· 4 min read
10 Great Resources on Game Analytics
Anders Drachen, Ph.D. is a veteran Data Scientist, Game Analytics consultant and Professor at the DC Labs, University of York (UK).
- Valve’s Mike Ambinder on Being Data-Driven
- The recipe for Candy Crush Saga: luck, skill and puzzles
- Understanding Your Players Using Near Real-time Data
- A Primer on Monetization of F2P Games
- Zombie Epidemics and You
- Cornell University’s Game Design Initiative on Game Analytics
- Predictive Analytics in the Gaming Industry
- 8 Tips to Help You Monetize Your Mobile Game App
- 3 Ways to Test the Accuracy of Your Predictive Models
- Understanding the Realities of Video Game Monetization
As game analytics continues to gain importance in the industry, there is a corresponding mass of writings and presentations becoming available. Therefore, I’ve put together another list of great new and older resources about game analytics, monetization, prediction and design, to keep you up to date with what is happening in the field.
Mike Ambinder gave an excellent presentation at the Steam Dev Days (January 15-16, 2014) about data driven decision-making. The presentation covers Valve’s approach to the acquisition, collection and interpretation of data across products and services. The emphasis on experimentation, migrating knowledge between games and operating iteratively and in a hypothesis-driven manner, is spot on.
The talk covers the infrastructure required to implement a data-driven approach to decision-making, as well as common problems, useful analyses, and lessons learnt as Valve built up their data-driven competency. The recording of this session is available on Youtube, and you can also access Mike’s slides here: Data to Drive Decision-Making
Heather Stark delivers an in-depth analysis of Candy Crush Saga, dissecting the mechanics and monetization aspects of the game. Given its one billion daily gameplays, Candy Crush is still under the intense scrutiny of everyone interested in topics such as cognitive bias, behavioral modification and conversion psychology. You can find the article on Gamasutra.
Michael Manoochehri from Google, and Luca Martinetti from Staq gave a presentation at GDC 2013 about analytics for game developers. They cover the basics of behaviour analysis, virality, user segmentation and understanding retention in near real-time using //staq and Google BigQuery. A recording of this presentation is available on Youtube.
Alex Konda writes about his experience at Ayzenberg, focusing on the nuts and bolts of F2P monetization. Along the way, he showcases some great examples of how to think in economics terms in F2P, dynamic pricing and even briefly outlines some of the core psychological theories in the domain like the flow theory and the impulse purchase theory. Read it on [a]listdaily.
Dmitry Williams talks about virality in games and how it operates, and the crucial role of social networks. He covers the basics of what virality is in a gaming context, and the key features/themes associated with it. He describes how measures such as k-factor, while great for getting an overview on the effectiveness of a campaign, condenses a lot of important information into one number, which can seem more precise than it is. By digging a bit deeper at an individual level, you’ll find out that it ignores important information about your player, notably how connected a specific player is, and her/his relative importance to the game. Read about what “going viral” means: Zombie Epidemics and You.
This is a set of slides from a lecture on game analytics from Cornell, presenting a great overview on the background of the rise of analytics in games, along with a bullet point introduction to game analytics fundamentals. This resource is great for people new to the subject, and includes great examples of practical applications: Game Design Initiative on Game Analytics.
Though Andrew Pearson focuses on the gambling industry, not computer games, this 2012 article provides an excellent introduction to the basics of predictive analytics in the context of behaviour. Pearson describes how predictive analytics work, the pros and cons, and provides a few case examples from gambling. He emphasises the development in customer analytics from simply reporting behaviour, to segmenting customers, to predicting the profitability, and finally to manipulating customer behaviour towards specific patterns that have the highest predicted profitability. Get a run-through predictive analytics in the gaming industry.
Priya Viswanathan provides a couple of useful tips for monetizing mobile game apps, focusing on the user. The blog post also includes links to other reads on the topic.
Predictive analytics is the new green, and Software Advice’s Business Intelligence blog, Plotting Success, recently ran an interesting article on testing the accuracy of predictive models. They interviewed three top data scientists about how they test the accuracy of their predictive models.
Gamasutra interviews a range of experts in game monetization about how difficult monetization is and what in-game economies actually are. Several insights are delivered by Mythic, Execution Labs, FamousAspect and Fortumo.