scholarly journals Churn Prediction using Machine Learning-An Analytical CRM Application

2020 ◽  
Vol 9 (5) ◽  
pp. 1948-1952
Keyword(s):  
Machine Learning ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Support Organizations ◽  
Forecast Models ◽  
Telecom Industry ◽  
Enormous Number

CRM represents (Customer Relationship Management).It is a classification of programming that covers many arrangement of utilizations that are intended to support organizations and furthermore to oversee huge numbers of the business forms like client information. CRM framework models incorporate stages worked to oversee advertising, deals, client support, and backing, all associated with assistance organizations work all the more viably. With a CRM framework, organizations can dissect client collaborations and improve their client connections. The data based forecast models utilizing AI systems have increased monstrous prevalence during the most recent couple of decades. These models have been applied in enormous number of areas like clinical conclusion, wrongdoing expectation, films rating, and so forth. Thus it is utilized in telecom industry where models of expectation have been applied for the forecast of not fulfilled clients who are probably going to change the administrations and furthermore the specialist organization. In telecom the money related expense of client agitate is tremendous henceforth numerous organizations have examined different variables, (for example, cost of the call, nature of the call, client assistance reaction time and so on.) utilizing different AI strategies. This work proposes different ML strategies for client agitate expectation.

scholarly journals Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
C. K. Praseeda ◽  
B. L. Shivakumar
Keyword(s):  
Customer Relationship Management ◽  
Relationship Management ◽  
Local Information ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Swarm Optimization ◽  
Customer Churn ◽  
Kernel Distance ◽  
Telecom Industry ◽  
Hybrid Kernel

Abstract Customer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs the appropriate algorithms to overcome the increasing problem of churn. This work proposed a churn prediction model that employs both strategies of classification and clustering, that helps in recognizing the churn consumers and giving the reasons after the churning of subscribers in the industry of telecom. The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier is employed in categorizing churn customers in the proposed approach. In this way, the compelling guidelines on retention have generated since the process plays a vital role in customer relationship management (CRM) to suppress the churners. After the classification process, the churn customers are divided into clusters through the process of fragmenting the data of churning customer. The cluster-based retention offers have provided by the clustering algorithm of hybrid kernel distance-based possibilistic fuzzy local information C-means (HKD-PFLICM), whereas the measurement of distance have accomplished through the kernel functions such as the hyperbolic tangent kernel and Gaussian kernel. The results reveal that proposed churn prediction model (FPSO- DKSVM) produced better churn classification results compared to other existing algorithms such as K-means, flexible K-Medoids, fuzzy local information C-means (FLICM), possibilistic  FLICM (PFLICM) and entropy weighting FLICM (EWFLICM). Article highlights Customer churn is a major concern in most of the companies as it influences the turnover directly. The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques. Churn prediction plays a crucial role in telecom industry, as they are in the position to maintain their precious customers and organize their Customer Relationship Management.

Big Data and Service Science

2016 ◽  
pp. 180-196
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

scholarly journals Pengembangan Sistem Student Relationship Management di Universitas Surabaya

Teknika ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 66-73
Author(s):  
Kristian Tanuwijaya ◽  
Liliana ◽  
Daniel Soesanto
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Student Relationship

Industri komersial saat ini menerapkan teknik Customer Relationship Management (CRM) untuk mendapat berbagai keuntungan seperti menyediakan informasi pada pelanggan, meningkatkan loyalitas dan kepercayaan pelanggan, serta mempelajari perilaku pelanggan. Pendidikan dapat dipandang sebagai industri bidang jasa. Pada model tersebut, peran siswa dapat dipadankan dengan konsumen. Karenanya, ada peluang untuk menerapkan CRM dalam dunia pendidikan. Oleh karena itu, penelitian dilakukan untuk mengembangkan aplikasi student relationship management untuk Universitas Surabaya yang mencakup rekomendasi beasiswa dan acara, pengingat event, dan perekrutan panitia. Aplikasi yang dikembangkan menggunakan platform web dan pembuatan rekomendasi dilakukan dengan algoritma machine learning yaitu random forest, deep learning, dan stacked ensemble. Berdasarkan hasil uji coba dan validasi, aplikasi tersebut dapat membantu mahasiswa mengetahui informasi seperti beasiswa yang dapat diperoleh dan kegiatan yang dapat diikuti. Dengan demikian, ketika kepuasan mahasiswa terhadap layanan yang diberikan oleh universitas meningkat, maka hubungan baik antara penyedia jasa, dalam hal ini universitas, dan konsumennya, dalam hal ini mahasiswa, dapat dijaga.

Big Data and Service Science

2014 ◽  
pp. 128-144 ◽  
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

ENTERPRISE CUSTOMER RELATIONSHIP MANAGEMENT BASED ON BIG DATA MINING

2018 ◽  
Vol 48 (3) ◽  
pp. 163-168
Author(s):  
X. T. LI ◽  
F. FENG
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Potential Core ◽  
Big Data Mining ◽  
Customer Classification ◽  
Classification Index

Based on the customer relationship management in the context of big data, focusing on B2C e-commerce companies, this paper constructs a customer classification index system, uses a factor analysis and Bagging model to study the sales data of an e-commerce business, and demonstrates the specific operation of customer relationship management under the background of big data. This paper finds that through the classification of past consumer behavior data, managers can distinguish between potential, core, and lost customers. The bagging model can predict the type of customer and guide the administrator to perform differentiated customer relationship management.

scholarly journals Customer Relationship Management and Machine Learning Technology for Identifying the Customer

2017 ◽  
Vol 1 (1) ◽  
pp. 12 ◽  
Author(s):  
Hidra Amnur
Keyword(s):  
Machine Learning ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Support Vector ◽  
Learning Technology ◽  
Organization Performance ◽  
Multi Criteria Decision Making ◽  
Response Model ◽  
Lost Potential

Customer Relationship Management needed for the company to know their customer more closed, and make two-way communication between company and customer. in CRM solutions are multi-criteria decision-making analysis tools that do not require prior assumptions to explore the weights and performances among project risk, project management and organization performance, based on research framework of stimulus-organism response model. in this study, Machine learning with Support Vector Machine algorithm is currently for classification task due to its ability to model nonlinearities CRM Solutions. With Machine Learning and CRM, Bank X optimize their profit, with manage their more benefit customer or find a new customer or get their lost potential customer back.

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