What is Customer Churn Prediction?

One of the main challenges of a growing business is figuring out how to retain more customers. Measuring customer churn is an effective way to gain insights into your business and improve your retention rate. It will also help you identify areas for improvement and feedback you should address.

Once you learn the importance of understanding and predicting customer churn, you can work on preventing it. Let’s start with an overview of what customer churn is.

What s customer churn?

Customer churn, also known as customer attrition, is the percentage or number of your customers who stopped using your product or service during a given period. Customer churn negatively affects your bottom line. You lose sales from the customers that left and have to spend more money to attract new clients. Adding up those marketing costs and lost revenue will provide an estimate of the cost of customer churn at your organization.

There are many reasons that customer churn can occur, including:

• State of the competition: Your business always has to think about competitors. If your competition offers something more appealing than your company or suddenly improves, you may lose customers. Avoid this by paying attention to pricing, evolving customer needs, and the overall customer experience.
• Changes to your products: Making changes to your product’s functionality is a common cause of customer churn, especially when customers believe that the new solution is worse. That’s why it’s so important to get customer feedback before changing products.
• Poor customer service: Poor customer service is a very common reason that people look for other products or services. So, you should always make it a priority to deliver good customer service and create a positive customer experience.
• Changes in customer needs: Sometimes, customers stop using your product or service because they feel they have already used it to its fullest. They may love your company and support it but just not need your offerings anymore. This can indicate you may need to expand the products and services you offer.
• Changes in customers’ financial circumstances: The reasons above all indicate voluntary churn, which refers to customers deciding to leave because they are not satisfied with the product or service, among other reasons. However, there are times when customers will be forced to churn, which is known as involuntary churn. This is usually due to changes in their financial circumstances, which cause them to be unable to pay for your product or service.

To reduce customer churn and its impact on your organization, you should track rates. As one of the most important customer success KPIs, customer churn rate can help you identify areas of improvement across your organization, like in customer acquisition, product, and support.

For example, a high churn rate might indicate that your current acquisition efforts are attracting the wrong type of customer. High customer churn could be a sign of issues with your product’s usability or customer support. Or it might uncover a need to improve customer onboarding and customer engagement to ensure they start and continue to actively use your product.

Customer churn rate is calculated by dividing the number of churned customers during a period of time by the total number of customers you had at the start of that period. Average churn rates vary by industry.

According to CustomerGauge’s recent B2B NPS® & CX Benchmarks Report, The State of B2B Account Experience, the median churn rate for the energy/utilities industry is 11% whereas the median rate for consumer packaged goods is 60%. Make sure you understand how your customer churn rate compares to other competitors in your industry. Then use past performance as well as these competitors’ performances as benchmarks for improving your rate over time.

What is customer churn prediction & analysis?

Customer churn prediction and analysis is the process of gathering relevant customer data such as purchase history, demographics, usage patterns, interactions, complaints, and feedback to identify factors that predict customer churn so you can identify customers who are at risk of leaving your business. This can help you forecast how many customers will leave your business and why so you can implement customer retention strategies to prevent churn and enhance decision-making around budget, hiring, and other aspects of your business.

The overall goal of customer churn prediction is to figure out how many customers will leave within a given time frame.

Why is customer churn prediction important?

Customer churn prediction is important for identifying patterns and indicators that are common among customers that churn so your business can take proactive measures to retain customers and minimize churn. This is important to all businesses, including companies trying to retain online subscribers to brick-and-mortar stores trying to keep repeat customers.

You can use customer churn prediction to:

• Identify customers at risk of leaving
• Identify customer pain points
• Create strategies to reduce customer churn and increase customer retention

Challenges of Customer Churn Prediction

One of the biggest challenges that businesses face when predicting churn is having access to the data they need. This is why the first step to customer churn prediction (and prevention) is to ensure that your customer data is well-organized, accurate, and up-to-date.

Some other challenges that may come up when predicting customer churn include:

1. Not Enough Data

While having relevant data can be a challenge, you may also not have enough of it. If there isn’t enough data, the churn prediction model you use may be insufficient. This will lead to inaccurate predictions.

