Framework for Targeting High Value Customers and Potential Churn Customers in Telecom using Big Data Analytics

Singh, Inderpreet; Singh, Sukhpal

doi:10.5815/ijeme.2017.01.04

Cited by 15 publications

(12 citation statements)

References 13 publications

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“…Improvement in complicated product life cycles cause retailers to employ big databased technologies to deploy product distribution strategies to reduce time and costs associated with them [43]. The key to utilizing data science and cloud computing platforms is to increase operational efficiency by unlocking insights buried in sensor and machine data through machine learning and pattern recognition techniques.…”

Section: Operational Analyticsmentioning

confidence: 99%

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

et al. 2019

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Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies' interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesised to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.

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Section: Operational Analyticsmentioning

confidence: 99%

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

et al. 2019

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“…Customer segmentation involves splitting the customer base into different subsets. A specific subsets with the same interest and spending habits [20]. Based on the TFM results as calculated above, customers can be divided into five parts: Rank F1 F2 F3 F4 F5 T1 T1F1 T1F2 T1F3 T1F4 T1F5 T2 T2F1 T2F2 T2F3 T2F4 T2F5 Time T3 T3F1 T3F2 T3F3 T3F4 T3F5 T4 T4F1 T4F2 T4F3 T4F4 T4F5 T5 T5F1 T5F2 T5F3 T5F4 T5F5 .…”

Section: Customer Categoriesmentioning

confidence: 99%

Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study

Wassouf

Alkhatib²,

Salloum

et al. 2020

J Big Data

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IntroductionThe telecom sector is witnessing a massive increase in data, and by analyzing this massive data, telecom operators can manage and retain customers. It is also important for companies to be able to predict the amount of income they may receive from their active customers. For this purpose, they need models able to determine customer loyalty. The cost associated with customer gain is usually higher than the cost associated Abstract Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well as customers who are churn. The emergence of big data concepts introduced a new wave of Customer Relationship Management (CRM) strategies. Big data analysis helps to describe customer's behavior, understand their habits, develop appropriate marketing plans for organizations to identify sales transactions and build a long-term loyalty relationship. This paper provides a methodology for telecom companies to target different-value customers by appropriate offers and services. This methodology was implemented and tested using a dataset that contains about 127 million records for training and testing supplied by Syriatel corporation. Firstly, customers were segmented based on the new approach (Time-frequencymonetary) TFM (TFM where: Time (T): total of calls duration and Internet sessions in a certain period of time. Frequency (F): use services frequently within a certain period. Monetary (M): The money spent during a certain period.) and the level of loyalty was defined for each segment or group. Secondly, The loyalty level descriptors were taken as categories, choosing the best behavioral features for customers, their demographic information such as age, gender, and the services they share. Thirdly, Several classification algorithms were applied based on the descriptors and the chosen features to build different predictive models that were used to classify new users by loyalty. Finally, those models were evaluated based on several criteria and derive the rules of loyalty prediction. After that by analyzing these rules, the loyalty reasons at each level were discovered to target them the most appropriate offers and services.

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“…Singh et al,2017 [25]proposed a (Recency-Frequency-Monetary) RFM examination direct the association to break down the client esteem on premise of which association can foresee which clients are probably going to react just as that won't react to their offer. Every client is assessed on premise of recency, recurrence and money related an incentive in RFM examination.…”

Section: Inderpreetmentioning

confidence: 99%

Churn rate prediction in Telecommunication systems

R*,

Ganesh

2019

IJEAT

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The broad view of chum rate measures the amount of individuals or items comes out of a collective group over a predefined time period. Many challenges linked with the churn rates in the telecom industry rise in increasing competition with the elimination of switching costs. The customer loyalty over the telecom industry exists along with the service provider struggling to offer improved services with the lower cost. Many author reviews depends on the churn rate prediction in the field of telecommunication sector discussed with the paper. The paper talk about an idea related with an application of churn rate for the technical review.

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Framework for Targeting High Value Customers and Potential Churn Customers in Telecom using Big Data Analytics

Cited by 15 publications

References 13 publications

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges

Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study

Churn rate prediction in Telecommunication systems

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