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    Social casino metrics industry trends and analysis

    social casino metrics industry trends and analysis

    May 1, social casino metrics industry trends and analysis. Febr. Zahl der Social-Casino- Spieler sinkt 13 Prozent zum Jahr, wie der Report 'Social. Internet-Casino ohne Echtgeld-Einsatz Prozent zum Jahr, wie der Report 'Social Casino Metrics: Industry Trends and Analyses' zeigt. With nationwide coverage. Febr. Zahl der Social-Casino-Spieler sinkt 13 Prozent zum Jahr, wie der Report ' Social Casino Metrics: Industry Trends and Analyses' zeigt. Aug 6, Pincus.

    Imagine being on the receiving end of half a million consumer messages a year—and any single one of them could contain information that causes serious trouble for your organization if not handled quickly.

    If the wrong message falls through the cracks, you could be exposed to a ton of risk immediately. Now imagine that you have only a few hundred employees responsible for making sense of all those messages.

    That was the problem facing a financial services organization — and the reason it turned to text analytics. But when this organization needed to take its text analytics capabilities to the next level, it turned to the world of science — specifically bioinformatics, in which scientists are working to match sequences of DNA.

    After all, DNA is represented by a series of letters that occur in non-random patterns, not unlike the words and phrases in emails and feedback forms.

    Using algorithms originally developed to compare DNA sequences, this organization cracked the code on thousands of messages received every day.

    Please enable JavaScript to view the site. Still not a member? Analytics Trends The next evolution. Add to my bookmarks.

    Six significant trends are in play. Download Analytics Trends The man-machine dichotomy blurs Analytics expands across the enterprise Cybersecurity: Download trend The man-machine dichotomy blurs.

    Are machines coming for us? Complementing one another There are likely to be a variety of ways in which smart people and smart machines will work alongside each other.

    LifeLearn Sofie In North America, most veterinarians are general practitioners, and while specialists may be available by referral, veterinarians are often required to have expertise across many disciplines, species, and breeds.

    The man-machine dichotomy blurs. Impact on society High. Impact on business High. Expected peak 5 years. Download trend Analytics expands across the enterprise.

    How quickly things change Today, building on analytics successes in discrete disciplines, leaders are beginning to take serious steps toward connecting these successes to create something bigger—something we call the insight-driven organization IDO.

    Laying the groundwork What does this look like in practice? Analytics expands across the enterprise. Impact on society Medium.

    Expected peak 3 years. Industries most impacted Financial Services, Retail, and Telecommunications. Business domains that will lead the charge IT, Marketing, and Production.

    On the Cybersecurity Front Line," February Going on the offensive Organizations such as these are beginning to employ more predictive approaches to threat intelligence and monitoring—in short, going on the offensive.

    A moving target creates new demands Companies adopting these types of offensive steps will no doubt find that they need new capabilities.

    Financial Data and Technology Firm For a leading data and technology firm that supports the financial services industry, cybersecurity is of paramount importance.

    Business domains that will lead the charge IT and Security. Download trend The Internet of Things—and people, too. A new source of innovation Innovation has always been a key force in transforming business and society.

    Real investment This innovation is taking place in both consumer-focused and business-to-business B2B industries. Building on existing infrastructure Many businesses are finding that much of the infrastructure they need for IoT applications is already in place.

    City of Boston Some of the earliest adopters of the IoT have been municipal governments. City of Boston Continued Boston was also one of the first cities to adopt "BigBelly" trash cans, which continuously report on how full they are so that they can be emptied more efficiently.

    The Internet of Things—and people, too. Download trend Companies bridge the talent gap. Getting creative International Data Corporation IDC predicts a need for , people with deep analytical skills in the US by and a requirement for five times that number of positions with data management and interpretation capabilities.

    Cisco Systems Having decided that analytics and data science skills are key competencies for its organization, Cisco Systems has created an aggressive program for cultivating data scientists and data-savvy managers.

    Companies bridge the talent gap. Expected peak 1 year. Business domains that will lead the charge HR and IT. Download trend Business borrows from the sciences.

    Scientists were into analytics before it was cool Any conversation about the new world of business analytics should come with a caveat: Cross-pollination between science and business This environment—marked by a reinvigorated interest in business analytics combined with separate-but-related advances in analytics in the sciences—is one that is ripe for cross-pollination.

    Financial Services Imagine being on the receiving end of half a million consumer messages a year—and any single one of them could contain information that causes serious trouble for your organization if not handled quickly.

