Why Modeling Churn Is Difficult? (TOP 5 Tips)

Modeling churn is difficult because there is inherent uncertainty when measuring churn. This uncertainty does not change churn’s status as an essential SaaS metric.

Why is churn a problem?

Churn leads to higher CAC & reduced revenue In fact, acquiring new customers is considerably more expensive than maintaining and upgrading existing customer relationships. The more customers you churn, the more money you must spend to recoup the loss of business by finding new ones.

How do you model churn rates?

The churn rate formula is: (Lost Customers ÷ Total Customers at the Start of Time Period) x 100. For example, if your business had 250 customers at the beginning of the month and lost 10 customers by the end, you would divide 10 by 250. The answer is 0.04.

Why use a churn model?

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. However, it is possible to estimate the probability through a churn model.

What factors affect churn?

The three leading factors that impact customer churn rate:

  • Average subscription length. Subscription length is the amount of time an average customer spends paying for a company’s goods or services.
  • Customer acquisition cost.
  • Customer lifetime value (CLV)

Is all churn bad?

Not all churn is equally bad for your business. Every time a customer makes the decision to leave your product, they’re making a statement—telling you something about your product, your service, or your brand. Every customer churned is an opportunity to understand how you can create a better product.

How can I reduce my churn?

How to Reduce Customer Churn

  1. Lean into your best customers.
  2. Be proactive with communication.
  3. Define a roadmap for your new customers.
  4. Offer incentives.
  5. Ask for feedback often.
  6. Analyze churn when it happens.
  7. Stay competitive.
  8. Provide excellent customer service.

Can churn be negative?

Negative churn is when the amount of new revenue from your existing customers is greater than the revenue you lose from cancellations and downgrades.

What is churn modeling?

A churn model is a mathematical representation of how churn impacts your business. Churn calculations are built on existing data (the number of customers who left your service during a given time period). A predictive churn model extrapolates on this data to show future potential churn rates.

Is a high churn rate good?

Churn is bad but inevitable, so it’s important to track and improve your churn rates over time. 5 – 7% annual churn is a great benchmark to aim for – if you’re an established, mature SaaS company, primarily targeting the enterprise. If you’re earlier-stage, or targeting SMBs, expect churn to be closer to 5% per month.

Can you predict customer churn?

So, Churn Prediction is essentially predicting which clients are most likely to cancel a subscription i.e ‘leave a company’ based on their usage of the service. That makes it a classification problem where you have to predict 1 if the customer is likely to churn and 0 otherwise.

What is customer churn and why is it important?

Customer churn is an important metric to track because lost customers equal lost revenue. If a company loses enough customers, it can have a serious impact on its bottom line. No matter how good a company’s product or service may be, it’s essential that they monitor their customer churn rate.

How do you identify churn?

To calculate your probable monthly churn, start with the number of users who churn that month. Then divide by the total number of user days that month to get the number of churns per user day. Then multiply by the number of days in the month to get your resulting probable monthly churn rate.

What are the main two reasons SaaS customers churn?

Top 12 Reasons Why SaaS Customers Churn

  • Poor customer service. Customers will use your product as long as it benefits them.
  • Fewer features in your SaaS product.
  • Pricing.
  • Multiple features in your SaaS product.
  • Payment method may not work.
  • Attracting wrong customers.
  • Product problems.
  • Scalability issues.

What is the best model for churn prediction?

Random Forest can yield good results with less data, so it’s one of the best classification models for churn prediction.

What is a churn and how does it affect a firm?

The churn rate, also known as the rate of attrition or customer churn, is the rate at which customers stop doing business with an entity. It is most commonly expressed as the percentage of service subscribers who discontinue their subscriptions within a given time period.

Why you should stop predicting customer churn and start using uplift models

Open access is granted under the terms of the Creative Commons license.

Highlights

The introduction of the maximum profit measure for assessing uplift models is a significant development. Uplift modeling for client retention is demonstrated in a real-world scenario in this presentation. Customer churn uplift models beat customer churn prediction models, according to the findings. It has been determined that uplift modeling is a superior tool for customer attrition and retention management, and that it increases the returns on marketing spend.

Abstract

When it comes to predictive analytics for data-driven operational decision-making, uplift modeling has garnered increased attention from both the business analytics research community and the industry as a more effective paradigm for predictive analytics for data-driven operational decision-making. While the research does not give strong empirical proof that uplift modeling beats predictive modeling, it does suggest that it does. We don’t have any examples of studies that directly compare both approaches, and the results of various experimental studies that report on the performance of predictive models and uplift models can’t be compared indirectly because the evaluation measures used to assess their performance are different.

It is an extension of the maximum profit metric, which was originally created for assessing customer churn uplift models, and is used for evaluating customer churn prediction models.

These metrics are then used to evaluate and compare the efficacy of customer churn prediction and uplift models in a case study that examines the use of uplift modeling to client retention in the banking industry.

Keywords

Data analytics using a prescriptive approach Modeling for uplift Predictions of customer churn Customer retention is important. Profitability at its peak in 2020 The Authors are credited. Elsevier Inc. is the publisher.

Customer Churn Prediction & Prevention Model

Churn (also known as customer attrition) is the process through which a customer (player, subscriber, user, etc.) terminates his or her business connection with a company. When a consumer has not interacted with a website or service for a specified period of time, the customer is said to have churned, according to industry standards.

turnover encompasses both lost income and the marketing expenditures associated with acquiring new consumers to replace the ones who have abandoned their shopping carts. Every online firm strives to reduce churn as a fundamental business objective.

