An analysis of churn management of telecom industry in China Introduction Traditional marketers emphasised that increasing sales volume should be the first in the market strategy. With the traditional strategy marketers are oriented toward single sales and they concentrate on products’ features rather than customer services, which leads to the result that it is limited to meet customers’ demands. (Payne et al. 1995) However, today’s competitive market strategies mainly focus on maintaining the relationship with targeted customers in specific segments, which call customer relationship management (CRM). Churn management, a vital component of customer relationship management (CRM), which is critical for a competitive customer relationship management strategy. Companies’ profitability increase year by year due to effective customer retention approaches. Conversely, a high rate of customer attrition tends to gobble up companies’ profits.
One good example to illustrate it is that the one of biggest electrical retailer Comet plans to close all stores in the UK because it loses a majority of clients in the keen competition with E-business retailers such as Amazon. Customer churn and customer retention are two significant aspects of churn management. The aim of churn management is to minimize the proportion of customer churn and maximize the proportion of customer retention. This essay attempts to assess researches to evaluate the importance of churn management.
Then the following section will concentrate on the customer lifetime value (CLTV) and churn analysis process. After that this essay will look into the situation of customer churn management in China telecom industry and find out what actions should managers to take to predict, manage and reduce customer attrition in order to achieve competitive advantages. Researches on churn management Customer churn always happens, not only in a saturated market but in an emerging market as well. There are two sets of various reasons why customer to switch to another companies or service providers.
One set of factors are controllable, for example, a customer passes away or his relocation to a distant location from companies. The other set is controllable, for example, subscribers are dissatisfied with the low quality products, poor after-service, uncompetitive price or competitor activity and word of mouth . The latter set is mainly about customer relationship and which can be improved. According to that, the marketing strategy should concentrate on it to improve customer loyalty and make profits.
Acquiring new customers contributes greatly to profits of corporations and may become a powerful profit engine. However, it is widely recognised that retaining existing customers is much cheaper than recruiting new customers. Gummesson(1999) argues that customer acquisition is 5 and 10 times more expensive than customer retention. The ‘front-end’ expenses of customer acquisition that go over the expenses of customer retention including five aspects in general: ‘personal selling, commission,data collection, supply of equipment , and advertising and other communication costs. (Egan ,2011) It reported that the net present client value have an apparent rise(25%-85%) in several industries including financial service and real-estate because of five presents of increase in retention rate. (Dawkins and Reichheld 1990) Securing a customer’s loyalty over time produces superior profits mainly in four espects (Egan, 2011 ): “reduced price sensitivity ,reduced switching to competitors, increased referrals and increased repeat purchase. ” However, it is impossible and unpractical for managers to retain all the clients to achieve “zero refection” because the price is not cheap as well.
Due to that, we have to focus on customers who can produce high value. Yet there is no a precise answer to the question: which kind of clients has high value and companies should be attract and retain? It suggested that a competitive marketing strategy should concentrate on the high-end customers because roughly ninety percent of profits in the retail banks only benefit from twenty presents of customers, in other words, the rest eighty presents are unprofitable or less profitable. (Storbacka, 1997) On the other hand, Lin el al. 2011) claim that it is not appropriate to abandon unprofitable clients and use a case about China mobile cultivating low-end subscriber market successfully. According to that, marketers should take action to minimize the proportion of customer churn, win back valued defectors, and attract more cost-effectively the right kind of customers in the future. Therefore, it is necessary to discuss with customer value for a effective churn management strategy. Customer value and churn analysis The aim of churn management is to maximize the customer lifetime value (CTLV) and meet profits targets.
Strouse (1999) underlines that this can be divided into four parts: • ‘Discovery of motives prompting existing customers to leave and of the features characterizing these customers. • Ability to increase loyalty and to provide services and products dovetailed with expectation of the targeted clients. • Ability to design actions for recapturing profitable customers. • Customization of sales and marketing campaigns directed to customers identified as probably profitable and loyalty. ‘ Although in theory CLTV is a useful as a measure , in practice it is difficult to implement.
For example, there is no precise definition on the span of lifetime. (Magson, 1998). In order to maximize the CLTV managers should do churn analysis to find out the characteristics of the churners and take actions to prevent the reflection. There are four steps of churn analysis process: • Firstly, data collecting, sample data from of customers who have already churned and preprocess the data, basically about customer personal information, purchase history, credit records, customer complain records. Secondly, data initial analysis, try to find out different correlation between the variables and the influence different variables on the customer loss. • Thirdly, modeling, use the related data mining tool and select the correct models. (support vector machine, decision tree, naive Bayesian classifier and other models) The entire modeling process is actually a continuous cycle process because the results from a model analysis may be input variable of another model. • Fourthly, model evaluation and application. When the model is determined, it can be applied to all the customers.
After it being put into practice, the following information can be collected: list of potential churners, the probability they churn and factors. Churn analysis in china telecom industry Currently, the mobile communication industry is fiercely competitive and dealing with the customer refection is a crucial issue for telecom services providers globally. Wong(6 2011) underlines that 30 percents to 40 percents of customers churn annually in telecom corporations around the world. There are two aspects of customer churn in telecom industry. Firstly, existing customers switch to other competitors; this is the main part of churn analysis.
