How to Predict Customer Churn: A Comprehensive Guide

While customer churn is a natural part of business, it presents a significant challenge for organizations aiming to maintain steady business growth through customer retention. Remaining vigilant in reducing churn is a great way to ensure stable revenue, profitability, and scalability. Retaining customers rather than acquiring new ones through marketing campaigns and promotions is usually more effective. This can be a crucial component for subscription-based companies that are subject to volatile churn metrics over a while. 

An imperative step to reducing churn is to figure out a way to predict possible situations that create high-risk customers. We’ve compiled a comprehensive guide on everything you need to know to predict customer churn. Using these resources, you can begin to forecast or identify patterns that contribute to churn and proactively reduce it.

Before we explore the different ways to approach churn analysis and prediction models, here are some standard terms you should know.

Voluntary Churn

This groups all customers who actively decide to cancel or end their relationship with the business. This can result from a poor customer experience, low-quality products or services, or competitors’ better options. Many of these aspects can be remedied by improving your customer experience. 

Involuntary Churn

Situations where customer relationships are lost due to factors such as failed billing, expired payment information, or even technical disruptions in the processing fall into this category.

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High-Risk Customers

Without addressing these issues, you can create a group of high-risk customers, which refers to their likelihood of churning.

Customer attrition

Part of your churn could be due to customer attrition, which is a natural, gradual reduction in the company’s customer base over some time. This is usually associated with changes to the customer’s lifestyle, including:

• Aging
• Relocating
• Other personal circumstances

Though both customer churn and attrition account for the loss of current customers, churn is largely driven by customer decisions as a result of a poor customer experience. 

Predicting a Risk of Churn

Combating customer churn is a proactive action, meaning you need to know what data and behaviors to look for before you can accurately predict it. You can leverage your customer interactions and data in many different ways to predict your potential churn rate.

One of the biggest limitations of some of these recommendations is inaccurate data. It’s essential to ensure that your data is configured correctly and that you have a management and analytics platform with the right feature selections to help you accomplish your goals.

Without clean data, you risk creating false positives in your modeling, which can classify an active customer at risk when they aren’t. These distractions can add up over time, reducing the amount of progress in your customer retention efforts.

What are churn prediction models, and why are they essential for businesses?

Churn prediction models are analytical tools that leverage data mining and machine learning techniques to forecast the likelihood of customers discontinuing their business with a company. Essential for identifying at-risk customers early, these models enable businesses to proactively implement retention strategies, potentially reversing decisions to leave and significantly impacting revenue and growth.

Customer Feedback and Sentiment

A tried and true route for predicting customer churn is to gather customer feedback and monitor customer sentiment. Customer feedback is a great way to gauge how your customers feel about your brand and their recent experiences. Customer surveys and follow-up e-mails after purchases or experiences with your customer service can highlight emotional triggers and frustrations you can apply later on to future customer churn prediction strategies. 

Surveys are helpful because you can offer them in various forms with as much or as little structure as possible. Structured surveys tend to be a series of questions with pre-defined choices, while unstructured surveys are surveys where customers can provide feedback in their own words.

When you send a survey to your active customers, it’s important to ask the right questions to gather data that will help you make informed decisions. With that in mind, using questions like the ones below paired with an optional comment box can get you the relevant data your customer success team requires.

Helpful questions to include:

• How happy or satisfied are you with your recent shopping experience?
• How likely would you recommend our brand to your friends and family?
• What issues or frustrations have you experienced with our brand? 
• What kind of features would make your buying experience even better? 
• How often are you using our products or services?

Each of these questions can gauge and help you identify patterns and trends. Responses can give you an inside look at your audience’s habits, buying frequency, and, most importantly, frustrations that have hindered their buying experience. Ensure you are using a data resource that can help you uncover these insights in an easy-to-understand way. 

By understanding what to look for, you can closely monitor your active customer base to ensure their satisfaction. Another way to do this is to monitor social media networks. 

If customer feedback surveys are more of the traditional route, monitoring social sentiment is the modern equivalent. Monitoring refers to using various resources to track, analyze, and communicate with followers on your brand across social networks. It works similarly to surveys but with faster response times.

This helps you address the problem before high-risk customers are lost. With proactive action, you can personalize offers to these at-risk audience segments and remind them they are valued. 

