不同類型的遊戲玩家的分類與影響

作者:Allison Bilas

在過去幾年裏,也就是自從免費遊戲崛起以來,盈利一直是遊戲產業中一大熱門話題。如何優化盈利,如何明確適當的價格點或者如何更好地轉換玩家只是關於這一主題的衆多討論中的滄海一粟。然而在本文中我們將通過完全不同的角度去討論有關盈利的問題。比起討論如何獲得更高的轉換率,我們將深入挖掘玩家與非盈利者之間的行爲差異。通過這些討論我們呈現給你有關玩家屬性的一些觀點,從而幫助你儘早在遊戲中做出相關的重要決定。

我們主要將討論兩大類別(非盈利者和盈利者)。但爲了更詳細進行解釋,我們將把盈利者分成三種類型:小魚(低核),海豚(中核)和鯨魚(硬核)。

以下是我們所總結的一些內容:

海豚玩家和鯨魚玩家更有可能只玩一款遊戲。一旦他們開始轉換並花錢,他們便更有可能忠實地留在遊戲中。

而非盈利者更傾向於玩一些不同的遊戲,並且每週的遊戲次數大概是1至6次。相反地,鯨魚玩家每週的遊戲次數更少。

當我們進一步着眼於從安裝到第一次購買的時間長短時,我們會發現鯨魚玩家需要更長的轉換時間,即通常是在安裝遊戲後10天。

接下來讓我們進行更詳細的說明!

方法學

第一個用戶羣組從未在任何遊戲中購買任何東西,而其它三個羣組擁有不同的消費分佈。

羣組1==非盈利者==1.14億–97.91%

羣組2==小魚==120萬–1.03%

羣組3==海豚==100萬–0.86%

羣組4==鯨魚==23萬–0.20%

只擁有2%的付費用戶並不是什麼讓人驚訝的事。在整個過程中,樣本中擁有25%的非盈利者的情況只會出現一次。在過去3個月裏13%的非盈利者只玩過2次遊戲。加起來便是在3個月裏有40%從未發生轉換的玩家只玩過2次以下的遊戲。最有可能的情況是所有的這些用戶將只是嘗試遊戲並離開遊戲,這也是目標用戶最常見的行爲:安裝一款應用,嘗試一次然後卸載它。

非盈利者:–

小魚玩家:少於50次

海豚玩家:50次至90次

鯨魚玩家:超過90次

有關參數:

玩遊戲的次數/特殊用戶數乘以特殊的遊戲次數。

每週平均遊戲次數/一個樣本在特定期間總的遊戲次數除以12周。

平均遊戲時間/每個羣組的平均遊戲時間。

第一次購買的時間/從安裝到第一次購買的時間。

結果

關於非盈利者與付費用戶之間的屬性趨勢,我們首先想要了解的是他們的遊戲模式。因此我們比較了每個羣組中玩了一次以上游戲的玩家百分比。

chart(from gamesindustry)

chart(from gamesindustry)

上述圖表便是每個羣組中玩了2次或3次遊戲的用戶比例。我們很容易看出玩了2次或3次遊戲的非盈利者的比例高於盈利者,我們甚至會考慮所有玩家將出現在其中三種盈利者羣組中。

以下是關於分解四種羣組以及他們在玩1至5次遊戲的百分比。從中我們可以看出鯨魚玩家更忠實於一款遊戲,而非盈利者則比付費玩家玩更多遊戲。

不同遊戲數量 非盈利者       小魚          海豚           鯨魚

1款遊戲            83.09%        93.93%   95.09%     95.34%

2款遊戲           12.10%          5.27%     3.35%        4.06%

3款遊戲            3.26%           0.66%      0.90%       0.44%

4款遊戲            1.12%            0.10%      0.36%       0.06%

5款遊戲以上   0.43%           0.01%       0.15%       0.03%

在分析四種羣組每週的遊戲次數時,我們發現99%的鯨魚玩家每週最多隻會玩4次遊戲,而小魚/海豚玩家最多隻會玩2次。相比之下非盈利者每週會玩每款遊戲8次。

遊戲次數    非盈利者      小魚           海豚            鯨魚

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%

盈利者每一款遊戲每週的遊戲次數明顯低於非盈利者。

關於盈利者的深度分析

上述的遊戲次數結果和觀察也向我們呈現出了盈利者的用戶留存情況。

chart(from gamesindustry)

chart(from gamesindustry)

上圖是三種盈利者羣組與非盈利者的用戶留存曲線的關鍵數據點。所以儘管你的鯨魚玩家每週的遊戲次數較少,但他們卻是留在遊戲中最久的羣組。而比起轉換玩家,非盈利者的流失率更高。這也與我們的另外一個有趣的分析點相關:硬核盈利者(遊戲邦注:也就是鯨魚玩家)需要花更長時間進行轉換。讓我們看看下圖:

chart(from gamesindustry)

chart(from gamesindustry)

