The use of predictive analytics is a hallmark of the most advanced marketing analytics teams.
In Gartner’s 2018 Marketing Data and Analytics Survey, we asked marketing analytics leaders: “Which of the following activities does your company’s data scientist (or advanced analytics resource) perform for your marketing analytics team?” Respondents were given a list of 13 activities, such as data visualization to preparing data for analysis.
A total of 57% of marketing analytics leaders with the most advanced teams (n=35 at Level 5) said their organization uses predictive analytics compared to 35% of the other respondents from less mature teams (n=468 at Levels 1 through 4). Out of 13 activities, this stands out as the largest difference between the two groups of respondents.
It’s no surprise why there’s such a large difference: doing predictive analytics is tough. Your team needs to possess the right skills, understand business priorities and deal with data accuracy. What’s the likelihood you’ll sink under the weight of your organization’s data or swim to successful results?
Gartner analyst Jason McNellis, who has led advanced analytics practices, recognizes the challenges faced by most marketing practitioners. As he points out: “As a marketing leader, you likely don’t have an informed, passionate stance on whether random forests, k-nearest neighbors or logistic regression should be used to create your prospect propensity model.”
As your analytics team incorporates predictive models in its work, you don’t need to obsess over the role of a Gaussian activation function in a neural network. Instead, prepare yourself for productive conversations without needing statistics by checking out Jason’s report, “A 4-Step Process for Marketers to Evaluate Predictive Model” (subscription required). In it, you’ll find a simple, intuitive way to assess an existing model or review a newly built one.