Analyzing social networks for customer sentiment can show how your customers feel about your brand in real time and should be part of any customer retention strategy. But when it comes to customer churn analysis and strategy, there is plenty of data for you to monitor. 

The Importance of Data & Monitoring Analytics

Data, in general, is critical to understanding how you can predict customer churn. You must analyze your data to see what metrics tie back to the high risk of churning. These can include data on customer lifetime value, customer acquisition costs, and overall customer engagement. Over time, the large amount of data collected will help you see the patterns before it’s too late and improve your retention efforts. This is done by creating various customer churn prediction models. 

How does a data-driven approach empower churn prediction models?

A data-driven approach provides the foundation for churn prediction models by supplying the raw insights needed to forecast customer behavior. By analyzing patterns in data such as purchase history, service usage frequency, and customer interactions, these models can accurately identify signals that a customer is at risk of churning. This allows businesses to not just react to churn, but anticipate and mitigate it effectively.

What goes into building and training effective churn prediction models?

Building and training effective churn prediction models involve selecting the right algorithms (like logistic regression, decision trees, and random forests) and employing machine learning techniques (such as supervised, unsupervised, or reinforcement learning). This process requires a significant amount of historical customer data to identify and learn from patterns of behavior that precede churn. The models are continually refined and retrained to adapt to new data and emerging trends, ensuring their accuracy and relevance.

Data Modeling & Machine Learning

Through the use of artificial intelligence and algorithms to create predictive analytics, you gain the ability to run simulations of active customer behavior. Through the patterns and trends, you are able to understand what factors are contributing to creating at-risk customers. This method relies on a lot of historical data to statistically identify the factors that drive customer behavior. This churn model can help you take proactive action to right the course and retain more customers. 

In order to create a predictive model to identify risks of customer churn rate, you can use various data points collected to identify churn probability. This refers to the likelihood of losing a customer based on behaviors and patterns. Some of the points that you should focus on include purchase history, frequency of use, and interactions with your brand. Your churn probability is an important component of your predictive modeling, focusing on the customers that require the highest priority to retain. 

In general, predictive data modeling uses these algorithms to help you predict what customers are at risk:

Logistic Regression

This statistical method uses a logistic function to model the correlation between dependent variables like churn or no churn and one or multiple independent variables like customer data. When it comes to identifying churn patterns in the data, it was most likely a result of logistic regression. 

While it can be simple to execute and is ideal for churn probabilities, it is limited to a linear relationship between the variables, meaning it may not always mirror real-world situations accurately. 

Decision Tree

As the name suggests, a decision tree algorithm uses a branch structure to create decision models and the numerous possible outcomes. From there, the data is split again into smaller sets based on relevant variables and continues to create a tree structure that shows the process of their decision-making. 

A decision tree remedies logistic regression’s non-linear limitations but can be sensitive to small changes or overcomplicate the possible outcomes within the decision tree. It works best with smaller data sets. 

Random Forest

As an ensemble learning method, random forests combine multiple decision trees to gain more accurate insights. This combats the typical limitations of just using one single decision tree. Once the mass of decision trees begins to create branches based on the variables, everything is averaged together or based on the majority outcomes of all of the trees. 

Even though random forests can provide accurate insights and capture non-linear relationships, it can be time-consuming to train and difficult to interpret or visualize based on the size of the datasets and individual decision trees. 

You can train these various algorithms using different approaches. Hence, it knows what to look for in customer data that indicates a churn risk to improve its predictions’ accuracy. These are the three most common training processes:

Supervised learning

For this type of machine learning, the algorithm reviews data already labeled to learn the patterns and relationships between the variables. From there it can apply what it has learned to new, unseen data to identify churn risks. You can adjust the data sets based on the model performance to refine the process for more accurate insights. 

Unsupervised learning

Contrary to the supervised learning approach, the algorithm starts with unlabeled data, making its own assumptions and using its own techniques to identify patterns with no guidance whatsoever. In a broad approach, a churn model using unsupervised learning can identify groups of customers with similar behaviors, which can quickly identify common traits or situations leading to the churn of customers. 

