| customer service data analysis: Advanced Customer Analytics Mike Grigsby, 2016-10-03 Advanced Customer Analytics provides a clear guide to the specific analytical challenges faced by the retail sector. The book covers the nature and scale of data obtained in transactions, relative proximity to the consumer and the need to monitor customer behaviour across multiple channels. The book advocates a category management approach, taking into account the need to understand the consumer mindset through elasticity modelling and discount strategies, as well as targeted marketing and loyalty design. A practical, no-nonsense approach to complex scenarios is taken throughout, breaking down tasks into easily digestible steps. The use of a fictional retail analyst 'Scott' helps to provide accessible examples of practice. Advanced Customer Analytics does not skirt around the complexities of this subject but offers conceptual support to steer retail marketers towards making the right choices for analysing their data. Online resources include a selection of datasets to support specific chapters. |
| customer service data analysis: Customer Analytics For Dummies Jeff Sauro, 2015-02-02 The easy way to grasp customer analytics Ensuring your customers are having positive experiences with your company at all levels, including initial brand awareness and loyalty, is crucial to the success of your business. Customer Analytics For Dummies shows you how to measure each stage of the customer journey and use the right analytics to understand customer behavior and make key business decisions. Customer Analytics For Dummies gets you up to speed on what you should be testing. You'll also find current information on how to leverage A/B testing, social media's role in the post-purchasing analytics, usability metrics, prediction and statistics, and much more to effectively manage the customer experience. Written by a highly visible expert in the area of customer analytics, this guide will have you up and running on putting customer analytics into practice at your own business in no time. Shows you what to measure, how to measure, and ways to interpret the data Provides real-world customer analytics examples from companies such as Wikipedia, PayPal, and Walmart Explains how to use customer analytics to make smarter business decisions that generate more loyal customers Offers easy-to-digest information on understanding each stage of the customer journey Whether you're part of a Customer Engagement team or a product, marketing, or design professional looking to get a leg up, Customer Analytics For Dummies has you covered. |
| customer service data analysis: Predictive Analytics For Dummies Anasse Bari, Mohamed Chaouchi, Tommy Jung, 2014-03-06 Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions. Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more. Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses Helps readers see how to shepherd predictive analytics projects through their companies Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies. |
| customer service data analysis: Big Data in Practice Bernard Marr, 2016-03-22 The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter |
| customer service data analysis: Data Analytics for Organisational Development Uwe H. Kaufmann, Amy B. C. Tan, 2021-07-26 A practical guide for anyone who aspires to become data analytics–savvy Data analytics has become central to the operation of most businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for Organisational Development: Unleashing the Potential of Your Data introduces a methodical process for gathering, screening, transforming, and analysing the correct datasets to ensure that they are reliable tools for business decision-making. Written by a Six Sigma Master Black Belt and a Lean Six Sigma Black Belt, this accessible guide explains and illustrates the application of data analytics for organizational development and design, with particular focus on Customer and Strategy Analytics, Operations Analytics and Workforce Analytics. Designed as both a handbook and workbook, Data Analytics for Organisational Development presents the application of data analytics for organizational design and development using case studies and practical examples. It aims to help build a bridge between data scientists, who have less exposure to actual business issues, and the non-data scientists. With this guide, anyone can learn to perform data analytics tasks from translating a business question into a data science hypothesis to understanding the data science results and making the appropriate decisions. From data acquisition, cleaning, and transformation to analysis and decision making, this book covers it all. It also helps you avoid the pitfalls of unsound decision making, no matter where in the value chain you work. Follow the “Five Steps of a Data Analytics Case” to arrive at the correct business decision based on sound data analysis Become more proficient in effectively communicating and working with the data experts, even if you have no background in data science Learn from cases and practical examples that demonstrate a systematic method for gathering and processing data accurately Work through end-of-chapter exercises to review key concepts and apply methods using sample data sets Data Analytics for Organisational Development includes downloadable tools for learning enrichment, including spreadsheets, Power BI slides, datasets, R analysis steps and more. Regardless of your level in your organisation, this book will help you become savvy with data analytics, one of today’s top business tools. |
