如何有效分析付費用戶(二)

作者:Vasiliy Sabirov

在上篇文章中我們討論了付費用戶的劃分以及RFM分析方法。

而這次我們將基於完全不同的原則繼續描述用戶劃分。你是否考慮過你的收益結構?誰能創造更多收益—-是新用戶還是舊用戶?新用戶和舊用戶各自的收入比例是多少?這種情況會隨着時間發展發生什麼變化?這便是我們將在本文中討論的內容。

整體的用戶結構

首先,我們將根據用戶的註冊時間將其劃分到不同時間段中。如何選擇不同部分完全取決於你,即基於你的業務性質以及參與項目的時間段。

不管怎樣我們建議最好延伸到5至7個部分。

例如:

第一個部分—-從註冊那天起到現在不足14天

第二個部分—-從註冊那天起到現在有14至30天;

第三個部分—-從註冊那天起到現在有1至2個月;

第四個部分—-從註冊那天起到現在有2至6個月;

第五個部分—-從註冊那天起到現在有6個月至1年;

第六個部分—-從註冊那天起到現在已經超過1年。

通過選擇特定部分,你便能夠基於時間分析創造一份有關用戶結構的報告。

這份報告顯示了什麼:

如果新用戶明顯佔據主導地位—-你便遭遇了用戶留存問題。你的項目不能長久地留住用戶。這也意味着你需要着眼於提高用戶留存或考慮如何從新用戶身上賺取利益。

 

user retention(from tuicool)

user retention(from tuicool)

如果舊用戶明顯佔據主導地位—-這也不是什麼好事。新註冊用戶是否出現什麼問題?也許是時候購買一些流量?你需要牢記的是你將會迎來更多用戶。舊用戶是不可能一直支撐着你前進—-遲早你的應用會開始下滑。

也許下一步你將審查你的用戶結構和動態—-該結構是如何隨着時間的發展發生變化。通常在這個階段會出現一些最有趣的事。

付費用戶的結構

最後,基於同樣方式分析收益:通過付費用戶的註冊時間進行劃分。

在關於收益結構的報告中,我們能夠更清楚地看到舊玩家和新玩家的利益差。實際上(遊戲邦注:在基於長期用戶留存的項目中),新玩家的平均消費單價較低,而舊玩家的平均消費單價較高。

就像在我們的例子中,收益呈現下滑趨勢(需要清楚的是在這個例子中付費用戶的數量是穩定的)。而出現這種情況主要是因爲舊玩家所創造的收益減少。

所以關於該項目我們的診斷是,這個項目在註冊了3個月以上的付費用戶方面存在問題。所以必須優化項目的長期用戶留存從而避免最後一部分用戶的流失。

數學模擬

基於上面的報告,你將能夠創造一個數學模型讓自己可以提前幾個月預測到收益。

需要什麼:

估算每個選擇部分的規模;

面向所有部分估算從部分N轉向部分N+1的可能性(即用戶在一個月內以及在接下來一個月內保持活躍的可能性?);

評估每個部分中每用戶平均收益(ARPU)。

通過結合我們模型中的所有數值,你便能夠創造有關你的用戶結構和收益在一個月,兩個月,三個月以及六個月內如何發生改變的模型。

此外,這一模型將讓你能夠基於流量和盈利評估各種實驗。

它將能夠回答這樣的問題:

如果我斷開付費流渠道而只留下病毒性傳播方式的話會怎樣?這是否會影響我在12個月內的收益?

如果我優化了2%的用戶留存(如30天的用戶留存),這將如何影響用戶和收益的結構?我將改變遊戲平衡並因此提升10%的80級用戶的平均消費單價。這時候我的收益比會發生怎樣的改變?

通過本文我們想要傳達一個簡單的理念:根據用戶註冊時間去了解你的用戶和收益結構是非常重要的。這能幫助你做出更有效的決策—-不管是關於市場營銷,盈利還是遊戲設計。

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

How to analyze paying users. Part 2, The structure of the revenue by time

by Vasiliy Sabirov

Last time we were talking about the segmentation of paying users, reminisced RFM-analysis, as well as whales and dolphins.