The Importance of Predicting Customer Churn

For any online business, the ability to anticipate that a specific client is at high risk of churning while there is still time to do something about it provides a significant extra potential income stream. Beyond the immediate revenue losses that follow from a client leaving a firm, the costs of originally gaining that customer may not have been fully recovered by the customer’s spending to date. To put it another way, it’s possible that getting that customer was a losing proposition. Furthermore, acquiring a new client is usually more difficult and expensive than maintaining a relationship with an existing customer who is already paying.

Reducing Customer Churn with Targeted Proactive Retention

Using churn analysis, marketers and retention experts can successfully retain customers who would otherwise leave the company. To do so, they must be able to: (a) predict which customers are likely to churn in advance; and (b) determine which marketing actions will have the greatest retention impact on each specific customer. With this knowledge, it is possible to reduce a significant part of client turnover from your business. While the concept of “proactive retention” is straightforward in principle, the practicalities of achieving this aim are incredibly difficult.

The Difficulty of Predicting Churn

Attempts are made to identify the specific customer behaviors and features that signal the likelihood and timing of customer churn through the use of churn prediction modeling methodologies. Any proactive retention campaign’s effectiveness is obviously dependent on the correctness of the approach employed. Ultimately, marketing will take no action if a consumer is ready to churn and the marketer is not aware of this. Additionally, specific retention-focused offers or incentives may be delivered mistakenly to satisfied, engaged customers, resulting in a reduction in revenues for no apparent reason.

The most often used churn prediction models are based on earlier statistical and data-mining methodologies, such as logistic regression and other binary modeling techniques, which have become increasingly popular.

A Better Churn Prediction Model

In order to accurately predict which customers will churn, Optimove employs a newer and far more accurate approach to customer churn prediction: at the heart of the company’s ability to accurately predict which customers will churn is a proprietary method of calculatingcustomer lifetime value (LTV) for each and every customer. It is based on cutting-edge academic research, and it has been further developed and enhanced over a number of years by a team of top-tier PhDs and software engineers. With extensive field testing and validation, this strategy has been demonstrated to be a precise and successful solution across a wide range of sectors and client circumstances.

A hierarchical system of ever smaller behavioral-demographic groups is created by the former, which intelligently and automatically segments the whole consumer base.

Based on the reality that individual consumers’ behavior patterns regularly vary over time, the latter approach is used.

It is possible to achieve an unparalleled level of churn analysis accuracy by integrating the most rigorous micro-segmentation available anywhere with a deep understanding of how consumers migrate from one micro-segment to another over time – including the ability to forecast those moves before they occur.

Beyond Customer Churn Analysis: Preventing Customer Value Attrition

In order to accurately predict which customers will churn, Optimove employs a newer and far more accurate approach to customer churn prediction: at the heart of the company’s ability to accurately predict which customers will churn is a proprietary method of calculatingcustomer lifetime value (LTV) for each and every client. LTV forecasting technology integrated into Optimove is based on cutting-edge academic research and has been further refined and enhanced over a number of years by a team of top-tier PhDs and software engineers.

According to Optimove’s customer churn prediction technology, which cannot be disclosed in detail because of the company’s “secret sauce,” the technique blends ongoing dynamic micro-segmentation with a unique, mathematically intensivepredictive behavior modeling system.

Depending on the changes in the data, this segmentation is dynamic and updated on a regular basis Based on the reality that individual consumers’ behavior patterns change often over time, the latter approach is used.

It is possible to achieve an unprecedented level of churn analysis accuracy by merging the most exacting micro-segmentation available anywhere with a deep understanding of how customers move from one micro-segment to another over time – including the ability to predict those moves before they happen.

Now What? Targeted Proactive Retention

In order to accurately predict which customers will churn, Optimove employs a newer and far more accurate approach to customer churn prediction: at the heart of the company’s ability to accurately predict which customers will churn is a unique method of calculatingcustomer lifetime value (LTV) for every single customer. It is based on cutting-edge academic research, and it has been further developed and enhanced over a number of years by a team of top-tier PhDs and software engineers. This strategy has been battle-tested and shown to be an accurate and successful approach in a wide range of industries and client circumstances.

A hierarchical system of ever smaller behavioral-demographic groups is formed by the former, which intelligently and automatically segments the whole consumer base.

The latter is predicated on the reality that individual consumers’ behavior patterns typically vary over time.

It is possible to achieve an unprecedented level of churn analysis accuracy by merging the most exacting micro-segmentation available anywhere with a deep understanding of how customers move from one micro-segment to another over time – including the ability to predict those moves before they take place.

Leveraging Churn Analysis

Predicting client attrition and optimizing marketing actions are the cornerstones of the proactive retention method developed by Optimove, Inc. As a result, Optimove goes beyond “actionablecustomer analytics” to automatically decide exactly what marketing action should be conducted for each at-risk consumer in order to achieve the highest potential level of retention for that client.

You Can Dramatically Reduce Customer Churn with Optimove!

Get in touch with us now, or request a Web demo, to find out how you can use Optimove to minimize churn by up to 50% with cutting-edge customer churn prediction and automated marketing action optimization. the most recent revision was made in December 2021

What is Customer Churn Modeling? Why is it valuable?

ByDataScience.com This is a sponsored post. Keeping existing clients is a primary goal for many businesses, as recruiting new consumers may be many times more expensive than maintaining current customers. A data-driven retention strategy’s most significant components are identifying the reasons why customers leave and assessing the risk associated with certain consumers. It is possible that the Achurn model will be the instrument that brings these pieces together and gives insights and outputs that will be used to drive decision-making throughout a company.