| customer service data analysis: Customer Service Performance Great Britain: National Audit Office, 2012-12-18 This report recognizes that HMRC has restored customer service levels from a low point in 2010, when problems with the new National Insurance and PAYE system increased the number of queries. HMRC has now dealt with long-term backlogs by employing 2,500 temporary staff, enhancing phone technology and improving productivity. In 2011-12, HMRC answered 74 per cent of phone calls, against an interim target of 58 per cent. This level of service is nevertheless low. So far in 2012-13, HMRC has improved its handling of post but its performance in handling calls has been varied. Depending on the tariff they pay their phone company, customers are charged from when their call is connected even if they are held in a queue. The NAO estimates that it cost customers £33 million in call charges while they are in the queue. Most of HMRC's numbers are still 0845 numbers which result in high call charges for some customers. It is, however, investigating alternatives. NAO analysis indicates that, by the end of 2012-13 and through 2013-14, HMRC could achieve its target of answering 90 per cent of calls. However, by 2014-15, HMRC will have reduced numbers of contact centre staff so will need to redeploy large numbers of back-office processing staff to answer telephones. There is also uncertainty about the impact on call volumes of large-scale changes, such as the introduction of Real Time Information and the transition to universal credit. |
| customer service data analysis: Applying Predictive Analytics Within the Service Sector Sahu, Rajendra, Dash, Manoj, Kumar, Anil, 2017-02-07 Value creation is a prime concern for any contemporary business. This can be accomplished through the incorporation of various techniques and processes, such as the integration of analytics to improve business functions. Applying Predictive Analytics Within the Service Sector is a pivotal reference source for the latest innovative perspectives on the incorporation of analysis techniques to enhance business performance. Examining a wide range of relevant topics, such as alternative clustering, recommender systems, and social media tools, this book is ideally designed for researchers, academics, students, professionals, and practitioners seeking scholarly material on business improvement in the service industry. |
| customer service data analysis: Data as a Service Pushpak Sarkar, 2015-07-31 Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions |
| customer service data analysis: Data Analysis Peter J. Huber, 2012-01-09 This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics. |
| customer service data analysis: Analytics at Work Thomas H. Davenport, Jeanne G. Harris, Robert Morison, 2010 As a follow-up to the successful Competing on Analytics, authors Tom Davenport, Jeanne Harris, and Robert Morison provide practical frameworks and tools for all companies that want to use analytics as a basis for more effective and more profitable decision making. Regardless of your company's strategy, and whether or not analytics are your company's primary source of competitive differentiation, this book is designed to help you assess your organization's analytical capabilities, provide the tools to build these capabilities, and put analytics to work. The book helps you answer these pressing questions: What assets do I need in place in my organization in order to use analytics to run my business? Once I have these assets, how do I deploy them to get the most from an analytic approach? How do I get an analytic initiative off the ground in the first place, and then how do I sustain analytics in my organization over time? Packed with tools, frameworks, and all new examples, Analytics at Work makes analytics understandable and accessible and teaches you how to make your company more analytical. |
| customer service data analysis: Gower Handbook of Customer Service Peter Murley, 1997 This new Gower Handbook covers an area of management that is now regarded as fundamental to the success of any organization, whether it is in the private or the public sector. A team of experienced professionals and practising managers have pooled their expertise to provide nearly 50 chapters of current best practice in all aspects of customer service management, making this a valuable addition to the renowned Gower Handbook series. |
