The business of advertising, Pro & Cons, cost and benefits. Creatives effectiveness

1. Data Management for
the Web
Giacomo Giorgianni

2. Outline:
 Briefing
 Monetization
 Auction Mechanism
 Types of online ads
 Approaches to online ads
 Case Study: Modelization of the effects of
different Creatives and Impressions history
 New Trends (?)
 Considerations

3. “QUITE” A BIG
BUSINESS
More than 36
BILLIONS $ in US
during in 2012!!
United States Only
Objectives:
•
Increase visibility of a brand (Brand ads)
•
•
Stimulate users to immediately buy some product
Collection of user’s data and behavior to deliver him more
appropriate ads
Main Actors: Advertiser, Publisher, User
Benefits for users:
•
•
Free usage of applications
(Too) Quick responses to their needs
Briefing

4. Briefing – Pro & Cons:
• Ubiquity
• Frauds
• Speed
• Fragmentation
• Low Cost
• Ad-blocking
• Measurability
• Banner Blindness
• Creative
• Privacy concerns
 Difficulty to
track users
• Customization
of target

5. Briefing – Some history
May 1978:
Gary Thuerk,
emailed
ARPANET's user,
DEC computer.
1993: First
clickable ad sold to
a Law firm by
Global Network
Navigator.
Have some time?
CHECK THIS OUT!!
HotWired made banner
ads mainstream
18 January 1994:
Large scale
(RELIGIOUS)
email born
SPAM.
1998: GoTo.com
the first search
advertising
keyword auction

6. Let’s talk about money:
Pay per click (PPC) 32% :
Payment based on number of
click received from ad.
Not good for brand awareness
Pay per action (PPA) 2%:
Ad is clicked and user performes
desidered conversion (purchase,
form fullfilling, … )
Pay per impression (PPI) 66%:
Based on number of times ad is
shown. Usually stock of 1000 (CPM)
Widely used in Display Advertising

7. Auction Mechanism:
 Bid for keywords or better position/ranking
 First-Price Auction:




English auctions: public offers
Sealed-bid auction: Single and secret offer
Winner pays the amount he bid (the highest)
Bid are lower than WTP of bidders  Want some profit
 Second-Price Auction:
 Highest offer wins, second offer is paid  REVENUE!!
 GSP (Generalized second-price): Ranking in slots
assignment based on bid+quality.
Widely used among Search engines

8. Main Types of ads:
 Email & Newsletter marketing:
An ad copy inside email message
Consent  Opt-in /Opt out
• Search advertising: Pop Under:
Pop – up &
Advertisements on resultsover the main
Small window pages
Based on queries. browser
Sponnsored
Search
• Display Ads:
Multimedia content appears on Web
pages.
4 main types of Display ads (Which
WE know very well!)
Email advertising

9. Interstitial
Next slide will be available in 7seconds.
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10. Frame ad
FLOATING ADS..
QUACK!!
AND SO ON…

11. Technological PoV: Approaches
Filtered:
Specification of general
Constraints (time, age..)
Untargeted:
Fixed ads displayed for
a scheduled time
period
Personalized:
- Ads exposed based on
user’s behavior (history, data…).
- Machine Learning and Web Mining
- CHALLENGING!!

12. Technological PoV: Challenges
Objective: Exploit users’ navigation history to
deliver better ads
General Problems:
Technical Problems:
• Preferences vary over
time
• Cold start
• Inaccuracy of
information
• Potential customer
vs Information seeker
• Appropriate learning technique
• Privacy constraints
• Boredom prevention

13. Study case:
 Facts:
 Individual who sees an ad occasionally treated as individual who sees it
repeatedly  different goodwill wrt the ad.
 Not all creatives have the same effect on individuals.
 Act: Mathematical model that consider:
• Importance of different ad
creatives along the campaign
• “Goodwill” advertising response model
• Effect of individual’s
ad impression history
on future exposures

14. Study case – The boring part:
Ad Stock:
A= Ad stock i = individual
t=time
α=decay
E = Effect of all creatives
AD= Effect of the
Whole Campaign
Wearout
Restoration
C= Effect of the creative j
R= Restoration Rate
ρ= restoration param.
τ= time from last exposure

15. Study case – The boring part:
Data Likelihoods
3 related processes (zero-inflated):
1) mit: Impressions arrival; Poisson
2) vit: Visits; Poisson distribution
3) sit: Conversions; Binomial
3 parameters in the likelihoods:
1) λ: Impression rate parameter
2) μ: Visit rate parameter
3) p: probability of conversion after
visit
NB: (1-r): take account of 0-inflation.
Modelization of visits and conversion
parameters as functions of Ad stock.
Xt: Vector of variables  time varying
Fixed effects
γ= vectors of coefficient
Offline advertising effect
1
2
3
Effect advertising on behaviour

16. Study case – Model test:
CONTEXT:
On Automobile Brand
10 weeks in Summer 2009
5809 individuals randomly selected
Data Observed (powered by Organic):
• N_Impression per creative
• N_session with at least one visit
• N_session with conversion
15 different creatives
Benchmark with 4 Models in the
Observation Period:
1. No Ads Effect
2. Campaign Ad Effect
3. Creative-Specific Ads Effect
4. Full Model

17. Study case – Results:
Indicator:
MAPE (Mean Absolute Percentage Error)
Low MAPE  Real behaviour
with less error
Ad Effect over time
Advertising Impression Effect
Model fit comparisons

18. Considerations:
PROBLEMs:
 Wear-in  “Cold Start”
 No Example Reported
 Theoretical model  Practical results
SUGGESTIONs:
 Scheduled ad-exposure
 Interaction among website and ad creative

19. QUESTION BREAK!
PLEASE, BE GENTLE
AND NOW, YOUR CHOICE
YOU’VE THE POSSIBILITY TO SHUT ME UP
(FINALLY!!), OR…

20. New trends:
CORRELATION
Video Advertising
Mobile Advertising
Social Media Marketing

21. Social media marketing:
Scope: Create brand awareness trough social web
Viral concept
(good or not)
eWoM
Earned media rather
Than paid media
COBRAs
(Ex. New Converse
sneakers to
Facebook)
Special deals with
Tweets or Repost
Usage of social networks
Interaction with
smartphones (QR code)
Direct interaction among
Companies and users

22. Mobile advertising:
In-App Advertising
Sms Advertising
Mms Advertising
Form of advertising via mobile phones
Ubiquity
CPI (Cost
per install)
Smartphone
Technologies
Battery concerns
Incent for Users
Interaction with
Classic Advertising
(Bar code/ QR code)
Video: most effective
mobile advertising

23. Video advertising:
•
Video content in a MPU
•
Streaming Events
•
Cut TV Spot before Streaming
Felix Baumgartners’
Jump: Big Adventure
 Around 10M users
watched streaming 
Big Visibility for
RedBull

24. Considerations:
• Is “AdBlock” a good thing?.

25. References:
 Statistical Techniques for Online Personalized Advertising: A
Survey (Maad Shatnawi and Nader Mohamed)
 Online Display Advertising: Modeling the Effects of Multiple
Creatives and Individual Impression Histories (Michael Braun,
Wendy W. Moe)
 Video + Tablets: The Mobile Catalyst for E-Commerce
(Forbes.com)
 IAB internet advertising revenue report
 Web Information Retrieval (S. Ceri, A. Bozzon, M. Brambilla, E.
Della Valle, P. Fraternali, S. Quarteroni)