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如何有效分析付费用户(一)

发布时间:2015-12-08 14:58:34 Tags:,,,,

作者:Josh Bycer

付费用户是指那些为你的产品花钱的人。我们必须了解他们的行为的所有细微差别:他们付钱的内容是什么,支付速度多快,以及付了多少钱。如此我们便能够更好地摸清楚他们的感受以及他们是否能够从对你的产品的投资中获得满足。实际上,甚至在免费在线游戏中,玩家所做出的每笔消费都是一种投资:一开始他们会先花钱,而最后他们会获得一些投资回报(ROI)—-通常是以现金或他们的体验情感呈现出来。因此你应该将这些用户当成投资者,即使他们只是中小投资者。

为了更好地理解这种行为,我们可以找到有关付费用户的分销与需求的特定分析方法与报告。但今天我们将把RFM分析作为了解你的付费用户结构的一种基本方法。

RFM表示:

R–时间,即多久前进行了最后一次的购买;

F–频率,即多长时间进行一次购买;

M–消费金额,即总的购买费用。

你需要提供三个与这些付费用户参数相关的标记。通常情况下,在理论材料中,用户评估是按照三点式进行(好,一般,差),但实际上在RFM分析中我们甚至会使用五点式或十点式规模进行用户评估。为了更简单地说明,让我们着眼于一个三点式系统例子:

R==1,自从用户上次花钱已经过去很长时间了;

R==2,上次消费距离不是很久;

R==3,用户最近刚刚消费;

F==1,用户很少消费;

F==2,用户会基于一定频率消费;

F==3,用户经常消费;

M==1,总的消费数额较小;

M==2,用户为项目支付了一定的数额;

M==3,用户支付了很多钱。

当然了,这时候问题便出现了:在这种情况下该如何理解很久以前/最近,经常/很少以及很多/较少。我们可能会基于两种方式去回答这一问题:

1.专家评估。没人知道你的项目比你优秀。因此,你可以自己定义很久以前和最近,以及很多和较少代表什么。让我们假设很久以前指的是一个多月以前,很少是指一个月一次或更少,较少是在整个消费期间投入的钱不到100卢布。

2.分位数和四分位数。让我们回想一下数理统计。根据其中的一个参数分配你的用户(例如在一段时期内的支付总数),选择所有用户中的前5%,并假设这些用户是花费较多的用户。恭喜你,你已经拥有5%的用户样本分位数。你还可以使用四分位数(游戏邦注:四分位数==25%的分位数–级别),并将第一四分位数当成较多,最后四分位数当成较少,而它们之间的数值便是平均的付费金额。而即使如此,当你使用分位数和四分位数时,你也不能漏掉主观评估,因此你需要在这时候再次着眼于第1种方法。

不管怎样,你需要花些时间在Excel表格(或其它工具)上按照时间,频率和消费金额去标记每一个付费用户。

现在是最有趣的部分。

你可以观察这些标记在你的付费用户间的分布,并判断哪些用户的数量最多。这能够帮助你更好地划分付费用户并规划市场营销行动,以获取更高利益。

一个简单的例子:

最近刚购买,但是购买频率很低(或者只购买了一次)—-全新付费用户。你该如何面对他们?表达出你的感激之情!你的目标是激励他们不断进行购买。就像许多研究表明的那样,用户的不断购买以及购买频率和数额都能够提高一款应用赚取百万美元收益的几率。

最近刚购买,且购买频率很高—-忠实用户。他们不需要额外的激励,但是你也应该想办法表达对于他们的忠诚的感谢(游戏邦注:如意外的奖励,惊喜等等)。

频繁购买,但上一次购买已经过去很久—-处于离开边缘的忠实用户。换句话说,这些用户的钱正逐渐从你的指缝间流走。所以你的目标便是提醒他们你的存在。也许一封简单的推送邮件便足以。或者你应该询问他们发生了什么改变以及为什么他们会离开。

很少购买,且上一次购买已经过去很久—-已经离开的用户。他们不可能成为你的忠实用户,这是在过去出现的某些内容所导致的。你可以提供给他们(不只是他们)一个行动建议(即使这对于你来说不一定是有益的),这可能会激励他们再次购买并回到产品中。即使不行的话你也能够明确他们不喜欢什么内容,并基于反馈去完善你的产品。

让我们想象以下情况:

1.项目X想要提高收益;

2.他们进行了RFM分析,结果如下:

1)忠实用户的流失率非常高;