| customer service data analysis: Marketing Analytics Mike Grigsby, 2018-04-03 Who is most likely to buy and what is the best way to target them? How can businesses improve strategy without identifying the key influencing factors? The second edition of Marketing Analytics enables marketers and business analysts to leverage predictive techniques to measure and improve marketing performance. By exploring real-world marketing challenges, it provides clear, jargon-free explanations on how to apply different analytical models for each purpose. From targeted list creation and data segmentation, to testing campaign effectiveness, pricing structures and forecasting demand, this book offers a welcome handbook on how statistics, consumer analytics and modelling can be put to optimal use. The fully revised second edition of Marketing Analytics includes three new chapters on big data analytics, insights and panel regression, including how to collect, separate and analyze big data. All of the advanced tools and techniques for predictive analytics have been updated, translating models such as tobit analysis for customer lifetime value into everyday use. Whether an experienced practitioner or having no prior knowledge, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Complete with downloadable data sets and test bank resources, this book supplies a concrete foundation to optimize marketing analytics for day-to-day business advantage. |
| customer service data analysis: Competing on Analytics Thomas H. Davenport, Jeanne G. Harris, 2007-03-06 You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics. |
| customer service data analysis: Predictive Marketing Omer Artun, Dominique Levin, 2015-08-06 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. |
| customer service data analysis: The Three C's: Communication, Customer Service, & Chatbots I. Edmondson, 2024-01-05 The world that our forefathers knew no longer exists, and the world in which most of us grew up is no longer here either. Science has taken us into a new world in which humans and their activities are now augmented by robots that can perform many of the functions that were previously believed to be only possible for humans to perform. The total impact of these changes is as yet unknown, but we do know that every facet of human existence has been and will continue to be impacted. There are many who fear for the future of mankind while others see possibilities for changes that will improve all facets of our lives. The one thing we do know -- life will never be the same again! |
| customer service data analysis: Analysis of Customer Satisfaction Data Derek R. Allen, Danica R. Allen, Tanniru R. Rao, 2000-01-01 As global competition increases, maintaining customer loyalty is more important than ever. Dissatisfied customers now have many options, with dozens of companies from around the world competing for their business. it is crucial for every organization to retain loyal customers by maintaining a high level of customer satisfaction. However, sustaining an environment conducive to customer satisfaction is a difficult task without a strong understanding of the data surrounding customer satisfaction surveys. This is the focus of Analysis of Customer Satisfaction Data, which clearly demonstrates how to interpret the data gathered in customer surveys while explaining how to use this information to improve overall customer satisfaction. Written by industry leaders with years of experience consulting top companies such as General Motors, Bank of America and Met Life, this book offers a step-by-step approach to customer loyalty research in an advanced yet understandable format. This book is a must read for anyone who is developing a customer satisfaction survey. - Richard Yorio Customer Satisfaction and Loyalty Manager Xerox Corporation. |
| customer service data analysis: Data Analytics: Principles, Tools, and Practices Gaurav Aroraa, Chitra Lele, Dr. Munish Jindal, 2022-01-24 A Complete Data Analytics Guide for Learners and Professionals. KEY FEATURES ● Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. ● Dive into Machine Learning, its tools, and applications. ● Coverage of applications of Big Data, Data Analysis, and Business Intelligence. DESCRIPTION These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. WHAT YOU WILL LEARN ● Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. ● Learn to manage data warehousing with real time transaction processing. ● Explore various machine learning techniques that apply to data analytics. ● Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. ● Acquaint yourself with Big Data tools and statistical techniques for machine learning. WHO THIS BOOK IS FOR IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. TABLE OF CONTENTS 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning |
