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Author: Kai Yang
Publisher: McGraw Hill Professional
ISBN: 0071593411
Category : Technology & Engineering
Languages : en
Pages : 430
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Book Description
Discover All the Advantages of Using Design for Six Sigma to Develop and Build Customer Value-Based Products Voice of the Customer Capture and Analysis equips Six Sigma you with the skills needed to create and deploy surveys, capture real customers need with ethnographic methods, immediately analyze the results, and coordinate and drive responsive actions. Quality expert Kai Yang explains how to utilize the statistical methods of Design for Six Sigma to identify key customer needs and assess the cost of poor quality. He then shows how to design robust products to meet those needs, optimize product life cycles, and accurately validate their findings. Voice of the Customer Capture and Analysis features a wealth of information on Six Sigma and value creation...customer survey design, administration, and analysis...ethnographic research...process management and Lean Product Development...the deployment of customer value into products-DFSS...and value engineering. This product design tool enables you to: Minimize sources of response and measurement error Discern customer preferences Design VOC research to minimize mistranslation Respond to analytical implications of VOC data Optimize design to decrease sensitivity of CTQs to process parameters With the help of Voice of the Customer Capture and Analysis, you can now acquire the skills needed to truly understand a customer's wants and needs, in order to develop and build optimal products. Most Design for Six Sigma product development teams fall short of truly understanding their customers' want and needs until it is too late. Market research studies and reports simply do not provide sufficient guidance. Today's Six Sigma practitioners need a comprehensive approach to designing and building customer value-based products. Voice of the Customer Capture and Analysis now gives you the ability to create and deploy surveys, capture real voice of the customer in the field, immediately analyze the results, and coordinate and drive responsive actions. This powerful product-development tool demonstrates how to utilize the statistical methods of Design for Six Sigma to identify key customer needs ...assess the cost of poor quality...design robust products to meet those needs...optimize product life cycles...and accurately validate their findings. By using the expert methods, strategies, and guidelines presented in Voice of the Customer Capture and Analysis, you can: Harness VOC data to create value-based products Employ Design for Six Sigma to optimize value creation Become proactive in gathering VOC information Improve customer survey design, administration, and analysis Accurately process VOC data Deploy customer value into products-DFSS Perform effective quality function deployment (QFD) Get the most out of value engineering Capitalize on creative design methods Utilize process management and Lean Product Development Apply statistical techniques and Six Sigma metrics This wide-ranging resource will give you the ability to minimize sources of response and measurement error ...clearly discern customer preferences...design VOC research to minimize the perils of mistranslation...respond to analytical implications of VOC data ...and optimize design to decrease sensitivity of CTQs to process parameters. Comprehensive and authoritative, Voice of the Customer Capture and Analysis provides you with all the tools you need to fully understand customer needs and wants_and then develop and build outstanding products that meet, or exceed, customer expectations.
