How and Why to Calculate and Predict Customer Churn

Why did the customer retention expert become a detective?

Because they were determined to solve the case of disappearing customers!

OK, that’s pretty silly. And hey, customer churn is no joke. 

You’ve worked so hard to acquire existing customers, not to mention spending considerable time and resources on that task. You want to keep customers around and, if anything, build a stronger relationship with them.

If you suspect you’re dealing with a high churn rate and want to improve your customer retention metrics, the first task is to calculate customer churn accurately and ensure you have an accurate handle on the problem. 

First, let’s dig into why churn matters so much for businesses. Then, we’ll take a look at how to calculate customer churn during a given period of time, and some potential ways to begin solving this challenging problem.

Why does churn matter so much for businesses?

In the realm of business analytics, accurately calculating and detecting churn is of utmost importance for ensuring customer retention and gaining insights into the overall health of a company’s customer base.

Churn refers to the rate at which customers terminate their relationship with a company within a specific time frame. Precisely measuring churn enables businesses to identify patterns, diagnose potential issues, identify warning signs, and devise strategies to bolster customer loyalty.

Thankfully, in the world of data analytics, we have some powerful tools and methods to understand and tackle customer churn head-on. By using data-driven insights, businesses can actually spot those customers who might be thinking of leaving, and then apply specific strategies to keep them around. This way, we can boost customer loyalty and make sure they stick with us for the long haul.

It’s alarming to note that businesses suffer significant financial losses due to churn, amounting to approximately $1.6 trillion annually. In certain industries, as many as a quarter of customers churn each year, resulting in substantial revenue loss and a less-than-ideal customer experience.

When a customer decides to leave, it can actually hit a business right in the wallet, costing them an average of $243. That’s not just a simple number – it takes into account things like how much customer acquisition cost in the first place, the recurring revenue that’s now gone, and even the potential damage to the brand’s reputation. Of course, it’s worth mentioning that this number can fluctuate quite a bit depending on the industry you’re in.

For example, for SaaS companies targeting smaller businesses, a monthly churn rate of 3-5% is to be expected. Conversely, those serving enterprises can anticipate a much lower churn rate of just 1%.

For those shiny new SaaS companies stepping into the market, it’s important to be prepared for some bumpy roads when it comes to churn rates. In fact, during their first year, it’s not uncommon to see churn rates climb as high as 15%. This initial phase presents its own set of hurdles as the company works hard to find the perfect match between its product and the market. It’s all about tweaking those customer acquisition and retention strategies until they hit the sweet spot.

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What are the traditional ways to calculate churn?

Determining churn rate involves various approaches, each offering unique insights into customer attrition. Let’s explore three commonly used methods:

Method 1: Churn Rate = (Number of customers lost during a period / Total number of customers at the start of the period) * 100

This method calculates the churn rate by dividing the number of customers lost during a given period by the total number of customers at the beginning of that period. Multiplying the result by 100 provides the churn rate as a percentage. This method offers a simple way to measure churn.

Method 2: Churn Rate = (Number of customers lost during a period / Average number of customers during the period) * 100

Instead of using the initial number of customers, this method employs the average number of customers during the period. Considering the fluctuation in customer count provides a more accurate representation of the churn rate.

Method 3: Churn Rate = (Total revenue lost due to churn / Total revenue at the start of the period) * 100

This approach takes a revenue-centric perspective by measuring the percentage of revenue lost due to churn. It involves calculating the total revenue lost from customers who churned during the period, divided by the total revenue at the start of the period.

How can predicting customer churn help?

Predictive churn software has a big job — because churn is a big problem for businesses.

Calculating churn is crucial for businesses seeking to gauge customer retention and identify potential issues that may impact their bottom line. Understanding churn allows businesses to allocate resources effectively, optimize customer retention strategies, and deliver exceptional experiences to retain their most valuable customers. 

But measuring churn after it’s already happened isn’t really ideal. Instead, anticipating customer churn offers significant advantages over reacting to it after the fact. By being proactive and predicting churn, businesses can take preventive measures and implement targeted strategies to retain customers before they even think about leaving. This approach allows companies to stay ahead of the game, minimizing the impact of churn and maximizing customer retention.

On the other hand, reacting to churn after it has already happened puts businesses in a tougher spot. It often involves scrambling to win back customers or find replacements, which can be costly and time-consuming. Plus, by that point, the negative effects of churn, like lost revenue and potential damage to the brand, have already taken their toll.

By predicting churn, businesses can allocate their resources more efficiently. They can focus on spotting early warning signs, analyzing customer behavior, and implementing personalized retention initiatives. Taking this proactive approach enables companies to be customer-centric, addressing issues in real-time and building stronger relationships with their clients.

Ultimately, the ability to predict churn empowers businesses to be proactive, optimize their customer retention efforts, and cultivate a more sustainable and successful customer base. It’s like having a crystal ball that helps them stay ahead of the curve and minimize the adverse impact of churn.

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Read how Hydrant used Pecan's predictive churn models to win back customers.

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How does Pecan predict churn?

Pecan AI’s low-code predictive analytics platform helps you predict and reduce churn quickly. Using your customer data and transaction data without additional data science resources, your data analysts can build sophisticated machine learning models. These models can:

  • Understand what influences customers’ potential to churn
  • Detect 85% of would-be churn
  • Lower churn by roughly 15-20%

You can build a churn model according to your specific business model (like a subscription) or assign each customer a specific churn likelihood score. That score is important to understanding and acting on the risk of churn. Specific scores generated by the churn prediction model let you create groups of customers. Those defined groups can each receive targeted offers, promotions, or messages designed to keep their business.

Importantly, Pecan also explains the factors contributing to each score. These explanations allow you to choose how to take action to retain at-risk customers.

graph showing ways to respond to potential customer churn
Knowing in advance whether a customer is likely to churn can inform more efficient outreach and fine-tune strategies.

You can also use the collected information across all your customers in various ways, beyond reducing customer churn. It can help you:

  • Plan future campaigns
  • Inform decisions about new products and services
  • Guide customer service initiatives
  • Shape strategies to boost customer satisfaction
  • Improve customers’ experiences with your brand

When we talk about predicting churn, it’s not just about keeping tabs on customers who walk out the door. It’s actually a powerful strategic tool that empowers businesses to do some pretty cool things. 

By understanding and predicting churn, companies can take proactive steps to foster loyalty, boost customer satisfaction, and pave the way for long-term success in a business landscape that’s constantly changing and evolving. It’s all about using churn as a springboard for growth and improvement.


Keep your customers today and in the future with Pecan’s predictive churn models. Ready to get started with building a churn prediction model yourself? Then go ahead and start your free trial. Or, if you’d prefer, we can give you a guided tour.

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