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
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.
On the cusp of a data analytics revolution
As the shift toward content-based marketing builds momentum, marketers will find themselves under increasing pressure to measure the effectiveness of their publishing efforts. At the same time, marketers have more data available to them than they ever did in the past.
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. By comparison, the number of data sources only increased by 20 percent between 2010 and 2015.
The central question is no longer whether marketers can apply the scientific method to available data. Rather, the question is now how best to do this? As the Salesforce report points out, “business leaders face a continued influx of data and still struggle to make sense of it all.”
Marketing data trends to watch in 2017
The urgency of these questions will only intensify as we approach and shift into 2017. These trends are being driven largely by a wealth of available data and emerging tools to make practical use of that data.
“Our entire focus and claim to fame is helping business people who choose to deal with data that is complex … without any IT investment, any data warehouse,” Amir Orad, CEO of business intelligence software company Sisense, recently told an Entrepreneur magazine contributor.
The marketing data analytics landscape is primed to undergo some significant evolution in the months ahead. Here are the top trends to track.
1. Data-driven engagement via automation
The marketing automation trend we have seen in 2016 will only strengthen in 2017, as the industry continues to demand higher levels of efficiency and effectiveness in an increasingly scrutinized and competitive environment. 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.
This means that marketers will have no choice but to increase their investment in marketing tools that are capable of more sophisticated segmenting for targeted engagement with existing and prospective customers.
Infinitely scalable and impervious to human error, “marketing automation can enhance your company’s relationship with customers and potential consumers by connecting with them as they engage with your content,” notes consultant Mary Wallace in a recent MarTech Today column. “It empowers marketers to send personal emails to their contacts without having to send hundreds of emails one at a time.”
2. The tyranny of the bottom line
Marketing has always required a combination of creativity and science — usually more of the former. This has made the impact of marketing initiatives trickier to measure accurately and their value harder to assess. The tides have turned, though, as marketing has become increasingly data-focused.
According to a recent study from Regalix (registration required), 63 percent of B2B marketers believe that analytics have helped increase their firms’ sales revenues by 11 to 50 percent.
The effect of this data revolution in marketing is twofold. On the one hand, marketers have far deeper insights into what works and what doesn’t. On the other hand, this means marketers are expected to make more effective use of available data analytics tools to show higher returns.
3. Tighter collaborations across teams
Marketing is no longer a fringe group practicing some dark art that somehow benefits the business. With greater transparency into all aspects of the customer life cycle, marketing is being recognized as a critical contributor to business success, and tighter integration with sales teams is crucial.
With marketing activity becoming more directly measurable, the pressure is on for better alignment in terms of evaluating performance. End-to-end business process metrics are merging with one another, and lead qualification criteria are evolving as a result.
What’s more, you can finally ignore those vanity metrics that don’t make substantive contributions to the bottom line. Insights derived from marketing analytics must be actionable by sales teams who must, in turn, give feedback to the marketing team about leads that do or don’t convert and which leads are more likely to convert, for example.
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.
4. Data analytics latency is history
Thanks to cloud storage solutions and sophisticated caching algorithms, today’s advanced business intelligence tools make it easier for marketers to collect data from multiple sources in different formats, analyze that data and distill actionable insights.
The benefits of this democratization include dramatic cost savings and near-real-time analysis of information accurately merged from distributed sources.
Simply breaking down traditional data silos was a powerful step towards greater efficiency. Analysis with minimal latency means more immediate decisions that often have profound ramifications for rapidly growing businesses based on key goal-based metrics such as:
the costs of content production and paid customer acquisition.
whether content is being effectively distributed.
true engagement with relevant audience members.
Taking marketing to the next level in 2017
Marketers who pay attention to these transformational trends heading into 2017 are better positioned to take advantage of new tools to collate, analyze and act on their data. Ultimately, these are the people who will be able to most effectively determine whether their marketing strategies are working and how best to optimize them.
Over the course of 2017, we’re likely to see increasing demand for data that’s more detailed and precise than ever before. Those who are capable of drawing insights from the data using complex analytics will leap ahead of the laggardly marketers who insist on relying on their “gut feel.”
We can already see the signs of this looming, fundamental change. They all point to one inescapable conclusion: Anything but a data analysis-driven marketing strategy for 2017 is a recipe for failure.
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