SlideShare a Scribd company logo
{
Big Data Hype
(and Reality)
‘The randomness inherent in
human behavior is the limiting
factor to consumer modeling
success.’
Three areas where better
prediction of consumer
behavior would clearly be
valuable are:
1)Film ratings
{
Netflix Competition
the error of Netflix’s own algorithm was about
0.95-meaning that its predictions tended to be off
by almost a full “star.”
After a 3 year gap and million dollar prize, the
average prediction of how a viewer would rate a
film improved by less than 0.1 star.
 When an activity is driven by
consumers’ whims, no amount of
ingenuity can produce the ability to
know what will happen.
2)Customer attrition
Analysts measure how accurate
the list of potential churners is
by using a measure called “lift.”
 challenge of predicting the
click-thru rate (CTR%) of an
online ad — clearly a
valuable thing to get right.
We should exclude search
advertising, where the ad is
always related to user intent,
and focus on the rates for
display ads.
3)Web advertising response
meaning that
today’s best
targeted
advertising is
ignored 98.6% of
the time.
 average CTR% for display ads has been reported as
low as 0.1-0.2%. that a seven-fold improvement from
0.2% amounts to 1.4%
Big data will see its biggest and
most important application in the
realm of artificial intelligence.
We NEED TO FOCUS
ON:
Predictive
analytics can
figure out how to
land on Mars, but
not who will buy a
Mars bar.
{
Marginal gains can perhaps
be made thanks to big data,
but breakthroughs will be
elusive as long as human
behavior remains
inconsistent, impulsive,
dynamic, and subtle.
Fun Fact!
{
So you should expect big data to have big impact.
And you can bet that it will help machines
interact more usefully with our unstructured,
changing, and sometimes downright confused
human ways. But if you’re counting on it to make
people much more predictable, you’re expecting
too much.
THANK YOU

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Big data hype

  • 2. ‘The randomness inherent in human behavior is the limiting factor to consumer modeling success.’
  • 3. Three areas where better prediction of consumer behavior would clearly be valuable are:
  • 6. the error of Netflix’s own algorithm was about 0.95-meaning that its predictions tended to be off by almost a full “star.” After a 3 year gap and million dollar prize, the average prediction of how a viewer would rate a film improved by less than 0.1 star.
  • 7.  When an activity is driven by consumers’ whims, no amount of ingenuity can produce the ability to know what will happen. 2)Customer attrition
  • 8. Analysts measure how accurate the list of potential churners is by using a measure called “lift.”
  • 9.  challenge of predicting the click-thru rate (CTR%) of an online ad — clearly a valuable thing to get right. We should exclude search advertising, where the ad is always related to user intent, and focus on the rates for display ads. 3)Web advertising response
  • 10. meaning that today’s best targeted advertising is ignored 98.6% of the time.  average CTR% for display ads has been reported as low as 0.1-0.2%. that a seven-fold improvement from 0.2% amounts to 1.4%
  • 11. Big data will see its biggest and most important application in the realm of artificial intelligence. We NEED TO FOCUS ON:
  • 12. Predictive analytics can figure out how to land on Mars, but not who will buy a Mars bar.
  • 13. { Marginal gains can perhaps be made thanks to big data, but breakthroughs will be elusive as long as human behavior remains inconsistent, impulsive, dynamic, and subtle. Fun Fact!
  • 14. { So you should expect big data to have big impact. And you can bet that it will help machines interact more usefully with our unstructured, changing, and sometimes downright confused human ways. But if you’re counting on it to make people much more predictable, you’re expecting too much.