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"Praise for the first edition: 'Peppers and Rogers do a beautiful job of integrating actionable frameworks, the thinking of other leaders in the field, and best practices from leading-edge companies.'--Dr. Hugh J. Watson, C. Herman and Mary Virginia Terry Chair of Business Administration, Terry College of Business, University of Georgia. 'Peppers and Rogers have been the vanguard for the developing field of customer relationship management, and in this book, they bring their wealth of experience and knowledge into academic focus. This text successfully centers the development of the field and its theories and methodologies squarely within the broader context of enterprise competitive theory. It is a must-have for educators of customer relationship management and anyone who considers customer-centric marketing the cornerstone of sound corporate strategy.'--Dr. Charlotte Mason, Department Head, Director, and Professor, Department of Marketing and Distribution, Terry College of Business, University of Georgia. 'Don and Martha have done it again! The useful concepts and rich case studies revealed in Managing Customer Relationships remove any excuse for those of us responsible for actually delivering one-to-one customer results. This is the ultimate inside scoop!'--Roy Barnes, Formerly with Marriott, now President, Blue Space Consulting. 'This is going to become the how-to book on developing a customer-driven enterprise. The marketplace is so much in need of this road map!'--Mike Henry, Leader for Consumer Insights at Acxiom. Praise for the second edition: 'Every company has customers, and that's why every company needs a reference guide like this. Peppers and Rogers are uniquely qualified to provide us with the top textbook on the subject, and the essential tool for the field they helped to create'--David Reibstein, William Stewart Woodside Professor of Marketing, The Wharton School, University of Pennsylvania."

"Peningkatan aktivitas pembayaran dan transaksi online mendorong transformasi produk dan layanan perbankan. Di era big data, ulasan menjadi penting bagi bank untuk mengetahui tingkat kepuasan nasabah sebagai masukan untuk perbaikan. Saat bank merilis aplikasi mobile banking di Google Play Store, pelanggan dapat memberikan ulasan tentang pengalaman mereka menggunakan aplikasi tertentu. Tujuan dari penelitian ini adalah untuk memahami sentimen pengguna aplikasi mobile banking melalui analisis sentimen. Metode Natural Language Processing (NLP) digunakan untuk mengekstrak data teks, meliputi: pra-proses, analisis sentimen setiap ulasan dan analisis lima dimensi kualitas layanan berbasis mobile. Beberapa masalah dan dimensi kualitas layanan harus ditingkatkan untuk memenuhi kebutuhan pelanggan. Dengan adanya kemungkinan pengguna untuk terus menggunakan mobile banking, bank dapat memprediksi perilaku pelanggan di masa mendatang.

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Increased online payment and transaction activities drive the transformation of banking products and services. In the big data era, reviews are important for banks to discover customer’s satisfaction levels as input for improvement. As banks release mobile banking applications in Google Play Store, customers can leave reviews regarding their experience using certain applications. The purpose of this study is to understand customer sentiment of mobile banking applications through sentiment analysis. Natural Language Processing (NLP) method is used to extract the text data, including: pre-processing, analysing the sentiment of each review and analysing the sentiment of five dimensions of e-service quality. Some issues and dimensions of service quality should be improved to satisfy customers’ needs. Discovering the probability of continuing to use mobile banking, a bank may predict the future behaviour of the customers."

"Studi tentang pengelolaan customer retention bersumber dari kebutuhan perusahaan untuk mempertahankan customer agar tetap loyal menggunakan produk ataupun layanan yang ditawarkan. Hingga saat ini customer retention menjadi salah satu perhatian utama dalam dunia bisnis karena menurunnya tingkat customer retention berdampak pada berkurangnya revenue. Big data mulai banyak dimanfaatkan sebagai sumber data untuk memahami suatu kondisi ataupun untuk memprediksi suatu behavior yang akan terjadi melalui berbagai pemodelan analisis data. Peristiwa berhentinya customer dari menggunakan produk ataupun layanan disebut customer churn. Penelitian ini menyajikan dua model untuk membantu suatu perusahaan jasa penyedia layanan online berbasis internet untuk menganalisis dan memprediksi future behavior berupa customer churn dan memahami kondisi yang menyebabkannya. Model prediksi customer churn yang dikembangkan menggunakan konsep logistic regression dan random forest. Hasil dari penelitian ini menunjukkan bahwa model yang dikembangkan bisa mengidentifikasi customer suatu perusahaan penyedia layanan online QWE.Inc yang berpotensi akan meninggalkan layanan. Selain itu penelitian ini juga menganalisis faktor-faktor yang memiliki pengaruh signifikan terhadap kondisi tersebut dan memberikan saran pengelolaan customer retention dengan program customer relationship management.

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The study of customer retention management is influenced by the need of the companies to keep their customers stay loyal to use their products or services. Customer retention is one of the main concerns in the business world until today, since the declining level of customer retention will result in the reduced revenue. Big data begin to be widely used as source of data to learn about condition or to predict behavior that may occur through various data analysis modeling. The event of the customer stop from using the product or service is called customer churn.

This study presents two models to help QWE Inc. an internet based online service provider company, to analyze and predict future behavior which is customer churn and understand the causes. Customer churn prediction models in this study have been developed using logistic regression and random forest concepts.

The results of this study indicate that the developed model can identify the customer of QWE.Inc that will potentially leave the service. In addition, this study also analyzed the factors that have a significant influence on these conditions and provide advice on customer retention management with customer relationship management programs."