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Scheduling of Dynamic In-Game Advertising

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Scheduling of Dynamic In-Game Advertising OPERATIONS RESEARCH informs ® Vol. 59, No. 1, January–February 2011, pp. 1–16 issn 0030-364X eissn 1526-5463 11 5901 0001 doi 10.1287/opre.1100.0852 © 2011 INFORMS OR PRACTICE Scheduling of Dynamic In-Game Advertising John Turner The Paul Merage School of Business, University of California, Irvine, Irvine, California 92697, [email protected] Alan Scheller-Wolf, Sridhar Tayur Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 {[email protected], [email protected]} Dynamic in-game advertising is a new form of advertising in which ads are served to video game consoles in real time over the Internet. We present a model for the in-game ad-scheduling problem faced by Massive Inc., a wholly owned subsidiary of Microsoft, and a leading global network provider of in-game ad space. Our model has two components: (1) a linear program (solved periodically) establishes target service rates, and (2) a real-time packing heuristic (run whenever a player enters a new level) tracks these service rates. We benchmark our model against Massive’s legacy algorithm: When tested on historical data, we observe (1) an 80%–87% reduction in make-good costs (depending on forecast accuracy), and (2) a shift in the age distribution of served ad space, leaving more premium inventory open for future sales. As a result of our work, Massive has increased the number of unique individuals that see each campaign by, on average, 26% per week and achieved 33% smoother campaign delivery as measured by standard deviation of hourly impressions served. Subject classifications: dynamic in-game advertising; video game advertising; display advertising; revenue management; linear programming; goal programming. Area of review: OR Practice. History: Received July 2008; revisions received March 2009, July 2009, December 2009; accepted February 2010. Published online in Articles in Advance February 8, 2011. 1. Introduction where ads are displayed. For this reason, and also because saturation competition Video games have incorporated static ads for decades: of additional constraints (such as , , and context, discussed below), scheduling in-game ads is for example, an old racing game may have a billboard significantly more complicated than scheduling banner ads. to this article and distributed this copy asthat a courtesy toalways the author(s). displays the same Sunoco ad. Recently, how- The ad server is operated by a network provider, an inter- ever, technology and business relationships have matured mediary between game publishers and advertising agen- to allow Internet-enabled consoles (e.g., Xbox, PCs) to cies. We focus on the scheduling problem faced by Massive dynamically serve ads over time, creating an entirely new Inc., a wholly owned subsidiary of Microsoft and a leading ad market: Dynamic in-game ad technology allows in-game copyright global network provider. Game publishers allow Massive to billboards to display different ads to different players based sell and serve ads in their games, and consequently receive on their demographic, the time of day, the day of week, and a portion of the generated revenues; ad agencies buy ad holds possibly other parameters. The in-game advertising indus- campaigns directly from Massive. try is growing quickly; at present, revenues are projected The operational problem network providers like Massive to reach $800 million by 2012 (Cai 2007). face is how to schedule and serve ads to players over At the heart of an in-game ad system is the ad server: time so as to make the best use of their inventory of ad INFORMS Additional information, including rights and permission policies, is available atWhen http://journals.informs.org/. a player enters a new level of a game, his console space. Campaigns purchased by an ad agency specify a tar- connects to the ad server via the Internet and requests new get number of impressions (ads seen by gamers), a rough ad graphics for billboards, stadium walls, and other loca- schedule for serving these impressions over time, and also tions where ads are shown in the level. The ad server decides a desired mix (e.g., 60% in sports games, 40% in the rest of which ads to serve to this player, functioning like a Web the games). A campaign’s delivery may also be restricted server delivering banner ads. But, unlike on the Web, where to certain geographic areas and/or times of the day. In addi- a selected ad is almost certainly seen, it is common for tion, the network provider must also manage (1) satura- only a fraction of selected in-game ads to be seen. Billable tion: it is undesirable for a single player to simultaneously ad time is thus recorded only when, as part of game play, see many copies of the same campaign; (2) competition: the player