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探索游戏的新内容和玩家留存理论

发布时间:2013-07-30 16:53:40 Tags:,,,,

作者:Alexander Engel

我之所以选择谈论这一话题,是因为当我们在阐述如何创造游戏的细节时,玩家们对此都很好奇,所以我决定在此与大家分享。

我所思考的问题是,你是如何比较留住新文件与为旧玩家创造内容的利益?同时我想声明的是,本篇文章会偏向理论化,所以不会反应我们(或其它游戏公司)的实际操作。

答案出人意料的复杂。该问题的基础在于,对于玩家来说一款游戏的乐趣可以维持多久。我仔细思考了这一问题,并想出了一个简单的等式去计算平均每个玩家需要花多少天时间才能玩遍所有游戏内容,即到最后他们已经找不到其它事情可以做了:

((H + C + X) * E) / P = L

H表示每周创造内容的小时,C表示已经存在游戏中的内容,X则是每周让玩家沉浸于其中的内容。乘以游戏诞生的周数(E),再除以玩家每天的平均游戏时间(P),你便可以得出平均玩家消耗掉游戏内容需要多少平均天数。找到这些变量你便能够发现复杂之处。

为了明确每周出现内容的时间,我们需要着眼于一些其它变量:带宽(D),即每周团队贡献于游戏中的时间;漏洞修复(B),每周团队致力于漏洞修复的时间;项目时间(J),每周团队致力于项目的时间(包括性能,其它平台,参数,商店等);以及创造1小时游戏内容需要花费多长带宽时间(K)。如此,H的公式将变成:

(D – (B + J)) / K = H

为了明确每周平均玩家所参与的玩家间活动,公式变得较为简单。将玩家每周参与的PtP活动总数乘以他们每周花费在PtP上的平均时间,再除以那一周登录游戏的玩家数量,最终便会得到有关X的公式:

(A * B) / D = X

为了测试这一公式,我们需要分配一些变量。一开始我擅自将随机数字带进这一公式中,不过这并不能代表我们的真实工作和所花费的时间。所以让我们假设这是关于一款假想游戏《太空牛仔》(我在前天晚上所创造的)的公式。

在分解后,我们最后的公式如下:

((((D – (B + J)) / K) / E) + (((A * B) / D) * E) + C) / P = L

让我们记住,这一假想游戏已在180天前发行了,它的名字是《太空牛仔》。我们一直在追踪这款游戏的玩家数量,并且可以将其整合到公式中。基于这些任意数字,我们最终获得的结果是:

((((180 – (120 + 40)) / 8) * 10) + (((200000 * 5) / 300000) * 10) + 100) / 2 = 91

是的,我知道自己的计算和公式非常混乱,一点都不完美。

所以最终的结果便是,在我们的任意游戏发行10周后,我们的任意玩家将会在玩了91天的游戏后耗尽所有内容。假设玩家每天平均游戏时间为2小时,每周发行新内容的时间为5个小时,每周2/3的玩家平均花5个小时于PtP活动中,每周共有30万名玩家在玩游戏。这也假设了我们会在10周之后停止创造内容,而玩家也会在10周之后停止进行PtP活动。

但是等等,我们似乎漏掉了什么!我们忘记了一些非常重要的事:并不是所有玩家在玩遍了所有内容前都是无限期地玩游戏。相反地,许多玩家会在游戏中的某个点上感到疲乏并选择离开游戏。世界上,对于大多数游戏而言,大部分玩家都是在注册后便选择退出,并再也不会回到游戏中。所以对于我们的假想游戏,让我们为100万名玩家在第0天(也就是我们的发行日)注册后设置一些虚构的流失率:

第0天——留存率:100%—-玩家:100万

第1天——留存率:75%—-玩家:75万

第15天——留存率:50%—-玩家:50万

第30天——留存率:40%—-玩家:40万

第90天——留存率:25%—-玩家:25万

所以我们的第一个公式所确定的是玩家耗尽所有内容的平均时间。我们还必须明确多少假想玩家会在遇到这种情况时继续游戏。使用上述数值,我们可以发现在第90天时会有25万名玩家仍在玩游戏。那时候,平均玩家将耗尽所有内容,即意味着12万5千名玩家将无事可做。而另外12万5千名玩家将拥有一些全新的内容,但是关于具体有多少内容则是个变量。

