Or I might remark that "those who cannot remember the past are condemned to repeat it.".*
Recently, I read a promotional email from one of my favorite market research vendors. The email’s first message was about propensity models, one of my favorite tools. I wondered if they really have the sample sizes necessary to create accurate scoring; in my experience, it requires very large samples that most firms cannot afford or execute.
Well, it’s 2021. Happy new year! And while the new year always seems brimming with possibilities in these early weeks, this year things seem especially uncertain, strange, and off-kilter. With COVID-19 vaccinations underway across the country, and the possible return to something that looks like life as we know it starting to look like true light at the end of the tunnel and not an oncoming train, we here at DDG are feeling hopeful for the future. But we also know that the events of the past year will continue to impact consumers, and businesses, for quite some time. In this blog we’d like to take a moment to look back at some of the insights we gathered in 2020.
In 2020, as we are painfully aware, the consumer got to stay at home. They managed their households, taught school, and went to work in their PJs. For many, a typical day consisted of surfing the web for things to watch and things to buy.
As of this writing (from my home office, of course), over 5 million people in the United States have contracted the novel coronavirus known as COVID-19. After a period of reopening businesses across the country, climbing case rates have driven many states to begin closing back down again. Almost seven months after the first U.S. case was reported, there are still no clear indicators of when life will return to normal.
Here are 3 things to know about implementing market segmentation.
Other methods have significant but grudgingly accepted flaws; Likert scales, for example, are subject to scale bias because people use scales differently (one person’s 7 might be another person’s 9). Ordinal rankers, where respondents simply rank their preferences from 1 to k in order of importance, can reliably provide information on which single item is most important, in aggregate, but discrimination between attributes is quickly lost after that. Further, most survey respondents can rarely make meaningful comparisons between more than a few items.