تجزیه و تحلیل ریزش مشتری: عوامل ریزش و اثرات میانجی گری از خرابی جزئی در صنعت خدمات ارتباطات راه دور تلفن همراه کره ای
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|12441||2006||17 صفحه PDF||سفارش دهید||9032 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Telecommunications Policy, Volume 30, Issues 10–11, November–December 2006, Pages 552–568
Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer's status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer's partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer's status change, or both; therefore, a customer's status change explains the relationship between churn determinants and the probability of churn.
Managing customer churn is of great concern to global telecommunications service companies and it is becoming a more serious problem as the market matures. The annual churn rate ranges from 20% to 40% in most of the global mobile telecommunications service companies (Berson, Smith, & Therling, 1999; Madden, Savage, & Coble-Neal, 1999; Parks Associates, 2003; Kim, Park, & Jeong, 2004). Customer churn adversely affects these companies because they stand to lose a great deal of price premium, decreasing profit levels and a possible loss of referrals from continuing service customers (Reichheld & Sasser, 1990). Furthermore, the cost of acquiring a new customer can substantially exceed the cost of retaining an existing customer (Siber, 1997). In a highly competitive and maturing mobile telecommunications service market, a defensive marketing strategy is becoming more important. Instead of attempting to entice new customers or lure subscribers away from competitors, defensive marketing is concerned with reducing customer exit and brand switching (Fornell & Wernerfelt, 1987). Reichheld (1996) estimated that, with an increase in customer retention rates of just 5%, the average net present value of a customer increases by 35% for software companies and 95% for advertising agencies. Therefore, in order to be successful in the maturing market, the strategic focus of a company ought to shift from acquiring customers to retaining customers by reducing customer churn. In order to better manage customer churn, companies need to fully understand a customer's behavioral churn path and the factors pertaining to the customer churn; however, these problems have not been fully addressed in the literature. First, previous studies mainly focused on finding a few specific factors (e.g., customer dissatisfaction, customer loyalty, etc.) pertaining to customer churn rather than investigating and empirically testing a comprehensive model encompassing relationships among various constructs, such as customer dissatisfaction, switching costs, service usage and other customer-related variables. For example, Keaveney (1995) only examined why customers switch their services and classified the reasons into eight general categories. Bolton (1998) investigated the role of customer satisfaction in a dynamic model estimating the customer's duration with the service carrier. Bolton, Kannan, and Bramlett (2000) found that members in loyalty reward programs overlook a negative evaluation of the company vis-à-vis its competitors in their repatronage decisions. Gerpott, Rams, and Schindler (2001) analyzed a two-stage model where overall customer satisfaction has a significant impact on customer loyalty, which in turn influences customers’ intentions to terminate their contractual relationship. Lee, Shin, and Park (2003) found some determinants of customer churn in the Korean broadband Internet access service market. Kim et al. (2004) investigated the adjustment effect of switching barriers on customer satisfaction and customer loyalty. Secondly, due to the proprietary nature of actual customer data, much of the research has dealt with consumer survey data asking consumers’ perceptions of service experiences and intention to remain. However, the survey data rather than the actual customer transaction or billing data may not fully represent the customer's actual future repatronage decision. Furthermore, due to cost concerns, most survey-based studies use a small sample of less than a thousand customer records (Keaveney, 1995; Bolton et al., 2000; Gerpott et al., 2001; Lee et al., 2003; Kim et al., 2004), which may undermine the reliability and validity of analysis results. In fact, there are several studies that are based on large-scale actual customer transaction and billing data. However, their objectives mainly focus on predictive accuracy rather than descriptive explanation. For example, detailed call data is used to predict the probability of customer churn (Mozer, Wolniewicz, Grimes, Johnson, & Kaushansky, 2000; Ng and Liu, 2000; Wei and Chiu, 2002); a subscriber's remaining tenure with the company is estimated using internal company databases (Drew, Mani, Betz, & Datta, 2001) and brand switching and adoption probabilities are forecasted using commercial databases (Weerahandi & Moitra, 1995). However, there are at least two exceptions, both of which use survival analysis to test hypotheses about churn predictors. One is a study by Bolton (1998) where actual customer transaction and survey data is used to analyze customer churn behavior in the cellular service market. Another is a customer attribution study where Poel and Lariviere (2004) analyzed an in-house data warehouse in the European financial service market. Compared with the previous studies, this paper has two distinct research objectives. The first objective is to develop a comprehensive churn model and empirically test it using a large sample of actual customer transaction and billing data, which is directly related to actual customer churn decisions. Identifying customer churn determinants, such as core service failures, customer complaints, loyalty programs, service usage, etc., may help managers improve company operations in terms of their marketing strategy, specifically customer churn prevention programs. The second objective is to identify both partial and total defection in a subscription-based telecommunications service industry. The majority of previous studies have focused on discovering the direct effect of independent variables on customer churn; however, this study is motivated by the idea that customer status may act as a mediator between churn determinants and customer churn, indicating that a customer's status change is an early signal of total customer churn. Some churn determinants are expected to affect customer status and their impacts on both customer status and customer churn are analyzed. From a managerial standpoint, understanding the mediating role of customer status would mean that companies will be able to manage churn better, which would not have been obvious in the previous models. This paper is organized as follows. In Section 2, a research model and hypotheses are developed regarding the factors pertaining to customer churn. The empirical method is described in Section 3 and the results of the analysis are discussed in Section 4. Finally, the implications of the study and areas for further research are presented in Section 5.
