Even as more effective data analytics tools started to become available, many marketers preferred to trust intuition over the data. Neil H. Borden prefaced his groundbreaking 1964 treatise, “The Concept of the Marketing Mix,” with the following statement that described the state of the art at the time: Marketing is still an art, and the marketing manager, as head chef, must creatively marshal all his marketing activities to advance the short and long term interests of his firm. Image Source The march toward practical marketing analytics surged ahead in the 1980s, with the development of what was known at the time as marketing “productivity analysis.” This was followed by enterprise marketing analysis systems in the early 2000s that gave larger companies the capability to better track their marketing efforts. According to the 2015 Salesforce State of Analytics Report (registration required for access), the number of data sources actively analyzed by businesses is expected to grow by 83 percent between 2015 and 2020. These trends are being driven largely by a wealth of available data and emerging tools to make practical use of that data. According to Acend2, 71 percent of companies currently use marketing automation, and another 23 percent plan to get started with it in the months ahead. On the other hand, this means marketers are expected to make more effective use of available data analytics tools to show higher returns. This “closed-loop reporting” process transforms marketing into a discipline that draws on relevant metrics to deliver practically useful insights and prospects to sales teams to use to realize higher sales and revenue. The benefits of this democratization include dramatic cost savings and near-real-time analysis of information accurately merged from distributed sources. whether content is being effectively distributed.
In the beginning, there was no light, and marketers were forced to rely on “gut feeling” when it came to assessing the success of marketing initiatives. We simply lacked the tools and information needed to scientifically conceive of and measure the effectiveness of our efforts.
Even as more effective data analytics tools started to become available, many marketers preferred to trust intuition over the data. Intuition became prized in the C-suite, and taking action based on gut feeling was valued over the hard work of crunching data and formulating rational strategies.
As recently as 2002, executive search firm Christian & Timbers found that 45 percent of executives “rely more on instinct than facts and figures to run their businesses,” according to a Harvard Business Review report. That’s a pretty alarming statistic.
Unfortunately for marketers driven by creative instinct, today’s businesses are increasing demands for marketing teams to be able to show a tangible ROI to justify their budgets. Marketers simply have to do better. Your gut feeling just isn’t good enough anymore.
From art to science
Marketers didn’t always have data analytics tools available to them to enable them to quantitatively and qualitatively measure the effectiveness of their campaigns. In the past, determining marketing success was primarily an intuitive exercise.
John Wanamaker, a 19th-century merchant and early marketing pioneer, once famously exclaimed, “I know half my advertising is wasted; the trouble is I don’t know which half.” Despite a growing and acute recognition of the need for more transparency, the technology just wasn’t there.
Neil H. Borden prefaced his groundbreaking 1964 treatise, “The Concept of the Marketing Mix,” with the following statement that described the state of the art at the time:
Marketing is still an art, and the marketing manager, as head chef, must creatively marshal all his marketing activities to advance the short and long term interests of his firm.
Borden was particularly interested in ascertaining how the elements of a marketing program could “be manipulated and fitted together in a way that will give a profitable operation.” He highlighted the need to ask “what overall marketing strategy has been or might be employed to bring about a profitable operation in light of circumstances faced by management?”
Despite the progress marketers had made towards a “use of the scientific method” to test which configurations of a marketing mix are most effective, Borden lamented that marketers had still not achieved a goal of establishing an empirical marketing science by the 1960s. Marketing remained largely in the “realm of art,” as he put it.
The march toward practical marketing analytics surged ahead in the 1980s, with the development of what was known at the time as marketing “productivity analysis.” This was followed by enterprise marketing analysis systems in the early 2000s that gave larger companies the capability to better track their marketing efforts.
Business intelligence followed soon afterward, coming together as a coherent discipline in the mid- to late 2000s. That paved the way for modern data analytics, powered by bleeding-edge cloud technology, predictive modeling and information APIs.
Perhaps the biggest leap since then was the democratization of data analytics within the last year or two. Today, we’re witnessing a dramatic shift in data analytics tools, which are becoming increasingly accessible to marketers from businesses of every type and size. Prices are dropping, cloud solutions are enabling mobility, drag-and-drop interfaces are replacing code-based ones, automation and AI are handling the heavy lifting and integrations are bringing silos down.
It’s easy to forget that until this point, effective data analysis was primarily the domain of larger corporations that could afford the necessary infrastructure to work with huge sets of unstructured data from diverse sources. This infrastructure typically included data warehouses, a complement of data scientists and expensive software to make sense of the data and deliver the results of the data analysis to decision-makers.
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