What is Churn?

When reduced to its most basic form, the churn rate is computed by dividing the number of client cancellations within a certain time period by the number of active customers at the beginning of that period. Using this straightforward approach, you may acquire significant insights about your business. For example, the total churn rate can serve as a baseline against which you can analyze the impact of your strategy. And understanding how turnover rate changes by time of week or month, product line, or client cohort can aid in the creation of basic consumer categories that can be targeted more effectively.

  1. Customers differ in their habits and preferences, which, in turn, determine their level of happiness with the service or their desire to terminate it.
  2. Churn modeling is typically most effective in situations like these.
  3. This is the definition of “churn modeling” that is most commonly used, and it is the one that we will use in this piece as well.
  4. When it comes to contractual situations, which are typically referred to as “subscription settings,” churn is especially important since cancellations are expressly documented.
  5. The difficulty in identifying an unambiguous churn event timestamp under certain circumstances is a difficult task.
  6. Separate underlying variables may be responsible for both voluntary and involuntary churn, depending on the situation.
  7. The main reason of voluntary client cancellations is likewise more difficult to uncover, which is why the majority of churn research focuses on voluntary customer cancellations.

While both voluntary and non-voluntary cancellations have a significant influence on income, it is advisable to focus a churn model on a single form of churn to get the greatest results.

Use Cases

Various statistical or machine learning approaches can be used to estimate the likelihood of customer turnover. Customer cancellation probabilities are predicted using these approaches, which analyze purchase and behavior data from previous purchases and interactions. It is possible for a well created model to serve as the basis for a wide variety of decisions and to flow into multiple internal tools or applications. For example, some frequent scenarios in which a churn model is employed are as follows:

  • Different statistical or machine learning approaches can be used to forecast the likelihood of churn. In order to anticipate the chance of cancellation per client, these algorithms use past purchase and behavior data. It is possible for a well created model to serve as the basis for a broad variety of decisions and to flow into multiple internal tools and applications. To give an example, the following are some popular applications of a churn model:
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Use Cases

Various statistical or machine learning approaches can be used to estimate the likelihood of churn. These strategies use past purchase and behavior data in order to estimate the likelihood of a client canceling their transaction. A properly-constructed model may be used to inform a wide range of decisions and can be fed into a variety of internal tools and applications as well. For example, some frequent scenarios in which a churn model is employed include:

  • Various statistical and machine learning approaches may be used to estimate the likelihood of churn. These strategies use past purchase and behavior data in order to estimate the likelihood that a consumer may cancel their transaction. A well-constructed model may be used to inform a wide range of decisions and can be fed into a variety of internal tools and applications. For example, some popular use cases for a churn model include the following:

Where to Start?

So, where do we begin when it comes to developing and implementing a churn model? From conception to deployment, the process of developing a successful model is divided into many major stages: Recognize the situation in which you find yourself. The first and most crucial step in developing a model is always to define a clear use case for it. This approach will not only establish who will utilize the model output and how they will use it, but it will also mandate the modeling method that the data scientists will employ.

  • Identify the stakeholders within your business who will be impacted by the result of the churn model.
  • This may be accomplished by having your data scientists develop a churn model and then distributing it to the engineering team for deployment.
  • It’s important to remember that gathering feedback from the many parties involved early on will make the model-building process much simpler.
  • Consider the scope of the optimization and the metric that is being optimized.
  • Explanation: (i.e., minimizing the number of low-dollar customers who are being enrolled in your campaign).
  • Finally, do something!

More in-depth coverage of churn modeling will be provided in subsequent postings, which will include an examination of the issues that modelers most commonly find, an overview of typical churn modeling methodologies, and other related topics.

Churn prediction model

No firm wants to see one of its most valued clients go. In the beginning, a firm’s primary focus is on obtaining new customers. As the company expands, it may offer more items to existing customers or attempt to persuade them to use its products more frequently. In the event that everything goes well, there comes a moment when the firm has grown to the point where it must adopt a somewhat more defensive strategy and concentrate on maintaining existing consumers. Whatever the quality of the user experience, there will always be a small number of customers that are dissatisfied and opt to quit.

One model that might help in this situation is the churn model, which is among others.

What is the churn model?

Predictive modeling predicts the likelihood (or susceptibility) of individual customers to quit a business at the level of the individual consumer. It shows us how likely it is that we will lose a client at any given point in time for any individual customer. According to the technical definition, it is a binary classifier that classifies clients into two groups (classes) — those who depart and those who do not. In addition to assigning them to one of the two categories, it will often provide us with a chance that the client belongs to that group, which is useful information.

The inclination to depart rather than the chance of leaving is what is being measured.

What is it useful for?

We can better target our rescue efforts if we know which clients are at the greatest risk of abandoning their accounts. For example, we may send out a marketing campaign to these customers, reminding them that they haven’t made a purchase from us in a while and possibly providing them a discount or other incentive. In addition to determining which clients to target, we can utilize the churn model to determine the greatest benefit price that is still beneficial for the business. In the above example, if we know that the predicted likelihood of a given client departing is 10% and that their yearly income is $100, we may calculate the expected value of future annual revenue as $90.

What do we need for the churn model?

A churn model, like any other supervised machine learning model, requires training data that includes both response (target) and explanatory variables (features). The model learns to best represent the link between features and the target based on the training data it has received. Typically, this is historical information, and we know which clients finally left and which did not. Those who have departed have a positive goal in mind (yes, they left). Those who oppose you have a negative aim (no, they did not abandon ship).

