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Now that in-game advertising is gaining traction, the next steps would be to allow marketers and advertisers to target their audience with the most relevant messages. How would they do that? Predictive Analytics to the rescue!
As per Wikipedia, Predictive Analytics are techniques that use past performance to assess how likely a customer would display a specific behavior in the future. An example application is credit scoring in financial services, where attributes such as credit history, loan applications, payment defaults, etc are used to assess how likely an individual would make future credit payments on time.
Now that the discipline has matured in one industry, it's time to port that to the new media world, particular in-game marketing and television guidance. A Business Week article suggested that predictive analytics would provide the intelligence to help marketers target consumers while playing online games. By analyzing historical campaign performance, and hundreds of gamer attributes, including session activities, downloads, number of friends and genre preferences, marketers can identify consumer preferences and behavior. In an online gaming, different messages can be targeted to different players, each getting a message relevant to them. With such targeting, marketers would be willing to pay a higher price (per capita) to reach the right consumers. Moreover, since social networking enables peer-to-peer influence, predictive analytics can also identify the influencers who have loyal fan followings, not unlike identifying the most popular kid in high school, only through data analytics.
In-game advertising is only one application. That discipline can be used in television guidance. Then additional (though more targeted and more relevant) advertising opportunities can be created. With personalization and recommendation engines powering TV Guide 2.0, why not employ predictive analytics to (1) add to the power of giving the consumers exactly the television (or video, in general) that they want, and (2) market to them at the same time, since we understand their viewing behavior already?
As per Wikipedia, Predictive Analytics are techniques that use past performance to assess how likely a customer would display a specific behavior in the future. An example application is credit scoring in financial services, where attributes such as credit history, loan applications, payment defaults, etc are used to assess how likely an individual would make future credit payments on time.
Now that the discipline has matured in one industry, it's time to port that to the new media world, particular in-game marketing and television guidance. A Business Week article suggested that predictive analytics would provide the intelligence to help marketers target consumers while playing online games. By analyzing historical campaign performance, and hundreds of gamer attributes, including session activities, downloads, number of friends and genre preferences, marketers can identify consumer preferences and behavior. In an online gaming, different messages can be targeted to different players, each getting a message relevant to them. With such targeting, marketers would be willing to pay a higher price (per capita) to reach the right consumers. Moreover, since social networking enables peer-to-peer influence, predictive analytics can also identify the influencers who have loyal fan followings, not unlike identifying the most popular kid in high school, only through data analytics.
In-game advertising is only one application. That discipline can be used in television guidance. Then additional (though more targeted and more relevant) advertising opportunities can be created. With personalization and recommendation engines powering TV Guide 2.0, why not employ predictive analytics to (1) add to the power of giving the consumers exactly the television (or video, in general) that they want, and (2) market to them at the same time, since we understand their viewing behavior already?
posted by KW at 9:06 PM
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