Published on

January 5, 2024

Machine Learning

Customer Churn Analysis: 6 Steps To Success

Churn is the silent killer of SaaS firms. Here are 6 practical steps to analyze customer churn and reduce customer attrition.
Nathan Wies
Software Engineer, Akkio
Machine Learning

Companies today are facing a major challenge: customer churn. With customers increasingly switching providers, companies must find ways to understand why they are leaving and act quickly to retain them. The key to retaining customers and reducing churn is to analyze customer data and take action to improve retention.

To do this, companies need to follow a process for customer churn analysis. Guessing why customers are leaving is not enough; companies need to take a systematic approach to understanding churn. 

In this post, we will explore the steps to analyze customer churn and how to reduce it with machine learning powered predictive modeling.

What is customer churn, and how can you address it?

Customer churn is any time a customer stops buying a company's products or services over a given time period. SaaS companies, for instance, experience churn whenever customers unsubscribe or otherwise drop off.

Companies that rely on subscription services or high-volume sales need to track and analyze customer churn to optimize customer experience, customer engagement, customer lifetime value (LTV), monthly and annual recurring revenue, (MRR), and other key performance indicators (KPIs).

Customer churn analysis is the process of examining data on existing and past customers. This data can include credit card usage, support tickets, and other types of customer interactions, which can be used to gain valuable insight into how to optimize customer success and reduce future churn, lowering the attrition rate.

By analyzing customer churn metrics, SaaS companies can gain an understanding of the types of customers they have and how to optimize their customer acquisition costs. With a better understanding of customer churn, they can retain more existing customers, acquire new, loyal customers, who are less likely to churn prematurely and realize a higher customer lifetime value.

The process of analyzing customer churn data can also help SaaS companies understand their customer's lifecycle, identify any time periods with a high churn rate, and identify any lost revenue due to voluntary churn or involuntary churn due to lack of functionality. With this churn data, subscription businesses can understand what actions they need to take, such as improving customer support or adding new features or functionality, in order to increase retention rate.

Using customer churn analytics, SaaS companies can gain real-time insights into their customer base, understand the types of customers churning out, and use cohort analysis to identify the most valuable customers.

What does churn analysis entail? What is the step-by-step process?

Churn analysis is a crucial tool to identify which customers might be at risk for churn and take necessary steps to retain them.

In this section, we will explain the step-by-step process of churn analysis to help companies better understand and utilize this vital tool. While this process can be done completely manually, we will focus on how machine learning can make this fast and painless and get you to improved customer satisfaction and retention fast.

1. Collect the relevant data from customers and assess your current situation

Data is the fuel for all analytical processes, and churn analysis is no exception. As companies move to understand customer behavior better and anticipate churn, the first step to conduct churn analysis is to collect the right data.

Demographic, firmographic, behavioral, and psychographic data are all key components in understanding customer churn.

Demographic data describes the basic characteristics of the person involved and includes things like age, gender, location, and income. For example, if you're an eCommerce store selling children's clothes, you may find that mothers are less likely to churn than fathers.

Firmographic data includes company size, industry, and company type. For instance, a large company with deep pockets may be less likely to churn than a small startup.

Behavioral data includes customer interactions with your company, such as website visits, purchase history, and customer service inquiries. This helps you identify potential churners and intervene. Clients who didn't receive a timely, helpful customer service response may be more likely to churn.

Finally, psychographic data includes customer attitudes, beliefs, and values. If you can identify shared values between customers, you may be able to target them with specific offers and messages that will make them less likely to churn.

By collecting the right data from customers, you can assess your current situation and take the necessary steps to reduce churn. With the right data in hand, you can create a churn reduction plan that is tailored to your unique set of customers.

While all these categories of data are considered useful, it is important to note that not all data is created equal. Just because you have collected some data on customer beliefs does not mean that it is accurate, more data is not always better, and it's important to consider both quality and source when selecting it.

Akkio’s Flow Editor showing a data table input to train a predictive model

2. Segment your customers

The second step in churn analysis is to segment customers based on relevant factors. While every individual and entire customer journey is unique, groups can share certain characteristics and behaviors that can be analyzed.

For instance, consider pricing plans. Customers who are paying a premium price may be less likely to churn than those on a lower-tier plan. Their investment in the platform is higher, and their cost to switch may be preventative. You can also segment customers based on the stage at which they left your company. Clients who left early in the onboarding process may have different needs than those who stayed until the end.

Using Akkio, machine learning models uncover segments in your data that are most predictive of impending customer churn. These segments allow you to tailor your churn reduction strategy to the individual customer or group. As shown here, you can see that we can categorize churn into likely, uncertain, and unlikely to churn while providing information on the similarities found within the group. This information can then be used by other teams to modify interactions such as sales, support, and customer feedback.

