What is customer churn?

It is an especially important metric for software-as-a-service (SaaS) businesses, many of which depend on monthly recurring revenues from subscriptions. They need to know whether customers are churning—or might be churning in the future—as that will have an immediate impact on their bottom line.

Customer churn, or customer attrition, is on the opposite of customer retention, which relates to companies maintaining their customer relationships. Minimizing customer churn should be a key component of any customer engagement strategy, which relates to all interactions a customer has with a business or brand, whether online or in store.

Prioritizing customer engagement, especially formulating a robust customer retention strategy, is an important protection against customer churn.

Companies should measure customer churn rates on a frequent basis, so they understand whether they are at risk for revenue loss.

Eliminating churn is an important task given how high customer acquisition costs can be. According to McKinsey¹, replacing the value of one lost customer can require the acquisition of three new customers. So companies should do whatever they can to reduce churn and retain those customers.

Churn affects B2B and B2C businesses slightly differently. There tends to be a higher churn rate in B2C businesses than B2B for a couple of reasons.

First, B2C customers do not need to get approval from a boss to start or finish a subscription, so they are more likely to impulse buy and impulse quit. Second, subscriptions are also likely to be cheaper, which means it’s easier to leave one service and start another.

On the other hand, B2B churn is often more impactful for those businesses.

Modern B2B companies either sell products or services. The former is often a one-time fee for an individual product. For those who sell software-as-a-service solutions, that is, SaaS companies, they can charge customers multiple times during the year for access to the service. The latter depends on their customers (subscribers) paying a monthly recurring revenue.

B2B businesses likely have fewer potential customers, or a more stringent sales pipeline. That’s because B2B businesses serve a specific set of customers, whereas most consumers need certain B2C products regularly (for example, groceries, household goods, bank services).

As such, customer churn has a greater impact on B2B businesses, especially if they provide products or services at a high price tag to a more select group of customers.

Increased churn can demoralize executives and employees, creating worry about their jobs and the vitality of the company. Because new customer acquisition is often time-consuming and expensive, it can distract companies from focusing on serving their existing customers, thus creating a cyclical effect.

That is one example of how losing customers can create exponential or cyclical churn. Another is by word of mouth. If one customer talks to other customers about how unsatisfied they were with a company’s products that might lead to more cancellations creating even greater churn.

There are two major types of customer churn: voluntary and involuntary.

This relates to customers’ change in preference. Examples of voluntary churn include customers choosing to no longer use a product in that category, switching to a competitors’ product, reaction to price increases or bad customer experience.

This relates to issues often beyond a customer’s control, such as a company no longer offering that product or service, technical or payment issues, and natural disasters. There can also be an unforeseen reason why a customer can no longer use a service. Examples include a company no longer paying for an individual to use a service or a change in occupation renders that product or service no longer valuable.

• Churn rate
• Monthly recurring revenue
• Revenue churn rate

Companies can measure the customer churn rate by dividing the total number of customers that left the company in a specific time frame from the total customers a company acquired. This number is then multiplied by 100 over a specific period of time.

Companies should benchmark customer churn figures, like other customer experience metrics, so they can identify potential issues. To calculate churn rate, companies can choose between given time periods, such as calculating the weekly, monthly or annual churn rate. A monthly churn rate might be good for SaaS companies with monthly recurring revenue (MRR) that need to understand their monthly rates.

Churn rate = (Lost customers/Total customers at the start of time period) x 100

Example: A company that tracked churn rate monthly lost 300 of their 75,000 customers. That means their churn rate would be 0.4%

Companies that set up real-time alerts to things like cancelled credit card payments or cancelled services can have a better handle on churn.

Churn rates vary depending on the type of business. According to Recurly, an average churn rate is 4%, where 3% is attributed to voluntary churn and 1% attributed to involuntary churn. For digital entertainment providers, the average churn rate is often higher. It tends to be lower for software and business and professional services.

Companies can and should also calculate revenue churn rates, which determine lost monthly recurring revenue (MRR) from existing customers over a determined time period.

MRR = Number of subscribers x average revenue per subscriber (ARPU)

Example: That same company that charges its 75,000 subscribers USD 15.00 a month for services has an MRR of USD 1,125,000.

This determines how much revenue is churning out of a company during a specific time period.

Revenue churn rate = (Revenue lost to churn/Total MRR in the time period) x 100

Example: That same company would have lost about USD 4,500 a month to churn. Compared to the total revenue that is a 0.4% revenue churn rate as well.

• Customer satisfaction score
• Net Promoter Score
• Customer Effort Score
• Product usage rates

CSAT surveys customers to determine whether their satisfaction level. It calculates how many are satisfied or very satisfied. The more satisfied customers that a company has, the less likely they are to have churn issues.

NPS asks customers how likely they are to recommend a company and its products to those in their networks. The number of low scores (6 or less) is subtracted from number of “promoters” (9’s and 10’s) and the net is converted into a percentage. Companies with high NPS scores not only need to worry less about churn, but also might find their customer acquisition costs drop somewhat due to positive word-of-mouth.