It is very common for newer businesses to lack data. Your predictions should become more accurate over time as you get data on customers and their buying behavior.

2. Too Many Data Features

When there is too much data, your model can include a lot of “noise.” That extra noise can lead to false predictions, particularly when there is unseen data involved. This highlights the need to clean up your data and keep it well-organized.

3. Too Many Nulls

If your data model has too many nulls, then the model can become useless.

How to Develop a Customer Churn Prediction Model in 7 Steps

Let’s go over the step-by-step process of developing a customer churn prediction model below.

1. Identify Your Goals

To start, you want to establish clear goals for your churn prevention model. Remember that you should be as specific as possible to your unique business and customers.

Some common goals for churn prediction models include:

• Identifying customers that are most at risk of churning

• Understanding why at-risk customers are likely to churn
• Improving the customer journey to retain at-risk customers

2. Consolidate a Database

As mentioned, one of the biggest challenges with churn prediction is having access to clean, organized, sufficient data. You need enough data, and it needs to be usable. Take your time consolidating your database—various tools can help you with this to speed up the process.

3. Segment Your Customers

As you organize your consolidated database, segment your customers. This is the time to look at historical data on customers to see who is likely to stop using your products and services. There are multiple ways that you can segment your customers, and the method you choose will depend on your goals. You can segment based on demographics, behavior, contract terms, revenue information, and more.

Here are some more specific examples of how you can segment your customers:

• Whether they are using their product for personal use or as part of a company
• How often customers use the product
• Which features customers use most
• How often users reach out to customer support
• How long they have been a customer
• The revenue you earn from the customer (including predicted customer lifetime value)
• How long is left until the product expires and requires renewal (especially for businesses with subscription models)
• Pricing tiers or plans (for service-based businesses and those with subscription models)

4. Identify Customers with a High Risk of Leaving Your Company

Once you segment your audience, start using that data to identify the customers that have the highest risk of churn.

For example, customers who use your product daily or have bought multiple products or services have a low risk of churning. In the case of software and some other service-based businesses, regular communication with customer support can indicate a low risk of churn.

Some of the biggest warning signs to look out for among existing customers include:

• Customers who use the product less than they have (or buy less than they used to)
• Customers who signed up for a service but never followed through
• Customers who never sent in support tickets (especially for software and SaaS)
• Customers who send in a lot of support tickets for the same or similar issues

5. Build, train, and validate your model

To find patterns in the data and make accurate predictions, you need a customer churn prediction model that uses some form of logic like binary classification or decision trees. Binary classification gives a true or false answer, such as asking if a specific action causes churn. Decision trees offer various scenarios, possibly more than two. Other common examples are logistic regression, random forests, and neural networks. You should also consider employing machine learning algorithms and statistical techniques to create your model.

Once you build your model, you have to train it. Use historical data where the churn status of customers is known to train the model to recognize patterns associated with churn. You should also assess the accuracy and performance of the model using validation techniques and metrics like accuracy, precision, recall, F1-score, and ROC curve

You can then apply this model to new data to predict the probability of churn for existing customers.

6. Look for Patterns to Identify Future Churn Cases

You can then analyze the predictions to understand the factors contributing to churn.

The following are some examples of the types of patterns you may find regardless of the type of model or data type you use:

• A lot of customers leave right after signing up for your service (maybe due to your plans being confusing or the onboarding process not being user-friendly)
• Churn right after product updates (possibly because of poor resources and information about the updates)
• Churn with longstanding customers (possibly from lack of communication)

7. Come Up with a Response Plan

Now that you have identified patterns associated with customer churn, you can create strategies or interventions to prevent or reduce churn. Your churn response plan may involve targeted marketing campaigns, personalized offers, improved customer service, or retention initiatives.

For example, say your customers leave right after signing up. This may lead you to discover an issue with your website copy. For example, maybe they found your subscription plans confusing and signed up for the wrong one. In that case, you may need to A/B test your landing page copy to provide more detailed descriptions of each plan. Or maybe the issue is onboarding. So you may add more tutorials to your blog, create a customer knowledge base, or train customer support to schedule a check-in right after a customer purchases a plan to make the process more user-friendly.

