Churn Management - Research Paper Example

Churn Management Table of Contents Table of Contents 2 Churn 3 Churn Management 3 Voluntary Churn 4 Involuntary Churn 4 Telecommunication Business and Churn Management 5 Focus of the Paper 6 Churn Prediction 6 Recommendation 7 Targeted Model 7 Industry Example 8 Churn Management 9 Recommendation 9 Mathematical Approach 10 Industry Example 10 Churn Reduction 11 Recommendation 12 Industry Example 12 Churn Researchers have found that increasing churn rate has emerged as management issues for companies in recent times. In simple words churn rate is customer attrition or turnover rate for companies. Churn rate is a common term used by telecommunication companies in order to define switching trend of customers. There is a common tendency among customers to switch various service providers such as insurance companies, telecommunication companies, healthcare companies and internet connection providers. Industry trend shows churn rate increases for some common reasons. Those reasons can be summarized in the following manner. Company provides service at a higher rate in comparison to other competitors hence customers switch to new telecom or internet service providers Service quality of the company gets deteriorated hence customers switch to new companies to get quality service Customers might get shifted to new geographical locations and in such cases customers might need local service providers of new geographical locations Churn Management Churn management is the systematic approach taken by companies to decrease churn rate and ensuring longer association of profitable customers with the organization. Modern companies use advanced churn management techniques to predict switching tendency of customers and define strategy to increase customer sustainability. Research shows that customer churn has emerged as major challenge for telecommunication and wireless industry (Kalakota, and Robinson, 2004, pp. 393-394). Research scholars have identified that churn management should focus on retention component in order to create a mathematical formulation. According to them churn management is directly related to life time value of customers. Empirical formulation of life time value of customer is given below. LTV= Life Time Value of customers, r= retention component, Churn (C) = 1- retention rate=1-r According to researchers churn refers to probability of switching by the customer in a given time frame from the viewpoint of customers. Meanwhile from the viewpoint of firm churn refers to probable reduction of customer base in given time frame. Market research shows that there can be two types of churn possible during life time of customer such as voluntary and involuntary. Voluntary Churn In voluntary churn customers decide to terminate relationship with the company. In 2006, Hadden has pointed out that voluntary churn can bifurcated into deliberate and incidental churn. In deliberate voluntary churn refers to those incidents where customers switch to new service providers due to dissatisfaction over service quality while incidental voluntary churn refers to those incidents where customers switch to local service providers due to shift of geographical location. Involuntary Churn Involuntary churn refers to strategic decision of the company to terminate relationship with unprofitable customers (Blattberg, Kim, and Neslin, 2009, pp. 607-609). Researchers have found that early detection churn problem can give the much needed time for companies to address the problem and increase profitability. Managers use statistical tools such as customer propensity and customer loyalty to harness customer relationship management (Kumar, and Petersen, 2012, p. 18). Telecommunication Business and Churn Management Telecommunication and wireless business has emerged as one of the most important industry driver in recent years. Telecommunication industry offers unique value proposition to customers in comparison to other business. The industry showed double digit growth rate for consecutive ten yers during late 1990’s and early 2000’s (Duke Teradata, 2002). Study shows that the honeymoon period is over for telecom service providers due to increasing churn rate. In previous business situation demand of wireless telecom service has been such that if a patron resolute to switch to another service provider then another new client was ready to replace the switching customer (Mozer et al., 1999). Industry report shows approximately 25% of global telecom customers churn annually. Recent report shows that telecom companies are monthly 2% periodically which means they are losing 2% of their business monthly basis. Following reasons can be mentioned as a root cause of churn in telecommunication business. Similarity in offering by telecom service providers make them vulnerable to churning. Customers get confused about selecting right service plan and in such case they subscribe in accordance to their intuition. In most cases it has been observed that customers subscribe those plans which provide them facilities such as high speed internet, talk time facility and low call rate. Competition in mobile phone market also increased vulnerability of churning to telecom operators. Market competition has forced mobile manufacturers to decrease price of handsets. In such situation customers are maintaining more than one mobile handset in order to fulfil multi dimensionality of requirement. Multiple mobile sets mean lack of Wireless Local Number Portability (WLNP) for carriers. In such cases customers switch to new service providers. There are more than thousand telecom service providers present in the world and each of them competing fiercely with each other. Internal competition between telecom service providers has increased the scope of switching among customers (Nath, and Behara, 2003). Focus of the Paper This paper will try to recommend available strategy and techniques to managers regarding predicting, managing and reducing churn related to telecommunication business. Churn Prediction Research scholars have suggested predictive model of identifying the time of churning. Predicting who and when factor of churn management plays cordial role on deciding future success of the process. Churn prediction model is used to predict how many customers will switch to new telecom service provider in given time period. Raw churn prediction chart can be to predict probability of switching in a given time frame. Hypothetical churn prediction chart is given below. (Source: Mattison, 2006, p. 255) Recommendation Targeted Model Telecom companies’ use predictive models such as targeted model in order to calculate churn activity of customers. Targeted model helps