Definition of Customer Churn
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.
source: Super Office
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 Churn Analysis
Customer churn analysis refers to the customer attrition rate in a company. This analysis helps companies identify the cause of the churn and implement effective strategies for retention
- Gather available customer behavior, transactions, demographics data and usage pattern
- Convert structured and unstructured data/information into meaningful insights
- Utilize these insights to predict customers who are likely to churn
- Identify the causes for churn and works to resolve those issues
- Engage with customers to foster relationships
- Implement effective programs for customer retention
The cost of acquiring a new customer is, on average, six to seven times the cost of retaining an existing customer. It's three times easier to cross sell to existing customers than to new customers. New customers are important for your growth, and so are existing customers! Given the statistics, existing customers are a great opportunity for up-selling and cross-selling. Your organization gets a greater ROI by selling to existing customers. Reducing customer churn helps you make more money.
source: Dunn Solutions
Reducing Customer Churn
- Focus your attention on your best customers: Rather than simply focusing on offering incentives to customers who are considering churning, it could be even more beneficial to pool your resources into your loyal, profitable customers.
- Analyze churn as it occurs: Use your churned customers as a means of understanding why customers are leaving. Analyze how and when churn occurs in a customer's lifetime with your company, and use that data to put into place preemptive measures.
- Show your customers that you care: Instead of waiting to connect with your customers until they reach out to you, try a more proactive approach. Communicate with them all the perks you offer and show them you care about their experience, and they'll be sure to stick around.
Research and Applications
Scholars have studied customer attrition at European financial services companies, and investigated the predictors of churn and how the use of customer relationship management (CRM) approaches can impact churn rates. Several studies combine several different types of predictors to develop a churn model. This model can take demographic characteristics, environmental changes, and other factors into account. Research on customer attrition data modeling may provide businesses with several tools for enhancing customer retention. Using data mining and software, one may apply statistical methods to develop nonlinear attrition causation models. One researcher notes that "...retaining existing customers is more profitable than acquiring new customers due primarily to savings on acquisition costs, the higher volume of service consumption, and customer referrals." The argument is that to build an "...effective customer retention program," managers have to come to an understanding of "...why customers leave" and "...identify the customers with high risk of leaving" by accurately predicting customer attrition.
Retail Services Applications
Financial services such as banking and insurance use applications of predictive analytics for churn modeling, because customer retention is an essential part of most financial services' business models. Other sectors have also discovered the power of predictive analytics, including retailing, telecommunications and pay-TV operators. One of the main objectives of modeling customer churn is to determine the causal factors, so that the company can try to prevent the attrition from happening in the future. Some companies want to prevent their good customers from deteriorating (e.g., by falling behind in their payments) and becoming less profitable customers, so they introduced the notion of partial customer churn. Customer attrition merits special attention by mobile telecom service providers worldwide. This is due to the low barriers to switching to a competing service provider especially with the advent of Mobile Number Portability (MNP) in several countries. This allows customers to switch to another provider while preserving their phone numbers. While mature markets with high teledensity (phone market penetration) have churn rates ranging from 1% to 2% per month, high growth developing markets such as India and China are experiencing churn rates between 3% to 4% per month. By deploying new technologies such churn prediction models coupled with effective retention programs, customer attrition could be better managed to stem the significant revenue loss from defecting customers. Customer attrition is a major concern for US and Canadian banks, because they have much higher churn rates than banks in Western Europe. US and Canadian banks with the lowest churn rates have achieved customer turnover rates as low as 12% per year, by using tactics such as free checking accounts, online banking and bill payment, and improved customer service. However, once banks can improve their churn rates by improving customer service, they can reach a point beyond which further customer service will not improve retention; other tactics or approaches need to be explored.
Churn or Customer attrition is often used as an indicator of customer satisfaction. However the churn rate can be kept artificially low by making it difficult for the customers to resiliate their services. This can include ignoring resiliations requests, implementing lengthy and complicated resiliation procedures to follow through by an average consumer and various other barriers to resiliation. Thus, churn can improve while customer satisfaction deteriorates. This practice is short sighted and will backfire. However, it was shown to be common in telephone companies and among internet providers.
Customer Churn Modeling
In its simplest form, churn rate is calculated by dividing the number of customer cancellations within a time period by the number of active customers at the start of that period. Very valuable insights can be gathered from this simple analysis — for example, the overall churn rate can provide a benchmark against which to measure the impact of a model. And knowing how churn rate varies by time of the week or month, product line, or customer cohort can help inform simple customer segments for targeting as well. However, churn is often needed at more granular customer level. Customers vary in their behaviors and preferences, which in turn influence their satisfaction or desire to cancel service. Therefore, a cohort-based churn rate may not be enough for precise targeting or real-time risk prediction. This is where churn modeling is usually most useful. The output of a predictive churn model is a measure of the immediate or future risk of a customer cancellation. This is what the term "churn modeling" most often refers to.
Note that the rows in the above matrix are not mutually exclusive: Involuntary churn can be present in either contractual or non-contractual settings. Churn is especially relevant in contractual circumstances, which are often referred to as a "subscription setting," as cancellations are explicitly observed. However, non-contractual businesses also benefit from modeling churn. The challenge, in those case, lies in defining a clear churn event timestamp. This is often done by finding a certain threshold for a period of inactivity and using it as a definition for the churn event. On the other hand, voluntary and involuntary churn might be caused by different underlying factors. Voluntary churn is often more prevalent than accidental churn due to events such as payment failures. It is also more difficult to determine the root cause of voluntary customer cancellations, which is why most churn literature focuses on voluntary churn events. While both voluntary and non-voluntary cancellations have a clear revenue impact, it is best to focus a churn model on only one type of churn.
The probability of churn can be predicted using various statistical or machine learning techniques. These methods process historical purchase and behavior data in order to predict the probability of cancellation per customer. A well-constructed model can inform a wide range of decisions and flow into numerous internal tools or applications. For example, some common use cases for a churn model are:
- Measuring feature impacts on the likelihood of churn in order to understand why customers choose to leave, which can inform long-term retention initiatives
- Creating churn risk scores that can indicate who is likely to leave, and using that information to drive retention campaigns
- Predicting the probability of churn and using it to flag customers for upcoming email campaigns
- Integrating outputs with internal apps, such as a customer call center, to provide relevant real-time churn risk information
- Discounting strategically with promotion campaigns to customers with a high cancellation risk
And many more ...
Where to Start?
So, where do we begin when creating and using a churn model? Building a successful model happens in several broad stages, from concept to deployment:
- Understand your use case: Establishing a clear use case for a model is always the first and most important step. This process will not only determine who will use the model output and how, but it also dictates the data scientists’ choice of modeling method.
- Identify users and stakeholders from each team: Identify stakeholders within your organization who will touch the churn model output. Consider this simple example: A customer service representative would like to see whether it is reasonable to offer a promotional price to a customer currently on a call. One way to do this is to have your data scientists train a churn model and give it to the engineering team to deploy. Once the outputs — in this case, churn risk scores — are integrated into the call center software, the customer call center representatives can use this information to make informed decisions about discounts. Keep in mind: This process will be a lot easier if you gather feedback from the involved parties early on to inform the model-building process.
- Identify key metrics optimization: Think about the scope and the metric being optimized. For instance, if the costs associated with your retention campaigns are high, then your model should be focused on reducing the number of false-positive hits (i.e., minimizing the number of low-dollar customers who are being enrolled in your campaign). Identifying the right metric will help to measure the model’s impact and corresponding return on investment.
Finally, take action! Execute on the initial goal and start using your model output.
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)