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
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.unifrankfurt.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.
Search Engine Advertising
2
What is search engine advertising (SEA)?
Paid search results
Keyword
Rank
1
2
3
4
5
6
Organic search results
Search Engine Advertising
3
Decision making after cooperation
Rules-based decision making
1.
If
then
keyword profit after acquisition costs > 10€
increase bid by 30%
Profit maximization
2.
If
then
rank > 5
increase bid by 20%
3.
If
keyword profit after acquisition costs < 0
& number of clicks > 100
& rank <= 3
decrease bid by 20%
then
Search Engine Advertising
4
PROSAD
(PRofit Optimizing Search Engine ADvertising)
Transactional
profit
Profit contribution
per conversion
Max!
Acquisition costs
per conversion
Search Engine Advertising
Number of
conversions
5
How does the bid influence transactional profit?
1
Profit contribution
per conversion
Acquisition costs
per conversion
Number of
conversions
Transactional
profit
2
Conversion rate
Clickthrough rate
Number of searches
4
Rank
3
Bid
Quality Score
Decision variable
Search Engine Advertising
6
Optimal bid
1
PCk
Profit contribution per
conversion
2
Conversion rate
CRk
3

'
k
4

'
k
Optimal bid
Percentage increase in
prices per click
Percentage increase in
clickthrough rates
Bid = PCk  CRk 
*
k
Search Engine Advertising
k'
 +
'
k
'
k
.
7
Learnings from field experiment
LOWER BIDS
Profit improvement per keyword per year:
ROI for lower budget:
+33.12€
+21%
Profit improvement potential of PROSAD for
SoQuero and its clients:
Search Engine Advertising
2.7€ million
8
Summary
Rules-based decision making difficult
•
•
•
Number of rules grows quickly
Likelihood of contradicting bidding suggestions high
Choice of specific parameter values in rules difficult
Profit optimizing search engine advertising easily feasible
•
•
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
Results of field experiment support profit optimizing SEA
•
•
Reduction of SEA budget by 38%
Increase in ROI by 21 percentage points
Search Engine Advertising
9