总的来说,“忙碌中的玩家”(游戏邦注:即并未离开并仍有内容可做的玩家)的数值情况如下:

player trends(from gamasutra)

player trends(from gamasutra)

我们可以估算,直到第180天,《太空牛仔》将剩下10万名玩家,因为他们停止玩PtP,并且耗尽了所有可行的内容,所以现在将没有人愿意再玩我们的游戏了。着眼于图表,我们早在第90天的时候就开始注意到这种情况了,那时候许多玩家开始选择退出,而其他玩家也已经完成了所有内容。如果我们能够创造更多内容的话便有可能改变这种情况,即能够提高玩家的留存率并吸引更多新玩家。

为此我们需要添加更多内容到表格中:每当我们去获取玩家时,会出现多少新玩家开始玩我们的游戏。为了呈现出这点,我混合了2种玩家群体,即包含了一群在游戏发行15天后获取的50万名玩家以及一群游戏发行30天后获取的25万名玩家。这从本质上改变了结构,因为除了最初的100万名玩家,还会有50万名在第15天开始游戏,并且在第30天后又会出现25万玩家开始游戏。结果如下:

175k players(from gamasutra)

175k players(from gamasutra)

这看起来还是很糟糕。直到第180天,尽管我们的玩家数量增加了75%,最终也只有少部分玩家仍继续游戏。所以作为游戏开发者,我们需要如何做才能阻止这种情况?我们可以想办法改变留存率。如果我们能够找到方法在每个阶段提高5%的留存率,我们便能在玩家耗尽所有内容前增加玩家数量。

直到第15天和第30天,便会有超过100万名玩家在玩《太空牛仔》。看来提升5%留存率的方法还蛮有效。但是因为在第180天时玩家也会耗尽所有内容,所以他们最终也会选择离去。而如果我们能够双倍增加内容,将耗尽日期往后延长,同时保持留存率,结果便会得到有效的完善:

double content(from gamasutra)

double content(from gamasutra)

如此,直到第180天,我们便拥有之前3倍的玩家仍在玩游戏。尽管玩家总数仍然很低。这便触及了图表的最大限度,因为我们只能延伸到第180天。我们将发现在180天后仍会有许多玩家继续玩着游戏,这将长于基础模式或留存模式。这一模式就像扣篮一样,但是这里所存在的最大问题是内容创造太过昂贵。在游戏中双倍增加内容需要我们投入更多的工作时间并组建更大的团队。我们在上述所提到的数值,也就是8个小时的开发时间只适用于1个小时的内容中。这便意味着如果要双倍增加内容(游戏邦注:从100个小时延长到200个小时),我们就需要投入800个小时的开发时间。这些时间都可以用于创造新图像,新工程,新设计,执行过程,QA测试,并确保我们不会破坏现有内容等工作中了。

我想要在此强调的是,创造更多内容并不是你可以轻松打开或关闭的旋塞。在Disruptor Beam(游戏邦注:专注于用户社区以及网络游戏领域的游戏开发商)中,并不是所有人都能在任何地方发挥自己的技能。就像我便不擅长创造游戏系统或编写代码。而雇佣其他人去完成一些额外工作则需要花费大量时间。不管是雇佣过程,面试,浏览简历还是执行其它扯到法律的工作,我们都需要花上大量时间才能找到一名合适的候选人。在那之后,这些候选人还需要经历相关培训才能真正开始研究问题。这将需要几周甚至几个月的时间,并且可能会在好几个月后才会带来帮助。

让我们着眼于《太空牛仔》最后的比较数值:

base vs content and retention(from gamasutra)

base vs content and retention(from gamasutra)

我们必须通过增加游戏的粘性而想办法达到平衡,即意味着提高玩家的留存率以及我们创造内容的速度,特别是在一开始。在某些情况下,你的大多数初期玩家将会退出游戏,但是你也可以通过游戏更新,扩展与改变将其再次带会游戏中。你需要记住的是,尽管内容创造非常昂贵,但是获取用户也不便宜。我们必须综合考虑方法所带来的利益及其需要花费的成本。

