I find that if I spend enough time scoping research projects and articulating (and documenting!) research objectives up front, writing the report becomes a more simplified exercise. However, there are times when things get complicated - new stakeholders come in, primary clients shift, corporate goals change after the study has been fielded, etc. So while there are times when it is straightforward there are times, of course, when it’s not.

Over at GreenBook, there’s an article on the quality of marketing research agency reports. Personally, over my career, I’ve seen a lot of bad reports. So many, in fact, that I’ve created types of bad reports in my mind: reports that are missing annotations, reporting that don’t tell a story, reports lacking objectives, reports that don’t tell me the ‘now what’, reports that treat qualitative data as quantitative, etc. This article lines up with many of my own classifications, and adds a few others.

I came upon this article about the use of AI in marketing research that grabbed my attention. Honestly, I keep hearing about the promise of AI, and the likely shifts the industry will see because of it. I certainly see the application in the examination of open ended data, but I’ve yet to fully understand how it’s going to shift the industry. I’m optimistic and definitely keeping an eye open to learn more.