小魚玩家大概會經過8天才進行第一次購買,而鯨魚玩家則需要最多18天的時間—-超過2倍。因爲鯨魚玩家的遊戲頻率較低,所以他們將較遲把握難度曲線;或者說他們更喜歡在消費真錢前先消費時間。

同時讓我們着眼於下圖玩家最喜歡的遊戲類型:

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

上述圖表指出,當你在分析玩家時你還需要考慮自己的遊戲類型。

基於遊戲類型,鯨魚玩家的百分比與消費者分佈也會不同。就像上圖那樣,你更有可能在一些小遊戲和角色扮演遊戲中找到鯨魚玩家,而益智遊戲與體育類遊戲玩家則更多屬於中核消費者。擁有更多硬核消費者去創造更多收益是取決於其它參數,而轉換率也是其中一份子。通過了解玩家的屬性,你便能夠更有效地分析遊戲的表現。

我們已經在方法學的部分解釋了將盈利者劃分爲三種類別的原因。而下圖是關於這樣的分解的更深入解釋以及他們所創造的收益百分比。

小魚玩家對於總收益的貢獻不到1%,而鯨魚玩家的收益貢獻高達86.6%。需要強調的是這些收益都是來自應用內部購買與廣告。

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

爲了更詳細地說明,我們按照平臺劃分了不同盈利者。結果便是Android玩家主要屬於小魚玩家(60%),而iOS玩家通常會花更多錢:大概有70%的iOS用戶屬於海豚玩家,只有15%屬於小魚玩家。

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

更有趣的是,我們發現iOS盈利者比Android盈利者更快進行第一次購買。讓我們看看下圖:

chart(from gamesindustry)

chart(from gamesindustry)

Android的小魚玩家發生轉變所需要的時間是iOS低核盈利者的9倍。這兩個平臺的鯨魚玩家的區別較少,而海豚玩家的區別也近乎沒有。但從整體上看,iOS用戶變成消費者的時間短於Android用戶。

爲了更深入瞭解盈利者的行爲,我們決定收集三種羣組及其在兩個主要市場美國和中國的範圍(從小魚到鯨魚)分佈。

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

chart(from gamesindustry)

就像我們所預測的那樣,大多數中國和美國盈利者都是中核玩家(遊戲邦注:比起中國美國擁有稍高的比例,即爲64%和48%)。

中國擁有比美國多2倍的鯨魚玩家(即爲37.37%和14.42%)。不僅如此,中國鯨魚玩家的平均消費也多於美國玩家,即爲347.39美元與283.9美元,而中核玩家的平均消費則爲120美元與67.24美元(相差了2倍左右)。

結論

首先讓我們着眼於我們的一些發現:

非盈利者:

在特定時間內會玩更多遊戲;

比盈利者更頻繁地遊戲;

盈利者:

會忠心於一款遊戲(特別是鯨魚玩家);

鯨魚玩家是最忠實的盈利者,然而他們也需要花費更長的轉換時間;

鯨魚玩家的遊戲次數不如其他玩家多;

iOS上的鯨魚玩家比Android上多;

Android用戶需要花更長時間進行轉換,特別是對於小魚玩家來說;

中國的盈利者的花費多於美國盈利者,特別是對於硬核玩家來說。

基於不同條件,這些結果也會有所不同,而這些內容只是我們在研究時對於結果的一些看法。

如果你的非盈利者玩了更多遊戲,且會更頻繁地玩遊戲,那可能是因爲他們非常擅於遊戲。儘管他們不一定會花錢,但是他們卻可能成爲你的遊戲的推廣者。

根據我們的調查結果,非盈利者不僅是擁有較強用戶粘性的玩家,同時他們每次還會玩多款遊戲。因此他們的注意力也更容易被分散。對於這些玩家來說,廣告服務可能會更有效。如果你能在這方面做好的話,你便無需去擔心玩家基礎:因爲他們總是會回到你的遊戲中,即使他們也會開始嘗試其它遊戲。

而關於鯨魚玩家的話,情況可能有所不同。因爲他們一次只會致力於一款遊戲,所以廣告在他們身上並不能發揮作用,你也有可能因此失去這些玩家。所以你應該先了解自己的玩家再去使用廣告服務策略。

實際上,因爲中國盈利者的消費多於美國盈利者,所以我們應該與瞭解真正目標市場的發行商展開合作。

本文爲遊戲邦/gamerboom.com編譯,拒絕任何不保留版權的轉發,如需轉載請聯繫:遊戲邦

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)