Mastering The Player Lifecycle


It’s been almost two months since I started the blog and began writing about The Player Lifecycle. Next I will write about how to set up your game analytics stack and The Player Lifecycle has a central role, moving from a theoretical context to a practical one.

There are some points I want to bridge that move.

The Player Lifecycle is about the player in the game

There is a lot that we do with game analytics that is outside the scope of the player in the game. We attribute acquisition sources to users. We make vanity metrics like Daily Active Users, Monthly Active Users and the Total Revenue the entry point for producers and stakeholders evaluation of the game’s performance. Data analysts, scientists and engineers support all data related initiatives, including those outside The Player Lifecycle scope.

The Player Lifecycle is about user behaviour analysis and research. Making it the central point of my work allows me to focus on what the game, as a product or service, delivers to the player: entertainment and fun.

Acquisition != UA

I’m afraid that using Acquisition in a vacuum might be confused with User Acquisition (UA).

UA happens outside The Player Lifecycle. As I mentioned UA influences The Player Lifecycle and is influenced by it but it is not part of it. UA influences The Player Lifecycle because UA strategies create cohorts of players with specific behaviours. We want to understand those behaviours. On the other hand, The Player Lifecycle influences UA because it provides the analysis for the strategies to be defined.

The Acquisition in The Player Lifecycle is more than UA. It is knowing the player as soon as we need to and making technical decisions on how to achieve it.

The several steps are not isolated

Observing each step in isolation is intuitive but counter-productive. It is possible to observe late retention as a function of engagement metrics and redefine acquisition metrics from what we learned from churn analysis. Nothing is self-contained.

Many great insights and powerful models are found when analysing monetisation and churn as a function of retention and/or engagement.

What each step gives you, other than the model itself, is an area of action. We seldom say when discussing an analysis “it is a retention problem” or “it is a monetisation problem” and even go deeper than that, e.g. “it is an early retention problem” or “it is a conversion problem”. In practice, this means that although I say that each step should not be used in isolation, they do isolate the problems.

If you can tell stakeholders, producers and designers where it itches, they’ll find out how to scratch it.

Mastering The Player Lifecycle

As far as an introductory theory of The Player Lifecycle, this is it. The rest is putting it to work. Mastering it is a lot like playing a good game: easy to learn, difficult to master. I certainly don’t call to myself the black belt of it.

You learn to master it as you use and analyse your game’s data in the light of The Player Lifecycle. You learn to find the idiosyncrasies of player behaviour across genres, platforms, countries, time spent in game, etc.

Now we move to practical things. Like setting up your analytics. This will be the focus of the next posts. I won’t stop posting about The Player Lifecycle. I will, however, make it on a more practical way.

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