Understanding and Leveraging Big Data for Audience Insights

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data?

Data mining, big data
Creative Commons license. | Credit: Flickr by KamiPhuc

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data? Some data management platforms (DMPs) have over 900 million consumer profiles globally with 10,0000 different data points associated with it. To get even close to that amount of data from consumer surveys, you’d have to run about half a billion surveys.

So while big data has been most impactful to programmatic advertising and media, it also provides a lot of opportunity for other marketing efforts and market research. Due to the insight it provides into consumer behavior, as budgets continue to shrink and the speed of decision making continues to increase, big data is necessary. 

The 4 V’s of Big Data

Today, big data is bigger than ever with more people engaging and utilizing tools that offer big data integration. However, incorporating big data is a marathon, not a sprint and companies have to take the right steps before making the leap. If you’re not already using big data, before getting started, you’ll want to familiarize yourself with the 4 V’s of big data:

  • Volume: the amount of data available
  • Variety: the different types of data
  • Velocity: how frequent, real-time, or up to date the data is
  • Veracity: how accurate and applicable the data is

The biggest challenge when it comes to data is the “veracity” of it. Because there is so much and such a variety of data, it can be difficult to assess its accuracy and application to your business. Discerning the signal from the noise is where most innovation teams will spend their time interpreting the data. In other words, veracity helps to filter through what is important and what is not, and in the end, it generates a deeper understanding of data and how to contextualize it in order to take action.

Data Veracity: The Most Important “V”

Data veracity, in general, is how accurate or truthful a data set may be. In the context of big data, however, it takes on a bit more meaning. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is. Removing things like bias, abnormalities or inconsistencies, duplication, and volatility are just a few aspects that factor into improving the accuracy of big data.

Unfortunately, sometimes volatility isn’t within our control. The volatility, sometimes referred to as another “V” of big data, is the rate of change and lifetime of the data. An example of highly volatile data includes social media data, where sentiments and trending topics change quickly and often. Less volatile data would look something more like weather trends that change less frequently and are easier to predict and track.

The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs and the output is pertinent to objectives. Interpreting big data in the right way ensures results are relevant and actionable. Further, access to big data means you could spend months sorting through information without focus and without a method of identifying what data points are relevant. As a result, the velocity of data and agile methods come into play here — big data should be analyzed in a timely manner, as is difficult, otherwise the insights would fail to be useful.

Big data is highly complex, and as a result, the means for understanding and interpreting it are still being fully conceptualized. While many think machine learning will have a large use for big data analysis, statistical methods are still needed in order to ensure data quality and practical application of big data for better marketing activation. For example, you wouldn’t download an industry report off the internet and use it to take action. Instead you’d likely validate it or use it to inform additional research before formulating your own findings. Big data is no different; you cannot take big data as it is without validating or explaining it. But unlike most market research practices, big data does not have a strong foundation with statistics—luckily, integrating it with survey data can help.

Integrating Big Data With Survey Data for Market Insights

While big data can answer when, where, and what, it can’t answer why. Integrating primary research, particularly with an agile methodology that can keep up with the velocity of big data, can help to analyze and connect the dots — easier said than done.

The obvious benefits of using big data in marketing includes gaining a better understanding of people, content, and media. By combining big data with survey data, you can identify a market opportunity, understand your target audience, and incorporate findings into your messaging and creative execution. Infusing survey research with big data also means the volume of questions that need to be asked are reduced as big data provides more answers. So our understanding of consumer behavior is going to grow exponentially over time as we bring these two worlds together. The ability to incorporate big data to use fewer questions can also deliver more speed and value.

The result is unique audience intelligence. The benefits of this approach mean removing the guesswork during activation, letting the audience identify the opportunity for you, creating more effective messaging, and ultimately increasing value on ad purchases. There are certainly challenges to infusing survey research with big data before organizations can reap the benefits. Since DMP’s were originally meant for advertising, they haven’t been made research-grade, so oftentimes there are errors or inconsistencies in the data related to an audience. However, there are solutions out there — and more to come for sure — that will be able to overcome the challenges and provide an accurate depiction of audience insights through big data.

What Does a Data Marketer Look Like?

The currency of nearly all marketing today is data. Ten years ago, we might have said much the same of digital marketing, and all the email, display, social, search, and mobile that’s came forward from it.

The currency of nearly all marketing today is data.

Ten years ago, we might have said much the same of digital marketing, and all the email, display, social, search, and mobile that’s came forward from it.

Twenty years ago, we could have said the same of database marketing and customer relationship management.

And wind back—measurability and accountability, the hallmarks of direct marketing—always have relied on data. We may have called it lists back in the day—but data are what lists have become. The inherent value of data is to know the shared attributes among the data elements and to use that knowledge.

Without a doubt, the “marketing of data” has evolved and transformed as much as marketing itself. Every day in our world, it’s not enough to have contact details on people, or any number of the hundreds of demographic, psychographic, contextual, social and behavioral overlays that may be available, we also need analytics power.

Recent research from The Winterberry Group underscores this point: data is now an $11 billion business in America, and that includes analytics services revenue. I recall an unofficial guestimate of a $2 billion data market back in the early 1990s, when that meant a North American directory of 30,000 plus response and compiled lists available for rental and exchanges.

Next month, the Data Innovators Group will host its annual Data Innovator of the Year Award dinner in New York. This year’s honoree is Auren Hoffman, CEO of LiveRamp (now owned by Acxiom), who says his mission “to connect data to every marketing application.” And so it shall be… Soon.

But who is going to all make it work? Let’s welcome the data marketer and the data scientists and strategists they employ.

Still, too many brands keep customer data in siloes. And while responsibly using offline data with online data is fast coming down the pike, marketing organizations need people in place who can help clients navigate the brave new world of data management platforms, data quality strategies, programmatic media exchanges, big data and small data, and all the algorithms that drive this important “stuff” often in real time. A list sale exists largely no more. Instead data is a pathway to opportunity, a challenge overcome, by way of a data-to-insights-to-strategy recommendation, and a discipline for testing and data quality that leads brands (and their agencies and data marketer partners) to succeed.

It’s more difficult than ever to be a successful data marketer, but our field is producing the partners that businesses, brands and chief marketing officers need. Now if we could just go find a few.

Thank you to the Hudson Valley Direct Marketing Association for enabling my participation at its recent “Meet the Masters” event. Ryan Lake (Lake Group Media), Mark Rickard (Rickard Squared) and Rob Sanchez (Merit Direct) are three CEOs of data marketing organizations who have a few suggestions on where we can all go to look.