Segmentation of telecom customers based on customer value by decision tree model

Han, Shui Hua; Lu, Shui Xiu; Leung, Stephen C. H.

doi:10.1016/j.eswa.2011.09.034

Cited by 63 publications

(41 citation statements)

References 18 publications

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“…But for different indicators, expert ratings may be subjective, and data processing methods are cumbersome. In [8], a customer segmentation method based on the customer life cycle is proposed. The characteristics of data and the experience of business experts were taken into account when constructing the judgment matrix.…”

Section: Expert Experience Scoringmentioning

confidence: 99%

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Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Bai¹,

Jia²,

Wang³

et al. 2020

Preprint

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A good recommender system can infer customers' preferences based on their historical purchase records, and recommend products that the customers may be interested in, saving them a lot of time and energy. For enterprises, it is difficult to recommend accurately to each customer, and the bad recommendation may be counterproductive. Customer loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. A hypothesis is proposed in this study: if companies can divide customers into different groups based on customer loyalty, the recommendation effect on certain groups is better than that on overall customers.In this study, customer loyalty is measured by four features of the RFML model. All customers are viewed as points on a four-dimensional space, which are clustered by the k-means model. Two recommendation algorithms based on complex networks are tested: recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and recommendation algorithm based on a single vertex set network and DeepWalk (SVDW). The experimental results show that customer loyalty has improved the effectiveness of the two algorithms over 14%, and the recommendation effect is the best on customer groups with a loyalty level of 4 (the highest level is 5). The recommendation algorithms with customer loyalty are better than using them alone.

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Section: Expert Experience Scoringmentioning

confidence: 99%

“…Calculate the similarity RD among the target customer node and all other nodes according to formula (8);…”

Section: Impact Of Customer Loyalty On Bgprmentioning

confidence: 99%

Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Bai¹,

Jia²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Wang and Wu (2012) suggested that it is necessary to focus equally on customer acquisition and customer retention in order to boost company performance. Moreover, a 2% increase in the customer retention rate is equal to a 10% decrease in the cost of customer acquisition in the mobile phone service industry in China (Han, Lu, & Leung, 2012). This means that mobile service providers that can retain their customers do not have the pressure of acquiring new customers; thus, they can save resources that would otherwise go to expensive customer acquisition and increase profitability.…”

Section: Background Of the Studymentioning

confidence: 99%

Analysis of Consumers’ Intention Values in the Choice of a Mobile Service Provider

Thaichon¹,

Sharma²,

Raina³

et al. 2016

AJBR

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This research aimed to explore the determinants of consumer intentions to purchase and switch in the mobile telecommunication industry in Australia. It adopted a qualitative, inductive approach to data collection using a sample of 21 Australian mobile service customers, identifying various determinants of overall service quality and values that play an important role in choosing a service provider. Based on the interview results, it was inferred that certain factors, including network coverage, price value, and voice and non-voice services, affect consumers' motivations to buy from or switch mobile service providers. Conversely, in-store service, information quality and website information support, and corporate social responsibility do not play a major role in consumers' decision. The findings enable the providers to make strategic decisions on service investment.

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“…Spiller, Vlasic, and Yetton (2007) claim that a 1% increase in the customer retention rate could add approximately 5% profit to an ISP, which also eases the stress of finding and acquiring new customers. In addition, a 2% growth in the customer retention rate is equivalent to a 10% decrease in the cost of customer acquisition (Han, Lu, and Leung, 2012). Hence, it is essential for ISPs to investigate and understand customer loyalty and retention by examining consumption behaviour.…”

Section: Literature Review Customer Switching Intentionmentioning

confidence: 99%

Who Stays, Who Switches: Different Segments of Internet Service Customers

Thaichon¹,

Lobò²,

Mitsis³

2016

AJBR

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High-tech services providers such as Internet Service Providers (ISPs) are facing many challenges and high switching rates remain one of the top concerns. Despite significant research done in the area of switching intention in the marketing literature, customer segmentation and its relationship with switching intention appears to be overlooked. This study aims to fill this gap in the Telecommunication industry by exploring the switching intention and perceptions of different ISP customers. Data was obtained from 2,059 Internet users located in all regions of Thailand via an online survey. This study identified three major distinctive groups of ISP customers based on their Internet usagewhich were classified as light, medium and heavy users. This study also explored the differences among light, medium and heavy users in terms of age groups, employment status, gender and area of employment. The motivations associated with particular types of behaviour of each segment are also discussed.

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Segmentation of telecom customers based on customer value by decision tree model

Cited by 63 publications

References 18 publications

Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

Analysis of Consumers’ Intention Values in the Choice of a Mobile Service Provider

Who Stays, Who Switches: Different Segments of Internet Service Customers

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