Slides that correspond to the paper: Skiera, Bernd About Nabout, Nadia (2013), "PROSAD: A Bidding Decision Support System for Profitable Search Engine Advertising", Marketing Science, Marketing Science, Vol. 32, Issue 2, 213-220 (also finalist of "The Gary L. Lilien ISMS-MSI Practice Prize")

1. Science-to-Practice Initiative
PROSAD: A Bidding Decision Support System for
PRofit Optimizing Search Engine ADvertising
Bernd Skiera, Nadia Abou Nabout
skiera@wiwi.uni-frankfurt.de
abounabout@wiwi.uni-frankfurt.de

2. Search Engine Advertising 2
Availability of video presentation and additional
exercises
Paper "PROSAD: A Bidding Decision Support System for PRofit
Optimizing Search Engine Advertising" was a finalist of "The Gary L.
Lilien ISMS-MSI Practice Prize.“ A video presentation and the
original PowerPoint slides of the presentation are available at
http://techtv.mit.edu/videos/18315-prosad.
Instructors can also contact the authors (skiera@skiera.de or
abounabout@wiwi.uni-frankfurt.de) for a larger deck of slides and an exercise
(including teaching note) that can be taught and that involves two small data sets
to further illustrate the decision support system.

3. Search Engine Advertising 3
Keyword
Organic search
results
2
3
4
5
6
1
Paid search
results
Rank
What is search engine advertising (SEA)?

4. Search Engine Advertising 4
Decision making after cooperation
1. If keyword profit after acquisition costs >
10€
then increase bid by 30%
Rules-based decision making
2. If rank > 5
then increase bid by 20%
3. If keyword profit after acquisition costs < 0
& number of clicks > 100
& rank <= 3
then decrease bid by 20%
Profit
maximization

5. Search Engine Advertising 5
PROSAD
(PRofit Optimizing Search Engine ADvertising)
Transactional
profit
Profit contribution
per conversion
Acquisition costs
per conversion
Number of
conversions
Max!

6. Search Engine Advertising 6
How does the bid influence transactional profit?
Transactional
profit
Profit contribution
per conversion
Acquisition costs
per conversion
Number of
conversions
Bid
Rank
Number of
searches
Quality
Score
Clickthrough rateConversion rate
1
2
3
4
Decision
variable

7. Search Engine Advertising 7
Percentage increase in
clickthrough rates
Percentage increase in
prices per click
Conversion rate
Profit contribution per
conversion
'
' '
* k
k k k
k k
Bid = PC CR .
+

 
 
Optimal bid
Optimal bid
1
2
3
4
kPC
kCR
'
k
'
k

8. Search Engine Advertising 8
Learnings from field experiment
ROI for lower budget: +21%
Profit improvement per keyword per year: +33.12€
LOWER BIDS
Profit improvement potential of PROSAD
for SoQuero and its clients:
2.7€ million

9. Search Engine Advertising 9
Summary
• Number of rules grows quickly
• Likelihood of contradicting bidding suggestions high
• Choice of specific parameter values in rules difficult
Rules-based decision making difficult
• Profit function equals number of conversions times profit per conversion after
acquisition costs
• Estimation of functional relations between
 rank and bid
 rank and clickthrough rate
Profit optimizing search engine advertising easily feasible
• Reduction of SEA budget by 38%
• Increase in ROI by 21 percentage points
Results of field experiment support profit optimizing SEA