Grouping the respondents into 3 broad categories, the survey reveals that a plurality 40% are “traditional marketers,” described as “venturing into predictive modeling on a small scale, but generally lack[ing] the organizational clout and deep, prescriptive insights to effect broad-scale change.”
Close behind, 37% of respondents are termed “constrained analysts,” who are either “struggling to move into more prescriptive analytics and modeling,” or whose scope hasn’t moved outside of marketing and sales.
Just 23% can be dubbed “marketing scientists,” who are both advanced in their analytical capabilities and possessing a broader scope, thus allowing them to effect greater changes in their organizations.
Not surprisingly, traditional marketers lag marketing scientists across several data-driven areas. For example, they are less likely to use a broad variety of sources (25% vs. 48%), to use a scientific approach to research (16% vs. 45%), and to emphasize data-based decision making (18% vs. 49%). Constrained analysts tend to sit nearer to traditional marketers than marketing scientists across those points: just 27% emphasize data-based decision making.
Combining the proportion from each group that emphasize data-based decision making with the proportion of the survey sample that each group accounts for yields the following result: 28.5% of marketers surveyed emphasize data-based decision making. Or, to flip the result, roughly 3 in 4 rely more on “hunches and experience” than data to make decisions.
About the Data: IBM surveyed 358 marketing professionals in Australia, Canada, India, the United Kingdom and the United States to find out how they use data to make business decisions. Respondents come from a wide range of organizations covering 17 industries: 50% work for small companies (100-999 employees); 25% work for mid-sized companies (1,000- 4,999 employees); and 25% work for large companies (5,000 or more employees).