The rise of the Big Data-driven CMO?

Data, which used to be a four-letter word reserved for IT, has suddenly become something that every CMO needs to think about, according to Kieran Kilmartin, marketing director (EMEA) for Pitney Bowes Software, who here offers some insights into how CMOs can refine their roles, teams and operations to become more data-centric.

In fact, there are now more than 4 million Gigabytes of information per internet user between the ages of 45 and 59, according to IDC. That's enough to fill up about 200,000 iPhones. Every year, those consumers create another 1.8 million gigabytes of data about their spending habits, their influencers, and their demographic information.

Gartner has estimated that, by 2017, this trend will drive CMOs to spend more on IT than the CIO. If you think about it, that should not come as a surprise. You can learn a lot from customers if each one of them is leaving the internet with enough data to fill a quarter of a million iPhones. But the question still keeping many CMOs up at night is "how"?

The humanisation of data
Marketing used to be about building a brand's reputation. Not that this isn't still true, but, historically, marketing involved creative campaigns that didn't have their successes or failures judged by cold, hard numbers. But that's not really true anymore.

As one CMO in the airline industry said recently: "The success of my role is far more about analytics and technology than it is about hanging out with my ad agency, coming up with great creative campaigns. We must increase campaign ROI."

Every CMO has to think about technology and data now. Over the past decade, data has become very, very human. Thanks to social media, analytics from websites and search, and more detailed statistics from online marketing campaigns, numbers aren't just sales anymore, they represent behaviours, decisions and impulses.

In short, with the right customer analytics software, marketers can actually record, in real-time, whether or not a marketing campaign is doing what they want it to do.

If a CMO can learn to couple this human data with traditional market data such as demographics and credit rating, a whole new world of personalised purchases opens up. Matching a customer's preferences, location, and behaviours with his or her demographics won't just help identify the exact product package they want, it will help to predict what they're looking for in the future.

Categorising this new set of data
Automated analytics software can help companies find exactly the customer they want to pinpoint for certain campaigns, because they can identify exactly where the customer is in the buying cycle: What's going to encourage them to take the next step? What has worked in the past? What's going to work in the future?

To segment all of this appropriately, CMOs should start thinking about big data in three distinct stages: the past view, the present view, and the predictive view.

1. The Past View represents the path that the customer has taken to get to his or her current place in the buying cycle. What have they bought in the past? How have they interacted with the business before?
    
2. The Present View represents the context with which the customer sees the business currently. What do they want? How do they feel? What channel are they using?
    
3. The Predictive View is the trajectory of the customer based on how they got there (past view) and what they're doing now (present view). CMOs can use this value to forecast for future behaviours, from upsell opportunities to churn risk.

The brand is no longer a static thing, either, because it will continually change depending on a customer's experience. That experience will build the brand and create the interaction points within this cycle of "3 P's". The next step lies in measuring it.

Take responsibility for big data
For a lot of companies, all of this data is already there - but it's stored in a lot of different places. Maybe customers who have bought a specific product are in a CRM. Maybe there's a big list of leads in an obscure folder on a different server.

If that data can be integrated and leveraged to reveal deep insights into customer behaviour and trends, it only makes sense for the CMO to take the reins. CMOs already know how important this is- recent research by Forrester and Heidrick and Struggles showed that "technology savviness" has risen to the top of the skills list that CMOs are hoping to improve.

This research shows a growing awareness of just how critical a multi-channel approach is for marketing. With so many different interactions happening in real-time across so many different channels, big data analytics is becoming integral to building a brand and building customer relationships that last.

To get started, here are the questions all CMOs have to ask about data:

• What's the most valuable data to my campaign? How can I capture and use it?
   
• Is this data coming from a reliable source? Is there more data in a different part of the business?
   
• What's the best way to reach out to customers with personal messages that are truly relevant?
   
• How can I best use data to segment prospects and customers?
   
• How do I empower salespeople with this information?
   
• How do I make sure everything stays coordinated and the customer experience is consistent, whatever the channel?

No matter what they're buying or what website they're visiting, customers want a personalised experience these days. They will seek out exactly what they're looking for and, if it doesn't meet their expectations, they'll immediately pass it over and move onto the next thing.

Thanks to big data, CMOs can actually see how these decisions happen and which customers are which. Rather than 'Chief Marketing Officer', many may actually become the 'Chief Data Officer' - turning numbers and data into actionable insights and powerful, customised marketing programmes.