Where Is It Now?

OK, it’s been two years in the making (actually it’s been thirty years in the making) but I finished it last month.  It’s about 55,0000 works (Some of them good).

It’s called A GUIDEBOOK FOR MARKETING ANALYSTS, A Conceptual Overview of Real Marketing Science.  Isn’t that a great title?  Yeah, I did not think so either but it may change.  The idea is to have a guidebook, NOT a textbook, for marketing analysts.  Pretty much the subject of this blog.  Same idea, same style, etc.

I have bookcases full of textbooks on marketing science, econometrics, marketing research, statistics, multivariate analysis, etc.  I use them sometimes (if the leg on my desk has become wobbly, pages from those books  will help prop it up).  I’m kidding of course.  They have their place.

But it is my experience that marketing analysts (or students about to become marketing analysts) appreciate a conceptual, guided overview of how to apply analytics to solve a marketing problem, without all the mathematic clutter of most academic tomes.  That’s the purpose of A GUIDEBOOK FOR MARKETING ANALYSTS.

It’s making the rounds in New York now.  I’ll keep you informed.


How Do You Know if You’re “Analytic”?

Okay, since some of this blog is aimed at students of analytics, how do you know if YOU are analytic?  Sure, sure , you’ve been pushed by your parents into taking a lot of math and science, etc., and are now in school studying analytics–but deep down, sometimes at night, you wonder if it is really for you.  It’s not about how much money you might be able to make, you sometimes wonder if you should change your major to something fun and interesting, maybe music or art or politics or history.

Or, you’re already working IN analytics and also question if you’ve made the right decision.  Do you fit in?  Can you be successful?  You’re early in your career and it’s not too late.  How does the prospect of doing SAS on dirty data and searching for insights with no time for the next 30 years sound?  If your heart skipped a beat, you should worry.


How do you know if you’re an analytic person?  You should love the simple joy that comes when seeing a variable that should be significant, be proved in the data.  The satisfied look of wonder pervades your face when the world makes sense.  That replaces the constant, cynical caveat-laden weariness we usually have to carry around.  That’s what got us into analytics in the first place, right?  People are confusing, full of irrational gray areas, but data is data, truth is truth.  When well-understood relationships make sense, it’s comforting.  When insights are found, it’s exciting.  Murder solved!  Puzzle completed!  And because it’s consumer behavior we are trying to predict–this helps us believe that maybe people are NOT so confusing.

So, look over your life.  Do you find enjoyment in black and white answers?  Do you naturally distrust any data / claims that you yourself have not been in to?  Do you like learning how things work, do you naturally and quickly see relationships (especially causal relationships) and are you constantly curious?  If the answers to these are mostly “Yes” then you might be analytic.


When I was in elementary school I was the class clown.  (Can you believe it?)  I have a strong introvert streak but also have always found it necessary to make the joke, point out the funny thing, and teachers usually hated me, the class clown.  I didn’t eat paste or do funny dances, it was always verbal.

Anyway, in third grade we were learning long division.  The previous couple of weeks the teacher had been warning us that LONG DIVISION was a very big deal, difficult, complicated, and would require all our attention, and she would have to mentor us along.  (She would be in no mood for class clowning when we started.)

So, the first day arrived and she motivated us to appreciate the central issue of long division, remainders, by asking, “Now, how can you divide 5 evenly?  You can’t.  Thus–”

I immediately shouted, “Yes, you can: two-and-a-half and two-and-a- half.  See, evenly.”

She sent me to the office.  The one time I was NOT being the class clown–I was being analytic–got me in trouble.  In truth, it served me right.  The statistics in that class proved that 9 out of 10 times whatever I said was worthy of sending me to the office.


So, another issue.  The real trouble is that being analytic in corporate America is not enough to be successful in analytics.  This is because most analytic folks are a little quiet, maybe introverted.  We can pretend it’s because of the left-brain domination where we get our sense of logic and rationality.  But to be successful in analytics you will have to be able to push yourself to find insights and present them to others.  You will often have to convince other people (sometimes those many levels above you, those that have the purse strings to carry the project forward).  So the key personality trait, as a test for analytic talent, is passionate curiosity.   That is, you are so excited by what you have found, you can easily overcome your natural shyness.  The love of discovery so drives you that you can';t keep your mouth shut and you tell the world that you have found the truth, and it’s shouted from the rooftops.

Therefore, I would say you are analytic if you love finding relationships in data.  But you can only be successful in analytics if  you are so thrilled by what you have found you must socialize that to everyone you can.  Right now.  Make sense?




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.   Marketingscience.biz 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.