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不同类型的游戏玩家的分类与影响

发布时间:2015-09-28 15:56:21 Tags:,,,,

作者:Allison Bilas

在过去几年里,也就是自从免费游戏崛起以来,盈利一直是游戏产业中一大热门话题。如何优化盈利,如何明确适当的价格点或者如何更好地转换玩家只是关于这一主题的众多讨论中的沧海一粟。然而在本文中我们将通过完全不同的角度去讨论有关盈利的问题。比起讨论如何获得更高的转换率,我们将深入挖掘玩家与非盈利者之间的行为差异。通过这些讨论我们呈现给你有关玩家属性的一些观点,从而帮助你尽早在游戏中做出相关的重要决定。

我们主要将讨论两大类别(非盈利者和盈利者)。但为了更详细进行解释,我们将把盈利者分成三种类型:小鱼(低核),海豚(中核)和鲸鱼(硬核)。

以下是我们所总结的一些内容:

海豚玩家和鲸鱼玩家更有可能只玩一款游戏。一旦他们开始转换并花钱,他们便更有可能忠实地留在游戏中。

而非盈利者更倾向于玩一些不同的游戏,并且每周的游戏次数大概是1至6次。相反地,鲸鱼玩家每周的游戏次数更少。

当我们进一步着眼于从安装到第一次购买的时间长短时,我们会发现鲸鱼玩家需要更长的转换时间,即通常是在安装游戏后10天。

接下来让我们进行更详细的说明!

方法学

第一个用户群组从未在任何游戏中购买任何东西,而其它三个群组拥有不同的消费分布。

群组1==非盈利者==1.14亿–97.91%

群组2==小鱼==120万–1.03%

群组3==海豚==100万–0.86%

群组4==鲸鱼==23万–0.20%

只拥有2%的付费用户并不是什么让人惊讶的事。在整个过程中,样本中拥有25%的非盈利者的情况只会出现一次。在过去3个月里13%的非盈利者只玩过2次游戏。加起来便是在3个月里有40%从未发生转换的玩家只玩过2次以下的游戏。最有可能的情况是所有的这些用户将只是尝试游戏并离开游戏,这也是目标用户最常见的行为:安装一款应用,尝试一次然后卸载它。

非盈利者:–

小鱼玩家:少于50次

海豚玩家:50次至90次

鲸鱼玩家:超过90次

有关参数:

玩游戏的次数/特殊用户数乘以特殊的游戏次数。

每周平均游戏次数/一个样本在特定期间总的游戏次数除以12周。

平均游戏时间/每个群组的平均游戏时间。

第一次购买的时间/从安装到第一次购买的时间。

结果

关于非盈利者与付费用户之间的属性趋势,我们首先想要了解的是他们的游戏模式。因此我们比较了每个群组中玩了一次以上游戏的玩家百分比。

chart(from gamesindustry)

chart(from gamesindustry)

上述图表便是每个群组中玩了2次或3次游戏的用户比例。我们很容易看出玩了2次或3次游戏的非盈利者的比例高于盈利者,我们甚至会考虑所有玩家将出现在其中三种盈利者群组中。

以下是关于分解四种群组以及他们在玩1至5次游戏的百分比。从中我们可以看出鲸鱼玩家更忠实于一款游戏,而非盈利者则比付费玩家玩更多游戏。

不同游戏数量 非盈利者       小鱼          海豚           鲸鱼

1款游戏            83.09%        93.93%   95.09%     95.34%

2款游戏           12.10%          5.27%     3.35%        4.06%

3款游戏            3.26%           0.66%      0.90%       0.44%

4款游戏            1.12%            0.10%      0.36%       0.06%

5款游戏以上   0.43%           0.01%       0.15%       0.03%

在分析四种群组每周的游戏次数时,我们发现99%的鲸鱼玩家每周最多只会玩4次游戏,而小鱼/海豚玩家最多只会玩2次。相比之下非盈利者每周会玩每款游戏8次。

游戏次数    非盈利者      小鱼           海豚            鲸鱼

0–2次        85.95%        99.71%    99.73%     96.28%

2–4次        7.66%           0.19%      0.22%        3.05%

4–6次        3.25%           0.05%      0.02%        0.46%

6–8次        1.83%            0.02%     0.01%         0.13%

8–10次      0.06%          0.01%      0.01%         0.05%

10次以上    0.57%         0.02%      0.01%         0.03%

盈利者每一款游戏每周的游戏次数明显低于非盈利者。

关于盈利者的深度分析

上述的游戏次数结果和观察也向我们呈现出了盈利者的用户留存情况。

chart(from gamesindustry)

chart(from gamesindustry)