Author: Kai Yang
Publisher: McGraw Hill Professional
ISBN: 0071593411
Category : Technology & Engineering
Languages : en
Pages : 430
Get Book Here
Book Description
Discover All the Advantages of Using Design for Six Sigma to Develop and Build Customer Value-Based Products Voice of the Customer Capture and Analysis equips Six Sigma you with the skills needed to create and deploy surveys, capture real customers need with ethnographic methods, immediately analyze the results, and coordinate and drive responsive actions. Quality expert Kai Yang explains how to utilize the statistical methods of Design for Six Sigma to identify key customer needs and assess the cost of poor quality. He then shows how to design robust products to meet those needs, optimize product life cycles, and accurately validate their findings. Voice of the Customer Capture and Analysis features a wealth of information on Six Sigma and value creation...customer survey design, administration, and analysis...ethnographic research...process management and Lean Product Development...the deployment of customer value into products-DFSS...and value engineering. This product design tool enables you to: Minimize sources of response and measurement error Discern customer preferences Design VOC research to minimize mistranslation Respond to analytical implications of VOC data Optimize design to decrease sensitivity of CTQs to process parameters With the help of Voice of the Customer Capture and Analysis, you can now acquire the skills needed to truly understand a customer's wants and needs, in order to develop and build optimal products. Most Design for Six Sigma product development teams fall short of truly understanding their customers' want and needs until it is too late. Market research studies and reports simply do not provide sufficient guidance. Today's Six Sigma practitioners need a comprehensive approach to designing and building customer value-based products. Voice of the Customer Capture and Analysis now gives you the ability to create and deploy surveys, capture real voice of the customer in the field, immediately analyze the results, and coordinate and drive responsive actions. This powerful product-development tool demonstrates how to utilize the statistical methods of Design for Six Sigma to identify key customer needs ...assess the cost of poor quality...design robust products to meet those needs...optimize product life cycles...and accurately validate their findings. By using the expert methods, strategies, and guidelines presented in Voice of the Customer Capture and Analysis, you can: Harness VOC data to create value-based products Employ Design for Six Sigma to optimize value creation Become proactive in gathering VOC information Improve customer survey design, administration, and analysis Accurately process VOC data Deploy customer value into products-DFSS Perform effective quality function deployment (QFD) Get the most out of value engineering Capitalize on creative design methods Utilize process management and Lean Product Development Apply statistical techniques and Six Sigma metrics This wide-ranging resource will give you the ability to minimize sources of response and measurement error ...clearly discern customer preferences...design VOC research to minimize the perils of mistranslation...respond to analytical implications of VOC data ...and optimize design to decrease sensitivity of CTQs to process parameters. Comprehensive and authoritative, Voice of the Customer Capture and Analysis provides you with all the tools you need to fully understand customer needs and wants_and then develop and build outstanding products that meet, or exceed, customer expectations.
Author: MicroStrategy Product Manuals
Publisher: MicroStrategy, Inc.
ISBN: 1938244540
Category : Computers
Languages : en
Pages : 216
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Book Description
A reference for the MicroStrategy Customer Analysis Module (CAM), part of the MicroStrategy Analytics Modules that come with MicroStrategy Architect. This guide provides a description, usage scenarios, and screenshots for all the packaged reports for CAM.
Author: MicroStrategy Product Manuals
Publisher: MicroStrategy
ISBN: 1936804972
Category : Computers
Languages : en
Pages : 221
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Book Description
Author: MicroStrategy Product Manuals
Publisher: MicroStrategy
ISBN: 1938244265
Category : Computers
Languages : en
Pages : 217
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Book Description
Author: MicroStrategy Product Manuals
Publisher: MicroStrategy, Inc.
ISBN: 1938244869
Category : Computers
Languages : en
Pages : 216
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Book Description
A reference for the MicroStrategy Customer Analysis Module (CAM), part of the MicroStrategy Analytics Modules that come with MicroStrategy Architect. This guide provides a description, usage scenarios, and screen shots for all the packaged reports for CAM.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 326
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Book Description
The project "Credit Card Churning Customer Analysis and Prediction Using Machine Learning and Deep Learning with Python" involved a comprehensive analysis and prediction task focused on understanding customer attrition in a credit card churning scenario. The objective was to explore a dataset, visualize the distribution of features, and predict the attrition flag using both machine learning and artificial neural network (ANN) techniques. The project began by loading the dataset containing information about credit card customers, including various features such as customer demographics, transaction details, and account attributes. The dataset was then explored to gain a better understanding of its structure and contents. This included checking the number of records, identifying the available features, and inspecting the data types. To gain insights into the data, exploratory data analysis (EDA) techniques were employed. This involved examining the distribution of different features, identifying any missing values, and understanding the relationships between variables. Visualizations were created to represent the distribution of