This customer service metric relates to how difficult a customer found from 1 (easy) or 7 (difficult). Companies with high CES might be at risk for customer churn because customers find interacting with the company to be difficult.

Whether customers are using a company’s product can help determine whether they are at risk for churn. For example, a customer who no longer logs in to use a product can be using a different product and are just waiting for the subscription to run out.

Why customer churn rates are calculated by using the same formula for all companies, there are several customer churn models that companies can use to track and predict future churn. To successfully build these, companies must analyze several questions, including why customers leave and stay, what factors lead to churn, and to what degree those factors are present or prevalent.

• Predictive Churn Models
• Preventive Churn Models
• Survival Analysis
• Anomaly Detection

This data-based model that ingests those data points and predict future churn rates without any intervention.

This takes the same approach as a predictive churn model, but maps out what techniques help minimize churn, such as pricing changes, new features or new approaches to customer support.

This model, also know as a time-to-event model, can predict when a customer churns based on their purchase data, historical data and current conditions. It is especially helpful for B2B companies with high-cost products that can develop resources to retain every client. It can also be valuable in the aggregate by helping companies understand if they’re at risk for a large churn event.

These models can identify potential events that might create an increase in customer churn, giving companies an opportunity to try to fix them or change strategy to minimize their impact. Examples of anomalies that companies should track are sudden increases in negative customer feedback, drops in usage and increases in return or refund requests.

Companies have several proactive tools and approaches to reduce customer churn along the customer journey, especially among at-risk customers.

• At-risk customer identification
• Customer success investment
• Excellent customer service
  
• Fair pricing
• Metric-based decision-making
  
• Stellar onboarding process
• Strong loyalty programs

Companies should invest in advanced customer relationship management (CRM) tools to track their customer activity. They can also use artificial intelligence technologies like machine learning to better analyze individual customer data, potentially identifying customers that are likely to churn before customer success teams would on their own.

This is different than customer service, which is more reactive to issues as they arrive. Customer success works with customers as they use a product or service to make sure they maximize their utility. It helps companies identify customers at a risk of churning. SaaS businesses have invested in client or customer success as a way to keep customers happy and using the products enough to justify their monthly costs.

Reducing churn can sometimes be as easy as improving the value of the customer service a company provided. Put another way, poor customer service can quickly turn happy customers into those who end up churning. Treat those customers who provide customer feedback with utmost respect and respond to those customer needs immediately.

Companies have several advanced technologies that they can now use to improve customer relationships. Using natural language processing (NLP) can help companies better crunch customer data to understand customer satisfaction, which they can put into models to determine average churn rate.

They can use artificial intelligence-based chatbots to answer simple questions from customers, freeing up their customer service representatives to handle more complicated issues.

Customers might like a company’s products or services, but stop buying if they think the price is too high for what they receive. Companies need to conduct pricing research to keep prices in line with the competition and what customers expect to pay.

Understanding and tracking certain metrics can help monitor potential churn. Both customer satisfaction scores (CSAT) and net promoter scores (NPS) help understand how happy customers are with a company’s products and services. CSAT asks customers to rank their satisfaction on a scale of 1–10. NPS asks customers how likely they are to recommend a product or service to their peers.

If these metrics begin decreasing, it is a likely sign that the company’s customer base is at risk of churn. Leaders know they need to act to improve the customer experience and reduce churn.

Creating brand loyalty is a great way to reduce customer churn. For example, company can offer incentives to existing customers, like discounts or free products for birthdays or if customers spend a certain amount of money. That is a great way to keep good customers happy with a company’s products. Encouraging loyalty can lead to other benefits, like increased potential for upselling.

One of the best ways to avoid losing customers is to focus on providing excellent services to new customers with at the beginning of the relationship. Companies can offer tutorials, FAQs and self-help guides to set up customers for success and better help them to use the company’s products.

Investing in artificial intelligence can help companies address customer churn issues in several ways.

• Better data collection and analysis
• Faster, more comprehensive customer support
• Models and simulations
• More satisfying, omnichannel customer experiences
• More accurate predictive analytics

Companies can use generative AI marketing to improve their internal marketing by producing multiple, personalized versions of the same email, providing greater resonance to individual customers in whichever channels they use.

Using conversational AI positive customer service experience can decide whether a customer churns or not. Companies should embrace conversational AI, whether using customer-facing chatbots or providing AI virtual assistants for customers.

AI can quickly collect all third-party public and first-party internal data and machine learning specifically can identify common themes that can help a company understand where their strengths and weaknesses are in serving customers’ needs.

AI can parse customer behavior data to better predict which customers are at risk of churning compared to employees looking at CRM information or spreadsheets.

Companies can use AI—powered customer digital twins (CDTs) to simulate customer experiences, helping companies understand purchase habits, what leads to churn, and how to better predict future purchases. CDTs can plot out days and weeks of customer journey maps, providing a holistic view of the entire customer experience.