When I began my marketing research career in the 1980s, I never would have imagined being confused with an IT director, let alone an AI researcher. The terms marketing research and market research are often used interchangeably, though market research suggests to some that MR is secondary analysis, for example, of a country or market. It is much more than that.
Here are a few things it isn't: Python, Bayesian statistics, Hadoop and ethnography. These are tools used by marketing researchers but the implements are not the field itself. For some background in what it is, here are a few official definitions:
According to Wikipedia, citing the American Marketing Association, marketing research is
the process or set of processes that links the producers, customers, and end users to the marketer through information used to identify and define marketing opportunities and problems; generate, refine, and evaluate marketing actions; monitor marketing performance; and improve understanding of marketing as a process. Marketing research specifies the information required to address these issues, designs the method for collecting information, manages and implements the data collection process, analyzes the results, and communicates the findings and their implications.
about listening to people, analysing the information to help organisations make better decisions and reducing the risk. It is about analysing and interpreting data to build information and knowledge that can be used to predict, for example, future events, actions or behaviours. This is where the real skill and value of market research lies.
It further defines research:
Research, which includes all forms of market, opinion and social research and data analytics, is the systematic gathering and interpretation of information about individuals and organisations. It uses the statistical and analytical methods and techniques of the applied social, behavioural and data sciences to generate insights and support decision-making by providers of goods and services, governments, non-profit organisations and the general public.
In the 1980s, marketing research was simpler - predominantly focus groups, in-depth interviews and survey research, the last of these consisting of telephone interviews, mail (postal) surveys and mall intercepts. The vast bulk of marketing research was customized, though there also were syndicated services and consumer, retail and media panels in some countries. Industries with extensive customer data, such as banking, were already doing a lot of what is now called data science. Mostly, this was done in-house, as is true today.
With the exceptions of Nielsen, IRI, AGB and a few other giants of the day, nearly all MR agencies were boutiques, often founded by academics or former academics. Companies with 100 or more full-time employers were rare. It would be an exaggeration to say that MR was New York, Chicago and London, but little was done outside of the Western nations and it was more concentrated in the Anglosphere within the West than today.
Statisticians and researchers with PhDs in disciplines such as sociology or psychology were more prevalent, or at least that is my impression. A newcomer had less to learn, but more of us came from academic backgrounds requiring at least some knowledge of research methods and statistics. Seminars designed to plug gaps in our knowledge were already plentiful and local chapters of the American Marketing Association were quite active in New York, Chicago and a few other cities.
By today's standards, much of the work was manual but in this "hands on" world we were forced to learn the basics. There was also less room for error because research was costlier and fieldwork took more time. We had to be focused and plan carefully. The entire research process was about as fast as it is today since we made fewer mistakes and wasted less time trying to fix them.
Questionnaires were also shorter and better designed since more marketing researchers were survey research professionals. Analytics and data visualization were more limited than today, but my company had SAS and more than a dozen networked mainframes, so it wasn't exactly the Stone Age.
Causation: The Why Beneath The Whatis an interview with Harvard epidemiologist Tyler VanderWeele, a noted authority on causal analysis. Causal analysis is a thorny topic but, in my opinion, a good understanding of it separates a researcher from a mere number cruncher.
This has been a brief summary of my personal impressions, based on my own experience. Doubtless, some readers, including other MR veterans, will disagree with parts of it, but I hope you've found interesting and helpful!