Big Data and Big Goals

This week, in the United States, Barack Obama was reelected as President. It was a close race that really came down to key swing states.

But this post isn’t about politics. It’s not about the good, bad, or ugly of the recent election season and its outcome.  It’s not about who won or lost and why we should love them or hate them.  Rather, it’s a reaction to Time Magazine’s article on the role that big data played in the President’s win. The story was featured on Anderson Cooper’s CNN news magazine last night.

The Time Magazine piece details the various tactics that the reelection team used to target key voters. That’s not the point of this post either, although I find it utterly fascinating.

This post is the product of a conversation I had about the article.  The individual with whom I was conversing expressed dismay that rather than focusing on the issues, the Obama team focused on using and manipulating data (and the voters) to their advantage.  It’s true – there wasn’t much focus on the issues this year (good, bad, or indifferent – remember, this isn’t about politics).

My reply was that communicating the issues wasn’t the goal. The data crunchers did exactly what they were supposed to do – they cranked out models that showed the Obama marketing team exactly who they should focus on, and what tactics they should use to reach them. All of their testing, and they did plenty, was focused on figuring out the best message for fundraising, recruiting volunteers, and getting people out to vote. They used big data to predict voter turnout – which was then used to plan the President’s final campaign visits.  The data was even utilized to decide the best media for ad placement – rather than using traditional optimization tactics, they were able to get laser-focused with ads right from the start.

The reason they did all of this? Because the goal was to get the President reelected.  And it worked.

But could the Obama team have used big data to focus on the issues, as my conversant lamented?  I believe they could have. If their marching orders were to get the word out to voters on key issues, they would undoubtedly have used the data in a different way.

For example, they could have found out who was likely to be concerned about health care, and tailored and tested email messages about the health care initiative to those individuals.  They could have communicated the President’s stance on gay marriage to gays, on women’s rights to women, on business issues to business leaders, etc.  And incentives could have been matched to those audiences – instead of dinner with Sarah Jessica Parker, maybe they would have offered a chance to attend a town hall to ask questions about economic reform.

What if the team didn’t have a clear goal? What if they were let loose to just crunch numbers and make recommendations?

Think about this for a minute.  I know plenty of PPC advertisers who say “we need to start a PPC campaign,” yet they have no idea what they want to get out of it. In fact, this is the most common reason that PPC engagements fail.

Nowhere is goal-setting more important than the application of big data. Data without goals is just worthless gibberish – not to mention a black hole of time- and money-sucking quicksand. But once goals are clearly established, the tactics become clear.

And in the end, big data is like politics. You can’t please everyone. But with clear goals in mind, you can please your boss.

Hot Off the Press! For more on the big data analytics lesson, check out this post from the Harvard Business Review.

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