CMOs: Are Big Data Challenges Holding You Back?

In today's marketing environment, decisions can’t be based simply on emotion or gut feel. They need to be backed by data. Anil Kaul, Co-Founder and CEO at Absolutdata shares insights into how CMOs are transitioning their teams into data driven marketing organizations

Creating a connection between brand and customer has been a key marketing goal for eons. The marketing industry itself, though, is in the midst of profound and rapid changes. Consumers want to feel an emotional pull towards a brand, but what they expect along with that — and what inspires and nurtures its growth — is complicated. There's little room for error, so many CMOs are turning to Big Data to accurately measure audience reach.

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How much of Big Data is hype? It's true that the marketing industry has been majorly affected by Big Data. Its benefits have been impactful and quick to be felt; the outcomes in reaching niche audiences and creating personalized campaigns are very noticeable. So yes, there have been marked changes and improvements in marketing due to Big Data adoption.

However, Big Data is not a “magic bullet”.  For one thing, CMOs are not data scientists and engineers; advanced technologies like those that power Big Data have traditionally been well outside a marketer’s wheelhouse. Although Big Data usage is becoming increasingly common, many CMOs feel underprepared. They sense the huge opportunity, but they're not sure that they can take on yet another technological advancement. 

Even if a CMO is not 100% confident in their ability to utilize Big Data, they can make the transition. In particular, three items are critical to a successful adoption: 1) Identifying business problems; 2) Analyzing the significance of data; and 3) Creating an analytics team.

Identifying the Business Problems Analytics Will Solve

In today's marketing environment, decisions cannot be based simply on emotion or gut feel. They need to be backed by data. But all the data in the world will not help if the business problems you need to solve are not clearly defined. 

Why not get analytics started and then adapt it to fix problems as they arise? If Big Data doesn't closely fit your organization's needs, there's a very real risk that you'll be reaping hype, waste, and disappointment rather than results. Plus, the experience will make adopting analytics later — when it's finally judged as useful — much harder. Marketers who map out their direction and outcomes fare better than those who jump in unprepared. Think of this planning phase as a safety net; tying initiatives to measurable goals delivers a high ROI on marketing spend.

Analyzing Data's Significance and Relevance

Once the business problems are defined, another series of decisions await. By its very nature, Big Data produces reams of information. Part of the CMO's job is to decide what is relevant. Which statistics have the strongest bearing on the tasks at hand? Remember, quality is always better than quantity when you're dealing with Big Data metrics. 

Another aspect of effective human data analysis is making sure that the right teams are getting the right information. Social media managers need different insights than PPC marketers; stakeholders need analytical information and occasionally some education about how the data is driving these results. 

As humans, we easily get distracted by facts that are interesting but not relevant. It's a CMO’s job to act as the gatekeeper of all this input, making sure that the most significant information is reaching those who need it. There's a fine line between too much data and too little. Stray too far in either direction and the entire marketing department will be impaired in some way. At the very least, their ability to capitalize on Big Data's valuable insights will be hindered.

Creating a Team of Analytics and Marketing Experts

Finally, let's circle back and talk about the technological aspect of Big Data. As a marketer, your skill set lies in a different area than a data scientist’s. No one is suggesting that you do a deep dive into statistics and machine learning and reinvent yourself. Instead, get people who are really brilliant and passionate about analytics on your team. To function at its best, an analytics team needs to have members who deeply understand their business and market as well as those who can manage the technical aspects of Big Data.

It may or may not be feasible to have an in-house analytics department. Some companies use an internal analytics team, while others outsource this to a Big Data firm. The analytics team can work with marketing as well as with other departments; in any scenario, the key to success is clear and frequent communication.

By using a smarter approach — deciding what to solve, sorting out the vital facts, and hiring experts to manage the technical side — the challenges of implementing Big Data become manageable. Once a data-driven approach to customer engagement is adopted, it becomes easier to connect with individual customers, holding their interest and building a stronger bond between the consumer and your brand.