平衡新玩家,用户留存以及新内容是区分一家成功在线游戏公司与不成功的公司的主要元素。在Disruptor Beam,我们便非常希望能够承担这种挑战,从而让玩家可以在《Westeros》中感受到最大的乐趣。

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

Exploring New Content and Player Retention Theory

by Alexander Engel

In one of my other lives, I am a writer. Many nights I sit down on my laptop and bang away at the keyboard, writing about video games and whatever else comes to mind. Sometimes I’ll be up late at night writing, annoying my wife with the clacking, when I’ll have a burst of inspiration and start clacking even faster, much to her dismay. I got on this topic today and, since our players like when we go into the details of how we make games, I decided to share it with our players.

The question I was thinking about was, how do you compare the benefits of retaining new players versus creating content for older players? Please note that the rest of the blog post is theory-crafting, and doesn’t necessarily reflect how we (or any other game company) operates.

The answer is surprising difficult. The root of the question lies in how long the game remains enjoyable for our players. I took some time to think about this and came up with a simple equation to figure out how many days of playing an average player will take to exhaust all of the content in the game, at which point they have nothing left to do and will churn out:

((H + C + X) * E) / P = L

Where H are the hours of content produced per week, C is the content already in the game, and X are the hours of player-to-player content engaged in per week. Multiplied by the number of weeks the game has been out (E) and divided by the average playtime per day in hours (P) you can come up with a rough idea of how many average days it will take for content to be exhausted for the average player. Finding those variables, however, is where the difficulty comes in.

To find the amount of content per week, we need to look at a couple of other variables: Bandwidth (D), which are the amount of hours per week the team can dedicate to the game; Bugfixing (B), which are the amount of hours per week the team dedicates to bugfixing per week; Project Hours (J), the number of hours the team dedicates to projects per week (performance, other platforms, metrics, shop, etc.); And how many hours of bandwidth it takes to produce a single hour of game content (K). Put that way, the formula for H becomes:

(D – (B + J)) / K = H

To find the amount of player to player activity an average player engages in per week, the formula is a bit simpler. You take the total number of players engaged in PtP per week (A), multiply it by the average amount of time they spend on PtP per week (B), and divide that by the total number of players that logged in that week (D). You end up with this formula for X:

(A * B) / D = X

So to test this formula,let’s assign some variables to each. I have taken the liberty to assign completely random numbers to this formula, which do not reflect our actual work or time spent in any way.Instead,pretend that they are for an imaginary game called Space Cowboys that I magicked into creation last night.

After being broken out, our final formula looks like this:

((((D – (B + J)) / K) / E) + (((A * B) / D) * E) + C) / P = L

Remember, this imaginary game was released 180 days ago and is called Space Cowboys. We have been tracking our Space Cowboys player numbers, and we can plug them into our formula. With these completely arbitrary numbers we can get a result:

((((180 – (120 + 40)) / 8) * 10) + (((200000 * 5) / 300000) * 10) + 100) / 2 = 91

Yes, I know my math and formulas are messy and not ideal.

So the final result is that, on average, after our arbitrary game has been out for ten weeks, our arbitrary players will exhaust all content after 91 days of gameplay. This assumes an average playtime of 2 hours per day and 5 hours of new content published per week, with ? of our players engaging in PtP play for an average of 5 hours per week, and 300,000 weekly players. This also assumes that we stop making content after 10 weeks and that players stop doing PtP after ten weeks.

But wait, there’s more! We forgot something vitally important: Not all players will play indefinitely until all content is exhausted. Instead, many players will become fatigued and churn out at some point in the game. In fact, for the vast majority of games, a huge percentage churn out after signing up and never play the game at all. So for our imaginary game, let’s set up some imaginary churn rates for an imaginary 1,000,000 players that signed up on Day 0, our launch day.

