Companies are spending big dollars on big data. Approximately 5.5% of marketing budgets currently are spent on marketing analytics and this is expected to increase to 8.7% in the next three years as reported in The CMO Survey. Expectations are running high and many companies are trying to figure out how to crack the code to generate good strategic insight from the data.
I’m in favor of the trend to capture and use data to drive decisions. However, that is where the problem lies. As the stash of data grows, companies are using a smaller percentage of it. I first asked the question, “In what percent of projects does your company use available or requested marketing analytics before a decision made” in February 2012 and the result was 37%, which I thought was the bottom. However, when asked that same question in August 2013, the percentage dropped to 29%. Figure 1 shows the continuous decline over the last 18 months.
This finding is not completely unexpected, however. Reviewing the thirty-year history of research on this topic, usage rates have always been low for many types of marketing information—marketing research, advertising research, and, now, social media research. This marketing analytics utilization gap is a challenge to big data’s contribution to the bottom line.
How bad is it? Some might argue that the ultimate benchmark of any type of market intelligence, including marketing analytics, is whether it improves a company’s customer insights. The CMO Survey asked top marketers to rate their firms on “developing and using customer insights” on a five-point scale where 1=poor, 2=fair, 3=average, 4=good, 5=excellent. Looking at this score over time, results indicate it remains average (3.4-August 2013, 3.5-February 2012, and 3.5-August 2009). Therefore, even as we have seen more mobile gambling spending on marketing analytics, we have not seen customer insights budge.
What should companies do? First, managers must begin with the end. Go-to-market plans, demand-generation activities, and sales activities must include specifics on what data should be collected and how they will be used. When plans and strategies have a built-in big data protocol, the down-stream utilization rate is likely to increase.
Second, companies need to invest in teaching managers how to use marketing analytics to develop insights, to drive decisions, to implement strategies, and to evaluate actions they have already taken. For just this reason, we teach a course in “Market Intelligence” here a Fuqua where we focus on the “use” of information, not its “creation.” Companies need to put a bigger emphasis on the use end of this marketing analytics equation. Agencies and consultancies can help by providing this type of training.
Third, companies need to find and keep the right talent so they can fully leverage marketing analytics. When asked “To what extent does your company have the right talent to fully leverage marketing analytics?” where 1=does not have the right talent and 7=has the right talent, only 3.4% scored their company a seven and 56% below average. Figure 2 shows the full distribution (mean=3.4, standard deviation=1.7).
Figure 2. Do Companies Have the Right Talent to Fully Leverage Marketing Analytics?
What type of talent should be sought? I’ll offer more thoughts on the human capital part of big data’s big puzzle in a follow-up blog.