  1. The importance of a well defined aim cannot be overstated.
  2. The churn model, on the other hand, may be used to both contractual (e.g., bank) and non-contractual (e.g., e-shop) customer relationships.
  3. Frequently, this information contains socio-demographic information, information about items possessed, information about history transactions, information about client-company interaction, information about e-commerce behavior, and so on.
  4. To put it another way, how long does it take between the day we look at clients using the various features and the day we can identify whether they have left the organization?

If that period of time is too short, we will not have enough time to respond in any meaningful way. It is possible that if the time period is very long, the model will be less accurate and up to date.

What does such a model look like?

Turn models that are used nowadays are frequently based on machine learning, and more precisely, on the binary classification techniques that were discussed above. The optimal algorithm for a certain case must be tested against a variety of other algorithms. There are many different algorithms available (specific training data, amount of data, etc.). The following two considerations must be taken into consideration regardless of whether you employ simple regression models, more complicated models such as random forest or generalized boosting models, or neural networks.

  1. Classifiers may be evaluated based on a range of performance indicators. Because churn is extremely low in the majority of businesses, it is not sufficient to examine the correctness of the churn model. For example, if the churn rate is 10% and the churn model for all clients predicts that they will not quit, the model will be 90% accurate. However, this is not beneficial. It is necessary to consider sensitivity (the number of clients who were detected by the model before they left) and precision (the number of clients who were recognized by the model before they departed)
  2. Among other factors. Furthermore, it is not recommended that the generated model be used as a black box in any way. Instead, make an effort to comprehend the boundaries on the basis of which judgments are made. In addition to pointing out problems in the model or data, this type of information may be quite beneficial to product and marketing teams. In the case of a discount, for example, knowing that the absolute amount of the discount has a less influence on churn than the relative amount of the discount allows us to design more successful marketing and pricing strategies.

What next?

The performance measures for classifiers are diverse. The accuracy of the churn model is not sufficient in most cases since churn is so low in most firms. It will be 90 percent accurate, for example, when churn is 10% and the churn model for all clients predicts that the clients will not quit. This, however, is not beneficial in any way whatsoever. You should consider, among other things, sensitivity (how many of the clients who really leave were identified by the model) and precision (how many of the clients who were identified by the model actually departed); and Furthermore, it is not recommended that the generated model be used as a black box for further analysis.

In addition to pointing out problems in the model or data, this type of information may be extremely valuable to product and marketing teams.

5 Barriers to Churn Prediction and How to Address Them

Lynn HeidmannPosted on March 15, 2017 in Use CasesProjects The fact that acquiring a new client can cost anywhere between five and ten times more than retaining an existing customer makes it seem apparent that all organizations should be involved in some form of churn avoidance. However, many firms’ methods result in them only addressing customer turnover after it is too late, which is a regrettably expensive (and typically inefficient) strategy of reducing customer churn. The other option is to use churn prediction.

Sounds too good to be true, doesn’t it?

This simple process of using predictive analytics to forecast customer churn is a highly accessible approach for even smaller and less experienced teams to get started in the field of machine learning – it’s just a matter of following theseven key stages to finishing a data project to get started.

Here are the top five obstacles reported as a deterrent to beginning a churn prediction project, as well as suggestions for overcoming them and getting started.

1. Admitting That Churn Is an Issue

The use cases and projects of Lynn Heidmann were published on March 15, 2017. The fact that acquiring a new client can cost anywhere between five and ten times more than retaining an existing customer makes it seem evident that all organizations should be involved in some form of churn avoidance. Customers turnover is a serious problem for many firms, and many businesses’ methods result in their addressing it after the fact, which is a costly (and in most cases useless) strategy. Instead, churn prediction can be used.

This seems like it’s too good to be true, right?

This simple process of using predictive analytics to predict customer churn is a very accessible way for even smaller and less experienced teams to get started in the world of machine learning — it’s just a matter of following these seven fundamental steps to completing a data project to get started.

Despite this, many firms continue to fail to comply. In this section, we’ll go over the top five obstacles that people have reported as being obstacles to starting a churn prediction project, as well as suggestions for how to overcome them and get started quickly.

2. Settling on the Right Definition of a User or Customer

Before determining what it means to churn, you must first determine whether users or customers are qualified to be deemed users or customers in the first place. Do you consider someone to have churned if all they do is sign up for an account but never use it? Or were they simply not interested enough to be called a customer in the first place?

3. Settling on the Right Definition of Churn

The next stage is to determine how long a period of idleness (or fading activity) should be considered churning in the business. Predictive models with artificially low churn rates may be created by defining a churn time that is excessively lengthy, resulting in the model not collecting enough individuals and therefore contradicting the goal of predictive modeling. It makes it difficult for marketing teams to evaluate churn prevention initiatives when the churn period is too short because they cannot distinguish between organic actions (users or customers who would have returned anyhow if the campaign had not been implemented) and effective campaigns.

4. Getting Data

This stage is really lot simpler than most organizations believe it to be, because the only information needed to build a churn prediction model is some kind of customer identification and the date and time of the customer’s most recent engagement with the company. Almost all firms already have this information at their disposal. It’s true that adding additional features and data will allow you to make more accurate and robust predictions, but this shouldn’t prevent you from getting started in the first place.