If customers are churning after product updates, then you may need to re-think how you create your product roadmap. To start, are you making sure that customers want the updates? How are you ensuring that they understand the reasons for the updates and how to use the new functionality? You may need to add more support and resources immediately before and after product updates to reduce churn.

Your long-term customers may also show patterns of leaving. In that case, you may need to add more touchpoints between them and customer support or product teams or offer them discounts or other incentives to ensure they remain satisfied.

How to Prevent Customer Churn: 5 Tips & Strategies

You don’t need to hire a team of data scientists to predict churn, as some very basic data science and customer analytics will give you a good starting point. Remember that the whole point of customer churn prediction is to be able to prevent churn.

You can also take the following steps to reduce churn.

1. Improve Your Customer Service

Satisfied customers are much less likely to churn, so you want to do your best to offer excellent customer service. A simple way to improve customer service is to ensure that you can easily respond to all messages from customers. Podium, for example, lets you make customer communication easier for everyone, as you will see all your messages in a single platform.

You will also want to improve other aspects of customer service and communication, such as response time and how satisfied customers are after talking to your support team.

Remember that customer communication and customer service give you the ability to engage customers before they even consider leaving. If you regularly reach out to customers or answer their messages promptly, you can take action as soon as you are aware of a problem. By being proactive, you can ensure it never gets bad enough for your customer to churn.

2. Consider a CRM

A Customer Relationship Management (CRM) system can be instrumental in preventing customer churn.

A CRM system consolidates customer information from various touchpoints, including interactions, purchases, preferences, feedback, and complaints, into a centralized database. This provides a 360-degree view of the customer, which allows your team to understand their needs and behaviors better so they can deliver personalized communications and support.

CRM systems also automate tasks, reminders, and follow-ups, ensuring that customer interactions are timely and consistent. Automated alerts can notify the sales or support teams about critical issues or opportunities with specific customers, enabling swift action.

In summary, a well-implemented CRM system serves as a powerful tool for businesses to understand their customers, personalize interactions, anticipate their needs, and proactively address issues—all of which are crucial in preventing customer churn and fostering long-term relationships.

3. Analyze Customer Reviews

Customer reviews are a valuable source of information that can help you identify factors that lead to churn as well as factors that increase customer satisfaction and loyalty. For example, a common pain point in negative customer reviews may be how confusing your product is. This may lead you to perform some UX testing so you can improve the usability and ease of use of your product.

On the other hand, a common theme in positive customer reviews may be how unparalleled your customer support is. In that case, you may build out your customer support team to ensure you can provide new as well as existing customers with this level of service.

With a free Google Review link generator, you can easily generate more customer reviews faster.

4. Take Customer Feedback Into Account

Reviews are just one form of customer feedback that can help improve your customer churn prediction model. You should also encourage customer feedback in other ways, such as sending out net promoter score (NPS) surveys or engaging with customers on your social media channels. This will enable you to address pain points before the customer experience worsens and causes customer churn. It will also help you proactively identify opportunities to improve customer satisfaction.

Don’t forget to ask for feedback from customers who have stopped using your products and services. Even if only a small percentage of them explain the reasons they left, it will give you optimal insights into the reasons for the churn. It can also allow you to send a follow-up after you address the issue and potentially win back the lost customer.

5. Incentivize Customer Loyalty

Loyalty programs offer rewards, discounts, or exclusive offers to incentivize customers to continue engaging with the brand. When customers feel appreciated and rewarded for their loyalty, they are more likely to continue purchasing from the company, feel an emotional connection with the brand over competitors, and refer friends and family.

By strategically implementing loyalty incentives, companies can nurture stronger relationships with their customers, foster loyalty, and continuously add value to their customers’ experiences, all of which will help reduce churn.

There are many ways you can begin building a loyalty program at your organization. For example, you may start texting exclusive offers to current customers with Podium.