Matrix multiplication via arithmetic progressions
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
A Two-Stage Model of the Promotional Performance of Pure Online Firms
Information Systems Research
Budget optimization in search-based advertising auctions
Proceedings of the 8th ACM conference on Electronic commerce
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
An empirical analysis of sponsored search performance in search engine advertising
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A Cascade Model for Externalities in Sponsored Search
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Sponsored Search Auctions with Markovian Users
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Bid optimization for broad match ad auctions
Proceedings of the 18th international conference on World wide web
Externalities in Keyword Auctions: An Empirical and Theoretical Assessment
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Mining advertiser-specific user behavior using adfactors
Proceedings of the 19th international conference on World wide web
Expressive auctions for externalities in online advertising
Proceedings of the 19th international conference on World wide web
Stochastic models for budget optimization in search-based advertising
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Dynamic dual adjustment of daily budgets and bids in sponsored search auctions
Decision Support Systems
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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.