    Business borrows from the sciences. Analytics Trends collections Access past Analytics Trends reports: A below-the-surface look Analytics Trends Social games are now offered at a fraction of the price through social platforms, such as Facebook, and are played on various mobile platforms, such as Android and iOS.

    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.

    Use these databases, websites, and books to find trends and statistics about the gaming industry, including information about markets, and consumers.

    Search this Guide Search. Gaming and Casinos Industry Guide: More Hospitality Industry Guides Airlines. Meetings, Incentives, Conventions, Events. Each report provides key statistics, market characteristics and segmentation, industry conditions, leading competitors, industry performance analysis and future outlook.

    Social Casino Metrics Industry Trends And Analysis Video

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    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.

    The video game subscription models fall into a few categories:. The largest game titles in this genre use pay-to-play subscription models. Game studios are now facing stiff competition as a direct result of the growth of mobile and social games.

    So while the larger titles are following the subscription model now, there may be an eventual shift towards a freemium model. For pay-to-play games, game analytics are focused on understanding who the most valuable players are, how they play, if they evangelize the game and influence others to play, and what their player personalities and motives look like.

    An important application of propensity modeling in this type of model is to identify those players with the highest propensity to do one of the following:.

    Data elements include traditional game-time dashboard key performance indicators KPIs , time to complete levels, solo versus interactive behaviors, avatar selection, interaction style indicators, gender of avatar, game strategy behavior variables, game-related tweets, social network activity, language, and more.

    Microsegmentation applications involve segmenting a player base to understand distinct segment preferences and behaviors to guide targeted game design, localization that reflects preferences of regional segments, and appealing targeted extension packages and additional content design see the References section.

    When you segment a player base and assign players real-time propensity scores, product, design, and marketing have detailed individual player intelligence to guide strategy and back-end measurement.

    This approach to players of games is no different from the traditional customer view towards applying advanced analytics for player retention, churn, and marketing response efforts.

    The main difference in this channel is the new variety of data and tremendous volume and velocity at which it is generated. The video game industry stands to benefit as they begin to leverage the efficiencies of combining traditional relationship databases such as Netezza with Hadoop and other NoSQL data stores and apply data mining tools such as R or InfoSphere BigInsights, which effectively manage out-of-memory data processing and analytics.

    Mobile and social game developers have been early adopters of big data technology, cloud computing solutions, and associated data mining applications.

    Zynga, for example, is well known in the industry for strategy based on cutting-edge player analytics to drive actionable user analysis.

    Player analytics allows social game studios to understand in real-time why users are abandoning a game and identify other players at risk of leaving the game so they can develop player retention strategies before those players quit.

    The same analytics applications optimize ad-generated interaction and in-game virtual goods sales. If a game developer is able to identify viral users that spread the player base, they can perform an outreach on social media sites with rewards to ensure the continuation of desired behaviors.

    Analytics in a mobile setting identifies players who represent the highest value in terms of propensity to purchase virtual goods, evangelize a game, or generate ad revenue.

    Another major issue with all game genres is game fraud, and analytics uncovers fraudulent player behavior for removal.

    Player segmentation based on variables related to device, platform, carrier, geolocation, and demographics facilitate more effective in-game and non-game brand and real-time partner-targeted offers.

    Both the game industry and technology are evolving at a rapid pace. Technological advancements in both sectors provide exciting opportunities for game developers and media studios to apply advanced analytics to further enhance design and optimize game monetization efforts.

    If new state legislation passes to legalize online gambling, a new focus on advanced analytics and microsegmentation to drive player monetization based on gambling patterns will emerge.

    Game fraud and analytics to combat this across all game genres will continue to be stressed. As more and more users gravitate to smart phones and tablets, more game business models will move to freemium models, and new player monetization models will emerge that require propensity modeling and segmentation for better targeting.

    Gamification will continue to spread, and the need for metrics associated with these programs will grow. The next decade promises to be fertile ground for specialized game analytics solutions and analysts focused on incorporating big data technologies to uncover rich insights into both player and consumer behavior.

    Linux Microservices Mobile Node. Game industry changes and trends There is a revolution happening in the game industry. Game analytics 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.

    Many of these off-the-shelf products adequately provide the expected standard list of metrics for social games see the References section , including: Technology advancements in databases Relational databases have been in widespread usage since their introduction onto the scene in the s.

    You must maintain schemas for each server. Denormalization is another method that involves grouping and indexing redundant data and often results in latency and issues with maintaining concurrency in relational database systems.

