Computational Social Science, Lecture 10: Online Experiments

Experimental Design
Sergei Vassilvitskii
Columbia University
Computational Social Science
April 5, 2013
Thursday, April 25, 13

Measurement
2
“Half the money I spend on advertising
is wasted; the trouble is, I don’t know
which half.”
- John Wanamaker

Measurement
3
“Half the money I spend on advertising
is wasted; the trouble is, I don’t know
which half.”
- John Wanamaker, 1875

Helping John:
4

Helping John:
Idea 1: Measure the ﬁnal effect:
– Track total store sales, compare to advertising budget
5

Idea 1:
Findings:
– Total sales typically higher after intense advertising
6

Idea 1:
Findings:
– Total sales typically higher after intense advertising
Problems:
– Stores advertise when people tend to spend
– Christmas shopping periods
– Travel during the summer
– Ski gear in winter, etc.
7

Correlation vs. Causation
8

Idea 1
Within Subject pre-test, post-test design.
9

Idea 2
“Measuring the online sales impact of an online ad or a
paid-search campaign -- in which a company pays to have
its link appear at the top of a page of search results -- is
straightforward:
We determine who has viewed the ad, then compare online
purchases made by those who have and those who have
not seen it."
10

Idea 2
“Measuring the online sales impact of an online ad or a
paid-search campaign -- in which a company pays to have
its link appear at the top of a page of search results -- is
straightforward:
We determine who has viewed the ad, then compare online
purchases made by those who have and those who have
not seen it."
– Magid Abraham, CEO, President & Co-Founder of ComScore, in HBR
article (2008)
11

Idea 2
Measure the difference between people who see ads and
who don’t.
12

Idea 2
who don’t.
Findings:
– People who see the ads are more likely to react to them
13

Idea 2
who don’t.
Findings:
– People who see the ads are more likely to react to them
Problems:
– Ads are ﬁnely targeted. These are exactly the people who are likely to
click!
– Don’t advertise cars in fashion magazines.
– Even more extreme online -- which ads are shown depends on the
propensity of the user to click on the ad.
14

Idea 3
Matching:
– Compare people in a group who saw an ad with people who are
similar, but didn’t see an ad, but are otherwise “the same.”
15

Idea 3
Matching:
Problems:
– Hard to deﬁne “the same.” Beware of lurking variables.
16

Ad Wear-out
17
What is the optimal number of times to show an ad?

Case Study: Ad Wear-out
Few:
– Don’t want user to be annoyed
– No need to waste money if ad
is ineffective
Many:
– Make sure the user sees it
– Reinforce the message
18
What is the optimal number of times to show an ad?

Observational Study
Look through the data:
– Find the users who saw the ad once
– Find the users who saw the ad many times
19

Observational Study
Look through the data:
– Find the users who saw the ad once
– Find the users who saw the ad many times
Measure Revenue for the two sets of users:
–
Conclusion: Limit the number of impressions
20

Correlations
Why did some users only see the ad once?
– They must use the web differently
– : Sign on once a week to check email
– : Are always online
21

Correlations
Why did some users only see the ad once?
– They must use the web differently
– : Sign on once a week to check email
– : Are always online
Correct conclusion:
– People who visit the homepage often are unlikely to click on ads
– Have not measured the effect of wear-out
22

Idea 3
Matching:
Problems:
23

Simpson’s Paradox
Kidney Stones [Real Data].
You have Kidney stones. There are two treatments A & B.
– Empirically, treatment A is effective 78% of time
– Empirically, treatment B is effective 83% of time
– Which one do you chose?
24

Simpson’s Paradox
Kidney Stones [Real Data].
You have Kidney stones. There are two treatments A & B.
Digging into the data you see:
If they are large:
– Treatment A is effective 73% of the time
– Treatment B is effective 69% of the time
If they are small:
25

Simpson’s Paradox
If they are large:
If they are small:
Overall:
26

Simpson’s Paradox Summary Stats
27
A B
Small 81/87 (93%) 234/270 (87%)
Large 192/263 (73%) 55/80 (69%)
Combined 273/350 (78%) 289/350 (83%)

Idea 3
Matching:
Problems:
– Simpson’s Paradox
28

Getting at Causation
Randomized, Controlled Experiments.
– Select a target population
– Randomly decide whom to show the ad
– Subjects cannot inﬂuence whether they are in the treatment or control
groups
29

Measuring Wear Out
30
Parallel Universe

Measuring Wear Out
31
Parallel Universe
Control Treatment
++

Measuring Wear Out
32
Parallel Universe
Control TreatmentControl Treatment
++

Creating Parallel Universes
When user ﬁrst arrives:
– Check browser cookie, assign to control or treatment group
– Control group: shown PSA
– Treatment group: shown ad
– Treatment the same on repeated visits
33