2)许多用户只进行了一次购买。

3.他们使用了一些触发内容去判断用户何时处于“上一次购买已经过了很久”或者用户在停止花钱前属于忠实用户等状态。而他们也在这些时刻提供给用户“他们难以拒绝的内容”(游戏邦注:如特殊行动,巨大的折扣,登录时来自推送通知或弹出窗口的信息);

4.重复购买的比例上升了,更多忠实用户留在了产品中;

5.利润增加。

上述提到的例子都只使用两个参数:时间和频率。

而添加消费金额参数到报告中将让你能够使用每个用户的支付金额。

除此之外,这样的分析也可以是基于用户数量或你从他们那赚到的钱。

我们还能够将消费金额–时间(即用户花了多少钱以及他们上次花钱是在多久前)和消费金额–频率(用户花了多少钱以及他们花钱的频率)结合在一起。

而基于一个参数框架去分析付费用户的最简单的方法便是根据时间,频率和消费金额去决定用户和他们的消费的分布。

根据付费用户在免费游戏中的消费规模而对他们进行的分析经常使用一些海洋生物作比喻:

鲸鱼—-带来巨大收益的用户;

海豚—-带来平均收益的用户;

小鱼—-带来较少收益的用户。

在这里我们并不是在谈论一次付费的总数,而是用户在整个付费期间所支付的总体金额。再一次地,这里的巨大,平均和较少的总额也是基于专家的评估。

通过分析每个部分的用户数以及你从每个部分的用户中赚取的钱数,你便能够判断该采取怎样的行动去提高收益。降低价格?提高价格?专注于“鲸鱼用户”的留存率?

data(from gamasutra)

data(from gamasutra)

在我们的devtodev.com中,我们根据用户的消费金额将其划分成五个部分,即多了“大鲸鱼”和“大海豚”。从讨论例子中我们可以看出收入的主要部分是来自“鲸鱼”和“大海豚”用户,因此市场营销规划应该主要专注于这类型用户。

而我们提到的这些内容只是分析付费用户的众多方法之一。还有很多其它问题和方法能够帮助你更好地定制自己的项目盈利方法。例如:

你的用户转换成付费用户的速度?是在第一次购买,第二次购买还是在第十次购买的时候?

用户愿意花钱买什么?为什么他们会成为付费用户?

你在用户的第一次购买时能赚到多少钱?在用户重复消费时又能赚到多少钱?

新手能带给你多少钱?而资深用户又能带给你多少钱?

而我们也会在之后的文章中告诉你所有这些问题的答案。

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

How to analyze paying users? Part 1, RFM-analysis

by Vasiliy Sabirov

So, paying users are those guys that bring money to your product. It is very important to know all the nuances of their behavior: what do they pay for, how fast and how much. It is important to know what they feel by doing this, do they get satisfaction from the investments they made into your product. In fact, even in the case of f2p online-game every payment made by gamers is their investment: at the beginning they pay, at the end they get some ROI, which can be denominated in a currency or in the emotions they experience. Therefore, you should perceive users as investors even if they are minority investors.

In order to better understand the behavior, distribution and needs of paying users there exist special analytical methods and reports. Today we will talk about RFM-analysis as one of the basic methods of understanding the structure of your paying audience.

RFM stands for:

R – Recency – how long ago the last purchase was made;
F – Frequency – how often purchases were made;
M – Monetary – the volume of purchases during all the time.

You give three marks that correspond to those parameters to every paying user. As a rule, in theoretical materials a user is assessed with a three-point scale (relatively speaking: good, normal, bad), however in practice we have also faced five- or even ten-point scales in RFM-analysis. To make it easier, let’s look at the example of the three-point system:

R = 1, it has been a long time since a user payed last time;
R = 2. last payment was made relatively short time ago;
R = 3, a user has payed recently;

F = 1, a user pays very rarely;
F = 2, a user pays with moderate regularity;
F = 3, a user pays often;

M= 1, the sum of all payments is small;
M = 2, a user have payed moderate amount of money to the project;
M = 3, a user have payed much money.

Of course, the question arises: how to understand in this case long time ago/recently, often/rarely and much/little. It is possible to answer this question in two ways:

1.Expert assessment. No one knows your project better than you. Therefore, define for yourself what is long time ago and recently, what is much and little. Let’s say, long time ago is a month and more ago, rarely is once a month or more rarely, little is not more that hundred rubles during the whole payment history.

