to be a gentle overview, more conceptual than statistical for the business analyst that just needs to know how to get on with their job.

Who is the Intended Audience for This Blog?

This is not meant to be an academic tome filled with mathematic minutia and cluttered with statistical mumbo-jumbo.  There will need to be an equation now and then, but if your interest is econometric rigor, you’re in the wrong place.  A couple of good books for that are Econometric Analysis by William H. Greene and Econometric Models, Techniques and Applications by Michael Intrilligator, Ronald G. Bodkin and Chang Hsiao.  So, this is not aimed at the statistician, although there will be a fair amount of verbiage about statistics.

If you’re all about (and only about) BI (business intelligence), which means mostly reporting / visualizing data, (if you live and die by creating KPIs) this is not for you.

This will not be a marketing strategy guide, but be aware that as mathematics is the handmaiden of science, marketing science is the handmaiden of marketing strategy.  There is no point to analytics unless it has a strategic payoff.  It’s not what is interesting to the analyst, but what is impactful to the business, that is the focus of marketing science.

So, to whom is this blog aimed?  Not necessarily at the professional (academic) econometrician / statistician, but there ought to be some satisfaction here for them.  And not necessarily for the student, but a conceptual overview is usually what students need most.  Primarily, the aim is at the practitioner.   The intended audience is the business analyst that has to pull a targeted list, the campaign manager that needs to know which promotion worked best, the guy that has to forecast next quarter’s demand units, the marketer that must DE-market some segment of her customers to gain efficiency, the marketing researcher that needs to design and implement a satisfaction survey, the pricing analyst that has to set optimal prices between products and brands, etc.

So What is Marketing Science?

As alluded to above, marketing science is the analytic arm of marketing.  Marketing science seeks to quantify causality. Marketing science is not an oxymoron (like military intelligence, happily married or jumbo shrimp) but is a necessary (although not sufficient) part of marketing strategy.  It is more than simply designing campaign test cells.  Its overall purpose is to decrease the chance of marketers making a wrong decision.  It cannot replace managerial judgment, but it can offer boundaries and guard rails to inform strategic decisions.  It encompasses wide areas from marketing research to database marketing.

What Kind of People in What Jobs Use Marketing Science?

Most people in marketing science (also called decision science, analytics, CRM, direct / database marketing, etc.) have a quantitative bent.  Duh.  Their education is typically some combination involving statistics, econometrics / economics, mathematics, programming / computer science, business / marketing / marketing research, strategy, etc.  Their experience certainly touches any and all parts of the above.  The ideal analytic person has a strong quantitative orientation as well as a feel for consumer behavior and the strategies that affect consumer behavior.  As in all marketing, consumer behavior is the focal point of marketing science.

Marketing science is usually practiced in firms that have a CRM or direct / database marketing component, or firms that do marketing research and analytics must be done on the survey responses.  Forecasting is a part of marketing science, as well as design of experiments (DOE), web analytics and even choice behavior (conjoint).  In short, any quantitative analysis applied to economic / marketing data will have a marketing science application.  So while the subjects of analysis are fairly broad, the number of (typical) analytic techniques tends to be fairly narrow.

Why Do I Think I Have Something to Say about Marketing Science?

Fair question.  My whole career has been involved in marketing science.  For more than 25 years I’ve done direct marketing, CRM, database marketing, marketing research, decision sciences, forecasting, segmentation, DOE and all the rest.  While my BBA and MBA are in finance and economics, my PhD is in marketing science.  I’ve published a few trade and academic articles, I’ve taught school at both graduate and undergraduate levels and I’ve spoken at conferences, all involved in marketing science.  I’ve done all this for firms like Dell, HP, the Gap, Sprint as well as consultancies like Targetbase, etc.  Over the years I’ve gathered a few opinions that I’d like to share with y’all.  And yes, I’ve been in Texas for over 15 years.

What is the Approach / Philosophy of This Book?

As with most bloggers of non-fiction, I wrote this because I would have loved to have had it, or something like it, far earlier.  What I had in mind did not actually exist, as far as I knew.

I had been a practitioner for decades and there were times I just wanted to know what I should do, what analytic technique best would solve the problem I had.  I did not need a mathematically-oriented econometrics textbook.  I did not need a list of statistical techniques.  What I needed was a (simple) explanation of which technique would address the marketing problem I was working on.  I wanted something direct, accessible, and easy to understand so I could use it and then explain it.  It was okay if the book / blog / website, etc. went into more technical details later, but first I needed something conceptual to guide in solving a particular problem.  What I needed was a marketing-focused book / blog explaining how to use statistical / econometric techniques on marketing problems.  It was good if it showed examples and case studies doing just that.  Voila’.

Generally this blog will have the same point of view as books like Peter Kennedy’s A Guide to Econometrics and Glenn L. Urban’s and Steven H. Star’s Advanced Marketing Strategy.  That is, the techniques will be described in two or three levels.  The first is really just conceptual, devoid of mathematics and the aim is to understand.  The next level is more technical, and will use SAS or something else as needed to illustrate what is involved, how to interpret it, etc.  Then the final level, if there is one, will be rather technical and aimed really only for the professional.

One thing I like about Stephan Sorger’s book, Marketing Analytics, is in the opening pages he champions action-ability.  Marketing science ought to be about action-ability.  I know some of you academic purists will read the following pages and gasp that I occasionally allow “bad stats” to creep in.  (For example, it is well known that forecasting often is improved if collinear independent variables are found.  Shock!)  But the point is that even an imperfect model is far more valuable than waiting for academic white tower purity.  Business is about time and money and even a cloudy insight can help improve targeting.  Put simply, this blog and marketing science is ultimately about what works, not what will be published in an academic research paper.

All of the above will be cast in terms of business problems, that is, in terms of marketing questions.  For example, the point is that a marketer, say, needs to target his market and he has to learn to do segmentation.  Or she has to manage a group that will do segmentation for her (a consultant) and needs to know something about it in order to intelligently question.  The problem will be addressed in terms of what is segmentation, what does it mean to strategy, why do it, etc.  Then a description of several analytic techniques used for segmentation will be detailed.  Then a fairly involved and technical discussion will show more additional statistical output.  Then an example or two will be shown.  This output will use SAS (or SPSS, etc.) as necessary.

Therefore, the philosophy is to present a business case (a need to answer the marketing question) and describe conceptually various marketing science techniques (in two or three increasingly detailed levels) that can answer those questions.  Then with SAS, etc., output will be developed that shows how the technique works, how to interpret it and use it to solve the business problem.  Finally, more technical details may be shown, as needed.  Okay?

So, now you know where we’ll try to go.  You can come along if you like.