features. These visualizations helped identify any patterns, outliers, or potential correlations in the data. The target variable for prediction was the attrition flag, which indicated whether a customer had churned or not. The dataset was split into input features (X) and the target variable (y) accordingly. Machine learning algorithms were then applied to predict the attrition flag. Various classifiers such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (NN), Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting, were utilized. These models were trained using the training dataset and evaluated using appropriate performance metrics. Model evaluation involved measuring the accuracy, precision, recall, and F1-score of each classifier. These metrics provided insights into how well the models performed in predicting customer attrition. Additionally, a confusion matrix was created to analyze the true positive, true negative, false positive, and false negative predictions. This matrix allowed for a deeper understanding of the classifier's performance and potential areas for improvement. Next, a deep learning approach using an artificial neural network (ANN) was employed for attrition flag prediction. The dataset was preprocessed, including features normalization, one-hot encoding of categorical variables, and splitting into training and testing sets. The ANN model architecture was defined, consisting of an input layer, one or more hidden layers, and an output layer. The number of nodes and activation functions for each layer were determined based on experimentation and best practices. The ANN model was compiled by specifying the loss function, optimizer, and evaluation metrics. Common choices for binary classification problems include binary cross-entropy loss and the Adam optimizer. The model was then trained using the training dataset. The training process involved feeding the input features and target variable through the network, updating the weights and biases using backpropagation, and repeating this process for multiple epochs. During training, the model's performance on both the training and validation sets was monitored. This allowed for the detection of overfitting or underfitting and the adjustment of hyperparameters, such as the learning rate or the number of hidden layers, if necessary. The accuracy and loss values were plotted over the epochs to visualize the training and validation performance of the ANN. These plots provided insights into the model's convergence and potential areas for improvement. After training, the model was used to make predictions on the test dataset. A threshold of 0.5 was applied to the predicted probabilities to classify the predictions as either churned or not churned customers. The accuracy score was calculated by comparing the predicted labels with the true labels from the test dataset. Additionally, a classification report was generated, including metrics such as precision, recall, and F1-score for both churned and not churned customers. To further evaluate the model's performance, a confusion matrix was created. This matrix visualized the true positive, true negative, false positive, and false negative predictions, allowing for a more detailed analysis of the model's predictive capabilities. Finally, a custom function was utilized to create a plot comparing the predicted values to the true values for the attrition flag. This plot visualized the accuracy of the model and provided a clear understanding of how well the predictions aligned with the actual values. Through this comprehensive analysis and prediction process, valuable insights were gained regarding customer attrition in credit card churning scenarios. The machine learning and ANN models provided predictions and performance metrics that can be used for decision-making and developing strategies to mitigate attrition. Overall, this project demonstrated the power of machine learning and deep learning techniques in understanding and predicting customer behavior. By leveraging the available data, it was possible to uncover patterns, make accurate predictions, and guide business decisions aimed at retaining customers and reducing attrition in credit card churning scenarios.
Author: Florent Buisson
Publisher: "O'Reilly Media, Inc."
ISBN: 1492061344
Category : Business & Economics
Languages : en
Pages : 361
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Book Description
Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way
Author: Jeff Sauro
Publisher: John Wiley & Sons
ISBN: 1118937597
Category : Business & Economics
Languages : en
Pages : 336
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Book Description
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.
Author: Nandi Dr. Rupam Dr. Gypsy, Kumar Sharma
Publisher: BPB Publications
ISBN: 938984567X
Category : Language Arts & Disciplines
Languages : en
Pages : 580
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Book Description
Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.
Author: David Parmerlee
Publisher: McGraw Hill Professional
ISBN: 9780071392037
Category : Business & Economics
Languages : en
Pages : 214
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Book Description
From the American Marketing Association, the world's largest and most comprehensive professional association of marketers, comes the AMA Marketing Toolbox series, a unique source of information, ideas and direction for anyone building an effective marketing program or who is interested in improving current marketing activities. The AMA Marketing Toolbox series will guide you through every critical marketing task and provide the tools you need--model formats, checklists, and boilerplate documents–-to implement those tasks quickly, accurately and effectively into your business. Auditing Marketing, Products and Marketing Plans is Step 1 in the Marketing Toolbox program. You'll learn how to identify your target markets, select the rights products for those markets and then plan how to reach them most effectively. Filled with worksheets, forms and tables for you to complete, Auditing Marketing, Products and Marketing Plans is step- and action-oriented, perfect for beginning marketers, students of marketing, small business owners, and entrepreneurs.