| customer service data analysis: 09 GRASPED Customer Service and Support Roadmap Steven Brough, 2024-02-19 The GRASPED Customer Service and Support Roadmap is an essential guide for startups focused on establishing excellent customer service and support systems. It outlines steps for setting up customer support channels, training on best practices, creating systems for handling inquiries, and actively using customer feedback to drive improvements. This roadmap is designed to help startups enhance customer satisfaction and loyalty by providing exceptional service. Its USP is the actionable and structured approach to building a customer service framework that prioritizes customer satisfaction and loyalty. Unlike generic customer service guidelines, this roadmap offers detailed steps, including case studies and actionable tips, making it a vital tool for startups aiming to establish a strong relationship with their customers. The GRASPED Customer Service and Support Roadmap provides startups with a comprehensive strategy for developing and implementing a customer service system that not only meets but exceeds customer expectations. It emphasizes the importance of customer feedback and continuous improvement in creating a loyal customer base. |
| customer service data analysis: ECIE 2023 18th European Conference on Innovation and Entrepreneurship Vol 1 Fernando Moreira, Shital Jayantilal, 2023-09-21 |
| customer service data analysis: Digital TV and Wireless Multimedia Communications Guangtao Zhai, Jun Zhou, Hua Yang, Ping An, Xiaokang Yang, 2022-04-16 This book presents revised selected papers from the 18th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2021, held in Shanghai, China, in December 2021. The 41 papers presented in this volume were carefully reviewed and selected from 110 submissions. They were organized in topical sections on image analysis; quality assessment; target detection; video processing; big data. |
| customer service data analysis: Applications of New Technology in Operations and Supply Chain Management Taghipour, Atour, 2024-08-26 The International Data Corporation (IDC) has unveiled a series of transformative predictions to reshape operations and supply chain management, leading companies to re-assess their processes. Applications of New Technology in Operations and Supply Chain Management offers an in-depth exploration of how emerging technologies are positioned to revolutionize the way businesses execute and coordinate their operations. The book delves into the adoption of digital technologies, the shift to cloud technology, and the emergence of real-time operational insights that can be accessed from anywhere. For instance, 2026 ushers in integrating digital tools for measuring carbon footprints and the increased use of robots in unconventional domains, such as remote inspection and maintenance. By 2027, augmented reality technology will take center stage, reducing operator and field worker errors. Furthermore, remote operations embrace satellite-based artificial intelligence or machine learning technologies, revolutionizing data collection and analysis at the edge. |
| customer service data analysis: Advances in Intelligent Data Analysis and Applications Jeng-Shyang Pan, Valentina Emilia Balas, Chien-Ming Chen, 2021-11-25 This book constitutes the Proceeding of the Sixth International Conference on Intelligent Data Analysis and Applications, October 15–18, 2019, Arad, Romania. This edition is technically co-sponsored by “Aurel Vlaicu” University of Arad, Romania, Southwest Jiaotong University, Fujian University of Technology, Chang’an University, Shandong University of Science and Technology, Fujian Provincial Key Lab of Big Data Mining and Applications, and National Demonstration Center for Experimental Electronic Information and Electrical Technology Education (Fujian University of Technology), China, Romanian Academy, and General Association of Engineers in Romania - Arad Section. The book covers a range of topics: Machine Learning, Intelligent Control, Pattern Recognition, Computational Intelligence, Signal Analysis, Modeling and Visualization, Multimedia Sensing and Sensory Systems, Signal control, Imaging and Processing, Information System Security, Cryptography and Cryptanalysis, Databases and Data Mining, Information Hiding, Cloud Computing, Information Retrieval and Integration, Robotics, Control, Agents, Command, Control, Communication and Computers (C4), Swarming Technology, Sensor Technology, Smart cities. The book offers a timely, board snapshot of new development including trends and challenges that are yielding recent research directions in different areas of intelligent data analysis and applications. The book provides useful information to professors, researchers, and graduated students in area of intelligent data analysis and applications. |
| customer service data analysis: Strategic Customer Service John A. GOODMAN, 2009-05-13 The success of any organization depends on high-quality customer service. But for companies that strategically align customer service with their overall corporate strategy, it can transcend typical good business to become a profitable word-of-mouth machine that will transform the bottom line. Drawing on over thirty years of research for companies such as 3M, American Express, Chik-Fil-A, USAA, Coca-Cola, FedEx, GE, Cisco Systems, Neiman Marcus, and Toyota, author Goodman uses formal research, case studies, and patented practices to show readers how they can: • calculate the financial impact of good and bad customer service • make the financial case for customer service improvements • systematically identify the causes of problems • align customer service with their brand • harness customer service strategy into their organization's culture and behavior Filled with proven strategies and eye-opening case studies, this book challenges many aspects of conventional wisdom—using hard data—and reveals how any organization can earn more loyalty, win more customers...and improve their financial bottom line. |
