by John Forsyth, Christine Moorman and Steven Spittaels
In the big data talent wars, most companies feel they’re losing. Marketing leaders are finding it difficult to acquire the right analytical talent. In the latest CMO Survey, only 3.4% senior marketers believe they have the right talent. Business-to-business companies have a bigger gap than business-to-consumer companies, as do companies with a lower percentage of their sales coming from the internet. And yet analytic skill is a must for effective marketing.
Results indicate that companies with above-average marketing analytics talent experienced significantly greater rates of marketing return on investment (MROI) than companies with below average analytics talent (+4.18% vs. +2.51%). When it comes to profits, the same pattern emerged—companies that are above average on analytics talent experienced profitability increases of +4.69% compared to companies below average on analytics talent +2.71%. In short, while using any analytical skill truly is better than none, strong analytical skills are measurably better.
So how do you find those people? Given how tight the market for analytical talent is – and how critical it is to a business growth – companies have to adopt different strategies for hiring and keeping people. Some large companies have taken to acquiring start-ups or developing “research labs” jointly with academic institutions or organizations. But there are a range of tactics companies of any size can use to improve their analyst recruiting.
The first is simply using more specific language. At one top retailer, the analytics team was looking to fill a direct marketing measurement position but was not satisfied with the direct marketing experience in the CVs the recruiting team was sharing with them. So the analytics and recruiting teams came together to redefine the characteristics of the ideal candidate. This collaboration led to searching CVs for a more targeted set of keywords (not generic “measurement” skills but advanced “segmentation” and “predictive analytics” capabilities). The new approach led to the discovery of dozens of qualified candidates. Similarly, at General Mills, recruiters looking for senior marketing analytics managers found that using more precise and discerning language cut search times in half.
A second strategy is to use an “always on” approach to recruiting. As John Walthour, Director, Growth Insights & Analytics at General Mills, noted, “We know these positions will continue to be in demand at General Mills and so we no longer wait for a specific position to arise.” Still other employers search constantly in stealth mode for the best talent. For example, Beth Axelrod, SVP of Human Resources for eBay, works with companies such as Gild, which identifies prospective employees on the hard-science side of marketing analytics by examining the quality of their open code.
A third component is beefing up management’s analytical skill. We find that senior executives often don’t have a clear sense of what’s needed from the analysis and, therefore, don’t ask questions that lead to helpful answers. Senior managers need to be educated to understand the basics and be able to ask good questions, such as probing the quality of the statistics being used or asking about how to incorporate new types of data types.
Finally, in order to hire the best analysts, hiring managers may need to recognize that some softer business skills won’t come in the same person. Instead of holding out for the perfect total package, one banking company solved this issue by creating a mixed team of hard-core statisticians and marketers who together mined the data, analyzed the results, and developed marketing campaigns based on those results. After three months, the team was delivering better analytical insights, and both customer activity and revenues were nearly 10 times higher.
Whatever the strategy, however, acquiring the right array of marketing analytics talent is critical to turning big data into a powerful capability for companies.