5. Getting Predictive

This phase is really lot simpler than most organizations believe it to be, because the very minimum of information necessary for a churn prediction model is merely some kind of customer identification and the date and time of the customer’s most recent encounter with the company’s representatives. This information is currently available to almost all firms. It’s true that adding additional features and data will allow you to generate more accurate and robust forecasts, but this shouldn’t prevent you from getting started in the first place!

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What makes predicting customer churn a challenge?

Maintaining control over client turnover is a critical component of running a healthy and profitable organization. In particular, most businesses with a subscription-based business model keep track on the turnover rate of their client base on a frequent basis. In addition, the expense of gaining new clients is usually rather expensive. As a result, predictive models of customer turnover are intriguing since they allow businesses to retain more of their existing customers at a greater rate than they otherwise would.

According on our experience modeling customer churn at Tucows, the following paper gives a discussion of some of these difficulties in further detail.

In particular, this is true for our Ting mobile clients, as seen by our highly recognized customer care experience. There are three major obstacles to overcome in this situation:

  • What exactly is a “churn event” in the business world? When is it permissible to refer to a client as a churner and when is it not? In the context of a subscription-based business, a churn event might be described as the termination of a subscription by either the consumer or the firm. Churn, on the other hand, becomes a hazy term in a firm that is not built on subscriptions. For example, a client can engage with an online retailer at any hour of the day or night. Consequently, what exactly does it mean to claim that a consumer has left in this situation? A workaround option is to consider a client to be a churner if they have not had any (buy) interaction with the company for the last 30 days. Despite the fact that this strategy is commonly utilized, it does not work for consumers that exhibit “burst behavior,” that is, customers who engage in intermittent but frequent encounters (see Figure 1). It is more suitable to use techniques like these while dealing with difficult consumers.

Typical customer activity patterns (a) regular customer activity (b) bursty sporadic activity are depicted in Figure 1.

  • How to compute (monthly) turnover rate? churn rate is aimed to quantify the percentage of customers departing a firm compared to its base size. This is quite ill-defined since one must then inquire as to when the company will cease operations, which means that the size of the customer base itself can no longer be presumed to be static. In an intriguing paper, a range of strategies for measuring churn rate while taking this difficulty into consideration are discussed. What kind of churn is it? Although customer turnover appears to be a single problem on the surface, in actuality there are many different forms of churn that occur, each driven by a different set of motivations. We have two unique categories of churn for our Ting customers at Tucows Inc, namely, voluntary and involuntary, which we clearly distinguish from one another. People who leave on their own initiative, such as by canceling their service or transferring their number to another carrier, are known as voluntary churners, whilst those who leave on our own initiative are known as involuntary churners. Due to the complexity of such patterns, it is preferable to have several models for each form of customer churn rather than one large model that captures everything. A different churn modeling strategy can result in considerable gains in forecast performance, as shown in this excellent blog.

Typically, the dataset used to model customer churn has the form of (features, label), where features are a collection of various customer characteristics and label is set to 1 if the customer is deemed a churner and 0 otherwise. Both features and label data provide a number of issues, including the following:

  • Raw data tables found in a company’s data warehouse are rarely in a format that is ideal for churn modeling since they are in a messy state. In order to turn this jumble of data into a usable format, it is necessary to undertake ETL (extract-transform-load) and feature engineering operations. Tasks include identifying and selecting potentially useful features, developing SQL scripts to extract them from database tables, removing outlier records (for example, customers with outlandish characteristics), performing various data transformations, such as the box-cox transformation to ensure data normality, and so on.

Data wrangling is often a time-consuming and critical element of the modeling process; nonetheless, it does not receive nearly enough attention, particularly in academia, despite its importance.

  • Low customer turnover rate: When a firm is in good form, customer churn is an uncommon occurrence, assuming that the business is profitable. This results in the so-called “class imbalance” problem, in which the number of churner customers is significantly fewer than the number of non-churner (majority) clients. When there is a large class imbalance, the churn model might make inaccurate predictions. This is related to the fact that most machine learning models learn by maximizing overall accuracy rather than by maximizing individual accuracy. A substantial class imbalance can result in a high accuracy for the churn model simply by forecasting all samples as belonging to the majority (non-churner) class without having to learn anything about the minority class. As previously noted, there are a variety of ways that may be used to relieve the problem of class imbalance. Examples of such approaches include simple down/up sampling as well as certain complex sampling methods, such as SMOTE. The concept of churn event censoring states that theoretically all customers will ultimately churn at some point, which means that given enough time, all dataset labels will be one. Consequently, clients who are considered non-churners when learning the churn model are only partially seen, i.e. their churn event is filtered, when learning the churn model (see Figure 2). The suppression of churn events is an issue for traditional machine learning algorithms, which require all dataset labels to be fully viewable in order to be effective. Survival models offer an appealing option in such settings, as will be addressed in further detail in the next section.

The following figure shows an example of (right) suppression of churn event data.

  • Feature responsiveness: There are two types of features that are utilized to estimate churn, namely, aggregate features (e.g., average monthly bill) and time series features (e.g., average monthly bill) (e.g. data usages over the last six months). Generally speaking, aggregate characteristics are less difficult to gather and model, and as a result, are more widely employed. However, throughout our research, we discovered that basic linear averaging for aggregation had a flaw in it. Simple linear averaging provides equal weight to all data, and this is the most common method. When it comes to churn modeling, this is not acceptable since consumers may display a dramatically different pattern near to their churn event, which cannot be accurately represented by simple averaging. This is especially true for clients who have been with the company for a long time. To deal with this problem, one option is to employ moving average methods, and more specifically, exponential moving average approaches, which allocate a (tune-able) larger weight to the most recent data.

