Bernd Skiera, Nadia Abou Nabout 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 Thank you very much for using our material to teach search engine advertising. We hope that you and your students enjoy the class! If you have any further questions regarding the material, please do not hesitate to contact us via skiera@wiwi.uni-frankfurt.de or 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.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.

What is search engine advertising (SEA)? Paid search results Rank Keyword 1 2 3 4 5 Let me tell you what SEA is: Imagine you search for “cruise vacation” on Google. You will typically receive two types of results: Organic search results: These are driven by Google’s algorithm and ranked according to the relevance of the result to the search query. Paid search results: These slots are auctioned off by Google, so that advertisers need to bid for them. They are usually displayed at the top of the screen and its right hand side. Nowadays, you might even see ads being displayed at the bottom of the page (Google is often experimenting with these kinds of things). In contrast to the organic search results, a click on a paid search result is NOT free of charge. Note that unlike traditional (online) advertising (e.g. display advertising or TV advertising), advertisers don’t pay per thousand impressions (cost per mille (CPM)-pricing) but for clicks (cost per click (CPC)-pricing). Your bid will not only influence whether you are displayed at all, it will influence the rank that you will be assigned and it will determine your price (together with the other bids). So it is a very important decision in SEA that deserves special consideration by the advertiser. 6 Organic search results

Decision making after cooperation Profit maximization Rules-based decision making If keyword profit after acquisition costs > 10€ then increase bid by 30% If rank > 5 then increase bid by 20% If keyword profit after acquisition costs < 0 & number of clicks > 100 & rank <= 3 then decrease bid by 20% We thus developed PROSAD (PRofit Optimizing Search Engine ADvertising), which maximizes a single success measure, namely profit. No rules are needed anymore. PROSAD determines optimal bids fully automatically. However, campaign managers are still allowed to bring in their gut feeling by overruling PROSAD’s bidding suggestions.

PROSAD (PRofit Optimizing Search Engine ADvertising) Transactional profit Max! Profit contribution per conversion Acquisition costs per conversion Number of conversions This profit measure can be one that only covers profit gained from transactions (here transactional profit). However, some advertisers state that the impression of an ad has value in itself (branding profit). Advertiser who believe that there is some branding profit generated by the ad impression might include branding profit as well. For the sake of simplicity, we concentrate on transactional profit only, which is maximized by the PROSAD system. Transactional profit per conversion equals the profit contribution per conversion (i.e., the money that you earn from one conversion) minus the acquisition costs per conversion (frequently called cost per order (CPO) in the industry). Multiplied by the number of conversions, one receives transactional profit for all conversions generated by a specific keyword.

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 First of all, we will show you each factor that the optimal solution depends on and then we will put the optimal solution together and show you the closed-form solution derived from the optimization problem. The optimal solution depends on two quite obvious factors: Profit contribution per conversion (high profit contributions allow for higher bid -> see slide 31 in appendix) and Conversion rate (high conversion rates allow for higher bid -> see slide 32 in appendix) And two less obvious factors: Differences in prices per click across ranks (if these differences are high, better ranks are far more expensive and advertiser should bid lower -> see slide 33 in appendix) Differences in clickthrough rates across ranks (if these differences are high, better ranks receive far more clicks and conversions and advertiser should bid higher -> see slide 34 in appendix) 3 Bid Quality Score Decision variable

Optimal bid Profit contribution per conversion Conversion rate 1 Profit contribution per conversion 2 Conversion rate Optimal bid 3 Percentage increase in prices per click The optimal bid is thus positively influenced by the profit contribution, the conversion rate, and percentage increase in CTRs. However, the trade-off in the bidding decision problem comes from the fact that the percentage increase in prices decreases the optimal bid. 4 Percentage increase in clickthrough rates

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 The results of the field test show that PROSAD suggested to reduce bids by on average 31.58%. As a consequence, the rank decreased by almost one rank. The clicks and conversions also decreased substantially. As a result, we generated a profit before acquisition costs that was 25.41% lower with PROSAD. However, the acquisition costs decreased far more (by 38.40%), such that overall, we generated additional profit per keyword of 6.37€ over the preiod of 10 weeks. PROSAD’s ROI is about 21% (the client’s ROI was 0%). Using PROSAD one would thus generate a profit increment of 33.12€ for an average keyword. This seems to be a tiny number. However, SEA is a penny business, but these numbers sum up to a huger profit improvement potential.

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