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What this means is that there is a cost-benefit relationship in game telemetry, which basically describes a simplified theory of diminishing returns: Increasing the amount of one source of data in an analysis process will yield a lower per-unit return.
A classic example in economic literature is adding fertilizer to a field. In an unbalanced system (underfertilized), adding fertilizer will increase the crop size, but after a certain point this increase diminishes, stops, and may even reduce the crop size. Adding fertilizer to an already-balanced system does not increase crop size, or may reduce it.
Fundamentally, game analytics follow a similar principle. An analysis can be optimized up to a specific point given a particular set of input features/variables, before additional (new) features are necessary. Additionally, increasing the amount of data into an analysis process may reduce the return, or in extreme cases lead to a situation of negative return due to noise and confusion added by the additional data. There can of course be exceptions -- for example, the cause of a problematic behavioral pattern, which decreases retention in a social online game, can rest in a single small design flaw, which can be hard to identify if the specific behavioral variables related to the flaw are not tracked.
User-oriented game analytics typically have a variety of purposes, but we can broadly divide them into the following:
To an extent, operational and tactical analytics inform technical and infrastructure issues, whereas strategic analytics focuses on merging user telemetry data with other user data and/or market research.
When you're plotting a strategy for approaching your user telemetry, the first factors you should concern yourself with are the existence of these three types of user-oriented game analytics, the kinds of input data they require, and what you need to do to ensure that all three are performed, and the resulting data reported to the relevant stakeholder.
The second factor to consider is to clarify how to satisfy both the needs of the company and the needs of the users. The fundamental goal of game design is to create games that provide a good user experience. However, the fundamental goal of running a game development company is to make money (at least from the perspective of the investors). Ensuring that the analytics process generates output supporting decision-making toward both of these goals is vital. Essentially, the underlying drivers for game analytics are twofold: 1) ensuring a quality user experience, in order to acquire and retain customers; 2) ensuring that the monetization cycle generates revenue -- irrespective of the business model in question. User-oriented game analytics should inform both design and monetization at the same time. This approach is exemplified by companies that have been successful in the F2P marketplace who use analysis methods like A/B testing to evaluate whether a specific design change increases both user experience (retention is sometimes used as a proxy) and monetization.
Up to this point, the discussion about feature selection has been at a somewhat abstract level, attempting to generate categories guiding selection, ensuring comprehensiveness in coverage rather than generating lists of concrete metrics (shots fired/minute per weapon, kill/death ratio, jump success ratio). This because it is nigh-on impossible to develop generic guidelines for metrics across all types of games and usage situations. This not just because games do not fall within neat design classes (games share a vast design space and do not cluster at specific areas of it), but also because the rate of innovation in design is high, which would rapidly render recommendations invalid. Therefore, the best advice we can give on user analytics is to develop models from the top down, so you can ensure comprehensive coverage in data collection, and from the core out, starting from the main mechanics driving the user experience (for helping designers) and monetization (for helping making sure designers get paid). Additional detail can be added as resources permit. Finally, try to keep your decisions and process fluent and adaptable; it's necessary in an industry as competitive and exciting as ours.
Magy Seif El-Nasr, Anders Drachen, and Alessandro Canossa are the editors of Game Analytics: Maximizing the Value of Player Data, a recently published compendium of insights from more than 50 experts in industry and research. This article is based on selected content from the book.