When it comes to predicting churn, there are two major obstacles to overcome. First and foremost, one must design and validate an effective churn prediction model by employing the appropriate methodology. Once the model is in production, it must be regularly monitored for changes in performance over time, and it must be re-trained or further developed if necessary. The techniques to churn modeling may be divided into three categories:

  1. Churn modelling is treated as though it were just a matter of learning a binary classifier in this technique, which does not take into account the previously described churn event censoring. The inability of this technique to deal with censorship, as well as its sensitivity to imbalances in class labels, especially in applications with low churn rates, means that it is the worse modeling choice in most situations. In our situation, we tested with two binary classifiers, namely, the random forest (RF) and the wide and deep neural network (W and DNN), to see which performed better (WD-NN). When tested in both the training and validation stages, both of these techniques demonstrated excellent performance (measured in terms of accuracy and recall metrics). However, when they were put to the test to anticipate churners for the next month, their performance was far less than ideal. We had three hypotheses about what was causing this decline in performance. First, models were analyzed to see whether or not there was any over-fitting. Over-fitting was not an issue for the RF model, which was as predicted (RF is generally robust to the choice of hyper-parameters). The WD-NN model, on the other hand, exhibited some overfitting, which was later eased by increasing the level of regularisation. We have a second issue with our dataset, which is a significant class imbalance caused by our low client turnover rate. The use of an SMOTE sampling approach was successful in alleviating this problem. Third, binary classifiers are unable to detect censorship in churn label data because of their inherent limitations. This insight led to the development of the survival regression methods that will be explained later. When it comes to time-to-event datasets, survival regression and survival analysis models are the well-known approaches of choice when it comes to modeling. For example, the Kaplan-Meier (KM) model is a prominent non-parametric survival analysis technique that is used in many situations. The KM model, given a collection of customers’ duration until a churn event (or censoring), calculates their overall survival curve, which is their chance of surviving through time as a function of time. Survival regression models go this a step further by introducing characteristics associated with consumers (as variables) into the modeling procedure over the course of the analysis. Various survival regression models make various assumptions about the linear correlations that exist between variables and the risk of a customer churn event. Consider the Cox and Aalen models, both of which make the assumption of multiplicative and additive connections, respectively. Survival regression models do not distinguish between consumers who churn and those who do not churn. Each client is given a survival curve that may be used to calculate the predicted time to churn based on the data provided. To put it another way, a customer can be considered churner if their expected time to churn is near (based on a predetermined threshold) to their existing length of tenure. The Cox approach was used for churn modeling in our scenario, and the results were promising. Similarly to the binary classifier approaches described above, the model demonstrated excellent performance (as measured by the concordance index) during both the training and validation stages. However, when it was put to the test to anticipate churners for the next month, its performance suffered. We had two basic hypotheses as to why this was happening. First and foremost, as previously noted, the problem of feature responsiveness was raised since our aggregate features were generated using a straightforward linear averaging procedure. The more complex aggregation approaches, such as exponential moving averaging, will be tested in order to increase the responsiveness of our aggregate features in the future. Secondly, the Cox model’s linear covariate to (churn) risk assumption, which may or may not be acceptable in our particular situation. Our hypothesis is that, as the US telecommunications industry has evolved over time, the impact of various features on the churn of our Ting customers has varied as well. It is therefore necessary to use more complex survival regression algorithms that are capable of capturing such non-linearity in the data. This insight led to the development of the hybrid models detailed further down. Recently, a variety of strategies have been presented to deal with survival classification issues requiring sophisticated non-linear customer churn risk functions, such as those incorporating customer churn risk functions. These approaches are often constructed by applying non-linear binary classification methods to censored survival data, which is an extension of the popular non-linear binary classification methods. These hybrid approaches, which are extensions of the Random Forest and deep neural networks respectively, include theRF-SRCanddeepSurvare two examples of such hybrid methods. We intend to explore with churn modeling in the future, utilizing these sophisticated tools.

For the purpose of completeness, we should highlight a recent approach dubbed WTTE-RNN, in which the author effectively flips the traditional churn modeling strategy on its head, which is worth mentioning. The suggestion is to anticipate the time until the next non-churn event, rather than the time until the next churn event (see Figure 3). Figure 3: Predicting the time until the next non-churn event in WTTE-RNN using a creative formulation

Concept drift

A decent churn model has been designed, verified, and implemented in production, as previously stated, but there is yet another obstacle to overcome. It has something to do with the dynamic nature of the churn problem and the concept of concept drift, both of which are related. Simply said, a churn model that is effective now may become ineffective in the future as a result of changes in the behavioural patterns of customers who are the source of churn. For example, the high cost of phone calls may be pushing clients to leave their current provider today.

  1. Concept drift approaches are discussed in detail in this section.
  2. We have already seen a reduction in churn as a result of responding to the fast feedback offered by our Ting customers.
  3. It is our intention to improve our churn prediction findings by increasing the size of our training dataset and by experimenting with churn modeling utilizing sophisticated hybrid methodologies.
  4. When used properly, the “responsiveness” of our aggregate characteristics may be considerably improved using an exponential moving averaging approach.
  5. With time series characteristics such as these, churn modeling should be able to uncover and incorporate more sophisticated consumer patterns into churn prediction.
  6. Between the two hybrid possibilities described, the RF-SRC looks to be the more appealing of the two since it would be easier to tune (similar to RF) than deepSurv and would be less expensive.

