Last October, a colleague and I speculated on how a special, powerful form of predictive analytics would revolutionize presidential campaigning—and, if successful, how it might be poorly received by the public thereafter. In our work, he and I focus more on financial, marketing, and online applications of this technology. But we bet the story would break within politics by 2016 or 2020.
Last October, a colleague and I speculated on how a special, powerful form of predictive analytics would revolutionize presidential campaigning—and, if successful, how it might be poorly received by the public thereafter. In our work, he and I focus more on financial, marketing, and online applications of this technology. But we bet the story would break within politics by 2016 or 2020.
Surprise: There’s no wait! After Obama’s win in November, we’ve learned they already did this. The president won reelection with the help of the science of mass persuasion, a very particular, advanced use of predictive analytics, which is technology that produces a prediction for each individual customer, patient, or voter.
This is the first story ever of a presidential campaign performing and proving the effectiveness of mass scientific persuasion.
The technology’s purpose is to predict for each individual, and act on each prediction. But you may be surprised to know what the Obama Campaign analytics team predicted. In this persuasion project, they did not predict:
- Who would vote Obama
- Who would vote Romney
- Who would turn out to vote at all
… and they didn’t even predict:
- Who was “undecided”
Instead, they predicted persuasion:
- Who would be convinced to vote Obama if (and only if) contacted
This is the new microcosmic battleground of political campaigns—significantly more refined than the ill-defined concept of “swing voter.”
Put another way, they predicted for which voters campaign contact would make a difference. Who is influenceable, susceptible to appeal? If a constituent were already destined to vote for Obama, contact would be a waste. If an individual was predicted as more likely swayed towards Obama by contact than not swayed at all, they were added to the “to-contact” list. Finally, to top it off, if the voter was predicted to be negatively influenced by a knock on the door—a backfired attempt to convince—he or she was removed from the campaign volunteers’ contact list: “Do not disturb!”
I interviewed in detail Rayid Ghani, Chief Data Scientist of Obama for America—who will be keynoting on this work at Predictive Analytics World in San Francisco (April 14-19) and Chicago (June 11-12)—for an article (January 21, 2013 in The Fiscal Times) and book chapter on this topic.
To make this possible, team Obama first collected data on how campaign contact (door knocks, calls, direct mail) faired across voters within swing states. Of course, such contact normally helps more than it hurts. But, since the number of volunteers to pound the pavements and dial phones is limited, targeting their efforts where it counts—where contact actually makes a difference—meant more Obama votes. The same army of Obama activists was suddenly much stronger, simply by issuing more intelligent command.
Therefore, they used the collected data not just to measure the overall effectiveness of campaigning, but to predict the persuadability of individual swing state constituents. Each person got a score, and the scores drove the army of volunteers’ every move.
Persuasion modeling (aka uplift modeling or net lift modeling) has been honed in recent years for use in marketing. It’s the same principle as for political campaigning, guiding calls and direct mail (although marketing more rarely employs door knocks)—but selling a product rather than a president.
I’ve extensively covered this technology, which is more advanced than “regular” predictive analytics. Normally, you predict human behavior like click, buy, lie, or die (the subtitle of my forthcoming book on the topic). In this case, you predict the ability to influence said behavior.
If consumer advocates consider mass marketing a form of manipulation, they may find in this work even more to complain about. Was the election Moneyballed? As mere mortals are we consumers, patients, and voters too susceptible to the invisible powers of advanced mathematics? Will privacy proponents whip out their favorite adjective-of-concern, creepy? Shouldn’t elections be about policies, not number-crunching?
No question, the power of persuasion prediction is poignant. Industries are salivating and pouncing.
Sometimes this kind of work truly helps the world. Less paper is consumed when direct mail is more focused, and consumers receive fewer “junk mail” items. Patients receive predictively improved healthcare. Police patrol more effectively by way of crime prediction. Fraud is similarly detected several times more effectively. Movie and music recommendations improve.
How can this power be harnessed without doing harm? And how is “harm” to be defined in this arena?