Teaching marketing research has given me the opportunity to connect with people who could be future leaders in the marketing research industry, which I find to be an exciting extension of my ‘day job’ heading-up research methods and best practices for Lightspeed. I am currently teaching Consumer Insights at Northern Kentucky University. Teaching undergrads marketing research has made me reevaluate how we in the industry talk about various topics and try to come up with simple ways to explain what we do. One of my first challenges was coming up with a framework that summarizes the uses of marketing research and the specific research techniques tied to each use. I was thinking this should be simple; however, I quickly realized I couldn’t find what I wanted, so I created my own framework.
Marketing research companies are experiencing low response rates and low engagement rates, so the industry is continuing to turn to technology to try to increase both. According to the Pew Research Center, nearly two-thirds of adults in the U.S. own a smartphone of some kind, and 46% say their smartphone is, “something they couldn’t live without.” Younger generations coming of age have never known a world without incredibly intelligent mobile devices. With the inevitable and exponential growth of technology, more streamlined mobile devices and the rise of the ‘always on’ consumer, these numbers will continue to grow dramatically.
A dozen years ago a debate raged in the marketing research community over the switch from probability sampling methods such as telephone RDD to nonprobability sampling methods as are typical with online access panels. In the interim years, most clients moved to online samples but there are still some that cling to probability methods. However, we now see the quality of probability samples being questioned because of low response rates for RDD. In an interesting twist, the very same techniques that nonprobability samples use to weight and model data now often need to be done on probability samples to account for nonresponse bias.