An interesting front page story in The Wall Street Journal today by journalist Nick Wingfield discusses how casual gaming giant Zynga cashes in on their millions of players. After developing Fishville, following in the footsteps of highly successful titles like Farmville, managers noted players spending in-game currency on one type of fish more so than others. The “translucent angler fish” was being purchased more than 6 times the rate of other virtual fish. So the company quickly developed a whole line of translucent sea creatures, charging as much as $4 (this time, in real world money) for more exotic varieties.
This formula has been very successful for the company. Although only about five percent of Zynga’s player base spends serious money in their games, so many millions of people play that the company rakes in millions. They rake in even more by figuring out what the players want through data analysis.
Zynga is transforming the game industry. Traditional videogame companies create games they think players will like, then sell them. Zynga offers free games through Facebook Inc.’s social network, then studies data on how its audience plays them. It uses its findings to fiddle with the games to get people to play longer, tell more Facebook friends about them and buy more “virtual goods.” At the heart of the whole process is Zynga’s ability to analyze reams of data on how players are reacting to its games.
“We’re an analytics company masquerading as a games company,” said Ken Rudin, a Zynga vice president in charge of its data-analysis team, in one of a series of interviews with Zynga executives prior to the company’s July filing for an initial public offering.
This formula for financial success has other companies following Zynga’s lead. Rather than spending millions developing a title with a short shelf life, companies are turning to free games with extras that cost money. The primitive graphics Zynga uses are generally derided by serious gamers, but Zynga aims for the mass market, much the way American beer brewers produce bland beverages that appeal to the most palates.
All of Zynga’s games go through what amounts to a giant ongoing lab experiment involving players. Zynga conducts hundreds of “A-B tests” within its games, in which two sets of players see virtual goods on sale with, say, subtle color differences to see which color sells better…
Sizhao Yang, a former Zynga executive who helped create its virtual farming hit “FarmVille,” says his development team figured out by analyzing virtual-goods-sales data that “people buy animals a lot more than tractors and other inanimate objects.” The findings led the “FarmVille” team to more prominently feature animals in its online store, he says.
Interestingly, Wingfield reports there is considerable tension in the company between the data jockeys and the game designers. The game designers have a certain idea of how a game should look and function. The analysts drive the direction of game development based on the data, leading to tension. Some designers have quit the company in protest. Still, data remains the keystone in Zynga’s game plan for the foreseeable future.
The Zynga story on data analysis comes on the heels of the recent International Conference on the Foundations of Digital Games in Bordeaux this summer (fdg2011.org). There, Brent Harrison and David L. Roberts over at North Carolina State delivered an interesting paper, Using sequential observations to model and predict player behavior. Here’s their abstract:
In this paper, we present a data-driven technique for designing models of user behavior. Previously, player models were designed using user surveys, small-scale observation experiments, or knowledge engineering. These methods generally produced semantically meaningful models that were limited in their applicability. To address this, we have developed a purely data-driven methodology for generating player models based on past observations of other players. Our underlying assumption is that we can accurately predict what a player will do in a given situation if we examine enough data from former players that were in similar situations. We have chosen to test our method on achievement data from the MMORPG World of Warcraft. Experiments show that our method greatly outperforms a baseline algorithm in both precision and recall, proving that this method can create accurate player models based solely on observation data.
While not fixating on the profit motives that Zynga has in mind, Harrison and Roberts offer clues to game designers in guiding player behavior in-game. Educational games could become more engaging:
The ability to accurately predict a player’s behavior in a game has a number of applications. While these applications are beyond the scope of this paper, we discuss two of them briefly here to better situate and motivate our approach. With a model of player behavior, we can create an experience that is unique to a user’s tendencies or preferences. For example, if we predict that the user will choose to fight a certain non-player character (NPC) rather than talk to it, that NPC can be made more willing to fight. Another application involves guiding players to parts of games that they may enjoy. Modern games often take place in large, sandbox worlds where the player is given total freedom. It’s quite possible that players may never see content that they would like because the sandbox is just so big. Predictions about a player’s behavior can be used to guide her to the parts of the game that she would enjoy.
Eschewing surveys, the authors recommend a purely data-driven approach (as does Zynga):
We feel that a purely data-driven approach has significant promise for creating accurate predictive models of player behavior in games without the difficulties associated with earlier modeling techniques. Very little research has been done in this area to date.
Read the entire paper for further discussion of the algorithm they developed. Very interesting.
Harrison, B & Roberts, D. L. (2011). Using sequential observations to model and predict player behavior. In Proceedings of the 2011 Foundations of Digital Games Conference. (FDG 2011), Bordeaux, France.
Wingfield, N. (2011, September 9). Virtual products, real profits. The Wall Street Journal, p.A1.