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分享以数据提升用户获取率的方法(1)

发布时间:2014-03-04 14:29:52 Tags:,,,

作者:Nick Lim

引言

用户是游戏的生命之源。为了获得持续的玩家来源,开发者和发行商不惜投入大量资金获取用户,从而提升了每用户获取成本。其中的复杂性甚为惊人,因为要获取用户你就有超过50个地方需要花钱。所以寻求最低获取成本,以及高质量的玩家之间的平衡,实属一场无休无止的战争。(请点击此处阅读本系列第2篇

我们将在本文解决的一个问题就是,我们该如何使用数据令资金最大化地获取玩家。在此,我们假设你能够部分或完全将玩家分配到不同的获取渠道。

target-audience(from payelp.com)

target-audience(from payelp.com)

获取优化问题

在最广泛层面,人们会比较每用户获取成本以及每名玩家的价值。例如,最近有文章称手机游戏的每名玩家获取成本为2.73美元,而每名玩家收益为1.96美元。为了实现贸易顺差,第一步就是找到低获取成本的渠道。让我们举这个例子,你花5000美元获取2000名用户。每名玩家的总体成本是2.5美元,使用Mobile App Tracking或Google Analytics等工具,你可以创造一个显示5000美元在不同获取渠道的分配表格。其表格内容如下:

表1

表1

正如你所见,渠道B从每用户成本角度来看更为昂贵。

你将遇到的另一个问题就是,并非所有用户成本都是相同的。对于免费游戏而言,付费用户远比免费用户更有价值。在我们的案例中,假设你从这2000名用户中获得了3600美元,你每名用户收益为1.8美元。那么我们的表格可以延伸如下:

表2

表2

很显然,虽然渠道A和C拥有相同的每用户成本,它们所带来的付费用户类型并不相同。有趣的是,渠道B拥有最高的每用户成本和最高的每用户收益,因此有理由继续通过这一渠道获取用户。这当然有点夸张,但你可以看出一些问题了。

使用每用户平均收益要注意一点:每用户收益实际上遵从了一个幂次法则。所以使用每用户平均收益可能很棘手也不靠谱。如果你关心这一点,你可以计算付费用户数量。

表3

表3

免费用户该如何?

现在精明的读者应该会发现几个问题:a.免费玩家该怎么办?b.如果付费玩家未来继续花钱呢?c.免费用户为游戏创造了一个优秀的玩家社区。那我们先从免费玩家切入……

免费玩家可以创造一个社区,未来也可能成为付费玩家。前者并不容易计量,但后者却可以。我们可以使用数据技术来预测有多少免费玩家在不久的将来会变成付费用户。虽然我们无法深入技术层面的问题,但我们却可能根据得分准确评价免费用户是否将转化为付费用户。根据这些得分,我们可以生成大量未来的预测付费用户,并令表格扩展如下:

表4

表4

绿色格子中的“未来付费玩家”是各个渠道所预测的未来付费用户数量。以下是其他公式:

总体付费玩家=当前付费玩家+未来付费玩家

每付费用户收益=总体付费用户*每用户收益

每付费用户成本=“成本”/“总体付费用户”

因为你可以在自己的计划中考虑未来付费用户,每个渠道的成本和收益现在也可以计入未来付费用户的考虑因素了。在我们的例子中,虽然渠道A和C拥有相同的每用户成本(2美元),但渠道A看似更佳选择,因为其每付费用户成本低于每付费用户收益。渠道A似乎会在之后引进能够转化为付费状态的用户。所以如果你还有一个提升玩家数量而又不耗损过多资金的目标,那么渠道A应该是个比渠道C更棒的选择。

下一步

所以我们现在所做的就是通过比较“每付费用户成本”以及“每付费用户收益”来估计每个渠道将引进多少付费用户,你可以对每个获取渠道的质量进行排名。

那么下一步就是将你所有的获取预算投入那些每付费用户成本与每付费用户收益差别最小的渠道对吗?不,每位认真的游戏营销人员都知道,这种做法并不可行,原因包括:a.该渠道可能没有足够的库存或用户;b.从一个渠道购买更多用户,适度地调整每付费用户成本,因为这可能提升竞标价格,并且额外用户并不一定质量相同。此外,你可能还有不同的限制因素要考虑,例如寻找创建社区的最小用户数量。

所以,虽然渠道B很适合引进付费用户,但它一开始却不会带来大量用户。如果你的游戏本质上具有“社交性”,带有PvP或分享功能,那你必须在任何时间都拥有免费或付费的最小用户数量。所以将所有的营销资金投入渠道B并非理想选择。

我们将在下文中解决如何在不同的渠道分配用户获取预算,考虑多个不同的限制因素和目标。(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

Improving user acquisition effectiveness with big data – Part 1 Predicting number of paying users by channel

by Nick Lim

The following blog post, unless otherwise noted, was written by a member of Gamasutra’s community.