| customer service data analysis: Managing Customer Relationships Using Customer Care Techniques Anna Brzozowska, Stanisław Brzeziński, Arnold Pabian, Barbara Pabian, 2024-05-15 In today’s global business environment, Customer Relationship Management (CRM) has become key to the success of many international enterprises. Managing Customer Relationships Using Customer Care Techniques: Strategy Development of an International Enterprise offers a comprehensive analysis of this crucial business aspect, focusing on how companies can effectively manage their customer relationships in the context of global expansion. This book stands out with its unique approach to CRM, blending theory with practice and providing readers with a deep understanding of how CRM influences the strategies of international enterprises. The book is divided into four main parts, each focusing on a different aspect of customer relationship management. The first part focuses on creating strategies in the context of customer relationships in international enterprises, the second part discusses the essence of the CRM concept in companies, the third part delves into the strategy of a global enterprise from the customer relationship perspective, and the fourth part centers on the evaluation and optimization of customer care strategy in modern business. Key Features: • In-depth analysis of the CRM concept in the context of international business. • Discussion on the evolution of the CRM idea over the years. • Introduction to integrated customer relationship management systems in global enterprises. • Analysis of the impact of social media on CRM. • Practical insights on measuring the effectiveness of customer care activities. Managing Customer Relationships Using Customer Care Techniques: Strategy Development of an International Enterprise is a must-read for managers, business consultants, business students, and anyone wanting to understand how to effectively manage customer relationships in an international business environment. |
| customer service data analysis: Sustainable Development Through Data Analytics and Innovation Jorge Marx Gómez, Lawal O. Yesufu, 2022-09-26 Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis. The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management. |
| customer service data analysis: Towards Ethical and Socially Responsible Explainable AI Mohammad Amir Khusru Akhtar, |
| customer service data analysis: Advanced Intelligence Systems and Innovation in Entrepreneurship Misra, Sanjay, Jain, Amit, Kaushik, Manju, Banerjee, Chitresh, Singh, Yudhveer, 2024-05-16 The foundation of any successful enterprise lies in a well-crafted IT strategy. In today's volatile economic climate, it is necessary to harmonize the exigencies of daily operations with the demands of future growth and development. As information technology continues to permeate every facet of our lives and industries, the nexus between entrepreneurship and innovation remains pivotal. Advanced Intelligence Systems and Innovation in Entrepreneurship delves deep into the intricate web that binds information technology (IT) strategy, advanced intelligence systems, and the dynamic landscape of entrepreneurship. Within these pages, experts dissect the anatomy of IT strategies, deciphering their critical role in achieving IT and business objectives. This book discusses intelligence systems, the very embodiment of artificial intelligence's transformative potential. These systems possess the capacity to perform tasks once reserved for human intelligence, making decisions, solving complex problems, and learning from data. Yet, the book does not shy away from addressing the thorny issues of employment, privacy, and security that accompany such profound technological shifts. This book underscores how futuristic technologies empower entrepreneurs to innovate sustainably, fostering business growth while safeguarding our environment. Entrepreneurs, in their quest for new and inventive products and services, wield information technology as a transformative tool. The need for organizational restructuring, aligned with the demands of these technologies, becomes evident, with case studies showcasing the impact of IT on entrepreneurial activities.This book is deal for scholars, researchers, students, industry professionals, entrepreneurs, intrapreneurs, educators, technologists, policymakers, and innovators. |
| customer service data analysis: CRM in Financial Services Bryan Foss, Merlin Stone, 2002 Packed with international case studies and examples, the book begins with a detailed analysis of the state of CRM and e-business in the financial services globally, and then goes on to provide comprehensive and practical guidance on: making the most of your customer base; systems and data management; risk and compliance; channels and value chain issues; implementation; strategic implications. |