上图是三种盈利者群组与非盈利者的用户留存曲线的关键数据点。所以尽管你的鲸鱼玩家每周的游戏次数较少,但他们却是留在游戏中最久的群组。而比起转换玩家,非盈利者的流失率更高。这也与我们的另外一个有趣的分析点相关:硬核盈利者(游戏邦注:也就是鲸鱼玩家)需要花更长时间进行转换。让我们看看下图:

chart(from gamesindustry)

chart(from gamesindustry)

小鱼玩家大概会经过8天才进行第一次购买,而鲸鱼玩家则需要最多18天的时间—-超过2倍。因为鲸鱼玩家的游戏频率较低,所以他们将较迟把握难度曲线;或者说他们更喜欢在消费真钱前先消费时间。

同时让我们着眼于下图玩家最喜欢的游戏类型:

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

上述图表指出,当你在分析玩家时你还需要考虑自己的游戏类型。

基于游戏类型,鲸鱼玩家的百分比与消费者分布也会不同。就像上图那样,你更有可能在一些小游戏和角色扮演游戏中找到鲸鱼玩家,而益智游戏与体育类游戏玩家则更多属于中核消费者。拥有更多硬核消费者去创造更多收益是取决于其它参数,而转换率也是其中一份子。通过了解玩家的属性,你便能够更有效地分析游戏的表现。

我们已经在方法学的部分解释了将盈利者划分为三种类别的原因。而下图是关于这样的分解的更深入解释以及他们所创造的收益百分比。

小鱼玩家对于总收益的贡献不到1%,而鲸鱼玩家的收益贡献高达86.6%。需要强调的是这些收益都是来自应用内部购买与广告。

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

为了更详细地说明,我们按照平台划分了不同盈利者。结果便是Android玩家主要属于小鱼玩家(60%),而iOS玩家通常会花更多钱:大概有70%的iOS用户属于海豚玩家,只有15%属于小鱼玩家。

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

更有趣的是,我们发现iOS盈利者比Android盈利者更快进行第一次购买。让我们看看下图:

chart(from gamesindustry)

chart(from gamesindustry)

Android的小鱼玩家发生转变所需要的时间是iOS低核盈利者的9倍。这两个平台的鲸鱼玩家的区别较少,而海豚玩家的区别也近乎没有。但从整体上看,iOS用户变成消费者的时间短于Android用户。

为了更深入了解盈利者的行为,我们决定收集三种群组及其在两个主要市场美国和中国的范围(从小鱼到鲸鱼)分布。

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

就像我们所预测的那样,大多数中国和美国盈利者都是中核玩家(游戏邦注:比起中国美国拥有稍高的比例,即为64%和48%)。

中国拥有比美国多2倍的鲸鱼玩家(即为37.37%和14.42%)。不仅如此,中国鲸鱼玩家的平均消费也多于美国玩家,即为347.39美元与283.9美元,而中核玩家的平均消费则为120美元与67.24美元(相差了2倍左右)。

结论

首先让我们着眼于我们的一些发现:

非盈利者:

在特定时间内会玩更多游戏;

比盈利者更频繁地游戏;

盈利者:

会忠心于一款游戏(特别是鲸鱼玩家);

鲸鱼玩家是最忠实的盈利者,然而他们也需要花费更长的转换时间;

鲸鱼玩家的游戏次数不如其他玩家多;

iOS上的鲸鱼玩家比Android上多;

Android用户需要花更长时间进行转换,特别是对于小鱼玩家来说;

中国的盈利者的花费多于美国盈利者,特别是对于硬核玩家来说。

基于不同条件,这些结果也会有所不同,而这些内容只是我们在研究时对于结果的一些看法。

如果你的非盈利者玩了更多游戏,且会更频繁地玩游戏,那可能是因为他们非常擅于游戏。尽管他们不一定会花钱,但是他们却可能成为你的游戏的推广者。

根据我们的调查结果,非盈利者不仅是拥有较强用户粘性的玩家,同时他们每次还会玩多款游戏。因此他们的注意力也更容易被分散。对于这些玩家来说,广告服务可能会更有效。如果你能在这方面做好的话,你便无需去担心玩家基础:因为他们总是会回到你的游戏中,即使他们也会开始尝试其它游戏。