This time we will also use segmentation, but on an entirely different principle. Have you ever thought about the structure of your revenue? Who brings more money – the new ones or the old ones? What is the ratio of the revenue from the new and the old users, how it changes over time? This is what we are going to talk about.

The structure of the audience as a whole

At first we divide our entire audience (both paying and not paying) into multiple segments of time from the moment of of their registration. How to select segments – the decision is exclusively yours and depends on the nature of your business and the period of engagement in the project.

Anyway, we recommend to go down to 5-7 segments.

Example:

1st segment – less than 14 days from the moment of registration;

2nd segment – from 14 to 30 days;

3d segment – from 1 to 2 months;

4th segment – from 2 to 6 months;

5th segment – from 6 months to 1 year;

6th segment – more than a year from the moment of registration.

By selecting custom segments, you may build a report on the structure of your audience at the time of analysis.

What does this report show:

If the newcomers clearly dominate – you have a problem with retention. The project can not retain user for a long term. And that means that you have to either work on retention, or think about monetization of the newcomers (for example, make the application paid one).

If the oldies clearly dominate – this is also not good. Is everything OK with the new registrations? May be it is the time to buy a bit of traffic? Remember that the more users, the more users. And oldies do not go far – sooner or later, the app starts to loose rating.

The next step may be to examine not only the structure of your audience but its dynamics – how this structure changed over time. Usually at this stage the most interesting things show up.

The structure of the paying audience

Let’s perform the same manipulations but now only for the paying audience. For example, by report “Users & Gross structure” from devtodev.

This example shows how the stability of the size of your paying audience hides the pitfalls, and the growth of one segment is offset by a decrease of other segments.
We see that the percentage of newcomers (less than 30 days from the moment of registration) is increasing, and the percentage of oldies (6 to 12 months from the moment of registration) decreases. Without the consideration of the structure we would not notice this.

A sign of the healthy application is that the segment of the oldies should be slowly, but growing – more and more users should reach this segment and stay there.

The structure of the revenue

Finally, in a similar manner revenue may be analyzed: by cutting it into segments by the time from the moment of registration of users that make payments.

In the report on the structure of the revenue all distortions for the benefit of oldies and newcomers are usually more vividly pronounced. The fact is that usually (in the projects based on long-term retention) the average check of the newcomers is small, while the average check of the oldies is large enough.

As we see, the revenue in our example has a downward trend (remember that the size of the paying audience in this case was stable). And a decrease in this trend is primarily due to a decrease in revenue from the oldies. Up to the green segment, inclusive, there is some stability, and then decrease occurs.

Our verdict on considered project – the project has problems with payments from users who registered 3 months ago and earlier. It is necessary to optimize the long-term retention of the project so that the natural flow of users in the last segment exceeded the natural outflow.

Mathematical modeling

With the above reports, you will be able to create a mathematical model of predicting your revenue for a few months in advance.

What is needed:

estimate the size of each of the selected segments;

for all segments calculate the probability of transition from the segment N to the segment N+1 (what is the probability of user being active during the month, remains active in the next month?);

calculate the average revenue per user (ARPU) of each segment.

By combining all calculated values in one model, you will be able to model how the structure of your audience and of revenue will change in a month, two, three, six.

Furthermore, this model will allow you to calculate the various experiments with traffic and monetization.

Examples of the questions it will be able to answer:

What if I disconnect the channel of paid traffic and remain only on the virality? How will this affect my revenue in 12 months?

What if I optimize retention (eg, 30 days retention) by 2%, how it will affect the structure of the audience and revenue?
I’m going to make a change in the balance of the game and thus raise the average check of user of 80’s level (which is reached after an average of six months of the game) by 10%. By what percentage my revenue will change?

And so on.

By this article we would like to convey to you one simple idea: it is important to study the structure of your audience and revenue by time from the moment of users registration. This will help you to make more informed and effective decisions, whether it’s marketing, monetization or game design.(source:gamasutra