Lately I've been meeting lots of new people. Some industry and startup people, some new friends and some from other departments in Berkeley.
When I tell them I'm a PhD student, they ask about the topic of my PhD.
When I say "Marketing", the reaction of surprise/disappointment splits into two, depending on their PC skills.
It is either: "Marketing - that's interesting" (a.k.a - "You seemed like a serious person until a minute ago"). If they are Israelis, or know me from the past, the response is "Marketing? You? But you seemed like a serious person".
This is why I decided to write this post to dispel some common myths and inaccuracies about what academic marketing research is, and what quantitative marketing is.
On a side note, I started my 3rd year roughly two months ago, and finally have time to focus more on my research and other hobbies. There's nothing like the sight of a blog being resurrected just after Halloween. It almost beats seeing The Rocky Horror Picture Show on Screen and Stage in SF. Just almost.
Marketing research deals with how people and firms interact and what affects those interactions. Examples include research on how individuals make decisions and what affects those decisions, how firms compete over consumers, how products are developed to match customer tastes or maximize profits and much more.
My personal (and this is strictly subjective) division of the marketing world is into Applied Psychology (Consumer behavior) and Applied Economics (Quantitative marketing). Marketing departments in business schools are typically divided into two parts - a consumer behavior (CB) side and a quantitative side.
CB people perform what I call "Applied Psychology" research. Their research is typically experimental, and includes running experiments and collecting data using surveys and other methods either in the lab or in the field. Many CB professors have a PhD in Psychology as a result.
The topics covered are typically related to individual decision making in various scenarios, and experiments are designed to single out the causes and effects of specific phenomena. Examples are how emotions influence our decisions, what role information and uncertainty has in decision making, how colors change our choices etc.
Quantitative marketing (which is what I do) involves a rigorous mathematical analysis of marketing settings. Quant marketing researchers typically do either theoretical research (sometimes called analytical or modeling), or empirical research. Sometimes quantitative marketing is called marketing science, but I have met "real" scientists that frown at this term, although both of us solve the same differential equations when we go back to our offices...
Theoretical research involves creating simplified models that describe specific phenomena in consumer/firm interactions and analyzing them using mathematical tools to gain insights about the behaviors of firms and consumers. Some famous models analyze how new products are adopted by early adopters and then by followers (the Bass diffusion model), what the optimal level of advertising is (Steiner-Dorfman model), and how firms compete in different scenarios.
Typical tools used in quantitative marketing research include game theory, microeconomic consumer theory and several optimization techniques. My research, for example, looks at websites that compete on organic search results on Google and other search engines, and analyzes their incentives to try and cheat the ranking algorithm, and the effect of such cheating on consumer happiness.
The second part of quant marketing includes empirical research. Empirical research involves collecting data that was typically created by an uncontrolled experiment, and using sophisticated econometrics to analyze the data and arrive at conclusions. If you've never heard the term "econometrics" before, think "applied statistics". Good conclusions from empirical work help explaining how consumers and firms arrive at decisions and also help validating theoretical models. Really good conclusions help forecast how changes in the market affect choices by consumers and firms.
There are many examples for empirical research. One example is research by Brett Gordon and Wesley Hartman about political advertising, that looks at how spending on advertising affects voters choices. Another example is research I am conducting that tries to define a new way to measure the impact of sequences of online ads on consumers.
Another up and rising topic of research (in the last 15-20 years or so) is behavioral economics, which looks at biases people have in their behavior (what economists might call "irrational behavior") and tries to build a mathematical model that explains and predicts this behavior. This area of research is a "bridge" of sorts between CB research and quant research, as it takes experimental results and wraps them with sound rigorous mathematical models.
When people (who have gone through an Economics grad program) ask me about marketing research: "isn't this what Economists do?", I answer: "Yes, they also do that". "So what is the difference?" they ask. My reply is typically: "Like engineering is applied physics or applied chemistry, so is business research applied economics". I am not sure I can say (yet) where the boundary of marketing research and economics lies, but they are very much related. You can find economists performing research that will be considered marketing, and marketers that perform research that can be considered economics.
As a summary point, quant marketing is becoming more and more relevant as time goes by. The ability to reach interesting conclusions about decision making many time hinges on availability of data - lots and lots of it. Given the advent of Internet data collection through advertising, tracking, surveying, social networking and just browsing, marketing researchers today have the data needed to reach those conclusions. But as always, some ointments sometimes have flies in them (so my dictionary tells me), and too much data is also problematic.
Which gets me to present day - marketing research today deals with handling these huge amounts of data, and building the smart mathematical tools that help us refine the insights hiding in the data. Those insights will hopefully help businesses create better products and services, that will lead to happier consumers.