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Marketing as a science is about objectively using data to support decision making. Marketing as a science is about looking for patterns in the market and in customer behaviors — within data, but also through more qualitative sources of information, such as conversations with prospects, peers, and third-parties. This is where data science is applied in the exploration of marketing data.
Marketing as a science is about embracing ideas from other scientific and engineering disciplines : psychology, economics, computer science, neuroscience, biology, industrial engineering, anthropology, sociology, etc. Marketing as a science is really about running good controlled experiments to test hypotheses. This is the heart of the scientific method applied to any discipline.
The common theme among all these principles? Science in marketing is the systematic quest for knowledge. Not absolute truth, but effective working knowledge that can be applied to growing a business.
Subscribe to my newsletter to learn how to incorporate better marketing techniques in your business. Continue Reading ». Surveys are nice, but they have their limitations. Thanks for this Scott. In my view, marketing is not a science, but a business function.
It can informed by science and be improved by implementing elements of the scientific method, but its purpose is not discovery, but profit. Further, it has serious consequences, like the ridiculous pursuit of tortured ROI calculations for non-material costs. Marketing is definitely not a hard science like chemistry or physics. Such academic research is certainly at the crossroads of several soft science branches of study.
But in business, most practicing marketers are not pursuing research in that context. If we forget about labels for a moment and focus pragmatically on what works, the wise use of scientific thinking in marketing strategy and execution is a competitive advantage.
In the end, I think I felt that if such a label helps marketers think differently and embrace these techniques, that are new to many, then a little imprecision in terminology is a small price to pay. But data science seems to have been embraced by a number of academic circles. Data science is a lame, abstract term. From a pragmatic standpoint, its hard to have rational discussions when the strawman on the table has the ability to predict anything from any data.
These are rare skillsets in one person. Not impossible. But rare. And incredibly unlikely. The other, bigger problem I have is this: data is dumb. Without syntax and semantics — i. Once a context is properly applied and gives that data meaning, then it becomes information.
Now, information is something I can sink my teeth into. Information is relevant. Information can be smartly applied. Understanding the impacts of anthropogenic sound on beaked whales. Cetacean Res. PubMed Abstract Google Scholar. Whales, sonar and decompression sickness. Nature , 1—2. Frankel, A. Results of low-frequency playback of M-sequence noise to humpback whales, Megaptera novaeangliae, in Hawaii. Google Scholar.
Behavioral responses of humpback whales to full-scale ATOC signals. ATOC and other factors affecting distribution and abundance of humpback whales Megaptera novaeangliae off the north shore of Kauai. Henderson, B. Open Letter to the Kansas School Board. Jepson, P.
Gas-bubble lesions in stranded cetaceans: was sonar responsible for a spate of whale deaths after an Atlantic military exercise? Macilwain, C. Nature McCarthy, E. New York, NY: Springer. McConway, K. Score and ignore. A radio listener's guide to ignoring health stories. Significance 9, 45— Mooney, C. Retrieved from Washington Post. The Republican War on Science. National Research Council, A.
Nature, A. Filling the void. Cheerleader or watchdog? CrossRef Full Text. Norse, E. Norse and L. Oreskes, N. Science and public policy: what's proof got to do with it? Policy 7, 69— Oreskes and J. Parsons, E. Navy sonar and cetaceans: just how much does the gun need to smoke before we act?
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