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Author: Tianpei Xu
Publisher: Overseas Chinese Press
ISBN: 9781639311422
Category :
Languages : en
Pages : 0
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Book Description
Owing to fierce competition among telecom companies, customer churn is inevitable and it is known that the cost of retaining existing customers is 5-10 times lower than the cost of obtaining new customers. The competitive telecom industry requires telecom companies to use Customer Relationship Management (CRM) to analyze customer churn. CRM analysts need to predict customer churn and understand the reasons for churn. In this dissertation, a CRM system which consists of an ensemble prediction system and a customer churn analysis system is proposed. An ensemble prediction system is composed of a stacking model and soft voting. XGBoost, Logistic Regression, Decision Tree, and Bayesian machine learning algorithms are used to develop a stacking model with two levels, and the three outputs of the second level are used for soft voting. The feature construction of the churn dataset is a process which builds new features from the original dataset by the grouping of customer behavior features to increase the feature space and discover potential information. Using four evaluation measures, the stacking model is examined with the original and new churn datasets. The results show that the proposed customer churn analysis system achieves 96. 1% and 98% accuracy for the original churn dataset and the new churn dataset, respectively. And it has a much better performance compared to other recent search results.
Author: Tianpei Xu
Publisher: Overseas Chinese Press
ISBN: 9781639311422
Category :
Languages : en
Pages : 0
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Book Description
Owing to fierce competition among telecom companies, customer churn is inevitable and it is known that the cost of retaining existing customers is 5-10 times lower than the cost of obtaining new customers. The competitive telecom industry requires telecom companies to use Customer Relationship Management (CRM) to analyze customer churn. CRM analysts need to predict customer churn and understand the reasons for churn. In this dissertation, a CRM system which consists of an ensemble prediction system and a customer churn analysis system is proposed. An ensemble prediction system is composed of a stacking model and soft voting. XGBoost, Logistic Regression, Decision Tree, and Bayesian machine learning algorithms are used to develop a stacking model with two levels, and the three outputs of the second level are used for soft voting. The feature construction of the churn dataset is a process which builds new features from the original dataset by the grouping of customer behavior features to increase the feature space and discover potential information. Using four evaluation measures, the stacking model is examined with the original and new churn datasets. The results show that the proposed customer churn analysis system achieves 96. 1% and 98% accuracy for the original churn dataset and the new churn dataset, respectively. And it has a much better performance compared to other recent search results.
Author: group of authors
Publisher: MAC Prague consulting
ISBN: 8088085101
Category : Business & Economics
Languages : en
Pages : 305
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Book Description
The conference proceedings - Multidisciplinary Academic Conference on Management, Marketing and Economics, Czech Republic, Prague (MAC-MME 2016)
Author: IEEE Staff
Publisher:
ISBN: 9781538640326
Category :
Languages : en
Pages :
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Book Description
International Conference on Inventive Computing and Informatics (ICICI 2017) is being organized on November 23 24, 2017 by the Ranganathan Engineering College ICICI 2017 will provide an outstanding international forum for sharing knowledge and results in all fields of science, engineering and Technology ICICI provides quality key experts who provide an opportunity in bringing up innovative ideas Recent updates in the in the field of technology will be a platform for the upcoming researchers
Author: Konstantinos K. Tsiptsis
Publisher: John Wiley & Sons
ISBN: 1119965454
Category : Mathematics
Languages : en
Pages : 288
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Book Description
This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.
