Churn prediction model

流失预测模型

Churn prediction model

流失预测模型

Musing about a use case that’s been with me for a decade

关于一个陪伴我十年的应用场景的思考

No company likes to lose valuable customers. In the beginning, a company typically focuses on acquiring new clients, then grows by offering additional products to existing clients or trying to get them to use their products more.

没有哪家公司喜欢失去有价值的客户。起初，公司通常专注于获取新客户，然后通过向现有客户提供附加产品或鼓励他们更频繁地使用产品来实现增长。

If all is going well, there comes a point when the company is large enough that it must also choose a slightly more defensive strategy and focus on retaining existing customers. Despite the best user experience, there will always be a group of clients who are not satisfied and decide to leave.

如果一切顺利，终有一天公司规模足够大，就必须选择更具防御性的策略，重点关注现有客户的留存。即使拥有最佳的用户体验，总会有一些不满意的客户选择离开。

The company then faces the problem of how to prevent these (voluntary) departures as effectively as possible. This is where the churn model, among others, comes to the rescue.

这时公司就面临一个问题：如何尽可能有效地预防这些（自愿）流失。这时，流失模型（以及其他工具）就成了救星。

What is the churn model?

什么是流失模型？

It’s a predictive model that estimates — at the level of individual customers — the propensity (or susceptibility) they have to leave. For each customer at any given time, it tells us how high the risk is of losing them in the future.

它是一个预测模型，在单个客户层面估算他们流失的倾向（或敏感性）。对于任意时间点的每个客户，它告诉我们未来失去他们的风险有多高。

Technically, it’s a binary classifier that divides clients into two groups (classes) — those who leave and those who don’t. In addition to assigning them to one of the two groups, it will typically give us the probability with which the client belongs to that group.

从技术上讲，它是一个二元分类器，将客户分为两组（类别）——流失的和未流失的。除了将客户分配到两个组别之一外，它通常还会给出客户属于该组别的概率。

It is important to note that this is the probability of belonging to the group of clients who leave. Thus, it is the propensity to leave and not the probability of leaving. However, it is possible to estimate the probability through a churn model.

需要注意的是，这是属于流失客户组的概率。因此这是流失的倾向，而非流失的概率。不过，通过流失模型是可以估算概率的。

What is it useful for?

它有什么用处？

By knowing which clients are at the highest risk of leaving, we can better target our rescue efforts. For example, we can reach out to these clients with a marketing campaign, reminding them that they haven’t purchased from us in a while, or even offering them a benefit.

通过识别哪些客户流失风险最高，我们可以更有针对性地开展挽回工作。例如，我们可以通过营销活动联系这些客户，提醒他们有一段时间没有从我们这里购买了，甚至向他们提供优惠。

In addition to knowing which clients to target, we can use the churn model to calculate the maximum benefit price that is still worthwhile. For example, if we know that the estimated probability of a particular client leaving is 10% and their annual revenue is $100, the expected value of future annual revenue is $90. Therefore, an offer that typically reduces the probability of leaving to 5% (the expected value of the revenue is then $95) will be worthwhile for this client, so long as it does not cost more than $5.

除了知道应该针对哪些客户之外，我们还可以利用流失模型计算仍值得提供的最大优惠价格。例如，如果我们知道某个特定客户的流失概率为10%，而他们的年收入为100美元，那么未来年收入的期望值为90美元。因此，一份通常能将流失概率降至5%（收入期望值则为95美元）的优惠，只要成本不超过5美元，对该客户就是值得的。

What do we need for the churn model?

流失模型需要哪些数据？

Like any supervised machine learning model, a churn model needs training data with response (target) and explanatory variables (features). Based on this training data, the model learns to best capture the relationship between features and target.

与任何监督式机器学习模型一样，流失模型需要带有目标变量和解释变量（特征）的训练数据。基于这些训练数据，模型学习如何最好地捕捉特征与目标之间的关系。

Typically, this is historical data, where we know which clients eventually left and which did not. Those who left have a positive target (yes, they left). Others have a negative target (no, they didn’t leave). Whilst features describe clients at a point in time when that outcome was not yet known.

通常情况下，这些是历史数据，我们知道哪些客户最终流失了，哪些没有。流失的客户具有正向目标（是，他们流失了），其他客户具有负向目标（否，他们没有流失）。而特征描述的是客户在尚未知晓该结果时点的状态。

A properly defined target is fundamentally key. In many cases this is simple (e.g., cancellation of last product), sometimes less so (e.g., no transactions in the last three months). However, it is possible to apply the churn model to both contractual (e.g., bank) and non-contractual (e.g., e-shop) client relationships.

