Suppose you are driving your car on a dark and curvy mountain road, on a rainy night, with a very foggy front windshield, dim headlights, and a perfectly clear rear view mirror to guide your path forward? It’s not a comforting scenario. Surprisingly, this is how many companies still operate when attempting to understand and improve their customers’ experiences and the resulting customer satisfaction (or JD Power/Net Promoter) ratings. They know a great deal about the past but very little about the future; they know a lot about the average customer but very little about the individual customer.
If you have been a silent advocate for all the benefits that market research (MR) can bring to your company, then we’re here to help you find your voice. Our Ultimate Guide to Proving Your Market Research ROI brings research to the forefront as a key player in achieving business objectives, optimizing strategies, and—most importantly—improving return on investment (ROI).
What it is An Exploratory Data Analysis, or EDA, is an exhaustive look at existing data from current and historical surveys conducted by a company.
Editor’s note: we interview Philip Atkins, director of client services, who talks about how returning to and clarifying project objectives can help ensure that research insights are not wasted with inactivity. As a market researcher, do you ever feel like other departments at your company don’t render the insights and recommendations gained in studies into actionable strategy? Here’s what it takes to get all the teams are on the same page. Get the right people sitting at the table
We often use SPSS or R tables to conduct hypothesis testing—also known as testing for statistical significance—between means. These outputs generally provide t-test results, and in SPSS at least, produce a little alphabetical footnote when a difference is statistically significant. It’s quick and easy, but the problem is that it’s wrong. Even using the Bonferroni adjustment (which compensates for the fact that there are more than 2 groups being compared), it will commit Type II errors (it says something is not significant when it is) as well as the more common Type I errors (saying something is significant when it’s not). So what do we do?
There are many ways to predict which of several concepts will “win” in a retail environment. In another blog, I describe some of the methods we employ to conduct a best-practice experimental design for testing concepts, packaging options, advertising copy, or anything else where several discrete choices exist.
So you want to field a phone study? Perhaps you've used this mode in the past, or maybe this is a brand new avenue for your company. Regardless of whether you are looking to run your first or fifteenth telephone project, the place to start is with sampling methodology.