Author: Omer Artun
Publisher: John Wiley & Sons
ISBN: 1119037336
Category : Business & Economics
Languages : en
Pages : 217
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Book Description
Make personalized marketing a reality with this practical guide to predictive analytics Predictive Marketing is a predictive analytics primer for organizations large and small, offering practical tips and actionable strategies for implementing more personalized marketing immediately. The marketing paradigm is changing, and this book provides a blueprint for navigating the transition from creative- to data-driven marketing, from one-size-fits-all to one-on-one, and from marketing campaigns to real-time customer experiences. You'll learn how to use machine-learning technologies to improve customer acquisition and customer growth, and how to identify and re-engage at-risk or lapsed customers by implementing an easy, automated approach to predictive analytics. Much more than just theory and testament to the power of personalized marketing, this book focuses on action, helping you understand and actually begin using this revolutionary approach to the customer experience. Predictive analytics can finally make personalized marketing a reality. For the first time, predictive marketing is accessible to all marketers, not just those at large corporations — in fact, many smaller organizations are leapfrogging their larger counterparts with innovative programs. This book shows you how to bring predictive analytics to your organization, with actionable guidance that get you started today. Implement predictive marketing at any size organization Deliver a more personalized marketing experience Automate predictive analytics with machine learning technology Base marketing decisions on concrete data rather than unproven ideas Marketers have long been talking about delivering personalized experiences across channels. All marketers want to deliver happiness, but most still employ a one-size-fits-all approach. Predictive Marketing provides the information and insight you need to lift your organization out of the campaign rut and into the rarefied atmosphere of a truly personalized customer experience.
Author: Ajith Abraham
Publisher: Springer Nature
ISBN: 303073689X
Category : Technology & Engineering
Languages : en
Pages : 1061
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Book Description
This book highlights the recent research on soft computing and pattern recognition and their various practical applications. It presents 62 selected papers from the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) and 35 papers from the 16th International Conference on Information Assurance and Security (IAS 2020), which was held online, from December 15 to 18, 2020. A premier conference in the field of artificial intelligence, SoCPaR-IAS 2020 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 40 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
Author: Michael J. A. Berry
Publisher: John Wiley & Sons
ISBN: 0471470643
Category : Business & Economics
Languages : en
Pages : 671
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Book Description
Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.
Author: Pablo Moscato
Publisher: Springer
ISBN: 3030062228
Category : Computers
Languages : en
Pages : 1000
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Book Description
This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the latest applications of new computer science methodologies. The chapters are contributed by leading experts in the associated fields.The chapters cover technical aspects at different levels, some of which are introductory and could be used for teaching. Some chapters aim at building a common understanding of the methodologies and recent application areas including the introduction of new theoretical results in the complexity of core problems. Business and marketing professionals may use the book to familiarize themselves with some important foundations of data science. The work is a good starting point to establish an open dialogue of communication between professionals and researchers from different fields. Together, the two volumes present a number of different new directions in Business and Customer Analytics with an emphasis in personalization of services, the development of new mathematical models and new algorithms, heuristics and metaheuristics applied to the challenging problems in the field. Sections of the book have introductory material to more specific and advanced themes in some of the chapters, allowing the volumes to be used as an advanced textbook. Clustering, Proximity Graphs, Pattern Mining, Frequent Itemset Mining, Feature Engineering, Network and Community Detection, Network-based Recommending Systems and Visualization, are some of the topics in the first volume. Techniques on Memetic Algorithms and their applications to Business Analytics and Data Science are surveyed in the second volume; applications in Team Orienteering, Competitive Facility-location, and Visualization of Products and Consumers are also discussed. The second volume also includes an introduction to Meta-Analytics, and to the application areas of Fashion and Travel Analytics. Overall, the two-volume set helps to describe some fundamentals, acts as a bridge between different disciplines, and presents important results in a rapidly moving field combining powerful optimization techniques allied to new mathematical models critical for personalization of services. Academics and professionals working in the area of business anyalytics, data science, operations research and marketing will find this handbook valuable as a reference. Students studying these fields will find this handbook useful and helpful as a secondary textbook.
Author: Francis Buttle
Publisher: Routledge
ISBN: 1856175227
Category : Business & Economics
Languages : en
Pages : 495
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Book Description
This title presents an holistic view of CRM, arguing that its essence concerns basic business strategy - developing and maintaining long-term, mutually beneficial relationships with strategically significant customers - rather than the operational tools which achieve these aims.
Author: Manjusha Pandey
Publisher: Springer Nature
ISBN: 9813365188
Category : Technology & Engineering
Languages : en
Pages : 172
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Book Description
This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9–12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.
