Getting Ahead of the Game: How a gaming studio gets customer insights from big data
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The document discusses how a gaming studio utilizes big data to obtain customer insights, focusing on gamer profiling, user satisfaction, and retention metrics. It highlights the importance of rapid turnaround and cost-effective solutions for analyzing data trends, such as demographics and gameplay statistics. The document also mentions the technologies used, including Azure Data Factory and Machine Learning, for processing and predictive analytics.
Getting Ahead of the Game: How a gaming studio gets customer insights from big data
- 1. Getting Ahead of the Game How a gaming studio gets customer insights from big data Sonia Carlson PRINCIPAL PROGRAM MANAGER soniacar@microsoft.com
- 2. Understand patterns!! No BI or Data Scientists Rapid turn-around Must be cost-effective
- 6. Gamer Profiling User Satisfaction service MTTR, failure rate, #complaints Usage Trends DAU/MAU, Average Session Length, Play Session Time, Retention Commerce billing, loyalty points, in-app purchases Demographics age, education, occupation, marital status, gender Social Network friends, leaderboards, matchmaking, anti-cheat Actions
- 8. 2277,2013-06-01 02:26:54.3943450,111,164.234.187.32,24.84.225.233,true,8,1,2058 2277,2013-06-01 03:26:23.2240000,111,164.234.187.32,24.84.225.233,true,8,1,2058-2123-2009-2068-2166 2277,2013-06-01 04:22:39.4940000,111,164.234.187.32,24.84.225.233,true,8,1, 2277,2013-06-01 05:43:54.1240000,111,164.234.187.32,24.84.225.233,true,8,1,2058-225545-2309-2068-2166 2277,2013-06-01 06:11:23.9274300,111,164.234.187.32,24.84.225.233,true,8,1,223-2123-2009-4229-9936623 2277,2013-06-01 07:37:01.3962500,111,164.234.187.32,24.84.225.233,true,8,1, 2277,2013-06-01 08:12:03.1109790,111,164.234.187.32,24.84.225.233,true,8,1,234322-2123-2234234-12432-344323 … Log Files Snippet (10s of TBs per day in cloud storage) User Table UserID FirstName LastName State … 2277 Austen Auterberry Oregon 664432 Olivar Selwick Washington 8853 Stacia Grimley California New User Activity Per Week By Region profileid day state duration rank weaponsused interactedwith 1148 6/2/2013Oregon 216 33 1 5 1004 6/2/2013Missouri 22 40 6 2 292 6/1/2013Georgia 201 137 1 5 1059 6/2/2013Oregon 27 104 5 2 675 6/2/2013California 65 164 3 2 1348 6/3/2013Nebraska 21 95 5 2
- 10. What technologies should I use? How can I get them to work together?
- 12. Azure Data Factory Azure Machine Learning HDInsight Orchestrates Usage and Customer Profile Data Data Movement and Processing Predictive Analytics Anti-cheat
- 14. "availability": { "frequency": “Hour", interval": 1 } Hourly 12-1 1-2 2-3 GameUsageActivity: (e.g. Hive):
Editor's Notes
- #3: Other services Collection & HW load balancing anonymous + MS login cross-platform cloud save Configuration A/B testing
- #17: Product Segmentation, [Retail] Log Processing, Product Recommendations Game Log Analytics (usage/trends) Automated Risk Processing (2) Click-stream analysis Cart abandonment analysis 360 degree customer view Purchase decision analysis (2) Fault-detection analysis Course Log Analytics (usage/trends) Product telemetry analysis Data movement Log analytics user and product profiling to drive targeted marketing product operations and health from product telemetry systems