When marketing researchers use the term "analytics," they are usually broadly referring to predictive analytics, or perhaps text mining and other types of analyses they associate with machine learning and AI.
Back in the '90s, though, "advanced analytics" could refer to econometrics and time series analysis, conjoint analysis and choice modeling, new forms of segmentation, and other non-standard types of multivariate analysis.
Whether or not you call them advanced analytics, in reality, the data source does not matter. People working in marketing research, psychology, sociology, political science, economics and many other fields have been conducting quite sophisticated analyses of survey data, for example, for a very long time.
Predictive analytics, text mining, machine learning and AI also have much longer history in MR than many may realize. They just weren't getting the buzz they now do.
Another area of confusion is that many marketing researchers are under the impression that the "bigger" the data, the more sophisticated the analytics.
In fact, the reverse is often true. Moreover, very familiar methods such as linear regression, binary logistic regression, principle components ("factor") analysis and K-means are widely-used in "big data" analytics.
In the business world, sophisticated analytics of any kind historically have faced several challenges. An obvious one is that most business people aren't statisticians or computer scientists and may find it confusing.
Part of this stems from the way they are sold and, by sold, I mean internally as well. I've learned over the years not to sell technique but to sell the concrete benefits of advanced methods to decision makers. I avoid mention of anything technical unless it is absolutely essential, which is rare. Advanced analytics should not be used as sales gimmicks, IMO, especially by people who do not understand them.
A lot of important decisions can be made without any fancy stuff.
✴️ @AI_Python_EN
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