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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.<ref>Defining Customer Churn [https://www.investopedia.com/terms/c/churnrate.asp Investopedia]</ref>
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== Definition of Customer Churn<ref>Defining what is Customer Churn [https://www.investopedia.com/terms/c/churnrate.asp Investopedia]</ref> ==
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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.
  
  
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'''The Difficulty of Predicting Churn'''<br />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.
 
'''The Difficulty of Predicting Churn'''<br />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.
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== Customer Churn Analysis<ref>Customer Churn Analysis [https://www.gainsight.com/your-success/what-is-customer-churn-analysis/ Gainsight]</ref> ==
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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
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*Gather available customer behavior, transactions, demographics data and usage pattern
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*Convert structured and unstructured data/information into meaningful insights
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*Utilize these insights to predict customers who are likely to churn
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*Identify the causes for churn and works to resolve those issues
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*Engage with customers to foster relationships
 +
*Implement effective programs for customer retention
 +
The cost of acquiring a new [[Customer|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.
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[[File:Customer_Churn.png|400px|Customer Churn Analysis Diagram]]<br />
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source: [https://www.dunnsolutions.com/content/customer-churn-reduction1 Dunn Solutions]
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== Research and Applications<ref>Research and Applications [https://en.wikipedia.org/wiki/Customer_attrition Wikipedia]</ref> ==
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'''Research'''<br />
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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)|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|data modeling]] may provide businesses with several tools for enhancing customer retention. Using [[Data Mining|data mining]] and [[Software|software]], one may apply [[Statistical Analysis|statistical methods]] to develop nonlinear attrition [[Causal Model of Organizational Performance and Change|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|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.
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'''Retail Services Applications'''<br />
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Financial services such as banking and insurance use applications of [[Predictive Analytics|predictive analytics]] for churn modeling, because customer retention is an essential part of most financial services' [[Business Model|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|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.
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Churn or Customer attrition is often used as an indicator of [[Customer Satisfaction|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 Service Provider (ISP)|internet providers]].
  
  

Revision as of 09:22, 11 October 2019

Definition of Customer Churn[1]

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[2]

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[3]

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[4]

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.


Customer Churn Analysis Diagram
source: Dunn Solutions


Research and Applications[5]

Research
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.


See Also

Customer
Customer Acquisition Cost (CAC)
Customer Centricity
Customer Data Integration (CDI)
Customer Data Management (CDM)
Customer Demographics
Customer Due Diligence (CDD)
Customer Dynamics
Customer Effort Score (CES)
Customer Engagement
Customer Engagement Hub (CEH)
Customer Experience Management (CEM)
Customer Lifecycle
Customer Lifetime Value
Customer Loyalty
Customer Needs
Customer Retention
Customer Service
Customer Service Management
Customer Relationship Management (CRM)


References

  1. Defining what is Customer Churn Investopedia
  2. Causes of Customer Churn NGData
  3. Predicting Customer Churn Optimove
  4. Customer Churn Analysis Gainsight
  5. Research and Applications Wikipedia