| customer service data analysis: Big Data Analytics In Education Midhun Moorthi C, 2023-11-21 Big data analytics refers to the application of sophisticated analytical methods to extremely extensive and heterogeneous datasets encompassing structured, semi-structured, and unstructured information. These datasets originate from various sources and range in size from terabytes to zettabytes. With the purpose of facilitating data-driven decision making, big data analytics entails the identification of correlations, trends, and patterns in vast quantities of unprocessed data. These procedures employ well-known statistical analysis methods, such as regression and clustering, and employ more sophisticated instruments to implement them on larger datasets. Since software and hardware advancements enabled organisations to manage vast quantities of unstructured data in the early 2000s, big data has been a popular term. Subsequently, the proliferation of emerging technologies, such as smartphones and Amazon, has further augmented the considerable volumes of data accessible to organisations. For the storage and processing of big data, early innovation initiatives such as Hadoop, Spark, and NoSQL databases were developed in response to the data deluge. Data engineers are constantly inventing new methods to process and integrate the massive volumes of complicated data generated by many sources, such as the internet, smart devices, transactions, networks, and sensors. Presently, emergent technologies such as machine learning are being integrated with big data analytics methods in order to uncover and escalate the magnitude of more intricate insights. |
| customer service data analysis: Ai & Quantum Computing For Finance & Insurance: Fortunes And Challenges For China And America Paul Schulte, David Kuo Chuen Lee, 2019-04-16 This book offers a framework and analysis for the current technological landscape between the United States and China across the financial and insurance sectors as well as emerging technologies such as AI, Blockchain, Cloud and Data Analytics and Quantum Computing (ABCDQ). Based on original lecture slides used by the authors, the book presents contemporary and critical views of emergent technologies for a wide spectrum of readers from CEOs to university lecturers to students. The narrative aims to help readers upgrade their technology literacy and to overcome the fear of AI posed by our lizard brain. |
| customer service data analysis: The Art of Data Analysis Kristin H. Jarman, 2013-05-13 A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning. |
| customer service data analysis: Business Intelligence for Telecommunications Deepak Pareek, 2006-11-29 Bringing together market research reports, business analyst briefings, and technology references into one comprehensive volume, Business Intelligence for Telecommunications identifies those advances in both methods and technology that are being employed to inform decision-making and give companies an edge in the rapidly growing and highly co |
| customer service data analysis: Changing Competitive Business Dynamics Through Sustainable Big Data Analysis Sukanta Kumar Baral, Richa Goel, Tilottama Singh, Erdener Kaynak, Vishal Jain, 2024-07-30 This research book compiles concise reviews on business trends that drive innovation and competitive advantages. The book includes 15 referenced chapters covering topics in advertising, agriculture, digital marketing, human resource management, healthcare and sustainability. Chapters focus on the use of disruptive technologies such as virtual reality, artificial intelligence and Internet of Things that harness the power of big data and visualizations to provide a framework for insightful analytics. Readers will be able to understand the practical applications and implications of these technologies so that they can apply them to their businesses. Special topics of interest are highlighted, including industry 4.0, women empowerment for industry 5.0, sustainability models for achieving UN SDG 9, over the top media platforms, and more. |
| customer service data analysis: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| customer service data analysis: The AI Revolution in Customer Service and Support Ross Smith, Mayte Cubino, Emily McKeon, 2024-07-16 In the rapidly evolving AI landscape, customer service and support professionals find themselves in a prime position to take advantage of this innovative technology to drive customer success. The AI Revolution in Customer Service and Support is a practical guide for professionals who want to harness the power of generative AI within their organizations to create more powerful customer and employee experiences. This book is designed to equip you with the knowledge and confidence to embrace the AI revolution and integrate the technology, such as large language models (LLMs), machine learning, predictive analytics, and gamified learning, into the customer experience. Start your journey toward leveraging this technology effectively to optimize organizational productivity. A portion of the book’s proceeds will be donated to the