Secondly, Lower average revenue per user (ARPU), for instance, a customer of China Mobile prefers low-end product Easyown instead of a high-end one GoTone. ARPU is a significant index to measure the profitability of telecom operators. There are two different methods used for evaluation of customer attrition and profitability. Firstly, RFM (recency, frequency and monetary) method is a effective method used for analyzing customer churn and CLTV. From RFM model, telecom corporations are able to decide whether a client is still ‘active’ and provide personalized communication and service. Ansari et al. , 2000) Secondly, Gorth (1999) argues that VAL (value, activity and loyalty) method is superior to the RFM method because the latter one is just based on “limited individual purchase history”. However, these two methods are of real worth for evaluate CLTV and predict customer churn. Based on them, I find out four kinds of churners of telecom companies that managers do not have to pay attention to. • Firstly, distinguish whether the customer have complete personal contact information. Customers are requested to fill out the real and correct information on application form.
For example, it is apparent that customers are more likely to quit on the condition that his information is false or incomplete. • Secondly, short-term contract customer and long-term contract customer. Those who sign a contract more than one-year tend to be high value customers. If some customers leave in first three months, they probably churn sales promotion. It is difficult to analyse their consume behavior and take actions to prevent defection. • Thirdly, distinguish whether the customer is on the ‘backlist’ of the companies. For example, it is less necessary to retain those customers’ defaults on paying bills or have other bad records. Fourthly, group customer and individual customer. In china employee of some large enterprise can reimburse mobile phone fees in their companies. In 2009, China Mobile had about 185 million group customers, and it accounted for 35. 4% of the entire clients. (China Mobile Website) When a customer leave the company he is no more a group customer and it have no effect on the company consume activity. For the telecommunication corporation there is no need to prevent this kind of customer churn. Marketers should follow the data mining method to establish customer churn model.
Data mining, in a sense, is the expansion of the statistics with a little artificial intelligence. And the significant objectives of churn analysis is to evaluate the characteristic of churners. Recommendations Firstly, optimize the wireless rate plans and help customers to choose appropriate one. Barboza(2011)argues that clients have complained for a long time that China Telecom and China Unicom (the two biggest telecom operators in China ) overcharge while delivering relatively slow speeds for broadband services. And it is outlined that the rate plans are complex and elecommunication clients frequently choose high price rate plans and overpay for their service. (6,79) And Wong(2011) found that customer refection rate can be lower with “rate plan optimization” . Giffgaff, one of the UK telecom operators, succeed in attracting and retain the customers with rate plan optimization. Every month Giffgaff emails their customers a letter named” Your best plan” to summary the consume detail and highlight the total savings last month and give Recommended Plan next month based on the statistics. It appears that relationship based on fair trade is more reliable.
Secondly, develop some new retention approaches to keep your customers. For example, China Mobile customers can earn points, which is redeemable for gift certificates or concert tickets as a reward to their loyalty. Initially China Mobile set up a website as a points exchange platform to reward their old customers. Soon it brought impressive profits because it became one of significant media channel for the manufacturer to promote their products or services. This plan encourages customers to choose the high-end brand GoTone rather than the low-end one as the clients get more points.
Conclusion If customer defection behaviors cannot be predicted in advance, companies may waste budget on ‘mass marketing approaches’. (Miguei el al. ,2012). Conversely, a precise churn prediction enable marketers find out the particular customer base churn, the time when churn behaviors usually happen and the reason why customers switch. Due to that, companies are able to satisfy those churners in advance and increase the rate of customer retention. Data mining is crucial for churn analysis evaluating the significant characteristics of churners and CLTV in China telecom industry.
However, one significant issue to outline is how to develop a effective strategy to retain the customers. Churn management is vital in customer relationship management system. However, an incredible churn strategy cannot work without an effective marketing strategy, even the biggest mobile manufacturer Nokia ever in the world. References Ansari, S. , Kohavi, R. , Mason, L. and Zheng, Z. (2000), ‘Integrating e-commerce and data mining: architecture and challenges’, WEBKDD’2000 Workshop on web Mining for E-commerce -Challenges and Opportunities. Barboza.
D(2011), ‘China Investigates Pricing by 2 Broadband Companies’ The New York Times, 10 November 10, 2011,p. B4 Dawkins, P. M. and Reichheld, F. F. (1990) ‘Customer retention as a competitive weapon’. Directors & Board Summer, pp. 42-7. Egan, J. (2011) Relationship Marketing: Exploring relational strategies in marketing. 4th edn. Essex:Pearson Education Limited. Gummesson, E. (1999) Total Relationship Marketing: Relationship Marketing Management from 4Ps to 30Rs, Oxford: Butterworth Heinemann Gorth, R. (1999), Data Mining: Building Competitive Advantage, Prentice Hall, Santa Clara, CA
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