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Science- to - Practice Initiative PROSAD: A Bidding Decision Support System for PR ofit O ptimizing S earch Engine

Science- to - Practice Initiative PROSAD: A Bidding Decision Support System for PR ofit O ptimizing S earch Engine AD vertising. Bernd Skiera, Nadia Abou Nabout skiera@wiwi.uni-frankfurt.de abounabout@wiwi.uni-frankfurt.de. Availability of video presentation and additional exercises.

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Science- to - Practice Initiative PROSAD: A Bidding Decision Support System for PR ofit O ptimizing S earch Engine

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  1. Science-to-Practice InitiativePROSAD: A BiddingDecision Support System forPRofit Optimizing Search Engine ADvertising • Bernd Skiera, Nadia Abou Nabout • skiera@wiwi.uni-frankfurt.de • abounabout@wiwi.uni-frankfurt.de

  2. Availability of videopresentation 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. What is search engine advertising (SEA)? Paid search results Rank Keyword 1 2 3 4 5 6 Organic search results

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

  5. PROSAD(PRofit Optimizing Search Engine ADvertising) Transactionalprofit • Max! Profit contribution per conversion Acquisition costsper conversion Number of conversions

  6. How does the bid influence transactional profit? 1 • Profit contribution per conversion • Acquisition costs per conversion • Number ofconversions • Transactionalprofit 2 • Number of searches • Conversion rate • Clickthrough rate 4 • Rank 3 • Bid • Quality Score Decision variable

  7. Optimal bid 1 • Profit contribution per conversion 2 • Conversion rate • Optimal bid 3 • Percentage increase in prices per click 4 • Percentage increase in clickthrough rates

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

  9. 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

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