نتیجه گیری انگلیسی
This study investigated factors leading to customer churn using a sample of 5789 actual customer transactions and billing data. In addition, the mediating effects of customer status between churn determinants and customer churn were analyzed. The following section summarizes the result, discusses implications and suggests areas for further study. First, this study developed and tested a customer churn model based on a large number of transaction and billing data. This actual data-based approach addressed the managerial problems that may arise from the discrepancy between customers’ perception or intention and their actual behavior in the market. For example, previous research based on customer survey responses suggests that for membership card program subscribers, the negative effects of dissatisfaction with the service provider are adjusted, thus they remain loyal customers. However, this study using a company-internal database found that the membership card program subscribers, in fact, are more likely to churn. This raises questions about such a program's effectiveness, which is discussed in later paragraphs. Secondly, this study not only confirmed some findings of previous studies regarding explanatory churn factors, but also identified new ones. As in line with the previous result, call quality, loyalty points and service usage level are found to be among the major factors influencing customer churn in the mobile telephony market. On the other hand, specific factors such as functional capability of handsets, membership card programs and customer status were significant factors newly found in this study. Regarding the functional capability of handsets, Gerpott et al.'s (2001) study of the German mobile service market showed that a customer's desire for a new handset did not have any significant effect on customer retention. On the other hand, the result in this study shows that low functional capability of handsets leads to customer churn. The difference could be explained by the availability of handset subsidies: In June 2000, handset subsidies were banned in Korea, but they were still available in Germany. From that fact, it can be inferred that the handset subsidy ban in the Korean mobile market increased the customers’ actual cost (e.g., the price of purchasing new handsets). Therefore, the cost for a new handset worked as a switching barrier in the Korean mobile telephony market. However, if a subscriber's handset is quite old and lacks much needed functional capability, the switching barrier from the money spent on his/her current handset can be reduced significantly; thus he/she would purchase a new handset and that event might trigger a decision to churn. In fact, the present analysis shows that customers with less desirable handsets are more likely to churn. Thirdly, despite the incomplete nature of the data, the result implies that membership card programs may not increase customers’ switching costs. Considering the fact that most competing mobile service providers offer similar types of benefits (discounts, coupons, etc.) through their own membership card programs, customers may have few incentives to maintain a contractual relationship with the current service provider simply because of the benefits that the current membership card program offers. In addition, it can be argued that the benefits from current membership card programs are not fully responsive to the level of customers’ service usage or duration. For example, service providers issue only two or three kinds of membership cards and the benefits are almost equivalent regardless of service usage level, therefore, effectiveness of the membership program is not justified. The same concerns are shared among managers in the company where samples were collected. They believe that membership programs do not achieve the original objective and the programs need to be overhauled. Comparing the effects of two types of switching costs on customer churn, it can be suggested that current membership card programs should be redesigned following the characteristics of the loyalty point reward programs. This is justified by the result that loyalty points rather than membership card programs work as a switching cost. For example, two major characteristics of loyalty point reward programs are: (1) the points are gained according to customer usage level, (2) the points are deducted following a customer's redemption of fringe benefits via the membership card. Therefore, membership card programs need to be transformed from the current almost unlimited fringe service provision program into a point-based fringe benefit provision program where customers’ points are gained or deducted according to their usage level and point redemption level, respectively. This is in line with a Dowling and Uncles’ (1997) reward scheme where a customer's motivation to make the next purchase is maximized. Fourthly, partial and total defection were examined by identifying the mediation effects of customer status. Failure to consider a customer's status change is likely to cause researchers or corporate executives to ignore the impact of indirect effects of churn determinants on customer churn and eventually to mishandle the customer churn. Factors such as number of complaints, loyalty point rewards, billed amounts, gender, calling plans and handset Internet capability influence the probability of churn both directly and indirectly and thus have partial mediation effects. Of special interest is a management program that is designed to handle customer complaints. The result of partial mediation of the customer complaints indicates that the effectiveness of the customer complaint management program can be measured in two different ways: one is to evaluate the extent to which the number of complaints lead to customer churn. Another is to measure the extent to which the number of complaints influence a customer's status change from active use to non-user (or other possible customer status), which is positively related to customer churn. Either measure can provide a checkpoint of whether the current customer complaint management program prevents customer churn in the way in which it was designed. Accordingly, it can be argued that if an effective service failure recovery program is implemented, the number of complaints registered to the customer service center may decrease the probability of both the direct customer churn and the customer's status change into non-use. Thus the company can eventually reduce the customer churn. Despite this analysis, there are some areas that warrant further study. First, data for some variables, such as account tenure (also called customer duration) and each subscriber's age, were not available; and customers’ perceived values on service satisfaction were not included in the data either. Therefore, a better model can be developed by including these variables. In particular, the account tenure will be a very important variable explaining customer churn. Secondly, the 8-month data collection period for this study was relatively short. An additional longitudinal study (i.e., panel data analysis) with a longer period of data collection and time-series data is necessary. Such a study would help researchers and managers to not only better estimate the expected tenure but also develop the current churn model further into a lifetime customer value model (Jain & Singh, 2002) where the economic impact of churn factors can be estimated. Finally, the impact of membership card programs needs to be further evaluated. Surprisingly, the analysis showed that members in the programs are more likely to churn than non-members. In fact, the program may increase customers’ experiences and expose them to other competitors (Bolton et al., 2000). On the other hand, people who decide to join the program may be the group with a higher propensity to churn than the average population. That is, self-selection bias may exist in the sample, therefore, careful research design and analysis are needed to evaluate the impact of the programs on customer churn.