Reinforcement learning

As the last example using this algorithm training approach to your churn model uses interactions with its environment and the feedback it receives to learn. The feedback is either a reward or penalty which the algorithm uses to learn which actions will lead to a positive outcome like retention and which lead a customer further down the path of churning. With this feature selection ability, reinforcement learning can be a great asset to your customer retention strategies.

Something to keep in mind as you start to as you start to compile your customer churn prediction models is that they need to be specific to your industry and business. Many factors can contribute to churn, so understanding the ones that apply to you will save you time in the long run as you train your algorithms.

The last portion of machine learning we’ll explore is the various predictive models it can create but before that, it’s important to stress that selecting the right model depends on various factors.

How do businesses choose the right customer churn prediction model?

Selecting the right churn prediction model depends on various factors, including the nature of the business, the complexity of customer data, and specific goals for reducing churn. Businesses often experiment with different models and approaches to determine which provides the most accurate and actionable insights.

Factors like model interpretability, the volume of data, and computational resources also play crucial roles in this decision-making process.

Binary Classification Model

These can be a result of using supervised learning algorithms like logistic regression or decision trees. The binary classification model is used to predict categorical outcomes, in this case, whether or not a customer will churn based on historical periods. 

Baseline Model

The baseline model offers a starting point for comparison with more of its complex counterparts. These models make broad assumptions based on the data set which can then be compared to more complex model results. Ultimately, you want to use it to train your more complex model to improve its prediction accuracy. 

It will be up to your organization to select the machine learning model that fits your goals or needs and, most importantly, the data you have collected. Exploring the different models and approaches could be beneficial in seeing which ones offer the most accurate insights.

Why is Predicting Churn Important?

You always want to ensure you do the most to maintain a steady base of active customers.

Here are two examples of how large companies have improved their retention rate by focusing more on predicting churn.

Netflix

The streaming behemoth is a subscription-based company at the end of the day, making it imperative to retain as many customers as possible each month. They actively fight to reduce churn by using predictive modeling. Using machine learning models, they can predict or identify a show or movie customers will likely enjoy. They have seen increased customer engagement and a reduced churn rate by recommending it. 

Spotify

Similarly, the music streaming platform uses machine learning to analyze user behavior, such as song skips, searches, and how often they use the app to identify groups that may be more at risk than others. They also use machine learning to personalize the experience for every user in the form of personal curated playlists, song recommendations, and more. This helps improve the customer experience which in turn reduces customer churn.

Calculating Churn Rate

Organizations need to keep a pulse on their customer retention. To do so, organizations should frequently calculate their churn rate to make sure it falls within a healthy range. Starting to see an increase month over month can be a better situation than realizing a full quarter later. For most mid-level or enterprise organizations, it’s strongly recommended that the monthly budget be calculated.

Calculating the churn rate is simple. All you need to do is divide the number of customers you lost in a specific period of time by the total number of customers you had at the beginning of that period.

If you started the month with 200 customers and lost 20 over a month, your churn rate would be 10%. Depending on the industry, that could be pretty high and need to be resolved.

Some common factors that can influence customer churn:

• Low quality of products or services
• Lack of communication
• Pricing
• Poor customer experience
• Competitive market

Keep these factors in mind as you aim to predict which of your customers are considered at high risk of leaving.

How Does Customer Churn Prediction Enhance Customer Retention?

Understanding and effectively predicting customer churn is not merely a strategy but a fundamental necessity for businesses aiming for sustained growth and customer satisfaction. By embracing the comprehensive guide laid out, businesses can navigate the complexities of customer retention with greater confidence and precision. From recognizing the subtle differences between voluntary and involuntary churn to leveraging advanced churn prediction models, the journey toward minimizing churn is both a data-driven and customer-centric endeavor.

Predictive analytics, powered by machine learning, aids in foreseeing churn risks and taking preemptive actions. Clean, accurate data is crucial for successful customer churn prediction. Integrating customer feedback enhances understanding of their needs. Choose the right churn prediction model tailored to your business dynamics. Identify at-risk customers and engage them with retention strategies.

Ready to Transform Your Churn Rate into Retention Success?

Unlock the full potential of your customer retention strategies with Chargebee’s advanced churn prediction models. Dive deeper into data-driven insights and proactively prevent churn before it impacts your growth. Don’t let churn diminish your hard-earned customer base—start predicting and preventing with precision today.

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