Churn Prediction: How to Retain Customers and Boost Revenue

What is churn prediction?

Churn prediction detects customers who are likely to discontinue using your products or services.

This prediction provides you with information on whether a customer will leave and the reasons why.

4 Reasons to Predict Customer Churn

With the help of customer churn prediction, you can have a head start reducing customer churn; here is the rest of the list for you to see how important churn prediction is:

1. Improve Your Retention Strategy: By predicting customer churn, you can identify at-risk customers early in the cycle and offer targeted, personalized services.
2. Reduce Revenue Loss: By predicting churn, you can address and eliminate it before your customers leave, minimizing your potential revenue loss.
3. Increase Customer Lifetime Value (CLV): Upon detecting at-risk customers and implementing retention strategies, you can increase your customers' lifespan.
4. Enhance Customer Experience: Addressing pain points and improving customer service quality after collecting feedback from at-risk customers will increase customer satisfaction.

Why Do You Need A Churn Prediction Model?

• Identify At-Risk Customers Early: Predicting customer churn enables you to intervene before they leave, allowing for timely retention efforts.
• Optimize Resource Allocation: Pinpointing high-risk customers who are more likely to churn allows you to focus your resources on them rather than spending them on all customers.
• Implement Product and Service Improvements: Upon understanding the reasons behind churn, you can address the issues that drive customers away by making informed improvements to your product.
• Personalize Retention Strategies: You can tailor your retention tactics to customer needs and behaviors in order to make them more effective in preventing churn.

How to Build a Churn Prediction Model

Here are the steps that you can follow to build a churn prediction model:

1- Collect Customer Data

In order to build a churn prediction model, you need to collect enough data that will serve as a foundation for your efforts.

This task falls into data scientists’ areas of expertise most of the time since feeding your model data sources is a critical step that should be led without errors.

You can retrieve these data sources from the software you’ve adopted throughout the customer lifecycle, including your CRM, customer service platform, and web analytics tools filled with customer data.

Another key point here includes the data you prepare, as it should be relevant to your case so your model doesn’t fail, leading to inaccurate predictions.

2- Analyze Customer Data

The second step to building a churn prediction model is to analyze the data you collected.

After organizing the raw information into structured data, you should focus on understanding the factors behind your customers' leaving.

To do so, you can employ methods that extract the attributes, illustrating habit patterns attached to specific customer interactions with your product, and ultimately revealing the decisive factors.

With these habit patterns presented, you can analyze churn trends and identify the reasons behind customer churn.

Other than key metrics such as MRR, ARR, New Customers Rate, Churned Customers Rate, ARPU, and LTV, you can track and analyze other metrics that might be specific to your type of product or business, such as:

• DAU (Daily Active Users), 
• MAU (Monthly Active Users), 
• Day 0 Retention, 
• and Day 7 Retention.

3- Predict Customer Churn

This step requires you to create a solid predictive model for the data analysis you did priorly, starting the identification process.

You need to pin down the customers who are highly likely to churn based on the indicators of churn.

For this step, you can examine data related to product usage, satisfaction surveys, and customer feedback to discover any link between these subjects and churn.

After this manual method, you can use a machine learning model by feeding all the data you’ve collected during the prior processes.

With your dataset, variables, and target fed into the platform, you’ll create a churn model that will provide you with accurate results in a score time.

The platform will first divide the target base and recognize the patterns they took based on each variable, facilitating the prediction sequence of customers with close tendencies.

When performed correctly, this step enables you to address early churn to fight factors between a good experience and new users.

Customer Churn Prediction Software: 3 Tools

1- Qualtrics CustomerXM

Qualtrics is a digital success platform that serves various use cases, including customer experience, employee experience, and strategy and research.

Qualtrics’ Customer Experience product allows you to examine your customers more closely and determine whether they’re likely to quit.

For example, you can match experience and operational data to see a more detailed chart representing customer behavior, which can help you accurately predict churn.

By automatically identifying which customers are at high risk, this platform enables you to improve retention.

2- ChurnZero

ChurnZero is customer success software that, with its forecasting feature, can run the churn prediction process on your behalf.

This platform helps you identify at-risk customers and forecast renewals and revenue, allowing you to see possible opportunities.

Thanks to machine learning, it’ll keep you updated with success insights obtained from customer data and custom survey answers—helping you make more data-driven decisions to prevent future customer churn.

3- Gainsight

Gainsight is a platform designed for customer success, helping success managers improve in several aspects.

Gainsight uses machine learning to provide accurate predictions based on in-product data that can determine the possibility of customer churning.

Allowing you to manage every customer profile on a dashboard, this platform ensures that not even one at-risk customer slips out of your hands.

Case Study on Customer Churn Prediction

Along with the tools above, you can use a robust tool like UserGuiding for churn prediction, especially for detecting early churn.

To prevent early churn, UserGuiding provides you with several features, such as:

• Product tours,
• Onboarding checklists,
• Tooltips,
• Hotspots,
• Announcement modals.

By using these features early on in the customer journey, you can ensure that your customers get an interactive experience in which they understand what you offer and how you differ from the rest of the market.

Keep in mind that helping your customers understand your core features and how you can improve their workflow will also impact churn. This will increase their engagement with your platform, preventing churn in the early stages.

Let’s give an example of the matter with Opinew’s case, the e-commerce business that utilized UserGuiding in order to reduce early churn.

For starters, Opinew had a complex platform for new users to find the value they were looking for, which is why the platform had a significant early-churn rate for the first 15 minutes of a user’s lifetime.

That’s where UserGuiding came in.

It increased new users’ time to value (TTV) by showing them exactly how they could benefit from Opinew through interactive guides with videos and product onboarding checklists.

These two features enabled Opinew to display what the new users were missing, thanks to different UX elements that caught their attention and led them towards value.

One of Opinew's key elements was in-app interactive guides, which informed new users about the platform’s main features and how to use them without getting lost.

After onboarding, new users needed to explore and use the platform themselves; Opinew ensured this sequence by creating a product onboarding checklist that displayed how they should move forward step-by-step.

Opinew’s support team recorded support videos and embedded them into guides that users could check anytime to provide instant answers with visuals once they have an inquiry.

Long story short, Opinew increased the activation rate of users by 10% and reduced the support team’s workload at the same time—all thanks to addressing early churn.

For prediction, you can also…

In terms of prediction, UserGuiding enables you to utilize product engagement analytics, both stand-alone or through integrations with third-party analytics tools.

Moreover, collecting customer feedback can create a solid foundation for churn prediction, especially for high-risk customers.

UserGuiding facilitates the survey creation process by providing various customization and personalization options on a robust builder page.

After creating a visually pleasing survey and selecting your audience through segmentation, you can publish your in-app survey and start collecting feedback from customers without disrupting their experience.

Here is a preview of a newly made survey to help you understand more about your customers’ probability of churning:

Conclusion

Churn prediction is about identifying which customers are likely to churn from your business.

To make correct predictions, you need to complete a three-step process, including data collection, data analysis, and creating your churn prediction model.

Using machine learning, several tools, such as Gainsight, Qualtrics, and ChurnZero, are on the market to predict churn and revenue simultaneously.

However, one of the best strategies you can adopt to fight churn is through addressing early churn.

In order to do so, you need to show your customers how they can benefit from your platform and engage them to the maximum level so that they become repeat customers, which is possible through utilizing UserGuiding.

See how Opinew utilized UserGuiding to prevent early churn ⬆️