Budget Optimization for Online Campaigns with Positive Carryover Effects
While it is relatively easy to start an online advertising campaign, proper allocation of the marketing budget is far from trivial. A major challenge faced by the marketers attempting to optimize their campaigns is in the sheer number of variables involved, the many individual decisions they make in fixing or changing these variables, and the nontrivial short and long-term interplay among these variables and decisions.
In this paper, we study interactions among individual advertising decisions using a Markov model of user behavior. We formulate the budget allocation task of an advertiser as a constrained optimal control problem for a Markov Decision Process (MDP). Using the theory of constrained MDPs, a simple LP algorithm yields the optimal solution. Our main result is that, under a reasonable assumption that online advertising has positive carryover effects on the propensity and the form of user interactions with the same advertiser in the future, there is a simple greedy algorithm for the budget allocation with the worst-case running time cubic in the number of model states (potential advertising keywords) and an efficient parallel implementation in a distributed computing framework like MapReduce. Using real-world anonymized datasets from sponsored search advertising campaigns of several advertisers, we evaluate performance of the proposed budget allocation algorithm, and show that the greedy algorithm performs well compared to the optimal LP solution on these datasets and that both show consistent 5-10% improvement in the expected revenue against the optimal baseline algorithm ignoring carryover effects.
KeywordsGreedy Algorithm Markov Decision Process Advertising Campaign Occupation Measure Markov Policy
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- 2.Altman, E.: Constrained Markov Decision Processes. Technical Report RR-2574, INRIA, 05 (1995)Google Scholar
- 3.Archak, N., Mirrokni, V., Muthukrishnan, M.: Mining advertiser-specific user behavior using adfactors. In: Proceedings of the Nineteenth International World Wide Web Conference, WWW 2010 (2010) (forthcoming)Google Scholar
- 4.Athey, S., Ellison, G.: Position auctions with consumer search. Working Paper (2008), Available at SSRN: http://ssrn.com/abstract=1454986
- 6.Charikar, M., Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: On targeting markov segments. In: STOC 1999: Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, pp. 99–108 (1999)Google Scholar
- 8.comScore. Whither the click? comscore brand metrix norms prove ‘view-thru’ value of on-line advertising (2008), http://www.comscore.com/press/release.asp?press=2587
- 9.Coppersmith, D., Winograd, S.: Matrix multiplication via arithmetic progressions. In: STOC 1987: Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing, pp. 1–6 (1987)Google Scholar
- 10.Dar, E.E., Mansour, Y., Mirrokni, V., Muthukrishnan, S., Nadav, U.: Budget optimization for broad-match ad auctions. In: WWW, World Wide Web Conference (2009)Google Scholar
- 12.Feldman, J., Muthukrishnan, S., Pal, M., Stein, C.: Budget optimization in search-based advertising auctions. In: EC 2007: Proceedings of the 8th ACM Conference on Electronic Commerce, pp. 40–49 (2007)Google Scholar
- 13.Ghose, A., Yang, S.: An empirical analysis of sponsored search performance in search engine advertising. In: WSDM 2008: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 241–250 (2008)Google Scholar
- 14.Ghose, A., Yang, S.: Analyzing the Relationship between Organic and Sponsored Search Advertising: Positive, Negative or Zero Interdependence? Marketing Science (2009) (forthcoming)Google Scholar
- 15.Ghosh, A., Sayedi, A.: Expressive auctions for externalities in online advertising. In: Proceedings of the Nineteenth International World Wide Web Conference, WWW 2010 (2010) (forthcoming)Google Scholar
- 18.Lewis, R., Reiley, D.: Retail advertising works!: Measuring the effects of advertising on sales via a controlled experiment on yahoo! Working paper, Yahoo! Research (2009)Google Scholar
- 20.Page, L., Brin, S., Motwani, R. and Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab (1999) Google Scholar
- 21.PricewaterhouseCoopers and the Interactive Advertising Bureau. IAB Internet advertising revenue report (2009), http://www.docstoc.com/docs/5134258/IAB-2008-Report