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What Analysing 400+ Games Has Taught Us

The following content is a report put together by our awesome data science team for GDC15. As we received very good feedback on it, we wanted to share it with you, hoping you’ll find these insights interesting and helpful.
Analytics for Mobile Game Developers
* This paper was originally published on the GameAnalytics blog

Over the past years, we have seen trends in our industry rise and fall in the blink of an eye. Each year brings with it a set of new fads in gaming, some make it, some falter, but one question is ever present: what makes a game successful?

In an attempt to answer this question, we’ve looked at the evolution of key game metrics over 90 days after launch, across 415 games released in 2014 and spreading across multiple genres and platforms.

Our key focus was to explore whether or not there is a difference in a game’s daily metrics that could indicate its success or failure.

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Our findings show that:

  • by the time games exit beta there is already a difference in metrics between successful and unsuccessful games. This discrepancy will be maintained over time;
  • after exiting beta, improving upon the initial metrics is difficult, as conversion, ARPPU and retention largely decay over time;
  • most successful games show a better handling of their initial installs, the so-called “Golden Cohort”. This stresses the importance of the very first players acquired, and the necessity of taking advantage of those early birds in terms of conversion, retention and ARPPU.

Explore these findings and discover how you can exploit them in making your next or current title a success. Find out where your focus should lie, and determine the key areas your investments should go towards.


Sample Description

Our sample included 415 free-to-play games that achieved more than 1000 installs in 2014. The sample’s distribution by genre and platform is presented in the chart below.

The results are broken down by day over a period of 3 months, starting with the date at which the games either launched (where this was evident from the data) or reached 1000 installs. The data is smoothed to capture the important patterns and leave out irrelevant noise.

GameAnalytics - A Game Analytics Tool For Game Developers

The distribution of genres of the 415 games mirrored what is seen in the app store.

GameAnalytics - A Game Analytics Tool For Game Developers

The 415 games analyzed were across iOS, Android and Facebook platforms.

Cohort distribution

The games included in our sample were divided by quantiles, on their cumulative revenue over the complete period of the 90 days after launch. Group 1 represents the most successful games – the 10% of games with the highest cumulative revenue, whereas Group 4 includes those games with cumulative revenue below the median.

This distribution was chosen as it provides a general understanding of the patterns found in each of the metrics, and their evolution over time, making it easy to conclude upon differences.

GameAnalytics - A Game Analytics Tool For Game Developers

For this analysis, we split the 415 games into four groups based on how much money they made over 90 days post-launch.

Metrics considered

GameAnalytics - A Game Analytics Tool For Game Developers
We considered these 6 metrics in what was most important in driving success.


What our findings come down to is the chart below, which illustrates the difference in the cumulative revenue increase rate when considering quantiles.

The apparent difference in the cumulative revenue increase rate when considering quantiles. Check out the log scale on the X axis!

But what determines this difference? While all games have a close start in terms of DAU, the performance of other metrics (namely retention, conversion and ARPPU) will imply a large difference in revenue almost immediately after the game’s launch. Let’s see how that happens.

Show me the money!

Conversion to paying appears to be one of the crucial metrics in the early stages of a game. The graph below clearly shows that Group 1 games are better at converting users into monetizers. An interesting insight here is that Group 2, though it starts out with a lower conversion rate than Group 3, performs better over time.

GameAnalytics - A Game Analytics Tool For Game Developers

Conversion to paying users: a crucial metric in a game’s early stages.

From our sample, successful games also achieve a high ARPPU from the very beginning. For the top 10% this comes close to $15. At the other side of the spectrum, as seen below, ARPPU for games in the lower 50% quantile is under $5 and declines rapidly, becoming approximately $0 after the first month.

GameAnalytics - A Game Analytics Tool For Game Developers

Successful games achieve a high ARPPU from the very beginning.

Let’s now corroborate the findings above: higher ARPPU and conversion rate will mean higher ARPDAU, so Group 1 games are all around better at monetizing their players, regardless of DAU size.

GameAnalytics - An Analytics Tool For Game Developers

Regardless of DAU size, Group 1 games are all around better at monetizing their players.


Retention and critical mass

Retention plays a key role in determining the success or failure of your game. As shown below, games in Group 1 have consistently higher retention, especially in the first weeks after being launched.

While groups 1 and 2 manage to maintain a stable Day 1 and Day 7 retention after the initial drop, for groups 3 and 4 this downward slope continues and reaches below 20% and 3%, respectively.

GameAnalytics - An Analytics Tool For Game Developers

Games in Group 1 have consistently higher retention, especially in the first weeks after being launched.

GameAnalytics - An Analytics Tool For Game Developers

For less successful games (Groups 3 & 4) Day 7 retention drops below 3%.

But the top tier games in our sample also start out with higher install rates (likely from app store featuring, promotion and acquisition efforts), which supports a higher DAU growth rate.

GameAnalytics - An Analytics Tool For Game Developers

Top tier games in our sample started out with higher install rates.

This circulates back to our initial findings around high conversion and retention rates. Here’s why: being better at converting and retaining players means Group 1 will take full advantage of the app store feature or heavy promotional efforts. The increase in DAU that the latter generate is the last piece of the puzzle, which clearly differentiates them from the other groups.


What does this all mean:

  1. Our analysis comes down to 3 key metrics which are the ones to concentrate on for reaching top tier: retention, conversion and ARPPU.
  2. By the time a game exits beta, the metrics will clearly indicate its success level. Moreover, low values in the key metrics at an early stage will most likely be hard to turn around.
  3. Most successful games take advantage of the golden cohort: the very first players of your game are your best bet. Invest resources in retaining and keeping them happy, the rest will follow as a result.
  4. Once your key metrics fall into place, invest your resources into maintaining their healthy state.

Of course, games are much more than numbers. And there are a lot of other variables that come into play. What our findings come down to is the importance of iterating before heavily promoting your game. This speaks not only for how critical the beta period is in your game’s lifecycle, but also stresses the importance of game analytics.

As those first metrics are hard to turn around once your game has launched, data analysis should be introduced into your development process as early as possible. Continuously monitor retention, conversion and ARPPU from an early stage. Transform these insights into actionable points, use them to improve the mechanics that influence them, and you could propel your game into that top 10%.

* This paper was originaly published on the GameAnalytics blog

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