In the future, we want to test this strategy out for ourselves. If you find customer churn modeling to be an interesting experience and believe you have a natural aptitude for it, please get in touch with us. We’re looking for new employees!:)

3 Major Challenges Enterprises Face in Building an Effective Churn Model

Companies must develop and implement an effective customer churn analysis model in order to monitor customer churn rates and maximize client retention in order to be successful in today’s dynamic business environment. It is always more expensive to acquire new consumers, which makes the predicted churn model desirable for organizations who want to retain clients while increasing earnings. Although estimating customer attrition appears to be straightforward at first glance, it is fraught with difficulties.

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Our customer churn analysis assists businesses in determining their churn risk and improving the efficacy of their retention initiatives.

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Challenges of Building a Predictive Churn Model

One of the most difficult issues that businesses encounter when developing a predictive churn model is determining which churn modeling technique is most appropriate for their needs. However, there is no one way for developing a predictive churn model that is effective in all circumstances. In business, machine learning techniques are most commonly utilized because of their efficiency and capacity to categorize and manage large amounts of complicated data. The survival analysis technique, on the other hand, makes use of survival and hazard functions in order to forecast which customers would churn within a specific period of time, such as a quarter.

Quantzig assists its clients by providing churn prediction models, which are used to develop a data-driven strategy for customer retention.

2. Features and exploratory analysis

Business organizations confront various bottlenecks and churn risks during the early stages of developing predictive churn models, including a lack of information, target leakage, and the necessity for appropriate feature conversions, among other things. Businesses must have the necessary skills and creativity in addition to domain knowledge in order to build robust predictive churn models. As a result, it is critical that companies conduct thorough exploratory analysis and develop auxiliary models before embarking on the development of an overall churn prediction model.

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3. Validating churn model performance

While developing predictive churn models, businesses confront a number of barriers and churn risks, including a lack of information, target leakage, and the requirement for appropriate feature conversions. It is critical that businesses conduct thorough exploratory analysis and develop auxiliary models before embarking on the development of an overall churn prediction model. In addition to domain knowledge, businesses must also have the necessary skills and creativity to build robust predictive churn models.

To Predict Churn, You Shouldn’t Predict Churn

Churn reduction is one of the most important factors in the success of a subscription media or commerce firm. Customers that remain loyal to your company provide predictable and compounding income for your company. If you don’t know how to forecast and avoid churn, you’ll wind up spending a lot attempting to attract new users on a continual basis. Machine Learning is enabling organizations to design more forward-thinking retention tactics than they have ever been able to do in the past. As a result, subscription-based businesses rely on machine learning to forecast and prevent churn from occurring.

As an added bonus, we advise that monitoring a user’s activity is preferable than monitoring the user’s actual churn date.

A User’s EngagementYour Churn

In order to be successful, a company must have the greatest number of healthy consumers feasible – not just the greatest number of subscribers. Users who are in good health are more likely to renew their subscriptions and stay with your company for a longer amount of time, as well as to serve as ambassadors for your company. Healthy users are individuals who are engaged with and derive value from your product on a constant basis, which is important for subscription businesses in the media or commerce industries.

In reality, according to a Vidora investigation, once a user has been inactive for a specified period of time, it is a question of pure luck when they really churn – most likely on the day they realize they are still a paying customer.

Creating models to forecast this moment of inactivity gives more accurate leading indicators of churn than building models to predict actual churn, as we have discovered as a result of our research.

Importantly, using inactivity as a proxy in our churn models still results in predicting true churn:

We created a churn model with inactivity as the goal label and tested it (i.e. the metric that is being predicted). Cortex enables our partners to create models that can predict a variety of labels or combinations of labels in real time. A user’s likelihood of engaging in any activity over the next ‘X’ number of days is included in this section. These models are developed on the basis of behavioral data that is transmitted into Cortex in real time, straight from your company. In Cortex, you can quickly and easily create a Retention Model with a few clicks.

We needed to make sure that the inactivity prediction model appropriately forecasted genuine churn before moving on.

We ran retention models on those users and looked at their actual churn rates to see how well they did. The graph above compares users’ activity retention model scores with actual churn rates for those users. That Cortex’s retention model accurately predicts real churn is demonstrated in this study.

Why is predicting using inactivity better than using actual cancellations?

As a result, we discovered that inactivity is a more effective target label than actual churn because the time when a user actually cancels their subscription is a highly noisy variable (e.g., when the user realizes they have an active subscription that is not being used), while understanding a user’s engagement can be explicitly measured. In order to illustrate this issue, the figure below shows the churn rate of a group of users as a function of the amount of days that they have been inactive on the site.

  1. With this in mind, once a user has been inactive for forty days, it is simply up to chance when they will decide to unsubscribe from the product.
  2. Take, for example, Sally, who has not done anything for 41 days.
  3. Because they have both reached the 40-day mark of inactivity, Sally and Harry have the same chance of churning the week after next.
  4. Making accurate forecasts of customer attrition is both tough and time-consuming work.
  5. Our team at Vidora has years of expertise dealing with a wide range of subscription businesses.
  6. We incorporated such approaches into an easy-to-use platform.

Building a Customer Churn Model with Machine Learning

As subscription services have risen to prominence in recent years, businesses have been increasingly cognizant of the need to identify which customers are likely to churn and which ones are not. Due to its capacity to handle large volumes of data and extract insights that are not immediately apparent to human brains, machine learning is becoming an increasingly common tool to assist businesses forecast and prevent customers from abandoning their shopping carts. The problem has been resolved! The future has here, and artificial intelligence has provided a solution to all of our issues!

