Marketing Analytics – Summary of Session 1

We started the second trimester today at ESSEC’s Singapore campus. I am teaching Marketing Analytics (Engineering) to two sections of 50 students each. In my first lecture I introduced the fundamental problem in front of marketers – how to justify their decisions to others who control the budget. Gone are the days when people could simply use experience, gut feel, intuition, etc. as valid criteria for selecting marketing strategies. Now nobody wants to bet even $1 on speculative marketing managers. Data driven marketing is the new norm. My course is an introduction to this new reality. In any marketing course, ‘brand positioning’, ‘segmentation’, ‘targeting’, ‘media planning’, etc. are common terminologies. Professors and students know what these concepts mean. Yet, given a real life business situation how many students will be able to actually come up with a strategic solution? Very few indeed.

Our Course Text – Principles of Marketing Engineering

Over the next five weeks, we will take a two-step approach. First, we will clarify a certain marketing concept, for e.g., positioning. We will then understand what type of information needs to be collected to plot a perceptual map showing the brand positioning on 2 or 3 dimensional space. Next we will use SPSS to do the data analysis using statistical techniques such as factor analysis. Finally, based on the perceptual maps, students will recommend actions. There will be hard numbers involved. For example, when the students suggest launching a new brand to exploit potential gap in the market, they will need to justify that by projecting the changes in the market shares. They will have to account for cannibalization of any existing brands from the same company that is supposed to launch a new brand. This will be a complex but fun exercise!

The other topics include decisions on segmentation using probability models, salesforce allocation, and conjoint analysis. As we started working with SPSS today, I used a dataset consisting of accounting information on several US firms over 2010 and 2011. The students’ first task is to build a sales response model and test it using the data. To what extent do the sales respond to advertising? The response model will not be very complicated yet we may end up using a logit-type curve (ADBUDG model), who knows?

I believe that modeling the data is not the most important thing. It’s just a small component of decision making. The critical parts are to read the analysis, interpret it, and then recommend a decision path. I don’t like blind data mining of millions of data points to come up with patterns that everyone believes are true. Unfortunately this is exactly what’s happening in the analytics area. Data mining coupled with intelligent experiments is the way to go. (More on this later). Bringing intuition to this party is like inviting Michael Lohan to speak at a conference on responsible parenting!

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