The thoughts and opinions expressed are those of the writer and not Gamasutra or its parent company.

Introduction

Users are the lifeblood of games.  To get a constant stream of players, developers and publishers are spending huge amounts of money, driving up the cost per user.  The complexity is daunting as there are easily over 50 different places where you can spend money to acquire players.  It is a never ending game, no pun intended, to find the channel that provides the lowest acquisition costs balanced with high quality players.

The question we will address in this article is how would we use big data to optimize the large amount of money that is used to fund player acquisition.  We will assume that you are able to attribute players to the different acquisition channels, either completely or partially.

Framing the acquisition optimization problem

At the broadest level, one starts to compare the cost per player acquired and the value of each player.  For example a recent article listed the cost per player for mobile games at $2.73 while the revenue per player was $1.96   In the quest to achieve a favorable balance, a first pass would be to look for channels with low acquisition costs.   Let’s start with an example where you spent $5000 to get 2000 users.  The overall cost per user would be $2.50  Using attribution tools such as Mobile App Tracking or Google Analytics campaign ids, you can create a table that shows how the $5000 is broken down by the different acquisition channel.  The table may look like this:

As you can see, channel B is more expensive from a cost per user perspective.

The next problem you will encounter is that not all users are equal; for a free-to-play game, a user who pays is vastly more valuable than a free user.  In our example, let’s assume that you got $3600 in revenue from those 2000 users, giving you an overall $1.80 revenue per user. So we can extend the table above to include the number of payers and the revenue from the payers.

Clearly even though channels A and C have the same cost per user, they are not bringing in the same type of paying users.  And interestingly channel B which has the highest cost-per-user also has the highest revenue per user, thus justifying continuing to acquire users through that channel.  This is of course somewhat exaggerated, but you get the idea.

One note about using average revenue per user: revenue per user actually follows a power law.  Here’s a previous post about this topic.  So using average revenue per user can be tricky and unreliable.  If you are concerned about that, you can instead calculate just the number of paying users.

How about those free users?

Now the astute reader would have raised a couple of questions: (a) what about the free players?  (b) what if the payers continue to spend in the future? (c) free users provide a good community of players in the game.  Let us start with the free players…

Free players can provide a community and also might become payers in the future.  The former is not easily quantifiable, but the latter is.  We can use big data techniques to predict how many of the free users will become paying users in the near future.  While we cannot go into depth on the technical aspects here, we can say that it is possible to accurately score free users on whether they will convert to paying status. (Interested readers can watch a 30min video on this at the Sonamine website)  Based on these scores, one can generate a number of predicted future payers and expand the table further like this:

The green column of “Future Payers” is the predicted number of future payers from each channel.  Here are the other formulas:

Total payers = Current payers + Future Payers

Revenue per paying user = Total payers x revenue per user

Cost per paying user = “Cost” / “Total Payers”

Because you have included the Future Payers in your calculations, the cost and revenue from each channel can now take into account the free users who might pay in the future. In our example, although Channels A and C have equal cost per user ($2), channel A would look to be a better choice because the cost per paying user is less than revenue per paying user.  Channel A seems to bring in users that convert to paying status at a later time.  And so if you have another goal to boost the number of players without losing too much money, then channel A would be better than channel C.

Next steps

So what we have done is to estimate of how many paying users each channel will bring in, and by comparing the “cost per paying user” and “revenue per paying user”, you can rank the quality of each acquisition channel.

So the next step would be to logically spend all your acquisition budget with the channel where the difference between cost per payer and revenue per payer is the lowest, right?  No, every serious game marketer knows this won’t work for several reasons : (a) the channel may not have enough inventory or users (b) buying more users from one channel measurably changes the cost per payer because it might drive up the bidding prices, and the additional users may not be of the same quality.  Moreover, you may have different constraints to work with, such as looking for a minimum number of users to create a community.

So while channel B is very good for paying users, it’s not bringing in a lot of users to begin with.  If your game is “social” in nature, with PvP or sharing features, you must have a minimal number of users, free or paying at any time.  So spending all your UA dollars on channel B is not an option.

In the next post, we’ll address how to allocate an acquisition budget among all the different channels using the portfolio approach, take into account multiple different constraints and objectives, and have some fun with excel and linear solver…(source:gamasutra


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