telecom companies to identify future churners and prepare churn prevention campaign for them. The process of deducing exact number of future churners is bit complex in comparison to calculating probability of switching. Three steps can be used by telecommunication business to predict future churning activity. In the first step analysts need to gather historical data of customers churned previously and then analyze specific behaviour of churned segment. Analysts need to collect various data such as payment schedule, phone usage rate, customer service contacts of all existing customers. In the second phase analysts need to scrutiny behaviour of existing customers of the company. It can be done by monitoring talk time usage of customers. For example, generally customers planning to churn reduce talk time three months prior to the final decision while others make call to customer service department in order to register complain six weeks prior to termination. Statistical models such as weighted average method can be used to analyze potential predictive behaviours. In the final stage analysts need to sum up weighted average score of all the factors in order to calculate probability of churning. General trend shows that more number of casual factors causes higher percentage of churn in comparison to definitive factors such as poor service quality, price sensitivity etc. Industry Example Hwang et al. have studied churning rate of wireless telecommunication industry by using logistic regression model. In their study they have used sample size of 16,384 customers to predict future churn probability. They used following formulae to predict future churning probability. P(X) is the probability of future churning. They found a threshold value of 0.5 in their study which suggests that 50% customers prefer to switch to new telecom service providers during the first year of subscription. Graphical representation also supports their inference. (Source: Mutanen, 2006) Churn Management Customer relationship management plays crucial role deciding the dynamics of churn management. Following steps are recommended for in order to satisfy requirements of customers. Recommendation Organization need to gather data about customer’s transactions and interaction with the company in order to gather knowledge (Lovelock, 2010, p. 365). Companies need to use data mining for predicting the outcome of churn management process. They need to focus on reducing time gap between potential attrition and customer outreach in order to produce higher lower percentage of customer churning. In this step telecom companies need to identify most profitable customer segment with the help of CLV or customer lifetime value model. They need to examine factors of customer defection on the ground of CLV model. Companies to use various software packages such as Sales force automation, CRM software and ERP solutions to reduce churn risk level for an individual customer. Proactive churn management is needed in order to decrease propensity of risk factors associated with churning. Mathematical Approach Dohn Lehman and Sunil Gupta have proposed CLV model for existing customers in order to calculate future churn rate (Kotler, 2009, p. 127). M= margin, R= retention rate, T= time period Researchers use this model in order to calculate churn percentage of future years. Although the model is based on probabilistic study but incorporating time value of money concept in the model can define exact churn percentage in predictive manner (Grover, and Vriens, 2006, p. 611). Industry Example In 2007, Sunil Gupta used CLV model to analyze retention rate for telecom companies operating in USA. Next portion of the study will analyze CLV data of individual customer of AT&T (telecom giant based in USA). They used the formulae of margin multiple r/1+i-t (Gupta, and Lehmann, 2008, pp. 255-265). Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Number of Customer 100 90 81 72 60 48 34 23 12 Revenue/Customer 100 110 120 125 130 135 140 142 Variable Cost 70 72 75 76 78 79 80 81 Margin/Customer 30 38 45 49 52 56 60 61 Acquisition Cost 40 Total Cost/Profit -4000 2700 3040 3240 2940 2496 1904 1380 732 Present Value -4000 2455 2513 2435 2008 1550 1075 709 342 (Source: Kotler, 2009, p. 127) [Note: I= 10% discount rate, every value is given in US dollar, approx annual churning rate is 11%] Churn Reduction Customer loyalty program can be used in order to reduce churn rate. Tools are available to quantify customer loyalty in terms of churn rate. Research data shows that in telecommunication industry average churn rate is 25% which is way above than other industries. Churn rate reduction plays significant role on increasing the bottom-line for the company (Jeffery, 2010, pp. 93-95). Telecom companies need to use following techniques to reduce churn rate. Recommendation Telecommunication companies need to provide loyalty bonuses to customers in order to prevent them from switching to another brand. Companies need to work on expectancy value model proposed by Philip Kotler. According to this model, telecom services subscribers assign value point on various attributes of provided service and then they summarize total points assigned on each attribute. They only subscribe those services having larger sum of value points (FitzGerald, and Arnott, 2000, p. 107). Service diversification is needed in order to achieve larger expectancy value sum. A conjoint model of service diversification and loyalty program should be implemented in order to decrease churn rate. Customer relationship management software should be designed in a customized manner in order to redress customer complaints regarding service quality. Industry Example Dental Care partner is a dental solution provider located in Cleveland, Ohio. The company faced problems regarding high customer churning rate during 1990’s. In such cases the health service provider used a unique business model to increase customer loyalty and service maintenance cost (Hill, and Jones, 2009, p. 161-165). Founder of the company used following techniques to increase customer loyalty. The healthcare company provided much needed supports such as medical equipments, administrative help, superior infrastructural facilities and marketing initiative to doctors. This support helped doctors to spend more time with patients and provide them health solution in cost effective manner. Although the company never initiated customer loyalty program formally however they developed strategic model churning management. Churn management strategy of the company helped them not only to reduce annual churn rate but increased annual revenue to $100 million also. 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