What do you mean it isn’t that simple?

In this piece, I’ll break down the four most typical obstacles that businesses face when implementing machine learning for churn prediction, as well as how to overcome them in order to ensure that your churn prediction efforts are delivering meaningful economic value.

Why Value is Critical for Machine Learning

We at RapidMiner talk a lot about the importance of machine learning and analytics in today’s world. None of the work done in data science is beneficial to a company unless it can demonstrate genuine value and an influence on the bottom line of the firm. Therefore, whenever we consider applying machine learning to address business challenges, we always want to be able to connect it back to the value that it is creating. In fact, we created a whitepaper titled “Talking Value” that explains how to quantify the value of a machine learning model in the context of consumer analytics and how to use it.

We could have used it as an example in Talking Value, but we didn’t because churn is such an intriguing topic and such a widespread use case.

Consider this piece to be an addition to Talking Value; reading the whitepaper in conjunction with this blog post will help you understand how to use the reasoning in the whitepaper to manage customer attrition while avoiding the most typical pitfalls.

Four Big Churn Modeling Challenges

Here are the four most significant problems that businesses face when attempting to forecast client turnover, as well as suggestions for how to overcome them.

Challenge 1: Sleepers

When it comes to churn detection and prevention, one of the most typical issues is that organizations have extremely lucrative contracts with customers who have been using your service for a long time—they signed up and never looked for other alternatives. These individuals are referred to as sleepers, and if you begin to communicate with them, you may wind up arousing a sleeping lion. It is possible that reaching out will encourage these sleepers to switch to a rival or perhaps to cancel their membership with you.

  1. How many of these slumbering individuals will depart if you reach out to them?
  2. The solution is to do a value evaluation.
  3. You’ll be able to put in the missed income into your value calculations, allowing you to assess whether or not it’s worthwhile to wake up these clients.
  4. This takes us to our next churn problem, which we will discuss later.

Challenge 2: Churn may not be predictable from your data

When it comes to churn detection and prevention, one of the most typical issues is that organizations have highly valuable contracts with customers who have been using your service for a long time—they signed up and never looked for other alternatives. These individuals are referred to as sleepers, and contacting them may result in the arousal of a sleeping lion. Reaching out to these sleepers may compel them to switch to a rival or perhaps to cancel their membership with you entirely if they are not satisfied.

How many of these slumbering individuals will leave if you make contact with them.

Make a value evaluation as the answer.

Essentially, you want to determine how many of these false positives will depart if you wake them up with a discounted offer to stay with you.

Afterwards, you may use that missed income into your value calculations to evaluate whether or not it is worthwhile to rouse these clients from their somnolences. There’s no doubt about it: that seems like it may be difficult to figure out. As a matter of fact, this brings us to our next churn test.

Challenge 3: Deploying often requires human trust

People who deal with data frequently lose sight of the fact that businesses are run by people. Before beginning a churn detection project, you should ask yourself a series of questions regarding the potential applications of your churn score. Is it causing automated emails or SMS messages to be sent? Will the information be utilized to notify a local agent who will then take appropriate action? In order for other people to behave on the basis of your model, you must rely on something that is outside of your control and outside of your data: human trust.

  1. The answer is as follows: You have two choices in this situation.
  2. By doing so, you effectively change them into data citizens who, instead of trusting you because they have no choice, trust you because they want to.
  3. The second approach that may be useful in this situation is explainable artificial intelligence.
  4. People’s confidence in a model’s predictions grows as a result of these findings.
  5. Nonetheless, it might be a useful first step in gaining support for your models from others.

Challenge 4: Predicting churn is NOT the problem

Considering that this is possibly the most crucial argument to make, I saved it for last. You don’t genuinely want to forecast turnover. To prevent churn, you should aim to increase the amount of income generated compared to what would have been generated if you had not taken any action. Take, for example, the case where my mobile service provider had a 100 percent accurate signal that I was likely to churn when I cancelled my mobile contract. They were free to contact or email me whenever they wanted with all of the deals they wanted.

I was looking for a competitive offer for a business plan rather than a low-cost, discounted plan.

What they could have done instead was give me different options that would work better for my requirements, and maybe add a modest discount for being a current client.

The solution: The finest churn models not only forecast who would churn, but they also prescribe a course of action to try and prevent it from happening in the first place.

There are two alternatives here.

You may then utilize business rules for the different models to generate targeted offers.

A second method would be to employ two models. One is used to anticipate churn, whereas the other is used to predict or prescribe the next best actions, i.e., the activity that has the greatest likelihood of reducing the likelihood of churn. This would be a conversion model.

So, Churn’s a Bad Use Case?

No! With more and more organizations migrating to subscription-based revenue models, being able to forecast and then avoid churn is possibly one of the most important machine learning use cases out there, as we mentioned at the opening. However, in order to have that kind of effect, you must ensure that your churn prediction and prevention procedures are correct. That is to say:

  • Taking into consideration the costs and advantages of awakening any sleeping. Making certain that your data can be used to anticipate churn
  • Finding support from the other individuals that are participating in the process
  • Providing prospective churners with the appropriate incentives to remain

Combine these points with the important takeaway from the talk about value book, which is that you must understand the revenue implications of any action you take, including things like false positives, and you’ll be well on your way to using machine learning to build a churn model that not only predicts when someone is about to churn, but also suggests an appropriate action to take to help keep them as a customer.

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