而关于鲸鱼玩家的话,情况可能有所不同。因为他们一次只会致力于一款游戏,所以广告在他们身上并不能发挥作用,你也有可能因此失去这些玩家。所以你应该先了解自己的玩家再去使用广告服务策略。

实际上,因为中国盈利者的消费多于美国盈利者,所以我们应该与了解真正目标市场的发行商展开合作。

本文为游戏邦/gamerboom.com编译,拒绝任何不保留版权的转发,如需转载请联系:游戏邦

It’s all about the players

By Allison Bilas

Monetization has been a hot topic in the games industry over the past years, ever since the rise of free to play games. How to optimize monetization, how to define correct pricing buckets or how to better convert players are just a few of the widely discussed questions concerning the topic. In this article, however, we’ll be approaching monetization from a different angle. Rather than discussing how to achieve a high conversion rate, we will dig into the differences in behaviour between converted players and non-monetizers. With this we’re looking to giving you some insights into these players’ profiles, based on which you should be able to identify them early in the game.

We will be working with two main categories (non-monetizers and monetizers), but 4 cohorts. For granularity purposes, we have broken down the monetizers category into 3 types: minnows (lowcore), dolphins (midcore) and whales (hardcore).

Here’s a preview of our conclusions:

Dolphins and whales are more likely to play a single game. Once they convert and spend money, they are more likely to stay loyal to that game.

Non-monetizers are prone to playing a lot of different games, having any number of weekly sessions from 1 to 6. In contrast, whales play less sessions per week.

A closer look at the time between install to the first purchase reveals that whales take a longer time to convert, with a median of 10 days since install.

Find out more in the Conclusions section!

Methodology

In order to achieve as much granularity and detail as possible on the behavior patterns, we sampled 175M active users in the past 3 months, divided into 4 cohorts based on their total amount spent.

The first cohort consists of the users who hadn’t made any purchase in any game, while the other 3 are based on the amount spent distribution, and its 50th and 90th percentiles.

Cohort 1 = Non-monetizers = 114M – 97.91%

Cohort 2 = Minnows = 1.2M – 1.03%

Cohort 3 = Dolphins = 1M – 0.86%

Cohort 4 = Whales = 230K – 0.20%

Having around 2% of paying users is not surprising. As much as 25% of the non-monetizers in the sample were only seen once over the whole time period. 13% of them had only 2 sessions in the last 3 months. That adds up to the 40% of the players that never converted having only 2 sessions or less in 3 months. Most likely, all those users tried the game and churned, being a reflection of a common behavior of the population: installing an app, trying it once, and uninstalling it.

Non-Monetizers Minnows Dolphins Whales

- Less than 50th From 50th to 90th More than 90th

- total less than $1 $1 – $32 total more than $32

Metrics considered:

Number of games played Number of unique users by number of unique games played.

Average number of weekly sessions Total sessions over the sample period divided by 12 weeks.

Average Session Length Average session duration per cohort.

Time to first purchase Number of days since install until the first purchase is done.

Results

The first thing we wanted to look at in terms of profile trends among non-monetizers versus paying users, was their games playing patterns. Therefore, we set out to compare the percentage of users playing more than one game in each of the cohorts.

The graph above shows the distribution of users playing either 2 or 3 games for the respective cohorts. It is easily spotted that the percentage of non-monetizers playing 2 or 3 games is higher than that of monetizers, even when considering all players pertaining to the 3 monetizers cohorts as falling into the same bucket.

For the numbers-loving people like ourselves, here’s how the tabular data looks like when breaking down the 4 cohorts and the percentage in which they play 1 to 5 games. These results point out that whales are usually more loyal to a single game, whereas non-monetizers play more games than paying users do.

# of Games non-monetizers Minnows Dolphins Whales

1 83.09% 93.93% 95.09% 95.34%

2 12.10% 5.27% 3.35% 4.06%

3 3.26% 0.66% 0.90% 0.44%

4 1.12% 0.10% 0.36% 0.06%

+5 0.43% 0.01% 0.15% 0.03%

Analyzing the weekly number of sessions played by the four cohorts, we found that 99% of whales play up to 4 weekly sessions, and minnows/dolphins only up to 2. Non-monetizers, however, can play up to 8 sessions per week per game.

