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在不同开发阶段计算手机游戏LTV的方法

发布时间:2016-05-12 14:16:57 Tags:,,,,

作者:Yaniv Nizan

关于LTV(游戏邦注:生命周期总价值)你需要问自己的第一个问题是,为什么你需要去计算它。如果你拥有一款基于免费模式的手机游戏,你最好能够假设生命周期总价值将成为你的游戏的主要KPI(关键绩效指标)。以下便是原因:

在设计阶段,你需要衡量相似游戏的LTV和CPI并以此去推测你的游戏是否赚钱。换句话说便是,你需要确保这是一次有价值的投入。

当你进入软发行时,你便会想去衡量用户终身价值并努力优化它,从而让它能够赶超预期的CPI。

在游戏的市场营销阶段,你需要找到CPI<LTV的部分并在维持这种情况的期间始终专注于这部分内容。

设计阶段的朴实LTV计算

如果你的游戏还未发行,你便还未拥有任何真实数据,即你只拥有一些假设内容。如此你便可以使用“朴实方法”。即将APRDAU乘以预期的单一用户生命周期。

软发行要求你创造一个用户留存模型

关于软发行阶段,你需要一个完全不同的方法。这一阶段你所面对的情况截然不同,因为这时候你已经拥有有关游戏用户留存和盈利的数据点。所以你将需要APRDAU和2天,7天,14天以及30天的用户留存比例。创造用户留存模型是一道复杂的数学公式,它需要统计回归模型,对数函数和积分计算。以下资源将能帮到你:

使用用户留存模型的LTV计算方式

使用电子表格创造用户留存模型

基于市场营销目的的LTV计算细分

当你的游戏准备好问世时,你将拥有一个全新的用户终身价值计算需求。这一次将是关于付费市场营销和用户获取。这时候你的目标便是拥有一个高于获取全新用户成本的CLV。但是你并不需要所有用户都必须满足这一条件,这只需要针对于特定用户便可。一旦你明确了这部分用户,你便可以投入更多时间从中获取更多用户。因为你的游戏已经拥有一定的用户基础和数据点,所以对于全新用户的全新LTV计算将能够依赖于这一数据。这时候的你已经拥有现有用户基础的终身价值数据。而对于全新用户的计算需要你去比较全新用户与现有用户的前7天的ARPU值,并将同样的比例用于你所拥有的LTV数值中。你可以参考这种快速有效的方法:http://blog.soom.la/2016/05/estimating-segment-ltv-segment.html

Mobile_App_LTV_Calculator(from blog.soom)

Mobile_App_LTV_Calculator(from blog.soom)

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Calculating LTV for a Mobile Game – Methods for Different Stages

by Yaniv Nizan

The first thing you should ask yourself about LTV is why do you need to calculate it. If you have a mobile game that is monetizing through free 2 play model, it is safe to assume that lifetime value is going to be a leading KPI for your game. Here is why:

In the design phases, you need to esimate the LTV of similar games and their CPI to ensure the game has viable financial projections. In other words – you want to make sure it’s a good investment

Once in soft launch, you would want to measure the customer lifetime value (CLV) and optimize it so that it exceeds the projected CPI at least for some segments

In the marketing phase of the game, you need to find segments where CPI < LTV and invest in them as long as this condition continues to be true

Naive LTV calculator for design phases

If your game hasn’t launched yet, you still don’t have any real data, just hypothetical one. From this reason, you would use the “naive method”. Simply multiplying the ARPDAU by the expected lifespan of a single user. You can refer to this LTV calculator as an example of how to do it.

Soft launch requires modeling out the retention function

For soft launch phases, you would need a different method however. The situation here is different as you already have data points about the retention and monetiaztion of the game. Specifically, you would need the ARPDAU and at least the followign retention ratios: 2-day, 7-day, 14-day and 30 day. Modeling out the retention function is a complex mathematical excersise that requires statistical regression, log functions and integral calculations. These resources can do the trick for you:

LTV calculator using retention function modeling

Modeling the retention fucntion with a spreadsheet

Segment LTV calculation is based for marketing purposes

As your game is ready for showtime, you will have a new requiment for lifetime value calculations. This time it will be about paid marketing and user acquisition. The goal here is to have a CLV that is higher than the cost of bringing a new user. However, you don’t need this condition to existing for all your users but only for specific segments. Once you find a segment like that, you can invest in bringing more users from this segment. Since your game already has a user base and data points for a period of time, the LTV calculation for a new segment can relay on this data. You would already have the lifetime value from the existing user base simply because enough time has passed. The calculation for a new segment requires comparing the ARPU of the first 7-days between the new segment and the existing user base and than applying the same ratio to the LTV you already have. Here is a quicker way to do it with a calculator for new segment LTV(source:gamasutra

 


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