nonprofit Future World Alliance, dedicated to K-12 AI ethics education. IN THIS BOOK YOU’LL LEARN About AI, machine learning, and data science How to develop an AI vision for your organization How and where to incorporate AI technology in your customer experience fl ow About new roles and responsibilities for your organization How to improve customer experience while optimizing productivity How to implement responsible AI practices How to strengthen your culture across all generations in the workplace How to address concerns and build strategies for reskilling and upskilling your people How to incorporate games, play, and other techniques to engage your agents with AI Explore thought experiments for the future of support in your organization “Insightful & comprehensive—if you run a service & support operation, put this book on your essential reading list right now!” —PHIL WOLFENDEN, Cisco, VP, Customer Experience “This book is both timely and relevant as we enter an unprecedented period in our industry and the broader world driven by Generative AI. The magnitude and speed of change we’re experiencing is astounding and this book does an outstanding job balancing technical knowledge with the people and ethical considerations we must also keep front of mind.” —BRYAN BELMONT, Microsoft, Corporate VP, Customer Service & Support “The authors of this book are undoubtedly on the front lines of operationalizing Gen AI implementations in customer support environments... and they know undoubtedly that at its core, support is about people and genuine human connections. This book walks you through their journey to keep people at the center of this technical tsunami.” —PHAEDRA BOINODIRIS, Author, AI for the Rest of Us |
| customer service data analysis: Customer Intimacy Analytics François Habryn, 2014-07-30 The ability to capture customer needs and to tailor the provided solutions accordingly, also defined as customer intimacy, has become a significant success factor in the B2B space - in particular for increasingly servitizing businesses. This book elaborates on the solution CI Analytics to assess and monitor the impact of customer intimacy strategies by leveraging business analytics and social network analysis technology. This solution thereby effectively complements existing CRM solutions. |
| customer service data analysis: Advanced Data Analytics with AWS Joseph Conley , 2024-04-17 Master the Fundamentals of Data Analytics at Scale KEY FEATURES ● Comprehensive guide to constructing data engineering workflows spanning diverse data sources ● Expert techniques for transforming and visualizing data to extract actionable insights ● Advanced methodologies for analyzing data and employing machine learning to uncover intricate patterns DESCRIPTION Embark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook. Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets. The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons. The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence. WHAT WILL YOU LEARN ● Construct streamlined data engineering workflows capable of ingesting data from diverse sources and formats. ● Employ data transformation tools to efficiently cleanse and reshape data, priming it for analysis. ● Perform ad-hoc queries for preliminary data exploration, uncovering initial insights. ● Utilize prepared datasets to craft compelling, interactive data visualizations that communicate actionable insights. ● Develop advanced machine learning and Generative AI workflows to delve into intricate aspects of complex datasets, uncovering deeper insights. WHO IS THIS BOOK FOR? This book is ideal for aspiring data engineers, analysts, and data scientists seeking to deepen their understanding and practical skills in data engineering, data transformation, visualization, and advanced analytics. It is also beneficial for professionals and students looking to leverage AWS services for their data-related tasks. TABLE OF CONTENTS 1. Introduction to Data Analytics and AWS 2. Getting Started with AWS 3. Collecting Data with AWS 4. Processing Data on AWS 5. Descriptive Analytics on AWS 6. Advanced Data Analysis on AWS 7. Additional Use Cases for Data Analysis 8. Data Visualization and Interaction on AWS 9. The Future of Data Analytics 10. Conclusion and Next Steps Index |
| customer service data analysis: Master VISUALLY Excel 2007 Elaine Marmel, 2008-03-31 If you prefer instructions that show you how rather than tell you why, then this visual reference is for you. Hundreds of succinctly captioned, step-by-step screen shots reveal how to accomplish more than 375 Excel 2007 tasks, including creating letters with Mail Merge, assigning formats to cells, editing multiple worksheets at once, and summarizing with PivotTables and PivotCharts. While high-resolution screen shots demonstrate each task, succinct explanations walk you through step by step so that you can digest these vital lessons in bite-sized modules. |