# of Sessions non-monetizers Minnows Dolphins Whales

0-2 85.95% 99.71%% 99.73% 96.28%

2-4 7.66% 0.19% 0.22% 3.05%

4-6 3.25% 0.05% 0.02% 0.46%

6-8 1.83% 0.02% 0.01% 0.13%

8-10 0.06% 0.01% 0.01% 0.05%

10+ 0.57% 0.02% 0.01% 0.03%

The number of weekly sessions per game for the monetizers is considerably lower than the number of sessions of non-monetizers.

An in-depth analysis of monetizers

The number of session results and the observations made above pointed us straight towards the retention of monetizers.

What you’re looking at is a model adjusted to the key data points of the retention curves of the 3 monetizer cohorts and the non-monetizers. So your whales, though playing a lower number of sessions per week, is the cohort that retains best across time. Non monetizers, however, will retain less than the users who converted. This ties in with another interesting fact our analysis showed: hardcore monetizer (whales) take longer to convert. Take a look below:

While the time needed for minnows to make their first purchase is around 8 days, for whales it gets up to 18 days – more than double. As whales play less often, it could be that they catch up with the difficulty curve rather late; or that they like to take their time before committing to spending.

We’ve also looked into the top genre player preferences. Take a look below:

The graphs above point out the fact that when analyzing your players, you have to take into consideration which genre your game is in.

The % of whales and the spender distribution is different depending on the genre. As can be seen on the results above, you are more likely to find a whale (and therefore have whales) on Trivia and Role Playing games, while Puzzle and Sport players tend to be mostly mid core spenders. Whether having a higher percentage of hardcore spenders results in a higher revenue depends on other metrics too, conversion being one of them. But understanding players’ profiles is definitely a good start for a successful analysis of your game’s performance.

We’ve explained in the Methodology section the reasoning behind how we divided monetizers into the 3 categories. Here’s a more in depth picture of this split and the percentage of the revenue they generate.

Minnows, represent less than 1% of the total revenue, whereas whales generated 86.6% of the revenue in our games sample. It is important to note at this point, that the revenue is derived both from IAPs and in-app advertising.

For a more precise view, we’ve broken down monetizers by platform. It doesn’t come as a surprise that Android players are in their majority minnows (60%), while iOS ones spend more money: as much as 70% of the iOS users are dolphins, with only 15% being minnows.

More interesting from this perspective, we have found that iOS monetizers make their first purchase in considerable less time than Android monetizers do. Take a look at the chart below.

It takes minnows on Android 9 times more days to convert than it would an iOS lowcore monetizer. The difference between whales on the two platforms is considerably lower (of less than a week), while for dolphins the difference is close to none. But overall, it takes iOS users less time to make a purchase than Android ones.

To get even deeper into the behaviour of monetizers, we decided to pull the data on the 3 cohorts and their distribution across our spectrum (minnows to whales) for two of the major markets (and very different at that): US and China.

As expected, the majority of the Chinese and US monetizers are midcore (with US having a slightly bigger percentage than China – 64% against 48%).

The interesting difference though, intervenes when it comes to whales. China has more than twice as big a percentage of whales (37.37%) than the US (14.42%). Not only that, but Chinese whales will also spend, in average, more than the US ones: $347.39 vs $283.9, with a median of $120 vs $67.24 (almost double).

Conclusions

First, let’s take a look at a breakdown of our findings, bullet-points style (we do love a good list!):

Non-monetizers:

play more games in a given period of time;

play more often than monetizers (at large);

Monetizers:

tend to be loyal to one game (especially whales);

whales are the most engaged of your monetizers, however they also take longer to convert;

whales play less in terms of number of sessions than any of your players;

there are more whales on iOS than on Android;

Android users will also take longer to convert, this goes especially for minnows;

Chinese monetizers spend more than US ones, especially the hardcore ones.

Though these results can mean different things depending on your context, here are a few of the thoughts related to the results that crossed our minds when researching this.

If your non-monetizers play more games, more often, they most probably are skillful at it. Though they may not be paying, they might be your promoters.

Our results have shown that non-monetizers are not only very engaged players, but they play more than one game at a time. Therefore, their attention is by default divided between multiple games. By these players, ads serving may not be perceived as a nuisance. Done right, you may even struck the right cord, without having to worry about decimating your player base: they will still come back to your game even if they do find another one they’ll start playing.

With whales, the story could be different. Having shown they commit to one game at a time, bombarding them with ads may not be in your best interest, as you might end up losing some of them. So, be judicious with your ads serving strategy, and know your players.

The fact that Chinese monetizers are spending more than US ones, got us thinking how far a good partnership with the right publisher that knows the market may take you.(source:gamesindustry)

 


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