As my colleague Lizzy Foo Kune has been busy unearthing the most important insights from the Gartner 2020 Marketing Data and Analytics Survey (see this blog), I have been focused on mining for a more specific set of insights: Does marketing analytics even matter? The answer may seem obvious, but it’s not. Last year some of my colleagues made the prediction that, “By 2023, 60% of CMOs will slash the size of their marketing analytics departments by 50% because of a failure to realize promised improvements.” To help Gartner clients fall on the positive side of that prediction, I wanted to research three things: Verify that maturity in marketing analytics matters. Do more mature marketing analytics teams get better business results than less mature teams? Gain some insight on how more mature analytics teams get better results. Analytics only adds value if it is acted on. Do more mature marketing analytics teams encourage marketers to take more action on the data? Establish patterns across the maturity scale that could guide and enhance marketing analytic roadmaps. Do less mature teams frequently take similar steps or make similar investments as they become more mature? I’ll have more to say about this in October, but let me preview some findings: Marketing analytics still matters. More mature teams are noticeably more likely to report superior financial results than less mature teams. They are also more likely to credit (at least a portion) of those results to marketing analytics by displaying noticeably stronger agreements to statements like “The insights provided by the marketing analytics team provides are essential to our company’s success.” Mature teams influence more decisions. More mature teams are noticeably more likely to influence more marketing decisions with marketing analytics. The biggest differences by maturity? “Changing the channels used for marketing or advertising supporting point.” In all cases—we asked nine different questions about decision frequency—more mature teams were more successful in influencing decisions. Mature teams have more control over their data and rely more on machine learning. It makes sense that data and machine learning are important, but what isn’t stressed enough is the common order of progression: data first, then analytics. And while this may seem like common sense—after all, data often fuels machine learning algorithms—it is not always easy to justify. Most organizations have no formal way to assess the value of the data itself. So, analytics being easier to justify (and more fun!), can divert too many resources away from data. I’m curious, do these results meet your expectations? Where comparable, these results are consistent with our 2018 survey (see “Survey Analysis: Lessons From Leading Marketing Analytics Teams”), but this year we dug deeper into certain topics. For example, unique to this year’s survey, about half our respondents were “consumers” of marketing analytics and half were “producers” of marketing analytics. While their responses were often similar, we are finding some interesting cases where they diverge. What hypotheses do you have about more mature marketing analytics teams?