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Glossary term

Customer churn

Quick definition

The number of customers an organization loses in a given timeframe.

Key takeaways


Churn rate is calculated by identifying the number or percent of customers who have stopped engaging with an organization or using their services within a specific period of time.

Simply looking at how many customers left may not offer as detailed a picture as looking at factors like the revenue loss that accompanied the customer churn.

Companies can predict churn by examining customer actions to identify behaviors that indicate a high churn risk.

With new technology, nearly every company can use churn data to their advantage.

Nate Smith is the Group Product Marketing Manager for Adobe Analytics. In his role, Nate oversees strategic marketing for Adobe Analytics and ensures the success of ongoing product releases. He has been involved in digital marketing for the past fifteen years and holds a BS in Information Systems and an MBA from Brigham Young University.

Q: What is the churn rate?

A: The churn rate is the percent of customers lost over a period of time and is typically attributed to a lack of compelling brand experiences that meet their needs, wants, or desires

Q: How do you calculate churn?

A: To calculate churn, choose a starting point and an end point. For example, the starting point may be today, and the end point may be a month from now. The goal is to see how many customers remain at that point in time. It’s essentially a division problem. If a company has 10 million subscribers on July 1, and 9 million subscribers on August 1, the churn rate for that period is 10 percent.

The variety of factors that can be used for calculating churn are almost endless, but all derive from the basic premise that a starting point and a future end point are identified. Beyond that, the calculation can vary. For instance, an organization might want to consider seasonality or other specifics within a time frame that could change the calculation.

Predicting churn can also introduce new considerations and often organizations will create predictive algorithms based on specific customer behaviors to see how those behaviors might affect churn.

For example, a business may look at the number of website logins as one of the factors for predicting churn. If the number of logins drops by a certain percentage, that could indicate a risk. A business could then take that information to identify customers that are at risk of churning and target them specifically. 

Organizations usually have calculations to predict the number of customers or the number of subscriptions based on their churn rate or churn rate indicators, but one of the most important insights derived from a churn rate is calculating profit or revenue.

Let’s say a company that sells enterprise software gets 100 new customers in a month, but by the following month, they’ve lost 10 customers, and now have 90. That’s a 90 percent retention rate, which might not seem bad. But if those 10 customers are responsible for 80 percent of revenue, the company has lost their most important customers. The revenue impact from that churn rate is astronomical — while the customer churn rate is only 10 percent, the revenue churn rate is 80 percent. Looking at multiple different results of churn provides a more complete snapshot of the health of a business.

Q: Why is predicting churn important?

A: Any organization can break down churn into components that they can use to make predictions. Running a predictive base churn rate on customers who are exhibiting certain behaviors helps organizations be proactive to reduce churn. Because churn is essentially the inverse of retention, reducing churn offers concrete benefits for companies. It costs money to acquire a paying customer. It's better to keep existing customers paying on an ongoing basis than to continually spend money trying to acquire new customers for a single transaction.

Q: What is churn risk?

A: Churn risk refers to how likely a customer is to abandon a business relationship. A company that is paying attention can identify warning signs, like a decreased number of logins, to determine the churn risk of a customer and take steps to retain them. Red flags will be different for each business, so organizations need to consider business-specific metrics, interactions, and target markets to identify the potential churn risk of their customers.

Q: Why do customers churn?

A: While there are many reasons a customer might churn, at the root of it, customers leave because a business is no longer meeting their needs or wants. When a business breaks down its churn, it can start identifying what they’re not delivering to their customers. 

For instance, let’s say an ad manager team spends $1 million on Google Ads. They come up with a really slick ad that's going to show up in search results as an offer of an inexpensive Hawaiian vacation. They're going to get a huge click rate and report it’s the best ad campaign ever. But if customers click through to a landing page that has no mention of Hawaii and customers can't find the Hawaii offer, they're immediately going to churn and not come to the website.

The ad campaign had a great click-through rate, but the churn rate was massive. The team might have gotten clicks, but they didn't get paying customers because the landing page did not meet the customer’s expectations, which was to see a button allowing them to book the advertised trip. Because the landing page was not tailored to the ad campaign, the ad campaign essentially wasted $1 million.  

Every industry faces similar problems. For a B2B company, the customer expectation could be post-sales service and support. If the customer doesn’t get that support, the next contract cycle could fall through. At the end of the day, if the customers are unsatisfied or unhappy with their current experience with a brand, and think they can do better elsewhere, they will leave.

Q: How do businesses reduce or prevent churn?

A: There are a few things companies can think about. One is proactivity. As soon as a company realizes they are losing customers and money, they should start doing analysis to break down the data by group, by revenue, by any other factor, and identify when and where people are leaving. By analyzing the actions of the customers who leave, companies can identify some potential causal factors, and run tests on site content.

As an organization acquires more data, it can determine the effectiveness of any changes made then continue to adjust. It's an ongoing process of refinement — investigate, refine, deploy, and then repeat the cycle until the problem is identified and addressed. Companies can also use artificial intelligence (AI) and machine-learning algorithms. These are game-changing technologies in the area of churn analysis and reducing customer churn.

Q: What is a good churn rate?

A: A good churn rate for a company really depends on the industry, the product, the delivery, and a number of other factors. Each company will have to determine their own standard, but generally, a churn rate below 10 percent is considered a good number to aim for.

Q: What mistakes do companies make when addressing customer churn?

A: One common mistake is thinking of churn in narrow terms. If a company is only thinking about the aggregate number of customers versus revenue or profitability, they are going to have blinders on when analyzing their churn rates.

It’s also critical to choose the right time frame when calculating an appropriate churn rate. For example, a pay-per-click ad campaign might only have a maximum churn rate of a week. Similarly, how companies calculate churn in November or December will probably be different from how they calculate churn in April, because of an increase in one-time customers around the holidays, so organizations need to take seasonality into account.

Q: How can customer churn benefit a company?

A: While a growing or high customer churn rate is an indication that a company is not meeting a customer’s needs or wants, it also can provide an opportunity for a company to re-evaluate their offerings and consider improvements. By looking at the churn rate with both long- and short-term lenses, a company can determine whether the cause of increased churn is a short-term problem, like an issue with the website, or a long-term problem, like a strategic shift or a change in the market. By paying attention to churn rates, companies can continually evaluate the experiences they provide to customers and keep evolving to meet their audience’s expectations.

Customer churn is approachable, especially with today’s mar-tech offerings. Technology has made churn calculation much easier. Any company, regardless of its size or maturity, can approach customer churn data with the right tools to improve the level of insights they obtain. Understanding churn and its business impact is no longer the sole domain of statisticians.

The speed and the ease with which marketers can understand churn has greatly improved compared to the days of manual calculations in spreadsheets. The introduction of AI and machine learning in the last few years to measure churn risk has been a game changer for most organizations. Now, nearly everyone touching the customer experience has access to automated churn analysis.

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