2.Quantiles and quartiles. Let’s recall mathematical statistics. Arrange your users according to one of the parameters (for example, to the sum of payments made during all the time), take, for instance, top-5% of all the users and say that these users payed much. Congratulations, you have already got five-percent quantile of your users’ sample. You can also take quartiles (quartile = quantile of 25% -level), and assess first quartile as much, last as little, and what is between them as an average size of a payment. Be that as it may, even when you use quantile and quartile you cannot do without subjective assessment, therefore, look at point 1 again.

Anyway, having spent some time in Excel (or somewhere else), you will give a mark to every paying user for recency, frequency and amount of payments.

Now is the most interesting part.

You can see how those marks are distributed among your paying users and what are the majority of users. This will allow you to segment your paying audience and plan your marketing actions aimed at raising profit.

A simple example:

Purchased recently, but rarely (or only one payment) – new paying users. What should you do with them? Express your gratitude! Your aim is to stimulate them to make repeated purchases. As many researches show, repeated purchases, their regularity and size raise the app’s chances to earn a million of dollars.

Purchased recently and purchase often – loyal users. They do not need additional stimulation, however you can find a way and thank them for their loyalty (unexpected bonus, surprise, just “thanks” – all these work).

Purchased often, but long time ago – loyal users on the verge of leaving. In other words, these are the money that right now slip through your fingers. Your aim is to remind them about yourself. Maybe, a simple push-notification will be enough. Maybe, it is worth asking them what has changed and why they are leaving.

Purchased rarely and long time ago – outflow of users. They didn’t become loyal, something in the past prevented them from doing it. You can propose them (and not only them) an action – even if it is not profitable for you – which would stimulate them to make a repeated purchase and to return to the product. Otherwise, at least you can try to find out what they didn’t like, and adjust the product based on the feedback.

Imagine the following case.

1.Project X wants to raise its income;

2.They conduct RFM-analysis, and it shows that:

1)the outflow of loyal users is very high;

2)many users make only one purchase.

3.They introduce some triggers to the project that allow to recognize the moment when the stay of a user in a status “one purchase” is too long or when a user that was loyal before stops paying. At these moments users get “an offer that they can’t refuse” (special action, big discount, information is delivered by push-notification or pop-up window when logging);

4.The percentage of repeated purchases rises, more loyal users stay with the product;

5.Profit.

Both of discussed examples operate only two parameters: Recency, Frequency.

Adding the Monetary parameter to the report will allow to use volumes of every user’s payments in addition.

Besides that, the analysis can be conducted based on the quantity of users or based on money that you get from them.

In addition, it is possible to look at the combination Monetary-Recency (how much do users pay and how long ago did they pay), Monetary-Frequency (how much and how often do users pay).

The easiest way is to analyze paying users in the frames of one parameter is to get distribution of users and their payments depending on time (long time ago – recently), on frequency (often – sometimes – rarely), on amount (much – average – little).

In particular, analysis of paying users according to the size of their payment in f2p-games is usually described with the help of the inhabitants of sea depths:

Whales – users that bring big sums;

Dolphins – users that bring average sums;

Minnows – users that bring small sums.

Here we don’t speak about the sums of one payment but rather about general sums accumulated during the whole payment history of a user. The differentiation on big, average and small sums is made based on expert assessment again.

By analyzing the amount of users in every segment and the amount of money that you get from every segment you will be able to understand, which actions are better for raising the profit. To lower prices? To raise prices? To focus on the retention of “whales”?

At our service devtodev.com we have divided users depending on the volume of their payment into five segments additionally defining “grand whales” and “grand dolphins”. In particular, the discussed example shows that the main part of the income is brought by “whales” and “grand dolphins”, therefore, the focus of the marketing forces should be on them.

This is only a part of methods that can be used to analyze paying users. There are many more questions, answers to which will help you to customize your project’s monetization better. There are only some of them:

How fast your users are converted into paying? During the first, the second or the tenth purchase?

What do users pay for? Why do they become paying after all?

How much money do you make on the first payments of gamers, how much – on repeated?

How much money do beginners bring to you, how much do “oldies”?

We will surely tell you about every method in detail in our future articles.

If you don’t want to wait, we invite you to the free webinar, which will be held on October, 27th 2015 at 12 p.m CDT.

During the webinar we will tell you about all the methods of paying users’ analysis, describe cases devoted to how analytics of paying users helps to raise the income. (source:Gamasutra

 


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