marketing data analytics

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Most marketing executives appreciate the potential benefits of analytics. So why don’t they usually consider data analytics when making key decisions?

Organizations expect to spend more on marketing analytics, even though most analytics does not influence their decisions. Spending on marketing analytics is expected to jump from 4.6 percent to almost 22 percent of marketing budgets in the next three years, a 376 percent increase, according to the latest CMO Survey.

Yet marketers say barely a third of available data are used to drive decision making in their companies, according to the survey sponsored by the American Marketing Association, Deloitte, and Duke University’s Fuqua School of Business.

In previous CMO surveys, marketing leaders have consistently said a small percentage of marketing analytics influence company decisions. Levels have ranged from a high of 37 percent in 2012 to the current 31.6 percent.

Main Reasons for Lack of Use

Top reasons of lack of use include:

Lack of processes or tools to measure success. This suggests that companies have not thought through how analytics will enter the decision making process or how analytics will help marketers understand the effectiveness of their actions, says Christine Moorman, T. Austin Finch Sr. professor of business administration, Fuqua School of Business, Duke University.

Lack of people with abilities in both data analytics and marketing practices. “This divide between rigor and relevance requires boundary spanners that are either analytical managers or analysts with managerial insight,” Moorman writes in Forbes. “Either way, there must be human capital that can connect the dots between marketing practice and analytics.”

Other research also reports that business leaders recognize the potential benefits of data analytics but express dissatisfaction with their ability to obtain those benefits. Research from ZoomInfo found that only about a third of professionals now practicing data-driven marketing call the strategy “very successful.”

Some experts argue that focusing more on “why,” or the meaning and context, will provide better insights. Concentrating less on data and more on context will help obtain the full benefit from social media listening. In other words, business managers must “think more like an anthropologist.”

Recommendations to Increase Use of Marketing Analytics

Moorman recommends that companies assess the quality of their marketing analytics. Only 35% of marketing leaders said they formally evaluate the quality of their marketing analytics. “Regular review of the analytics used and not used may spur important conversations about the role that marketing analytics could play in driving firm decision making, which, in turn, should lead to stronger processes for doing so,” she writes.

Moorman and Fuqua School of Business students Sylvia Yang and Shiwani Kumar offered 10 recommendations on how to improve use of marketing analytics in a Forbes article. These are a few of them.

  • Don’t build a fun facts factory. Marketing analytics should resolve key business questions—not produce a collection of fun facts.
  • Nest analytics into marketing decisions. Embed data analytics into the decision-making process. Design capabilities to point marketing executives to the right data at the right time.
  • Crawl before walking. First take baby steps to build data with integrity to build trust in your information. Strong foundations of trust enable bigger leaps of faith.
  • Manage analytics for a multidimensional view of the customer. Knock down silos between marketing, operations, and sales data that contain glimpses of the customer at different stages of the purchase journey. That can provide a more complete view of the customer and greater insights.

Bottom Line: Marketing leaders generally understand that marketing analytics can provide powerful benefits. Nonetheless, they continue to struggle to incorporate analytics into their decision-making. Research illuminates reasons for those difficulties and offers recommendations for overcoming the challenges of finding valuable insights in data analytics.