Segmentation analysis of likely churn for telecommunications customers when predicting with Akkio
Segmentation analysis of likely churn for telecommunications customers when predicting with Akkio

In addition, you can consider industry factors such as economic trends, global issues, labor or union policies, and geographical factors such as legal policies, currency, and conversion rates. For example, customers in countries with unstable currencies may be more likely to churn than those in more stable economies, as the monthly cost could go up for them without a change in the base currency.

3. Analyze historical data

The third step in churn analysis is to analyze historical data. By looking at the data from previous months and years, you can understand how many customers are leaving each month and year. This allows you to predict future trends and make better decisions about where to focus your efforts.

The issue here is that this high-level, manual analysis overlooks the more detailed patterns in your data. Using advanced machine learning powered analytics tools, you can uncover more granular trends and correlations, such as the impact of customer service interactions on churn. In the below example, you can see how customers on paperless billing were notably less likely to churn, this could then be applied back into an eco-friendly paperless billing initiative on the customer services side.

Top Fields report in Akkio showing correlations in the dataset

With sophisticated algorithms, you can identify which customers are most likely to churn and why. This allows you to develop targeted strategies to reduce churn. As an additional example. you may find that clients who receive a personalized message after their first purchase are less likely to churn.

Analyzing historical data also helps you identify outliers and anomalies and exclude them from later profiling. By recognizing patterns in customer behavior, you can pinpoint potential churners before they leave and intervene accordingly. You can also automatically remove them in plain English thanks to our innovative Chat Data Prep feature.

4. Calculate your churn rate

Calculate customer churn rate by dividing the number of customers who stopped using your product during a given period by the total number of customers using it at the beginning of that period.

For instance, if 100 customers were using your product in March but only 90 remained by April, then your monthly customer churn rate for March would be 10%. Annualized, a 10% monthly churn rate is 72%, where you would continue to lose 10% of the remaining customers each month. In comparison, a 3% monthly churn rate is 31% annualized.

Clearly, the higher the churn rate, the more urgently you need to take action to improve customer retention. If your churn rate is high, you will likely want to choose correctional measures that are more drastic and easier to implement. If your churn rate is lower, you can invest time and resources in better customer research, customer service, and innovation.

By understanding your churn rate, you can better prioritize your resources and focus on the areas that will yield the greatest results over customer lifetime. This allows you to minimize customer churn and maximize customer retention.

5. Use predictive analytics to identify trends in churn by customer segments

The fifth step in churn analysis is to use predictive analytics to identify trends in churn by customer segments. By analyzing churn using machine learning and data science models, you can identify trends such as at which stage customers are most likely to leave, for what reason, and which channels customers prefer to communicate in.

You can also use predictive analytics to predict churn and future trends based on past data, as well as identify opportunities for improvement. The best way to apply predictive analytics is to use a tool like Akkio, which is a solution that comes with ready-to-use statistical models built for churn prediction.

Traditionally, businesses would need to build data pipelines, collect and clean data, and then manually build complex statistical models. These models take months to produce, with countless hours of back and forth with data science teams to produce a one-time static model with data that is now out of date. With Akkio, however, the entire process is simplified, fast, and automated, making it easy to identify churn trends and take the necessary steps to reduce customer attrition.

Try a churn predictive analysis ML model

Below, you can see what an Akkio churn analysis model looks like. Simply enter some sample values to generate a prediction of whether the customer will churn.

6. Take action

Finally, it's important to remember that your insights and customer metrics are only as good as the actions you take. Once you've identified areas for improvement, make changes to your product or service and monitor their impact on customer churn over time.

For instance, if you identified customer service issues as the primary driver of churn, you can invest in better training and better tools. Alternatively, if you identified a poor onboarding experience as the primary driver of churn, you can invest in a better onboarding process.

It may be that competitive pressures are driving customers away. In this case, you can invest in better pricing strategies, better marketing, and better product features.

Akkio's platform makes it easy to take action on your insights. With Akkio, you can create segmented customer journeys and tailor your messaging to each segment. This allows you to target specific customers with offers and messages that will make them less likely to churn.

You can also use Akkio's analytics and reporting tools to monitor the impact of your changes over time. This helps you identify other areas for improvement and repeat the process until your customer churn rate is as low as possible.

By following these six steps, you can effectively analyze your customer churn rate and take the necessary steps to reduce it. With the right data, predictive analytics, and actionable insights, you can create an attrition reduction plan that is tailored to your unique set of customers.


Customer churn is a major challenge for businesses today. It's important for companies to understand why customers are leaving and take action. 

By following the six steps outlined in this post—collect the right data, segment customers, analyze historical data, calculate customer churn rate, use predictive analytics to identify trends, and take action—companies can effectively reduce customer churn and maximize customer retention.

Akkio is an easy-to-use platform that makes it easy to analyze customer churn. With Akkio, companies can create segmented customer journeys, tailor messaging, and monitor the impact of their changes over time. By using Akkio's predictive analytics and reporting tools, companies can reduce customer churn and maximize customer loyalty.

Beyond churn analysis, Akkio can be used for a variety of other business purposes, such as fraud detection and lead scoring. Try Akkio today and see how it can help your business.

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