Day 0 – Retention Rate: 100% – Players: 1,000,000

Day 1 – Retention Rate: 75% – Players: 750,000

Day 15 – Retention Rate: 50% – Players: 500,000

Day 30 – Retention Rate: 40% – Players: 400,000

Day 90 – Retention Rate: 25% – Players: 250,000

So what our first formula established was the average time for players to run out of content. What we also have to determine are how many of our imaginary players will even be playing by the time they hit that wall. Using the numbers above, we can expect that at day 90, we will have 250,000 players playing our game. At that time, the average player will have run out of content, meaning that 125,000 of those players will have nothing to do. The other 125,000 will have something new to do, but how much will vary.

Put together, the chart of “Engaged Players,” i.e. the players who have not churned out and still have content to do, begins to look something like this for our numbers:

So by Day 180, we could expect Space Cowboys to have 100,000 remaining players, except since we stopped making content on Week 10, and since they stopped playing PtP, they all exhausted the available content and now we have no one left who wants to play our game. Looking at the graph, we really start to see the effects as early as Day 90, since by then many players will have churned out, and others will have completed content. We could have changed this if we’d either built more content, increased the retention of our players, or acquired new players.

So we need to add something more to the table: The incoming numbers of new players who start out, effectively, at Day 0 every time we acquire them and have them start playing our game. To show this, I added two more player cohorts into the mix, with a cohort of 500,000 players acquired on Launch Day + 15 and another cohort of 250,000 players acquired on Launch day + 30. That changes the mix substantially, because we have the cohort of 500,000 players effectively starting at Day 0 on Day 15 of our initial batch of 1,000,000 players, and another cohort of 250,000 players starting at Day 0 on Day 30 of our initial batch, and day 15 of our second cohort. The end result is this:

Still pretty grim. By day 180, even though we increased the total number of players by 75%, we end up with only a tiny fraction still playing the game. So what can we do, as a game developer, to stop that? Well, we could change our retention rate. If we found a way to boost our retention rate by 5% at every step, we would have a boost in players playing our game before they exhaust content:

By days 15 and 30, we would have around 100,000 more players playing Space Cowboys. Not too bad for boosting our retention rate up 5%. However, we still end up churning out players by Day 180 because they ran out of content. If we changed that and doubled our content, pushing our exhaustion date out to 180 days, yet kept our retention rate the same, we would see a greater improvement:

By Day 180, we have almost tripled the number of players still playing. The total number of players, however is still low. This brings up a limitation of my graph, because I only extended it out to Day 180. We’ll see a long tail of players that will continue playing after day 180 which should be substantially longer than the Base or Retention model. This model seems like a slam dunk, but the biggest problem here is that content is expensive. Doubling our content in the game would require many, many hours of work and a much larger team. Our numbers above posited that 8 hours of development work was needed for one hour of content. That means for doubling the existing content in the game – from 100 hours of Space Cowboys to 200 hours of Space Cowboys – we would need 800 hours of development time. That time would go to new art, new engineering work, new design, implementation, QA testing, and regression to make sure we didn’t break any of our existing game content too badly.

I’d like to note here that creating more content isn’t a faucet that you can just turn on and off. Not everyone at Disruptor Beam can equally apply their skills everywhere. I am not too good at creating game systems or coding, and some of our engineers would probably run away screaming if I asked them to take support tickets. Hiring people to do additional work also takes plenty of time. From the hiring process, to interviews, resumes, and other legal work, it can take some time to find a candidate. After that comes the lead time before they can begin, and then they have to be trained before they can dig into the issues. That lead time may vary from a few weeks to a few months, so even doubling team size may not show benefits until (sometimes) several months later.

Anyway, back to Space Cowboys. The final numbers comparing the approaches look like this:

We have to balance increasing the stickiness of a game, meaning the rate at which players are retained, especially during those beginning days, and the rate at which we produce content. At some point, most of your original players have churned out, but you also have the opportunity to reacquire them through further updates, expansions, and changes. Keep in mind that while creating content is very expensive, acquiring customers can also be quite expensive. We have to balance the cost of each with the benefit it provides.

Balancing new players, retention, and new content is part of what separates a successful online game company from an unsuccessful one. Here at Disruptor Beam, we’re excited to take on this challenge for our players, so they can keep having fun within Westeros.(source:gamasutra)


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