The churn rate, also known as the rate of attrition or customer churn, is the rate at which customers stop doing business with an entity. It is most commonly expressed as the percentage of service subscribers who discontinue their subscriptions within a given time period. It is also the rate at which employees leave their jobs within a certain period. For a company to expand its clientele, its growth rate (measured by the number of new customers) must exceed its churn rate.
Causes of Customer Churn
There are a multitude of issues that can lead customers to leave a business, but there are a few that are considered to be the leading causes of customer churn. The first is poor customer service. One study found that nearly nine out of ten customers have abandoned a business due to a poor experience. We are living and working in the era of the customer, and customers are demanding exceptional customer service and experiences. When they don’t receive it, they flock to competitors and share their negative experiences on social media: 59% of 25-34-year-olds share poor customer experiences online. Poor customer service, therefore, can result in many more customers churning than simply the one customer who had a poor service experience.
Other causes of customer churn include a poor onboarding process, a lack of ongoing customer success, natural causes that occur for all businesses from time to time, a lack of value, low-quality communications, and a lack of brand loyalty.
Predicting Customer Churn
The Importance of Predicting Customer Churn
The ability to predict that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge additional potential revenue source for every online business. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already been covered by the customer’s spending to date. (In other words, acquiring that customer may have actually been a losing investment.) Furthermore, it is always more difficult and expensive to acquire a new customer than it is to retain a current paying customer.
The Difficulty of Predicting Churn
Churn prediction modeling techniques attempt to understand the precise customer behaviors and attributes which signal the risk and timing of customer churn. The accuracy of the technique used is obviously critical to the success of any proactive retention efforts. After all, if the marketer is unaware of a customer about to churn, no action will be taken for that customer. Additionally, special retention-focused offers or incentives may be inadvertently provided to happy, active customers, resulting in reduced revenues for no good reason. Unfortunately, most of the churn prediction modeling methods rely on quantifying risk based on static data and metrics, i.e., information about the customer as he or she exists right now. The most common churn prediction models are based on older statistical and data-mining methods, such as logistic regression and other binary modeling techniques. These approaches offer some value and can identify a certain percentage of at-risk customers, but they are relatively inaccurate and end up leaving money on the table.
Customer Acquisition Cost (CAC)
Customer Data Integration (CDI)
Customer Data Management (CDM)
Customer Due Diligence (CDD)
Customer Effort Score (CES)
Customer Engagement Hub (CEH)
Customer Experience Management (CEM)
Customer Lifetime Value
Customer Service Management
Customer Relationship Management (CRM)