This needs to be understood. Data mining has reached the point that
it can predict your behavior with serious accuracy. The unanswered
question here is just how much investment in time and money is needed
to provide this data?
I ask that because the political cauldron has seen a no holds barred
dash to the finish line at what is becoming catastrophic expense.
Most of that money is outright wasted although no contractor will
ever let on. In fact it has become an advertising war contributing
little to understanding the candidates.
Obama is a great example. During the first campaign, he was kept in
a bubble to hide many controversial aspects of his history. These
have never been examined and will never be. We were sold a feel good
story that certainly had its high points. Yet that is true for any
candidate who gets up there.
What we now have is gladiatorial rather than magisterial. It is a
money fueled race to the bottom that is barbaric and only works in
the minds of the advertisers who sell this nonsense.
Recall that the popular vote itself shifts no more than zero to three
points in either direction around the mean of fifty fifty. What this
means is that all this noise and effort is targeted at one to two
folks out of every hundred who will vote. The rest are voting the
same old way.
This approach then allows the campaign to identify the must sees. A
mutual program to advertise and conduct civil discourse would be
better for all under a tight set of rules and would get the same
result while allow the focus to go on the folks who do need
persuasion.
Team Obama Mastered
the Science of Mass Persuasion - And Won
22 January 2013
by Eric Siegel
Eric Siegel opens a
window on the new marketing technologies of persuasion modeling and
predictive analytics successfully used in the Obama re-election
campaign.
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 had bet the story
would not break within politics until the years 2016 or 2020.
Surprise: There's no
wait! Since 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 may be 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 toward
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 and labeled: "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
my forthcoming book.
To make this
campaigning possible, team Obama first collected data on how 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 just the same
(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. 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 health care. 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?
For more information
about predictive analytics, see the Predictive Analytics Guide;
for more on persuasion modeling see this whitepaper.
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