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举例分析重复购买行为中的LTV计算方法

发布时间:2011-08-29 16:00:08 Tags:,,,

作者:jeremyliew

对许多行业来说,重复购买行为都是一个关键的价值驱动因素。许多公司将用户复重购买行为的比例视为重要运营指标。这在稳定状态下很管用,但有时候却容易误导正迅速发展的企业。从定义上来看,发展意味着企业获得许多新用户(即首次使用服务的用户),而新旧用户掺杂的情况却可能影响实际重复购买行为的判断。

calculate(from mobileorchard.com)

calculate(from mobileorchard.com)

我比较习惯通过断代分析方法区分重复购买行为,并以此计算用户终身价值(LTV)。断代分析方法可以让你在知晓实际结果之前,预先估算出用户终身价值。(请点击此处查看LTV和CAC计算公式)那么我们究竟该如何计算LTV呢?

以下是我发布的一份断代分析示例表格,使用了一些虚构的代表性数据进行说明。(请点击此处查看完整excel表格

table(from docs.google.com)

table(from docs.google.com)

在这份示例表格中,我们假设其数据来源于一个订阅型服务,其上线运营期为一年。首先,我们要根据用户首次订阅服务的时间将其分组。然后我们在第N月底计算留存率(游戏邦注:这里的留存率是指在N月后仍在订阅该服务的用户在所有用户中所占比例),并在表格中以蓝色数据显示。从中可以看出,针对从1月开始订阅服务的用户,我们可以搜集到12个月的留存数据,而从2月开始订阅的用户,我们就只有11个月的留存数据,其后几个月均以此类推。

平均每个分组的数据,你可以得到1个月、2个月等的平均留存率。随着分组的成熟发展,我们可平均统计的数据点集就越少,这就越可能造成计算误差,但这种断代分析法仍不失为一种预测项目运营成效的实用措施。

典型的订阅服务项目通常会出现一种情况:经历初始阶段之后,订阅服务的用户月流失率会逐渐持平。从该表格中同样可以看出这种迹象,在首月之后,其用户月流失率一般为-6%。

假如你从自己订阅服务中找到了这个模式,就可以使用同个月流失率推断几年内的流失情况。从以上表格中我们可以推断用户在今后5年的平均终身价值为9.77个月。

假如你的订阅服务收费标准是20美元/月,毛利率为90%(例如将客户服务等成本计算入内),那么你的一名新用户终身价值就是9.77X20美元X90%=176美元。了解这一点有助于你设定用户获取成本的上限(游戏邦注:不过实际上有许多人选择计算CAC/LTV的比例,并致力于将其结果控制在25%至35%之间)。

这里的示例适用于将活跃用户视为重要价值驱动力的订阅型服务,不过你也可使用类似分析方法评估其他涉及重复购买行为的项目。社交服务的关键衡量指标可能是活跃性(例如本月有多少用户发布照片),社交游戏的重要参数则是同一时期用户购买虚拟商品的消费金额。你可以根据项目发展速度决定测量周期。许多社交游戏每天或每周都会进行断代分析,而一些电子商务公司(他们的用户购买频率更低)则以季度为周期进行分析。这会决定你预测项目发展情况前搜集数据的时间长度。

不同的计费系统会让这种分析更加复杂化(例如年度计费系统就会影响用户平均终身价值),最好至少在项目运营初期使用稳定的计费系统,在相同计费情况的基础上进行断代分析。不过,这种断代分析法也是一种查看计费系统、注册流程、产品功能等发生变化时对留存率所产生影响的有效工具。

游戏邦注:原文发表于2010年7月19日,所涉时间及数据均以此为准。(本文为游戏邦/gamerboom.com编译,如需转载请联系:游戏邦

How to estimate Lifetime Value; Sample cohort analysis

by jeremyliew

In many businesses, repeat purchase behavior is a key driver of value. Many companies track % of repeat purchases as a key business metric. This is useful in steady state, but can sometimes be quite misleading if the company is showing substantial growth. By definition, growth implies many first time customers, and the mix of these new customers can distort the view into how much repeat purchase behavior is actually occuring.

I prefer to try to analyze repeat pruchase behavior, and hence, estimate lifetime value, by doing cohort analysis. This is approximate by definition, but it can give you some sense of lifetime value well before you actually see a full customer lifetime, which can help in accelerating decisions about marketing and customer acquisition.  I recently posted about how you can improve LTV and CAC for your subscription or repeat purchase business.  But how do you estimate Lifetime value?

I’ve uploaded a spreadsheet with a  sample cohort analysis, using representative but dummy data to illustrate how to do this.

In this particular example, I look at a hypothetical subscription business. Assume that the business has been in operation for one year. First, divide the users into cohorts depending on when they initially subscribed to the service.  I calculate retention at the end of month N by dividing the number of subscribers still subscribing after month N by the total number of subscribers that started in each cohort.  These are the numbers in blue. Obviously, for the subscribers that started in month 1, we have 12 months of retention data, for the subscribers that started in month 2 we have 11 months of retention data, and so on.

By averaging across the cohorts, you can get an average retention rate at the end of one month, two months and so on. As the cohorts age, there are fewer datapoints to average over, and hence the potential for error is greater. However, it is still a useful exercise to get an early indication of how the business looks.

A typical pattern found in subscription businesses is that after a steep drop off after an initial period, month-on-month attrition rates tend to level off. You can see a similar pattern in this example, where after the first month, month-on-month attrition rates are around -6% (ie month N subs ~ 94% of month [N-1] subs).

If you see a pattern like this, you can extrapolate forward using the same month-on-month attrition across several years. As you can see in the model, we extrapolate an average lifetime of 9.77 months by extrapolating forward over 5 years of data.

So if you were a subscription business charging $20/month with 90% gross margins (after accounting for customer service costs for example), then you would attribute a lifetime value for a new customer of 9.77 x $20 x 90% = $176. This sets an upper bound of what you would be willing to pay to acquire a customer (although in practice, you would prefer to see a ratio of CAC/LTV in the 25-35% range).

This example is for a subscription business where the key value driver is the number of active subscribers. However, you can conduct similar analysis on any type of repeat behavior business. In a social business the metric might be activity (e.g. how many users posted a photo this month), and in a social game the metric might be dollars spent in virtual goods that period. The measurement periods may vary according to the tempo of the business. Many social games do their cohort analysis on a daily or weekly basis,  whereas some ecommerce companies whose purchases are less frequent may do their cohort analysis on a quarterly basis.  This will dictate how long you have to collect data before you have enough data to project forward.

Different billing mechanisms can complicate this (e.g. an annual billing system will by nature skew average lifetime upwards) and while these can be important levers, it is usually helpful to hold billing constant and compare cohorts on a same-billing basis, at least initially. However, this cohort analysis is also useful tool to see what the impact of changes in billing, registration flow, product features etc can have on retention as you can often see an increase in early month retention from later cohorts.(source:lsvp.wordpress


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