6. Film Ratings
As a company that thrives when people consume more content,
Netflix routinely serves up personalized recommendations to
customers based on their feedback on films they’ve already
viewed. This is a prediction challenge; Netflix must venture an
informed guess that, if someone gave a certain rating to movie
a, they will rate movie b similarly.
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7. Customer attrition.
Now consider the bane of wireless service providers: the churn
in their customer bases. If predictive analytics drawing on big
data could accurately point to who in particular was about to
jump ship, direct marketing dollars could be efficiently deployed
to intervene, perhaps by offering those wavering customers
new benefits or discounts.
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8. Web advertising response.
The challenge of predicting the click-thru rate (CTR%) of an
online ad — clearly a valuable thing to get right, given the sums
changing hands in that business. We should exclude search
advertising, where the ad is always related to user intent, and
focus on the rates for display ads.
The average CTR% for display ads has been reported as low as
0.1-0.2%. Behavioral and targeted advertising have been able to
improve on that significantly, with researchers reporting up to
seven-fold improvements. today’s best targeted advertising is
ignored 98.6% of the time.
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9. 9
Activities that are governed by physics
and precise laws like the force of gravity
can be predicted to an amazing degree.
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But when an activity is driven by
consumers’ whims, no amount of
ingenuity can produce the ability to know
what will happen.
Predictive analytics can figure out how to
land on Mars, but not who will buy a Mars
bar.
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Big data analytics can improve predictions,
but the biggest effects of big data will be in
creating wholly new areas.