What Is Big Data?

Big Data defined:  Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Quite simply, Big Data is the collection of all of the little things we do as humans.  For marketers, this means the small actions consumers take and the influences they encounter as they move through the traditional marketing funnel.  That marketing funnel in its most basic form is the journey a consumer takes – moving from awareness to interest to consideration to intent, evaluation, and purchase.

Big Data was once only available to select organizations with the resources to collect and analyze it.  Big Data is now accessible to everyone, thanks in large part to technological advancements.  These technological advancements have made collecting data from disparate online and offline sources, pinning that data to persistent universal IDs, and analyzing that data to better understand consumer behavior, easier than ever.

Why is Big Data Important?

Marketers are ever consumed with efficiently finding the right consumer, at the right time, and delivering the right message.  The old adage in advertising used to be “I know that 50% of my advertising is working … I just don’t know which 50%”.  Now, knowing which 50% isn’t good enough.  Marketers need to know individual level data, such as “Who is the right consumer?”, “When is the right time to reach them?”, and “Which message will best influence them to convert?”.

Up until recently, collecting and analyzing the data necessary to answer those questions has been difficult and expensive.  Now, thanks in large part to the ubiquity of Software As A Service providers specializing in data management and execution, it is easy to not only accurately collect greater amounts of consumer data from disparate sources (eg: social, mobile, display, email, direct mail, etc.), but also put that data to use.

These solutions have become so ubiquitous that even mid-sized companies can afford to collect and store vast amounts of consumer data, analyze that data to create more predictive consumer models, index those models against individual cross channel consumer identities to create audiences, execute those models in the form of targeted cross channel audiences, and then measure the results – both within and outside the click stream.

Does that sound complicated?  It is!  But now thanks to technology advances in advertising, almost any company can do it.  Where companies had to rely on third parties and data collectives to generate data, they’re now able to collect and digest consumer data economically, at scale.  The critical component has moved from data collection and storage to data analysis.

This isn’t a fully automated process.  It still requires teams of “smart people” to implement.  (Data Scientists have reached near celebrity status, commanding larger salaries and greater influence in the corporate structure.)  Though those teams are smaller than there were before, and they have greater resources to make more informed decisions.

What Does The Future Hold?

As the accessibility of Big Data (and the solutions that support them) continues to grow, we’ll see several things:

1. Brands will collect greater amounts of data, as devices become more inter-connected and data storage costs continue to drop.
2. Brands will demand more access to their data.   We’re seeing this now, as Consumer Product Goods (CPG) companies are demanding access to the supermarket shopping data of consumers who purchase their products using loyalty cards, and also access to person level measurement / attribution data from walled garden publishers like Facebook and Google.
3. As more first party data becomes available, certain third party data attributes will become more commoditized. Brands will realize the value of their consumer data in the open marketplace, and take steps to monetize it.  We’re already seeing several adtech companies launch platforms to easily enable Brands to monetize their data.
4. A continued focus on accurate Identity.  Pinning consumers to universal persistent IDs (at the individual and household level) across online and offline channels.  This goes hand-in-hand with an increased effort towards identifying and tracking consumers at all stage of the marketing funnel (aka purchase lifecycle).
5. Leveraging Big Data to reduce the cost of customer acquisition. This is the ultimate goal of marketers … sell more for less.  And as Big Data enables us to learn more about consumer behavior, we can expect the traditional marketing funnel to adapt as well.