navigates through the level and passes locations campaigns of two competing brands—e.g., Coke and 1 Turner, Scheller-Wolf, and Tayur: Scheduling of Dynamic In-Game Advertising 2 Operations Research 59(1), pp. 1–16, © 2011 INFORMS Pepsi—should not be served to the same player; and (3) advertisers. In these models, the advertiser chooses among context: ads should not seem out of place within the advertising vehicles (e.g., newsprint, television, radio) to game—e.g., Coke ads belong on virtual soda machines, maximize some combination of reach (audience size), fre- Tide ads do not. quency, and campaign duration subject to a budget con- The size, scope, and complexity of Massive’s problem straint (see textbooks by Rossiter and Danaher 1998 and are such that even if there were no system uncertainty, Gensch 1973). One typical assumption is wearout (the optimization of their ad server would be difficult. But, effectiveness of an ad decreases as the number of expo- of course, uncertainty is present—there are three primary sures to the same person increases); papers in this line sources: (1) the acquisition of new games, (2) the sale of research include Thompson (1981) and Simon (1982), of new campaigns, and (3) error in inventory forecasts of which arrive at an optimal pulsing strategy of advertising ad space. This last factor, uncertainty in the amount of expenditure over time. Although some of these concepts are ad space, arises because the number of players, the types relevant, it is most instructive to compare our model with (demographics) of players, and the ad space that the play- those that take the perspective of a third party scheduling ers actually see during game play are not known when the many advertisers, such as models that plan TV commercials scheduling problem needs to be solved. Thus, campaigns and webpage banner ads. Structurally, in-game advertising sometimes fall short of their impression goals or deviate sits between TV and Web advertising: It has well-defined from the desired pattern of delivery; in that case, the net- contracts like TV, yet decisions are made at a very fine work provider offers the advertiser a make-good: the cam- granularity, as on the Web. paign is extended or the advertiser is offered a refund or TV Commercials. Advertisers purchase 60%–80% of credit for future use. the year’s ad space during a two- to three-week period in We present the first planning/scheduling model and algo- May called the up-front market; the remaining ad space rithm for dynamic in-game advertising. Our model has two is sold first-come-first-serve in the scatter market. In con- components: (1) a linear program called the Weekly Plan trast, in-game ads are sold throughout the year, so the divi- is solved periodically to establish target service rates, and sion between up-front and scatter markets is not profound. (2) a packing heuristic called the Real-Time Algorithm is Therefore, papers (e.g., Araman and Popescu 2010) that run whenever a player enters a new level, to serve impres- determine the optimal up-front versus scatter trade-off are sions in accordance with these service rates. Benchmarking not directly applicable. our model against Massive’s legacy algorithm using histor- Bollapragada et al. (2002) use goal programming to pro- ical data, we observe (1) an 80%–87% reduction in make- duce a sales plan for a single campaign that allocates com- good costs (depending on forecast accuracy), and (2) a shift mercial slots from TV shows such that the advertiser’s in the age distribution of served ad space, leaving more pre- preferences are honored as closely as possible. Inventory mium inventory open for future sales. Massive has begun a is booked for each advertiser in sequence, allowing each staged implementation of our model, and to date has bene- advertiser to request changes to their plan before it is fitted from a 26% average increase in the number of unique finalized. Audience size is assumed to be deterministic individuals that see each campaign each week, and 33% and there is no mention of make-goods allocation when to this article and distributed this copy assmoother a courtesy to the author(s). campaign delivery, as measured by standard devi- schedules are not executed as planned. This is a static up- ation of hourly impressions served. front market problem; in contrast, we consider the dynamic We proceed as follows: In §2, we review the literature problem in which new campaigns and games materialize on media planning and scheduling, and describe existing over a rolling horizon. Furthermore, our Real-Time Algo- copyright models for broadcast television and webpage banner ads. rithm is significantly different than the low-level ad-slotting We introduce the problem in §3, the Weekly Plan LP in §4, “ISCI Rotator” algorithms used for TV because in our case and the Real-Time Algorithm in §5.
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