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Optimizing Sponsored Search Advertisements for Revenue Under Budget Constraints

This study examines the optimal delivery of sponsored search ads on search engines like Google and Yahoo! within the constraints of advertisers’ budgets. The research focuses on the bidding process, query frequencies, and how advertisers can maximize revenue by strategically navigating auction dynamics. Utilizing simulations over 5000 queries, we compare our optimization algorithm to a greedy approach, finding significant improvements in revenue and efficiency. Future investigations will delve into less frequent queries and advertiser responses to this method.

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Optimizing Sponsored Search Advertisements for Revenue Under Budget Constraints

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  1. Sponsored Search Cory Pender Sherwin Doroudi

  2. Optimal Delivery of Sponsored Search Advertisements Subject to Budget Constraints Zoe Abrams Ofer Mendelevitch John A. Tomlin

  3. Introduction • Search engines (Google, Yahoo!, MSN) auction off advertisement slots on search page related to user’s keywords • Pay per click • Earn millions a day through these auctions • Auction type is important

  4. Sponsored search parameters • Bids • Query frequencies • Not controlled by advertisers or search engine • Few queries w/ large volume, many with low volume • Advertiser budgets • Pricing and ranking algorithm

  5. Solution • Focus on small subset of queries • Predictable volumes in near future • Constitute large amount of total volume

  6. Sponsored search parameters • Bids • Query frequencies • Advertiser budgets • Controlled by advertisers • Pricing and ranking algorithm • Generalized second price (GSP) auction • Rankings according to (bid) x (quality score) • Charged minimum price needed to maintain rank • Goal: take these parameters into account, maximize revenue

  7. Motivating example Reserve price is 

  8. Problem Definition • Queries Q = {q1, q2, q3, ..., qN} • Bidders B = {b1, b2, b3, ..., bM} • Bidding state A(t);Aij(t) is j’s bid for i-th query • djis j’s daily budget • viis estimate of query frequency • Li = {jp : jp B, p = 1, ..., Pi} • Lik = {jik : jik Li, l ≤ Lik ≤ P}

  9. Ranking and revenue • Bid-ranking - • Revenue-ranking - • So, for slate k, • Price per click: • Independent click through rates • Revenue-per-search: • Total revenue:

  10. Bidder’s cost • Total spend for j:

  11. Linear program • Queries i = 1, ..., N • Bidders j = 1, ..., M • Slates k = 1, ..., Ki • Data: dj, vi, cijk, rik • Variables: xik • Constraints: • Budget: • Inventory:

  12. Objective function • Maximize revenue: • Value objective: • Clicks objective:

  13. Column Generation • Each column represents a slate • Could make all possible columns • But for each query, exponential in number of bidders • Start with some initial set of columns • j: Marginal value for j’s budget • i: Marginal value for ithkeyword • Profit if • Maximize

  14. ebay.com nextag.com ? tigerdirect.com priceline.com How to maximize? • If small number of bidders for a query, enumerate all legal subsets Lik, find maxima, see if adding increases profit • Otherwise, use algorithm described in another paper

  15. Summary (so far) • Various bidders vying for spots on the slate for each query • Constrained by budget, query frequencies, ranking method • Solve LP for some initial set of slates • Check if profit can be made by adding new slates • Re-solve LP, if necessary • Can be applied to maximize revenue or efficiency

  16. Simulation Methodology • Compare this method to greedy algorithm • For greedy, assign what gets most revenue at the time; when bidder’s budget is reached, take them out of the pool • Used 5000 queries • For 11 days, retrieved hourly data on bidders, bids, budgets • To determine which ads appear, assign based on frequencies fik = xik/vi • After each hour, see if anyone has exceeded budget

  17. Simulation Results • Current method better than greedy method, when optimizing over revenue or efficiency • Larger gain for revenue when revenue optimized • Revenue and efficiency are closely tied

  18. Gains when efficiency is maximized

  19. Gains when revenue is maximized

  20. Impact on bidders

  21. Limitations • Illegitimate price hikes for other bidders if one person exceeds budget in middle of hour • Assumption that expected number of clicks are correct • For the purposes of the simulation, expect these to affect greedy and LP optimization similarly

  22. Future work • Focus on less frequent queries • Frequencies harder to predict • Some work has been done (doesn’t incorporate pricing and ranking) • Keywords with completely unknown frequencies • Parallel processing for submarkets • Investigate how advertisers might respond to this method • Potential changes in reported bids/budgets

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