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Five Ways Marketers (CMOs, Too) Can Master Data

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"Big Data" is more than a buzzword for CMOs these days. Understanding and using data is now an integral part of a CMO's job.

Trying to turn data's perceived value into something real within the walls of their organizations is the mission of the day for senior marketers. And we're far from achieving that goal. A study by the CMO Council found that 61% of CMOs still have work to do to understand Big Data.

But the journey is certainly worthwhile. Marketers have been under pressure for years to turn their art into more of a science. And an evidence-based approach to marketing analytics—including measuring campaign effectiveness, customer experience, customer lifetime value, and drivers of acquisition and retention—is more than possible, freeing important decisions from the vagaries of human bias.

Through our work with marketing organizations, we've learned some important lessons marketers can apply as they adopt a more data-centric approach to their craft.

1. Decide what information is necessary for success


Amid a blizzard of analytics software being sold to CMOs—many with outstanding marketing and graphical interface capabilities, making them all the more attractive—lies the truth of the data.

Corporate executives, however, often hold preconceived notions of precisely what they want to extract from the data. And, unfortunately, the real world isn't that convenient. Key data that the organization thought it had... isn't there—it's in error or not structured or integrated properly.

A bigger issue is whether the data proves a statistical relationship at all—and, if so, whether it's an impactful one.

CMOs and others taking leadership on analytics projects should consider ignoring issues of process, technology investment, and what the final end state will be until they've had a serious look at the numbers and declared what is possible. Consider it an R&D project, but a qualified feasibility analysis needs to be conducted to determine the five most impactful data relationships available to the company today. Envisioning future possibilities through additional data acquisition and technology is helpful as well.

Taking a deep look at the means before any decisions are made about the ends will save a world of budget and heartbreak when the numbers tell something different than was hoped for.

2. Get organized and educated

Nearly every organization overestimates the capabilities of its current state of data. A company's data often hasn't been designed to communicate with others, which doesn't help when it's time to interpret insights.

As a result, all CMOs must prioritize data before applying it to their organizations' holistic marketing views. But that can't be done by technology alone. It requires time, people, money, and extensive effort. Yet, it's an essential step to ensure your data guides your campaign in the right direction.

So get to know the world of data management. Familiarize yourself with database administrators, data managers, or data infrastructure engineers. They will help you gather, acquire, clean, organize, integrate, and structure the data for analysis. Some estimate that this function takes 90% of the time required for results from data science. It's not as expensive talent-wise, but it's an essential first step that will require a good deal of foundational patience.

3. Practice consistency

You must keep applying data to enjoy its long-term benefits. Continual use of evidence-based knowledge strengthens your marketing strategies and will likely provide better results.

Continue to add events to your database so patterns become more evident, allowing you to make smarter real-time decisions to predict customer behavior, achieve higher promotion response rates, and price optimally.

In the future, this method will allow you to make the jump from basic to predictive analytics. The latter uses data science to make statistically sound calculations for what lies ahead and takes your marketing tactics to the next level.

4. Bring in outside help

With an abundance of data comes the responsibility of knowing how to accurately interpret it. For CMOs, that isn't a skill that always comes at the ready.

For increased accuracy, you need to buy, borrow, or rent data scientists. They can be hard to find, because most have been snatched up by data-native businesses, but their expertise is paramount.

Estimates of the number of available and qualified data scientists vary, but by quantitative and qualitative measures a definite shortfall exists. What's more, to get the best, you must have a supportive culture that allows scientists to create real impact and multiple quick-building, impactful projects for them to work on; otherwise, they'll leave you.

What would be left, somewhat harshly speaking, are the less-qualified folks who employ point-and-click software that's abundantly available but has questionable accuracy in finding truly causal relationships.

Essentially, you have to find someone who understands the math behind the automated calculations.

If it doesn't sound as if your organization is ready to attract and retain data scientists, there's an alternative solution: You can "rent" them.

5. Find a good fit with the right expertise

Data scientists occupy a spectrum of talent, ranging from good to great to elite. Because the rewards are so high for the elite data scientists and there is no certification for their expertise, people lacking appropriate experience may claim to be data scientists.

To use data effectively in your marketing strategies, find the best expertise available. Filter through your options to make sure your data scientist has the skills and tools to help you reach your goals.

Practically speaking, it may be helpful to hire a data scientist "consigliere" who can give you honest counsel on how to build your data team and likely has a good network of friends and colleagues to bring on board. Heck, his or her LinkedIn and Twitter networks alone may be worth the fee.

* * *

Whether you're employing data for descriptive purposes or you're ready to get on the cutting edge of the truly predictive "Big Data" opportunities that are possible, it is never too late. In fact, by following these best-practices, you will quickly move to the front of the pack.


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John P. Kelly leads predictive analytics at Berkeley Research Group, which works with marketing, sales, and operations leadership to harness the power of data science.

LinkedIn: John P. Kelly

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