正确设定目标变量从根本上说是关键。在许多情况下这很简单（例如，取消最后一个产品），有时则不那么简单（例如，过去三个月内无交易）。不过，流失模型既适用于契约型客户关系（如银行），也适用于非契约型客户关系（如电商）。

Features include any data that can help identify clients who churn. Often this includes socio-demographic data, data on products owned, historical transactions, client-company interaction, e-commerce behaviour, and so on.

特征包括任何有助于识别流失客户的数据。通常包括社会人口统计数据、持有产品数据、历史交易数据、客户与公司互动数据、电商行为数据等。

It is also important to be careful about how far in advance we want to estimate the propensity to leave. In other words, how long is the time between the day we look at clients through the available features and the day we can tell if they have left? If that time is too short, we won’t have much time to make any kind of response. If, on the other hand, it is too long, the model will be less accurate and up to date.

同样重要的是，要谨慎考虑我们想要提前多久估算流失倾向。换句话说，从我们通过可用特征观察客户的那一天，到我们能够判断他们是否已经流失的那一天，之间的时间是多长？如果时间太短，我们就没有太多时间做出任何反应；如果时间太长，模型则会变得不太准确且不够及时。

What does such a model look like?

这样的模型是什么样的？

Modern churn models are often based on machine learning; specifically, on the binary classification algorithms mentioned above. There are a number of these algorithms, and it is necessary to test which one best fits a specific situation (specific training data, amount of data, etc.). Whether you use simple models such as logistic regression, more complex random forest or GBM, or venture into neural networks, you need to pay attention to the following two things.

现代流失模型通常基于机器学习；具体来说，基于上述二元分类算法。这些算法有很多种，需要测试哪一种最适合特定的场景（特定的训练数据、数据量等）。无论你使用的是逻辑回归等简单模型、更复杂的随机森林或GBM，还是涉足神经网络，都需要注意以下两点。

Classifiers have a variety of performance metrics. Since churn is very low for most companies, it is not enough to look at the accuracy of the churn model. For example, if the churn is 10% and the churn model for all clients says they will not leave, it will have 90% accuracy. But this is not useful. So, among other things, you need to look at sensitivity (how many of the clients who actually leave were detected by the model) and precision (how many of the clients identified by the model actually left).

分类器有各种性能指标。由于大多数公司的流失率非常低，仅看流失模型的准确率是不够的。例如，如果流失率为10%，而流失模型对所有客户的预测都是“不会流失”，那么它将有90%的准确率——但这毫无用处。因此，除了准确率外，你还需要关注敏感度（模型正确检测出了多少实际流失的客户）和精确率（模型识别出的客户中有多少确实流失了）。

Furthermore, it is advisable to not use the resulting model as a black box. Rather, try to understand the parameters based on which decisions are made. Not only can this reveal flaws in the model or data, but it can also be very useful information for product and marketing teams. For example, if we know that the absolute amount of discount has less impact on churn than the relative amount of discount, we can use this to create more effective campaigns and pricing strategies.

此外，建议不要将最终模型当作黑箱使用。相反，应尝试理解决策所依据的参数。这不仅能揭示模型或数据中的缺陷，还能为产品和营销团队提供非常有价值的信息。例如，如果我们知道折扣的绝对金额对流失的影响小于折扣的相对比例，我们就可以据此制定更有效的营销活动和定价策略。

What next?

接下来该做什么？

Once you have the churn model ready, you need to plug it into the day-to-day running of the company. This involves monitoring, evaluating and updating it on an ongoing basis (whether that’s simply re-training it or even adding new features).

一旦你准备好了流失模型，就需要将其融入公司的日常运营中。这包括持续监控、评估和更新模型（无论是简单地重新训练，还是添加新的特征）。

Consequently, you can start to automatically detect events that tend to increase the propensity to leave that need to be responded to as quickly as possible.

这样一来，你就可以开始自动检测那些往往会导致流失倾向上升、需要尽快应对的事件。

External data consultants can help you with both. But beware, it is crucial for the churn model (even more than for other data projects) to involve people with experience and a feel for the specific situation in the company and industry.

外部数据顾问可以在这两方面为你提供帮助。但请注意，对于流失模型而言（比其他数据项目更甚），让具有经验和对公司及行业特定状况有感觉的人员参与进来至关重要。

The article was originally written in Czech and published on Bizztreat Blog.

本文原文以捷克语撰写，发表于Bizztreat Blog。

As ever, I’m indefinitely grateful to Chelsea Wilkinson for patiently shaping my thoughts into a publishable format.

一如既往，我无限感激Chelsea Wilkinson耐心地将我的想法润色成可发表的形式。

Thanks for reading!

感谢阅读！

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