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针对415款游戏运营表现的分析结果

发布时间:2015-04-24 12:59:15 Tags:,,,,

作者:Allison Bilas

在过去数年中,我们已经看到行业中游戏不断兴衰与更替的趋势。每年游戏领域都会出现一些新秀,有些就此走红,有些缓慢前行,这不禁令人思考:让一款游戏成功的要素是什么?

为了解答这个问题,我们查看了2014年415款跨越多种题材和平台的游戏发布90天后的关键游戏参数。

我们专注于探索游戏的日常参数能否预示其将来的成功或失败。

我们的发现显示:

*在游戏退出beta测试的时候,成功与不成功的游戏之间在参数上就已经出现了差距。这种差距还将随着时间发展而存在。

*在退出beta测试的时候,要提升原始参数颇为难度,因为转化率、ARPPU和留存率都会随着时间发展而衰退。

*多数成功游戏在初始安装量上都有较好的表现,这再次强调了虏获初期玩家的重要性,以及利用这些早期玩家实现转化率,留存率和ARPPU的必要性。

希望这些发现有助于你制定开发下一款游戏的决策。或许你可以从中找到自己需要关注的层面,确定你应该在哪些关键区域加大投入。

分析方法

样本描述

我们的样本包括415款采用免费模式的游戏,它们在2014年安装量均超过1000次。以下图表根据题材和平台划分样本。

这项调查历时3个月左右,始于游戏发布或达到1000次安装量的日期。这些数据仅显示重要的模型,排除了不相关的信息。

byGenre2(from gameanalytics)

415款游戏在应用商店各类题材中的分布情况(from gameanalytics)

 

Platform_dist(from gameanalytics)

415款游戏在iOS、Android和Facebook平台的分布情况(from gameanalytics)

分组

我们样本中的游戏根据分位数来归类,考察的是它们发布90天之后的累计收益。第1组是最为成功的游戏,累计收益最高,占比为10%,而第4组则是累计收益低于中间水准的游戏。

选择这种分组方式是因为它概况了每种参数及其发展趋势,更有利于查看其中的差异。

Thecohorts(from gameanalytics)

The cohorts(from gameanalytics)

(为了便于分析,我们将415款游戏根据它们发布后90天的收益表现划分成4组)

涉及参数

Metrics(from gameanalytics)

Metrics(from gameanalytics)

(我们认为这6种参数是判断游戏成功与否的最重要因素)

结果

以下图表就是我们的发现,它显示了这些游戏在累计收益增长率上的差距。

Cumulative-Revenue1(from gameanalytics)

Cumulative-Revenue1(from gameanalytics)

但究竟是什么因素决定了这种差距?所有游戏刚开始时的DAU都很相近,但其他参数(如留存率,转化率和ARPPU)的表现几乎在游戏发布之后就能马上反映出收益上的巨大差距。让我们看看究竟为何如此。

收益

从免费到付费的转化率显然是游戏早期阶段的关键参数之一。以下图表清楚显示了第1组游戏在转化用户方面表现更出色。而第2组游戏刚开始在转化率上虽然逊色于第3组,但其表现会随着时间发展逐渐提升。

Conversion1(from gameanalytics)

从免费到付费用户的转化率(from gameanalytics)

从我们的样本情况可以看出,成功游戏在刚开始时也拥有较高的ARPPU。在这10%游戏的ARPPU将近15美元。而以下图表显示,那些收益表现不良的50%游戏的ARPPU值低于5美元,并且会快速下降,在首个月之后甚至降至0美元。

ARPPU1(from gameanalytics)

成功的游戏一开始就会获得较高的ARPPU(from gameanalytics)

现在我们来总结一下以上的发现:更高的ARPPU和转化率将意味着更高的ARPDAU,所以第1组游戏无论其DAU规模大小,它们都能够更好地从用户那里实现盈利。

ARPDAU1(from gameanalytics)

ARPDAU1(from gameanalytics)

(无论DAU规模大小,第1组游戏总有更好的收益表现)

留存率和临界质量

留存率是决定游戏成功与否的关键因素。正如上文所述,第1组游戏均具有较高留存率,在刚发布数周时尤其如此。

第1和第2组游戏在第1天和第7天的留存率较为稳定,而第3和第4组的留存率却会持续下滑,并分别降至20%和3%。

1DRetention1(from gameanalytics)

第1天留存率(from gameanalytics)

7DayRetention1(from gameanalytics)

第7天留存率(from gameanalytics)

我们样本中占比10%的顶级游戏同样拥有更高的安装率(有可能源于应用商店推荐,推广和其他营销措施),这有利于实现较高的DAU增长率。

DAU1(from gameanalytics)

这些顶级游戏拥有较高的安装率(from gameanalytics)

这个结果呼应了之前该类游戏较高转化率和留存率的发现。原因在于:第1组游戏擅于转化和留存用户,这就意味着它们能够充分利用应用商店推荐功能或展开强有力的推广工作。这有利于增加DAU,之后又会产生更好的收益,从而令其超越其他组的游戏。

总结

1.我们的分析关注的是留存率,转化率和ARPPU这三个游戏获得成功的关键参数。

2.在游戏退出beta浿时,这些参数能够清楚显示其成功水平。此外,在早期呈现低迷的关键参数,几乎难以在后期扭转颓势。

3.多数成功游戏都能够有效虏获首批玩家:游戏首批玩家是获得成功的筹码。开发者对此投入资源并取悦这批玩家,可以取得良好效果。

4.当你的关键参数达标时,就要投入资源令其保持良好状态。

当然,游戏并非仅仅局限于数据分析的产物。这其中还会涉及到许多变量。我们的发现只能说明在大力推广游戏之前进行迭代的重要性。它不但强调了beta阶段在游戏生命周期中的重要性,也表明了进行游戏分析的必要性。

因为早期参数在游戏发布后就难以扭转,所以开发者很有必要在项目开发过程中尽早引进数据分析环节。在早期阶段就不断监测留存率,转化率和ARPPU。并将这些观察结果转化为具有可行性的操作,以此来优化能够影响关键参数的游戏机制,这样你就有可能令自己的游戏跻身那10%的行列。(本文由游戏邦编译,转载请注明来源,或咨询微信zhengjintiao)

What Analysing 400+ Games Has Taught Us

by Allison Bilas

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.

Share this report on Twitter?

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.

METHODOLOGY

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.

RESULTS

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.

CONCLUSIONS

What does this all mean:

Our analysis comes down to 3 key metrics which are the ones to concentrate on for reaching top tier: retention, conversion and ARPPU.

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.

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.

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%.(source:gamasutra)


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