Skills Development: A Data-Driven Marketing Resolution for 2017

Skills Development: A Data-Driven Marketing Resolution for 2017


In Internet Years, “Data-Driven Marketing” has been a cliché for, well… almost as long as “Internet Years.” So why are marketers still lagging when it comes to data competencies?

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According to a recent analysis, 45% of job postings for marketers ask for data analytics skills but only 3% of marketers possess data analytics competency.[1] At the same time, 80% of enterprises and 63% of SMBs have deployed or planned to deploy big data analytics processes in 2016.[2][3]

According to a report by the Aberdeen Group, the message for marketers is clear: The adoption of data analytics tools is outstripping marketers’ ability to derive value from them. And the more sophisticated those tools are, the less likely even “Best-of-Breed” marketers are to use them.[4] In the absence of greater skills, collecting more data doesn’t help marketers—it only leads to more chaos and confusion.

So skills matter. Companies should consider investing in analytics skills training for their existing marketing staff, and they might consider hiring employees with the technical skills to train other staff members. (Our VP of Marketing, Kiyoto Tamura, has an MS in Computer Science and a BA in Mathematics from Stanford University.) None of this is new. But the important point is if your company is already investing in Business Applications for Marketing Analytics, it’s worth taking the time to calculate how much it’s costing you for your staff not to be adequately trained. After all, if you are spending money on software, you expect a return on that investment. How much does having the requisite skills in house, or not, impact that return?

For marketing professionals, of course, the self-interested reasons to obtain analytics skills have only gotten more compelling over time. In fact, it used to be that, for companies that didn’t have dedicated analysts, analytics was the domain of Marketing. As we enter 2017, however, changes in the analytics landscape have inverted the situation in ways that may change Marketing beyond recognition.

The End of “Marketing” As We Know It?

Could it be that “marketing” is on its way to disappearing as a job description? Scott Brinker thinks so. In his series 5 Disruptions to “Marketing,”[5] he posits that the most sophisticated users of digital marketing, the “digital natives” Amazon, Netflix, AirBnB, Uber, et al., no longer do “marketing” in the old sense of being a “wrapper around the product.” In fact, what they do doesn’t look much like marketing at all:

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I put “marketing” in quotes because these digital natives have not grown primarily through the kinds of wrapper-like digital marketing that most businesses apply. Most digital natives don’t do a lot of content marketing, their own social media presences tend to be relatively modest, and you don’t see much display advertising from them.

Their main marketing method, if you can call it that, has been “growth hacking.” They build their digital products to optimize for customer experience: easy to try, delightful to use, compelling to upgrade, valuable to share, effortless to keep, and ever-evolving to meet changing preferences and expectations.

Digital natives still have Marketing departments, of course, it’s just that their role is greatly diminished. When the Product team owns the customer experience, then Marketing is relegated to delivering communications. The integration of data across an ever-wider spectrum of company functions might ultimately lead, Brinker suggests, to a democratization of “Marketing,” with Sales, Customer Service, Billing, etc. all having some component of “Marketing” integrated into their job functions.

This need not lead to the diminishment of Marketing’s role, of course. Depending on a company’s structure and goals, it might lead to an expansion of the Marketing role, as Marketing owns the Brand, and all of a company’s activities, including Product, are seen as expressions of the Brand.

The key thing for marketers to understand is that, now more than ever, customer experience is marketing. Data is a tool for understanding your customers intimately and forming relationships with them. To the degree that data collected by teams in your company is not shared with and intelligible to other teams, that understanding will be incomplete. At best your company will provide an inconsistent customer experience; at worst you will be relegated to frantically putting lipstick on a pig and hoping your customers won’t notice.

The Customer Experience Imperative is one of the hallmarks of Digital Transformation. If marketers don’t want to become irrelevant, they must not follow the Digital Transformation — they must become leaders of it. And in a world of constant disruption, investment in data skills may be not only critical to their own survival — it may be the key to the kingdom.

The Data Moat Imperative

The importance of customer experience to growth has some surprising implications for the importance of data. Apple Maps famously flopped on its first release, and a year later, customers still complained that the user experience was vastly inferior to Google Maps. Stephen O’Grady pointed out that the problem with Apple’s product wasn’t the software—it was the data. By the time Apple Maps came out, Google had been collecting data, both from third-party sources and (critically) through usage of the Google Maps app for years.[6] Google’s data, which led to much more accurate directions for the Google Maps user, gave them an insurmountable user experience advantage over Apple. They would continue collecting more and more data in the future, and Apple could never hope to catch up.


O’Grady coined the term Data Moat to describe this kind of competitive advantage. The term captures the idea that, while other kinds of competitive advantage can be easily snuffed out by a savvy and/or powerful competitor, large advantages in data cannot. The superiority of Google Maps is not a huge problem if you’re Apple, whose business model and brand loyalty make it (almost) a non-issue. But as O’Grady points out, most companies are not Apple:

…yet most software businesses today are competing on the same basis; competing as if the sales of software alone is a sufficiently compelling and projectable revenue model. Even without considering the value of the data, this is a problematic assumption. Open source, software-as-a-service and cloud are lowering costs for customers, and in so doing, lowering the revenue pool available to vendors.

No matter how much or little marketers care about dashboards and conversion rates, they should care very, very much about the impact of data on their companies’ competitive advantage. Working to a Data Moat a key area in which Marketing can provide leadership in the Digital Transformation of an organization.

Data differentiation puts a different spin on data collection. While from the perspective of decision-making, a company might not want to bother collecting more data before they have the skills in place to make sense of that data, the Data Moat Imperative suggests the opposite. Developer turned VC Leo Polovets suggests you start collecting data as early as possible, no matter whether you know what to do with it or not (you might find uses for it later, and you can always get rid of it later if you decide it isn’t valuable), and that you collect as much data as you can.[7][8][9]

Of course, all of this puts an even higher premium on data skills development, especially since, as Polovets says, part of the value of your data lies in the hard-to-copy applications it lets you provide. Among other things he lists, having great data helps you:

  • Provide great content recommendations to customers
  • Provide more accurate ad targeting for more revenue
  • Optimize pricing and offer pricing transparency

And so on.[7]

Invest in Data Skills Development in 2017


The idea that Marketing should invest in building data skills can be quantified, of course. As Douglas Hubbard points out in How to Measure Anything,[10][11] if a business decision has value, that value can be estimated and measured, even if the variables are uncertain. Instead of simply throwing money at the problem, take a data-driven approach. Look at your business goals, and at least try to make at least a back-of-the-envelope calculation about the expected ROI of data skills education for you and your team.

But do invest. Start now. The data natives have a head start on you and the world is not going to stop. The Digital Transformation will continue, and you should be on the forefront of it, not bringing up the rear.

My resolution is to help you. This article is intended as the first in a series for Marketers to build their data skills in 2017. If you can’t wait until then, you can get started here: So you want to be a data scientist?

Until then: Per data ad astra!