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What to Ask Yourself When Using Big Data to Understand Online Audiences

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The only way any of us can understand online audiences is through data. Companies like Nielsen and comScore have traditionally gathered that data by tracking a sample over time. But increasingly, what we know about audiences comes to us through servers.

Servers collect vast amounts of information on what consumers are doing online every second of every day. To some businesses, this Big Data speaks for itself and offers a crystal-clear lens with which to see and manage audiences. But much of the buzz surrounding Big Data is hype.

To make effective use of Big Data, marketers should be clear about its strengths and weaknesses. Here are five questions to keep in mind when you're seeing the marketplace through Big Data.

Census or Sample?

Big Data is often thought of as a census. If that were true, it would be great. You wouldn't have to draw a sample and carefully "weight" individual responses to reflect the population. But frequently, Big Data really means a big sample.


For instance, some companies claim that gathering data from digital set-top boxes (STBs) can create a census of the TV audience. In practice, however, STB ratings are based on samples that are cobbled together and extensively weighted to resemble the total TV audience. Those samples are large enough to offer a lot more granularity than traditional methods, but they're not close to a census.

Moreover, most big databases are adjusted in some way. You may think that the topics trending on Twitter reflect a simple headcount, but trending metrics are tweaked in ways that aren't widely reported. Providers of "currency" measures are often audited, so it's easier to know the recipe behind the numbers. But how many of the newer metrics are cooked up is a mystery. If you use them, you should assume you're not getting an unadulterated look at the audience; you're probably wearing corrective lenses.

Preference or Behavior?

Social media platforms can capture comments that reflect people's likes and dislikes, but most Big Data measures behaviors (e.g., views, downloads, shares, purchases, etc.). It's tempting to interpret behaviors as an expression of preferences. In fact, economists use choices as a measure of "revealed preferences." But people do things for all kinds of reasons.

Ask yourself, "Do people view something because they like it or because they just stumbled into it? Do they share something because they approve, disapprove, or want to build their personal brand?" Even the meaning of "likes" can be a puzzle. Is it really about liking or social affirmation or just plain fraud?

Big Data is often a by-product of using digital platforms to deliver media or provide services. Its great appeal is that Big Data is cheap and abundant, and it seems capable of providing valuable new insights. But there's also a danger in trying to wring too much out of data that was not designed to measure motives or states of mind.

Local or Global?

Any website can produce a treasure trove of data. Its servers can generally see how many visitors the site has, what visitors view, how much time they spend on each item, and what actions they take. But this is local on-site activity. Seeing what visitors are doing the rest of the time is hard, and yet doing so is important.

For example, there's interest among publishers in using "attention minutes" as a measure of engagement and perhaps a kind of currency. But are visitors who spend time on a website loyalists who are otherwise hard to reach or are they just people who spend a lot of time on the Web? Without a more global source of data, it's hard to know those visitors' real value. If visitors are heavy Web users, they'll be easy pickings for programmatic buyers and maybe less valuable than their attention minutes suggest.

Insight or Currency?

Even if you can't necessarily see what website visitors are doing off-site, there are plenty of insights to be gained by what a server can see. One powerful tool is A/B testing. With A/B testing, websites can assess the power of different page designs to trigger consumer actions. But using Big Data for insights and using it for currencies are two different propositions. Changing a currency—even a modest, commonsense change—is as much about politics as data. Reaching an industry-wide consensus takes time. For example, shifting to "viewable impressions" took IAB well over a year to orchestrate. Those negotiated currencies can be less than optimal for any one player. So, marketers may have to juggle two sets of metrics: those that provide insights and those that facilitate buying —and they're not always the same.

Prediction or Self-Fulfilling Prophesy?

One of the most heralded powers of Big Data is its ability to predict everything from which products consumers should buy to which websites they'll find useful. But predictions in the social world are a tricky thing. If you're modeling a stable behavior and keeping the results to yourself, like forecasting TV ratings, your forecasts are unlikely to alter the final result. But feeding predictions back into the populations you're observing, such as recommending what "people like you" should buy, can change how people behave. These "self-fulfilling prophesies" alter the very reality they purport to predict. So Big Data is an active participant in shaping the online marketplace. In which case, Big Data is both a cause and an effect of consumer behavior.

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Big Data offers valuable new ways to see and shape audiences, and we should use it for all it's worth. But all data, big or small, has limitations. And the tools we can build from data will have biases and blind spots. The best way to use those tools is to know what they can and can't do for you.


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James Webster is a professor of Communication Studies at Northwestern University and the author of The Marketplace of Attention: How Audiences Take Shape in a Digital Age.

LinkedIn: James Webster

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