Creating Parallel Universes
When user ﬁrst arrives:
– Check browser cookie, assign to control or treatment group
– Control group: shown PSA
– Treatment group: shown ad
– Treatment the same on repeated visits
Advertising Effects:
– Positive !
– But smaller than reported through observational studies
34

Online Experiments
Advantages:
35

Online Experiments
Advantages:
– Can reach tens of millions of people!
• Can estimate very small effects. Lewis et al., "Here, There, and Everywhere:
Correlated Online Behaviors Can Lead to Overestimates of the Effects of
Advertising." (WWW 2011). Estimate effects of 0.01%!
36

Online Experiments
Advantages:
– Can be relatively cheap (Mechanical Turk)
37

Online Experiments
Advantages:
– Can be relatively cheap
– Can be recruit diverse subjects
• “20 students in a large Midwestern university.” Try to avoid subjects from WEIRD
societies (Western, Educated, Industrialized, Rich, and Democratic).
38

WEIRD People
Which line is longer?
– Henrich, Joseph; Heine, Steven J.; Norenzayan, Ara (2010) : The
weirdest people in the world?, Working Paper Series des Rates für
Sozialund Wirtschaftsdaten
39

WEIRD People
40

Online Experiments
Advantages:
• Can estimate very small effects.
– Access: subjects in other countries, geographically diverse
– Can be quick
41

Online Experiments
Advantages:
• Can estimate very small effects.
– Access: subjects in other countries, geographically diverse
– Can be quick
Challenges:
– Limited choice in range of treatments (no MRI studies)
– Do people behave differently offline?
42

External Validity
Major Challenge in all lab experiments:
– Virtual and physical labs
– Do ﬁndings hold outside the lab?
Enter:
– Natural Experiments
43

Natural Experiments
The experimental condition:
– Is not decided by the experimenter
– But is exogenous (subjects have no effect on the results)
44

Case Study: Ad-wear out
Back to Ad-wear out.
Natural Experiment:
– When there were two competing campaigns, the Yahoo! ad server
decided which campaign to show at random!
– This was by engineering design -- both campaigns got an equal share
of pageviews. (Less complex, easy to distribute than a round robin
system)
45
Few:
– Don’t want user to be annoyed
– No need to waste money if ad is
ineffective
Many:
– Make sure the user sees it
– Reinforce the message

Natural Experiment:
– When there were two competing campaigns, the Yahoo! ad server
decided which campaign to show at random!
– This was by engineering design -- both campaigns got an equal share
of pageviews. (Less complex, easy to distribute than a round robin
system)
Experiments:
– Compare behavior of people who saw the same total number of ads,
but different number of each campaign.
46

47
Yes:
– Some advertisements see a 5x drop in click-through rate after the
ﬁrst exposure
– These typically have very high click-through rates
No:
– Others see no decrease in click-through rate even after ten exposures
– Have lower, but steady click-through rates

Case Study 2: Yelp
Does a higher Yelp Rating lead to higher revenue?
How to do the experiment?
48

Case Study 2: Yelp
How to do the experiment?
– Observational -- no causality.
– Control -- deception.
– Natural?
49

Case Study 2: Yelp
Natural Experiment:
– Yelp rounds ratings to the nearest half star.
– 4.24 becomes 4 stars, 4.26 is 4.5 stars
50

Case Study 2: Yelp
Natural Experiment:
Data:
– Raw ratings from Yelp
– Restaurant revenue (from tax records)
51

Case Study 2: Yelp
Natural Experiment:
Data:
– Raw ratings from Yelp
– Restaurant revenue (from tax records)
– Finding: a one star increase leads to a 5-9% increase in revenue.
52

Case Study 3: Badges
How do Badges influence user behavior?
Specifically:
– The “epic” badge on stackoverflow.
– Awarded after hitting the maximum number of points (through posts,
responses, etc.) on 50 distinct days.
53

How do Badges influence user behavior?
Specifically:
– The “epic” badge on stackoverflow.
– Awarded after hitting the maximum number of points (through posts,
responses, etc.) on 50 distinct days.
Experimental Design:
– Within subject pre-post test (again)
– Look at user behavior before/after receiving badge
– Averaged over different user, different timings, (hopefully) all other
factors.
54

Results:
55

Overall
Experimental Design is hard!
– Be extra skeptical in your analyses. Lots of spurious correlations
Experiments:
– Natural and Controlled are best way to measure effects
Observational Data:
– Sometimes best you can do
– Can lead interesting descriptive insights
– But beware of correlations!
56

Computational Social Science, Lecture 10: Online Experiments

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Computational Social Science, Lecture 10: Online Experiments