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These games differ from traditional video and massively multiplayer online MMO games. Traditional hardcore gamers are predominantly male, between the ages of 18 and 34, who buy boxed console and computer games that they pay for with cash and credit cards and play solo or within a limited interactive environment.
The rapid adoption of mobile games represents a fundamental change in the gaming landscape, expanding who, how, and why gamers are playing.
Social gamers tend to be 54 percent female, use mobile payments and PayPal for purchases, and play across platforms and devices see the References section.
Of further interest are stark differences that are emerging in game revenue patterns across devices. Recent surveys conducted by Newzoo point to iOS games generating 85 percent of in-game revenues over Android and other platforms.
Industry analysis points to an expected decline in console games and related hardware and apparatus sales, with web-based games becoming the go-to gaming platform.
This translates into big opportunity for analytics providers. Although the global game industry is now larger than the music industry and on par with the size of the film industry, game developers are only starting to adopt advanced analytics to support game development, product design, targeted marketing efforts, and data-driven in-game monetization optimization.
The corresponding game analytics industry is still greatly underserved. A list of niche analytics vendors for social and mobile games continues to expand, with representation by Kontagent, Flurry, Mixpanel, Totango, Claritics, and Google Analytics.
There are far fewer vendors focusing on the computer and MMO games, and no single analytics provider appears to focus on delivering cross-game platform analytics.
Many of these off-the-shelf products adequately provide the expected standard list of metrics for social games see the References section , including:.
Traditionally, video and social games have had distinctly different business models. Now there is more of a convergence of business monetization approaches between game types.
Historically, video games relied on a subscription-based model requiring gamers to make an upfront game purchase and subscribe to an ongoing monthly fee.
This business model is being replaced by some games with the testing of free-to-play models that closely follows the emerging freemium model typically offered by social games see the References section.
Currently the terms mobile and social are often used interchangeably; however, they will likely become distinct game genres as game distribution patterns develop.
They will expand game access and greatly increase the corresponding potential player universe. Whereas some games today are available only on certain sites such as Facebook, on single platforms such as computer or Xbox, or on specific devices such as iPhone or iPad, eventually many game titles will be released across more platforms in a more site- and device-agnostic fashion.
Social and mobile games make money monetize in a few ways, and related business and monetization models are changing dynamically. Some games, such as the iOS-based W.
Other games, such as those on Facebook, rely on the sale of virtual goods. A third popular form of game monetization is in-game advertising. Companies such as TapJoy offer in-game banner ads, video offers, and full-page interstitial advertisements.
There is a growing list of companies that offer hybrid models aimed at both raising awareness of new games and monetization optimization. As games and associated monetization models evolve, the relevance of underlying analytics to identify segments of players, how they play, propensity to click on an in-game ad, or pay cash for virtual goods becomes exponentially greater.
Current providers offer standard metrics, and some of these vendors, such as Flurry and Kiip, offer virtual good or ad-recommendation optimization engines.
While the game analytics industry is making big strides towards catching up, it is still arguably in its infancy. Stores of virtually untapped social data reserves at the player ID level exist as potential predictors that can be tied to segmentation and propensity to purchase models that can drive value-based game development, localization, targeted in-game offers, and ads.
This social data can also measure the success of net promoter scores, identify player evangelists, and track redemption of in-game offers.
In addition to the mainstream game monetization approaches detailed above, another major marketing trend is emerging that is worth mention, and is also relevant from an advanced game analytics focus.
Gamification is one of the hottest enterprise trends for , involving the use of game mechanics and design to motivate people and drive specific behaviors see the References section.
In a consumer behavior sense, gamification involves the introduction of game elements such as leaderboards, badges, trophies, points, virtual currency and credits, and more to reward desired behaviors.
Healthcare companies such as UnitedHealth Group have integrated gamification and video game strategies to promote the self-management of wellness and healthy behaviors.
As these programs gain popularity and are more widely implemented, game analytics solutions to measure impacts of gamification programs on customer engagement and loyalty will be in demand.
With all of the expected growth around video, social, and mobile games and the increasing enterprise adoption of gamification, one might expect the associated game and gamification analytics solutions to be more mature.
A large barrier exists to nimble game-and player-related analytics that has hampered rapid advancements in this space. The sheer mass of in-play, leveling up, skill achievement, in-game purchase, and peripheral game data at the individual player level presents a challenge for traditional database structures.
Legacy relational database management systems RDBMS were not built to manage, store, and process the petabytes of data generated by modern MMO and mobile or social games.
New big data solutions are based on NoSQL technology see the References section and are far better suited to manage the rapidly changing data volume, sources, and format of structured, semi-structured, and unstructured data, and filter datasets to a manageable level as an input to on-the-fly analytics solutions.
Relational databases have been in widespread usage since their introduction onto the scene in the s. While there has been explosive growth in user bases of online applications and resulting data generated from online and mobile systems during this time, new solutions better suited to managing data on such a big scale were not introduced and in wider-spread usage until recently.
Different methods of extending the capacity of legacy systems were introduced. New NoSQL technologies avoid the common shortcomings of relational databases and the resulting need to employ the scope-extending methods described in the previous paragraph.
These databases can handle structured, semi-structured, and unstructured data. Additional data can be added to the stores at any time, irrespective of format, and be immediately available for analysis.
These databases can easily handle hierarchical nested data structures and are elastic, automatically spreading data across servers as data expands and contracts, without impacting performance see the References section.
There are a variety of NoSQL database types, including document stores, column stores, key-value stores, XML databases, and graph databases.
There are over NoSQL databases in use, and that number continues to expand. Apache Mahout is a machine learning engine that provides classification, clustering, and collaborative filtering.
Open source R has been integrated to run massively parallel statistical processes directly in Hadoop nodes. Also popular are commercial NoSQL options that integrate with Hadoop and other open source tools and greatly extend these capabilities with analytics, text mining, in-application processing, map reduce functions, and graphing options.
One such example is the recently released IBM InfoSphere BigInsights see the References section that is built on the Apache Hadoop platform and is available in both a basic no-charge option and a more powerful enterprise edition.
Imagine a large MMO video game. This game genre includes powerhouse games such as World of Warcraft, the most profitable video game in history, and the recently released Star Wars the Old Republic.
Use these databases, websites, and books to find trends and statistics about the gaming industry, including information about markets, and consumers.
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