    Distributed caching, which caches recent data in memory, is useful when data is needed. The application web, game, social network, search engine, and so on first checks a distributed caching system, such as memcached, for the needed data instead of going back to the relational database.

    The video game subscription models fall into a few categories: Pay-to-play is where players must pay a monthly subscription fee. Free-to-play usually involves an upfront software cost but no additional payments.

    Freemium allows players to access game content and play for free but offers options to pay for additional content and access.

    An important application of propensity modeling in this type of model is to identify those players with the highest propensity to do one of the following: Continue a subscription Return to play a game after a subscription pause Encourage new players to subscribe Become skilled and persuasive guild leaders Data elements include traditional game-time dashboard key performance indicators KPIs , time to complete levels, solo versus interactive behaviors, avatar selection, interaction style indicators, gender of avatar, game strategy behavior variables, game-related tweets, social network activity, language, and more.

    Mobile and social game analytics examples Mobile and social game developers have been early adopters of big data technology, cloud computing solutions, and associated data mining applications.

    Summary Both the game industry and technology are evolving at a rapid pace. Read where Tim Cross predicts video games will be the fastest-growing form of mass media over the next decade.

    Do You Speak Metrics? Do You Speak Metrics?: Freemium and the virtual goods phenomenon: Learn more about the freemium business model.

    Join today to follow developerWorks tweets. Download the Basic Edition at no charge. Ironically, concerns about cybersecurity could—and perhaps in some cases should—slow the adoption of other trends that drive innovation.

    Organizations with a sophisticated approach to cybersecurity are no longer satisfied with locking the doors after the robbery has been committed.

    Organizations such as these are beginning to employ more predictive approaches to threat intelligence and monitoring—in short, going on the offensive.

    This may mean automated scanning of Internet "chatter" by far-flung groups and individuals who may intend cyberharm. It may involve analyzing past hacks and breaches to create predictive models of which threats are likely to surface next.

    Companies adopting these types of offensive steps will no doubt find that they need new capabilities. At the very least, extensive collaboration between analytics and cyber professionals may be required.

    And cybersecurity projects will need to rapidly move up the priority list for analytics groups. For a leading data and technology firm that supports the financial services industry, cybersecurity is of paramount importance.

    Externally, this group is analyzing huge swaths of human behavior data, in channels such as social media and IRC, to understand the most likely sources of threats and to know when employees may be traveling in global hot spots of threatening activity.

    These models are not currently fully automated but instead are "incredibly helpful in focusing our human analysts," says this cybersecurity leader.

    Innovation has always been a key force in transforming business and society. Increasingly, innovation is occurring as the result of aggregating and analyzing data to create new products and services.

    This innovation is taking place in both consumer-focused and business-to-business B2B industries. Many businesses are finding that much of the infrastructure they need for IoT applications is already in place.

    Auto insurance firms, for example, are now using customer smartphone data to power "pay as you drive" applications.

    Some health insurance firms are monitoring—and giving discounts for—customer fitness activities as revealed by wearable tracking devices.

    In B2B industries like shipping, long-distance trucks and locomotives equipped with GPS and other sensor devices enable companies to offer services to optimize routes, analyze driving, and make recommendations on the cheapest places to fuel up.

    While considerable effort remains to develop IoT standards and link up sensor-based data, there are already many possible applications that can provide value today—including helping people improve fitness, enhance efficiency, and save money.

    Some of the earliest adopters of the IoT have been municipal governments. Boston, with a number of high-profile university labs and tech startups, has been one of the most aggressive adopters.

    Meanwhile, citizens can find empty parking spots using their smartphones—then use the phones to pay for parking.

    They are also using apps to measure and report on street conditions. Boston was also one of the first cities to adopt "BigBelly" trash cans, which continuously report on how full they are so that they can be emptied more efficiently.

    Elsewhere, "smart benches" can be used for charging smartphones and reporting on noise and pollution levels. While the application of the IoT to city administration has really only just begun, Boston, along with cities such as Singapore, Amsterdam, Toronto, and others, is part of the vanguard of addressing how smart, connected devices can transform urban living.

    Forty percent of respondents to a MIT Sloan Management Review survey say they have difficulty hiring analytical talent. Only 17 percent of "analytically challenged" firms say they have the talent they need.

    Among companies reported to be "analytics innovators," 74 percent said they had the analytics talent needed. International Data Corporation IDC predicts a need for , people with deep analytical skills in the US by and a requirement for five times that number of positions with data management and interpretation capabilities.

