Across many industries, large and small organizations are using analytics and data science to offer greater insight and customer service. The gaming industry is almost well placed in that - particularly with online and social gaming - the companies already keep a vast amount of data on gamers. The challenge remains to make use of this data in a way that offers true value for money whilst enhancing the user experience.

I had recently attended the Gaming Analytics Innovation Summit 2014 (May 1-2, 2014) at San Francisco, CA, organized by the Innovation Enterprise. The summit brought together acclaimed speakers and attendees for deep insight into how the gaming industry uses analytics and data science. For speakers, it had a line-up consisting of 20+ leading executives working in Analytics, Data Science & Business Intelligence in gaming. Through real-life business case studies and deep-dive discussions, the summit offered solutions and insight from the leaders in the Gaming space.

To help its readers succeed in their Analytics pursuits, KDnuggets provides concise summaries from selected talks at the summit. These concise, takeaway-oriented summaries are designed for both – people who attended the summit but would like to re-visit the key talks for a deeper understanding and people who could not attend the summit. As you go through it, for any session that you find interesting, check KDnuggets as we would soon publish exclusive interviews with some of these speakers.

Highlights from selected talks on day 1 (Thu, May 1)

Here are highlights from selected talks on day 2 (Fri, May 2):

Lenin Gali, Senior Director, Game Analytics Services, Ubisoft shared the challenges and gratification he got through his journey until now at Ubisoft.

At first he shared some facts about gaming industry:

• 58% of Americans play video games
• There are an average of two gamers in each game playing U.S. household
• Average U.S. household owns at least one dedicated game console, PC or smartphone
• Average age of game players is 30 years
• Total consumer spend on games industry during 2012 was $20.77 billion

 
He emphasized that today’s game enterprise needs: acquisition, retention, monetization, performance, real-time actions, predictability and delivery of an enhanced 360-degree consumer experience. Contrasting with traditional BI, he characterized Big Data Analytics as innovative, iterative, real-time, predictive and interactive process.

While listing the challenges, he stressed upon bridging business and production data silos, data governance, master data management (MDM), tactical vs. strategic goals, and ROI(to account for sustainable investment for growth). In his assessment, he observed a lot of opportunities including highly scalable workforce, highly scalable technologies, 360-degree consumer insights, and enterprise data programs. He concluded his talk mentioning that the biggest goal of predictive analytics at gaming enterprises should be to understand the player and deliver actionable insights.

Bryan Neider, Senior Vice President, Global Operations & Shared Services, Electronic Arts talked about “Navigating the Blue Ocean Opportunity Using Data Science & Analytics”. He mentioned that the gaming industry has witnessed rapid growth over the last decade, due to increasing number of devices, improving user experience and advanced capabilities of gaming devices. The market would be growing to $70B+ this year, driven by emerging markets and platforms. The business models have been evolving as per the following trends leading to a new way of how gaming firms think about access and usage of their IP:

• Boxed products  -->  Digital Services
• Pay Upfront  -->  Pay Over Time
• Channel Distribution  -->  Direct Distribution
• Play Alone  -->  Play with Friends

EA is transforming its core competencies in Big Data & Predictive Analytics. EA Digital Platform is comprised of four components called: Descriptive, Diagnostic, Predictive and Prescriptive Analytics. In the past year, EA had a dominating market share of 35% due to its corporate digital transition continuing at full-speed & early leadership in next generation Analytics.  Realizing true business value from Big Data requires transformation in both analytics skillset and business culture.

For EA, Analytics is an intersection of Art (Art of Game Design) and Science (Science of Product Management). The Analytics-based Games-as-a-Service business model is transforming the development and business culture at EA. In the end, he recommended:

• Be patient and purposeful (develop multi-year roadmap, test regularly)
• Build and buy strategy (invest in internal capabilities and leverage external leading solutions)
• Kaizen philosophy (holistic change management)

 
Spiros Christakopoulos, Senior Manager, User Acquisition, Sega delivered an interesting talk on “Mobile User Acquisition with Big Data”.   He defined Mobile User Acquisition (UA) Analytics System as an information system that uses mobile’s data to enable efficient allocation of marketing capital. He discussed how to build an automated ROI focused marketing analytics system using: Back End infrastructure that is easy to scale, 3rd party attribution and integration methods, UA analytics solutions, Game Data real time funnels necessary to make marketing decision and a simplified version of a nLTV(Life Time Value) calculation.  He explained the system that is currently employed by SEGA and is used for multiple existing titles including highly popular titles like Sonic Dash.

Mobile User Acquisition systems have seen considerable changes within the last two years, such as:

• General Demographic Data --> User Specific Data
• Subjective Creative Process --> Objective Data Driven Process
• Box Shipping --> Games as a Service

 
Marketing Analytics System helps make decisions for capital allocation in order to turn marketing into a profit center. Giving an overview of Mobile UA Ecosystem, he explained the following key steps in the Analytics process: Ingest & Normalize, Store & Process, Analyze, Visualize, and Optimize.

The key marketing metrics to observe during data analysis are:

• Creative Optimization (CTR, CVR)
• UA Optimization (CPI, CPC)
• Social Metrics (K Factor, Organic Lift)
• Capital Allocation Metrics (ROI, Chargeback%)

 
Spiros ended the talk saying: “More Data will lead to More Automation, which in turn will lead to More Efficient Market”.

Related:

• Gaming Analytics Summit 2014: Day 1 Highlights
• Is data mining the new tool for gamers seeking pre-launch secrets?
• Qualitative Analytics: Why numbers do not tell the complete story?