| customer service data analysis: Customer Service Supply Chain Management Alexandre Oliveira, Anne Gimeno, 2014-06-17 DRIVE MORE VALUE FROM YOUR SUPPLY CHAIN BY IMPROVING THE WAY YOU MANAGE CUSTOMER SERVICE Optimize linked interactions across your entire customer service environment Implement customer-centric strategies, including customer-based supply chain segmentation and lifelong customer logistics management Use the business-driven customer service model to align customer services management to business goals, and measure your progress Customer Service Supply Chain Management offers expert guidance for managing your supply chain to deliver more innovative and profitable customer experiences. Pioneering supply chain management experts Alexandre Oliveira and Anne Gimeno provide a comprehensive overview of the topic, detailed descriptions of each high-value approach, and modern applications and best practices proven at leading companies worldwide. Complementing theoretical texts, they offer deep knowledge of how pioneering customer service management techniques are actually applied in the field. This book’s content will be exceptionally helpful to both practitioners and students in all areas of supply chain management, customer service, and marketing, including participants in leading certification programs. To build a truly customer-centric business, you must integrate, balance, and optimize four sets of relationships: product, customer, service, and process. By doing this, you empower your business to deliver the high-profit solutions your customers really want: personalized packages of products, services, support, education, and consulting. Customer Service Supply Chain Management offers a complete model and blueprint for achieving these goals. Global supply chain innovators Alexandre Oliveira and Anne Gimeno show how to systematically address key issues ranging from organizational structure, governance, and strategy to day-to-day tactics and operations. Oliveira and Gimeno help you assess where you stand now, identify gaps and priorities, and move rapidly towards greater effectiveness. They introduce realistic examples, applications, and best practices: all designed to help you translate theory into practice, and practice into profits. USE CUSTOMER SERVICE SUPPLY CHAIN MANAGEMENT TO: GROW SALES VOLUME: Increase market share Accelerate revenue cycles Reduce lost sales Support marketing and sales initiatives IMPROVE CUSTOMER EXPERIENCE: Add customer value Optimize cost to serve Deliver the right service at the right cost GROW MARGINS: Reduce cost of sales Improve asset management Balance service levels and cost structures |
| customer service data analysis: Fintech For Finance Professionals David Kuo Chuen Lee, Joseph Lim, Kok Fai Phoon, Yu Wang, 2021-11-29 As technologies such as artificial intelligence, big data, cloud computing, and blockchain have been applied to various areas in finance, there is an increasing demand for finance professionals with the skills and knowledge related to fintech. Knowledge of the technologies involved and finance concepts is crucial for the finance professional to understand the architecture of technologies as well as how they can be applied to solve various aspects of finance.This book covers the main concepts and theories of the technologies in fintech which consist of big data, data science, artificial intelligence, data structure and algorithm, computer network, network security, and Python programming. Fintech for Finance Professionals is a companion volume to the book on finance that covers the fundamental concepts in the field. Together, these two books form the foundation for a good understanding of finance and fintech applications which will be covered in subsequent volumes.Bundle set: Global Fintech Institute-Chartered Fintech Professional Set I |
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
想问一下大家web of science文献检索点不动 只能用作者检索怎么 …
手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
什么是CRM系统？它的作用是什么？ - 知乎
CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
请问金融系统中提到的KYC是做什么用的？ - 知乎
KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
consumer、customer、client 有何区别？ - 知乎
对于customer和consumer，我上marketing的课的时候区分过这两个定义。 customer behavior：a broad term that covers individual consumers who buy goods and services for their own use …
Consumer与customer有区别吗？具体作什么区别？ - 知乎
Mar 18, 2014 · 一般把 customer 翻译做 “客户“ 比如你是杜蕾斯的生产商，那么中国总代，上海曼伦商贸有限公司，就是你的customer，然后从曼伦进货的全家就是曼伦的customer，然后隔 …
Windows 10 business 和 consumer 中的专业版有什么不同？ - 知乎
Mar 14, 2020 · Windows10 有business editions 和 consumer editions 版。其中每个都有 专业工作站版，可这2个专业工作…
想问一下大家web of science文献检索点不动 只能用作者检索怎么 …
手机电脑打开都是这样 我想用文献检索 不想用作者检索啊啊啊啊啊
什么是CRM系统？它的作用是什么？ - 知乎
CRM（Customer Relationship Management），即客户关系管理系统.。 是指利用软件、硬件和网络技术，为企业建立一个客户信息收集、管理、分析和利用的信息系统。通俗地讲， CRM就 …
请问金融系统中提到的KYC是做什么用的？ - 知乎
KYC看着高端，其实我们每个人都经历过。例如，当你去银行开户的时候，都必须要提交身份证件，甚至有时候还要提交家庭住址证明。这便是一个最简单的KYC。(也叫做CIP - Customer …
什么是SCRM？为什么企业要做SCRM？ - 知乎
SCRM翻译后的全程是：Social Customer Relationship Management ，可以看到这里的“S”原来是“Social”，也就是“社交”的意思。 尽管只是多了一个S，却将原先CRM呈现的客户管理行为转 …
什么是跨境电商，你们了解多少？ - 知乎
跨境电子商务是指不同国度或地域的买卖双方经过互联网以邮件或者快递等方式通关，将传统贸易中的展现、洽谈和成交环节数字化，完成产品进口的的新型贸易方式，当前主流的跨境电商形 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
新媒体的KOL、KOC是什么? - 知乎
KOC有双重身份，即Customer和Creator，KOC是消费者的同时也是创作者，是对消费者的消费决策起到关键作用的群体。 KOL与KOC在本质上截然不同，是两个群体。前者是推，而KOC是 …