    Some organizations are taking a multipronged approach by supplementing campus recruiting with alternatives—from turning to managed analytics to cultivating in-house talent.

    With a rising number of analytics and data science programs at universities—more than in the US alone—recruitment efforts in analytics are red hot today.

    Organizations recruiting at these campuses will likely find more success if they work closely with the programs on internships and student projects.

    Once recruited, these graduates are more likely to stay and do productive work if they have meaningful career paths and have the ability to work with others with similar skills and backgrounds.

    Some companies are consciously developing ecosystems of external providers. One, for example, has selected multiple services partners in the areas of business intelligence, predictive analytics, data science, and cognitive technology.

    The company continually monitors the efforts of these partners to recruit and develop qualified people and to keep up with new technologies and methods.

    These are by no means extreme steps. Smart companies are realizing that analytical talent is critical to their success and in short supply.

    They know they must get serious about preparing or partnering with this strategic workforce if they hope to successfully execute their strategies.

    Having decided that analytics and data science skills are key competencies for its organization, Cisco Systems has created an aggressive program for cultivating data scientists and data-savvy managers.

    The company has launched a five-month training program in partnership with two universities to teach employees from all functions the fundamentals of data science.

    To date, more than employees have been trained. And for those who acquire the necessary skills, Cisco has created a well-defined career path in data science, with several roles that offer increased responsibilities and compensation over time.

    The company has created a number of physical Data Labs that serve as platforms for helping different parts of the company act on the opportunities identified through analytics.

    Any conversation about the new world of business analytics should come with a caveat: Businesses have been engaging in analytics for years—decades, even.

    It may be more accurate to say that analytics is experiencing a major renaissance, ushered in by big advances and investments in technological and data capabilities.

    As a result, business analytics has reached a next level of maturity. If anything, there may be a stronger case for the sciences leading the vanguard of analytics.

    Universities, research labs, and other science-focused organizations have been applying and refining analytics approaches to solve some incredibly complex problems through the years, in everything from molecular biology and astrophysics to the social sciences and beyond.

    This environment—marked by a reinvigorated interest in business analytics combined with separate-but-related advances in analytics in the sciences—is one that is ripe for cross-pollination.

    Already we are beginning to see techniques borrowed from the world of science and applied to business challenges. In one example, an organization leveraged tools used by DNA researchers as the keys to unlocking insights buried in tens of thousands of emails.

    These developments are in their nascent stages now, but there are plenty of signs of a coming explosion in shared analytics tools, techniques, and processes between the sciences and the business world.

    Some signs of the inevitable merging of science and business capabilities have already been widely observed. In one high-profile example, a prominent, well-funded startup lured dozens of scientists from a major research university—a coup for the company and a tough loss for the university.

    Look for more ripple effects—good and bad—as the worlds of business and science continue intermingling. From major airlines and insurers to oil and gas and beyond, the business community is actively hunting for science-based approaches that can give them the edge.

    Imagine being on the receiving end of half a million consumer messages a year—and any single one of them could contain information that causes serious trouble for your organization if not handled quickly.

    If the wrong message falls through the cracks, you could be exposed to a ton of risk immediately. Now imagine that you have only a few hundred employees responsible for making sense of all those messages.

    That was the problem facing a financial services organization — and the reason it turned to text analytics.

    But when this organization needed to take its text analytics capabilities to the next level, it turned to the world of science — specifically bioinformatics, in which scientists are working to match sequences of DNA.

    After all, DNA is represented by a series of letters that occur in non-random patterns, not unlike the words and phrases in emails and feedback forms.

    Using algorithms originally developed to compare DNA sequences, this organization cracked the code on thousands of messages received every day.

    Please enable JavaScript to view the site. Still not a member? Analytics Trends The next evolution. Add to my bookmarks. Six significant trends are in play.

    Download Analytics Trends The man-machine dichotomy blurs Analytics expands across the enterprise Cybersecurity: Download trend The man-machine dichotomy blurs.

    Are machines coming for us? Complementing one another There are likely to be a variety of ways in which smart people and smart machines will work alongside each other.

    LifeLearn Sofie In North America, most veterinarians are general practitioners, and while specialists may be available by referral, veterinarians are often required to have expertise across many disciplines, species, and breeds.

    The man-machine dichotomy blurs. Impact on society High. Impact on business High. Expected peak 5 years. Download trend Analytics expands across the enterprise.

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