Big data and Games Part 3 - Option value of data

In the Big data and games - part 1 - why ask why we described the characteristics of big data, how it will challenge the culture of asking and knowing why before acting. Big data and games - part 2 - algorithms and experts, and how management must utilize both these algorithmic experts and human experts  in the appropriate contexts.  In this final part 3, we will discuss the utility value of big data and games.  As before we will rely heavily on Mayer-Schonberger and Cukier, as well as the colossal "Games Analytics" edited by El-Nasr, Drachen and Canossa.

First let us talk through how data is different from other types of assets.  Many resources such as coal or oil can only be used once.  

Additionally some assets such as cars diminish in value and utility the more you use them.  Some digital assets such as software may have license restrictions only allowing one person to use it at any one time.   Data does not have these restrictions.  It can be used repeatedly without losing its value; it can be used by many different people simultaneously.  

These unique properties of data make it very difficult to estimate the actual value of the data.  This is because the data can be used for purposes which we have not yet thought of.  In the example of predicting which manholes in New York City would explode, "[f]ew executives at Con Edison in New York could have imagined that century old cable information and maintenance records might be used to prevent future accidents." (p. 103)  In effect data has an future option value of which it is very difficult to estimate.

In "Big Data", the authors describe three ways to unleash this option value: basic reuse; merging datasets; and finding twofers.

Basic data reuse and primary use
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Data that is collected is usually applied for the immediate use. In games, there are many primary uses of captured data, including but not limited to troubleshooting and customer service to verify user actions, economy balancing, smoothing user level progression, identifying sticking points, ferreting out gold farmers and bot accounts, reducing fraudulent charges.

But this data can be reused for different purposes.  Hitwise allows search query data to be used by marketers to learn about consumer preferences.  Farecast uses air travel ticket prices to help consumers predict whether prices will rise or fall.  SWIFT, the global interbank system for wire transfers, uses the wire payments info to offer GDP forecasts.  In the case of games, game logs originally used for customer service queries can be used to target marketing campaigns and promotions; user in-game behaviors used to level up players can be used to predict likely churners and purchasers.   Player churn predictions can be used for optimal server provisioning.

Merging datasets

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Recombinant data can make new things possible.  Most of us have used "mashups", which are essentially merged different datasets or services. Let us example the example of Zillow, a real estate service.  Zillow combines data about recent home purchases, information on for-sale listings, US census information about the geographic location, school information from the local governments, and places it all on an intuitive map.  Zillow makes it very easy for home buyers because it combines all the different sources of data into a coherent service.  

Game developers can therefore combine the data they are collecting with other external datasets. For example, most mobile game servers record the IP address of the player session.  When you combine this IP information with a mapping table of geolocation or internet service provider, you get a new kind of data that can be very valuable.  Mobile phone companies are willing to pay up to half million dollars to understand the switching patterns of their consumers. Game developers have the best view of these patterns from their game play logs, but only if it is combined with the IP - mobile operator information. The same applies for the switching behavior of home internet service providers.

One common problem in acquiring users likely to stay and pay, is that the advertising networks have different targeting criteria.  So game developers need to use broad targeting criteria such as "income range" or zip code.  By merging US census data on income and paying status, a game developer can arrive at a blended payer percentage for each zip code or each income range.  This helps the marketing manager to estimate the value of targeting ads to different zip codes and income ranges.

"Twofers"

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When data capture is designed with multiple uses in mind, you have the opportunity to get twofers.  This third method focuses more on the front-end of the process rather than after the data has already been captured.   A good example is that when Google street view cars were driving around snapping pictures, it was also collecting wifi information and tagging it with GPS data.  This allows Google to have its own WIFI-location database to use for geo-tagging whenever outdoor GPS coordinates were not available.  

One example in games is that many customer service call systems allow the user to provide feedback on the quality of the customer service.  This automated feedback system can be also used to gather some simple one question feedback about the game, thus helping development improve the game design.  

Preparing to leverage data

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How can a game developer or publisher prepare itself to take advantage of their data?  Here are some areas to get started with.

• keep track of what data is being collected, deleted and stored.  Since data is the raw material, it is critical to start an inventory of where the data lives in your company.  This is not a call to build an enterprise data warehouse; rather just to start to build a process to keep track of where data lives and the rules behind their storage and deletion.  This can be as simple as a very large spreadsheet or as fancy as master metadata management.  
• establish consistent goals for the data.  The goals of data use should align with the goals of your organization.  If your goal is to make the most entertaining game possible, challenge the team to use data to help make the most entertaining game.  If the goal is to achieve the highest payer percentage, then challenge your team to use the available data to achieve this goal.
• accountability.  Task one person to get the most out of your data.  Practically this person could have titles such as "chief data officer", "chief analytics officer". She should have KPIs such as "% of data elements with multiple applications" and be running projects such as "integrating player level data into marketing email targeting criteria" or "using player activity predictions to dynamically provision server resources for optimal player experience."    

This concludes the 3-part series on big data and games.  Of course, there are many other facets which we have not explored and this is just the tip of the iceberg.  Feel free to contact me at nick_at_sonamine_dot_com or check out the website www.sonamine.com  Would love to hear from you and start a dialog.

References

Mayer-Schonberger, V and Cukier K.  Big Data: A revolution that will transform how we live, work, and think.  Houghton Mifflin Harcourt, Boston, New York 2013 (Amazon link)

Magy Seif El-Nasr,  Anders Drachen,  Alessandro Canossa.  Game Analytics: Maximizing the value of player data. Springer-Verlag, New York 2013.  (Amazon link)

Originally published at Gamasutra Blog Site.

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