“We were initially approached by an online game provider that used a ‘freemium’ model — players could play for free, but could receive upgrades by paying a fee to become premium users,” says William Rand, an assistant professor of business management at NC State and co-author of a paper on the work. “The company wanted to know what incentives would be most likely to convince players to become premium users. That was the impetus for the work, but what we found is actually relevant for any company or developer interested in incentivizing user investment in apps or online services.”
A preliminary assessment indicated that access to new content was not the primary driver in convincing players to pay a user fee. Instead, player investment seemed to be connected to a player’s social networks.
To learn more, the researchers evaluated three months’ worth of data on 1.4 million users of the online game, including when each player began playing the game; each player’s in-game connections with other players; and whether a player became a premium user.
Using that data, the researchers created a computer model using agent-based modeling, a method that creates a computational agent to represent a single user or group of users. The computer model allowed them to assess the role that social connections may have played in getting players to pay user fees. They found that two different behavioral models worked very well, but in different ways.
“We found that the best model for accurately predicting the overall rate of players becoming premium users was the so-called ‘Bass model,’ which holds that the larger the fraction of direct connections you have who use a product, the more likely you are to use the product,” Rand says.
However, the researchers found that the best model for predicting the behavior of any specific individual was the complex contagion model.
“The Bass model looks at the fraction of your direct connections who adopt a product, whereas the complex contagion model simply looks at the overall number of your direct connections who adopt,” Rand says.
Both techniques have utility for businesses. For example, being able to predict how many players would become premium users could help a company make sustainable business decisions; whereas being able to predict the behavior of an individual player may help a company target players who are near the threshold of becoming premium users.
“By merging these two modeling approaches, we created a tool that would allow a company to predict how many additional premium users it would gain, depending on various degrees of investment in marketing to individual players who are the threshold of becoming premium users,” Rand says. “This could be used to make informed decisions about how much to invest in ‘seeded,’ or targeted, marketing in order to capitalize on word-of-mouth marketing.
“The bottom line here is that the approach we took to developing this tool could be used to develop a custom tool for any company that’s marketing an online product or service via word of mouth,” Rand says.