Consumer Behaviour and Analytics
Consumer Behaviour and Analytics

Author: Andrew Smith

Publisher: Taylor & Francis

Published: 2023-11-08

Total Pages: 231

ISBN-13: 1000982998

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The second edition of Consumer Behaviour and Analytics provides a consumer behaviour textbook for the new marketing reality. In a world of Big Data, machine learning and artificial intelligence, this key text reviews the issues, research and concepts essential for navigating this new terrain. It demonstrates how we can use data-driven insight and merge this with insight from extant research to inform knowledge-driven decision-making. Adopting a practical and managerial lens, while also exploring the rich lineage of academic consumer research, this textbook approaches its subject from a refreshing and original standpoint. It contains numerous accessible examples, scenarios and exhibits, and condenses the disparate array of relevant work into a workable, coherent, synthesized and readable whole. Providing an effective tour of the concepts and ideas most relevant in the age of analytics-driven marketing (from data visualization to semiotics), the book concludes with an adaptive structure to inform managerial decision-making. Consumer Behaviour and Analytics provides a unique distillation from a vast array of social and behavioural research merged with the knowledge potential of digital insight. It offers an effective and efficient summary for undergraduate, postgraduate or executive courses in consumer behaviour and marketing analytics, and also functions as a supplementary text for other marketing modules. Online resources include PowerPoint slides.

Behavior Analytics in Retail
Behavior Analytics in Retail

Author: Ronny Max

Publisher: AuthorHouse

Published: 2013-09-26

Total Pages: 213

ISBN-13: 1491806303

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What is the value of a bricks-and-mortar store? As retailers move to a multichannel world where the winners must overcome the challenges of pricing transparency, personalized marketing, and supply chain controls, most sales still occur in the physical site. Behavior Analytics is the science of studying the behavior of people. Schedule to Demand is a subset of Behavior Analytics, a method that correlates between traffic, sales and labor data, in order to optimize the productivity of employees and position them where they matter most. In Behavior Analytics for Retail, we will introduce the core metrics of Schedule to Demand; design the requirements for a Customer Service Model of the store, inside the store, and at the checkout; present technology options and accuracy requirements; and offer insights through case studies. Regardless of how the future will shape retail, the physical store will continue to exist, and thrive. We propose a framework for retailers, and others, on how to optimize store operations and profitability, and enhance the shopping experience by measuring, monitoring and predicting the behavior of employees and customers.

Behavioral Data Analysis with R and Python
Behavioral Data Analysis with R and Python

Author: Florent Buisson

Publisher: "O'Reilly Media, Inc."

Published: 2021-06-15

Total Pages: 361

ISBN-13: 1492061344

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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

Behavior Analysis with Machine Learning Using R
Behavior Analysis with Machine Learning Using R

Author: Enrique Garcia Ceja

Publisher: CRC Press

Published: 2021-11-26

Total Pages: 370

ISBN-13: 1000484254

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Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

Retail Analytics
Retail Analytics

Author: Emmett Cox

Publisher: John Wiley & Sons

Published: 2011-10-18

Total Pages: 176

ISBN-13: 1118099842

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The inside scoop on boosting sales through spot-on analytics Retailers collect a huge amount of data, but don't know what to do with it. Retail Analytics not only provides a broad understanding of retail, but also shows how to put accumulated data to optimal use. Each chapter covers a different focus of the retail environment, from retail basics and organization structures to common retail database designs. Packed with case studies and examples, this book insightfully reveals how you can begin using your business data as a strategic advantage. Helps retailers and analysts to use analytics to sell more merchandise Provides fact-based analytic strategies that can be replicated with the same success the author achieved on a global level Reveals how retailers can begin using their data as a strategic advantage Includes examples from many retail departments illustrating successful use of data and analytics Analytics is the wave of the future. Put your data to strategic use with the proven guidance found in Retail Analytics.

Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era
Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era

Author: Keikhosrokiani, Pantea

Publisher: IGI Global

Published: 2022-06-24

Total Pages: 484

ISBN-13: 1668441705

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The emergence of new technologies within the industrial revolution has transformed businesses to a new socio-digital era. In this new era, businesses are concerned with collecting data on customer needs, behaviors, and preferences for driving effective customer engagement and product development, as well as for crucial decision making. However, the ever-shifting behaviors of consumers provide many challenges for businesses to pinpoint the wants and needs of their audience. The Handbook of Research on Consumer Behavior Change and Data Analytics in the Socio-Digital Era focuses on the concepts, theories, and analytical techniques to track consumer behavior change. It provides multidisciplinary research and practice focusing on social and behavioral analytics to track consumer behavior shifts and improve decision making among businesses. Covering topics such as consumer sentiment analysis, emotional intelligence, and online purchase decision making, this premier reference source is a timely resource for business executives, entrepreneurs, data analysts, marketers, advertisers, government officials, social media professionals, libraries, students and educators of higher education, researchers, and academicians.

Business Analytics: Data-Driven Decision Making
Business Analytics: Data-Driven Decision Making

Author:

Publisher: Cybellium

Published:

Total Pages: 225

ISBN-13: 1836795742

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Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com

Pricing, Online Marketing Behavior, and Analytics
Pricing, Online Marketing Behavior, and Analytics

Author: G. Viglia

Publisher: Palgrave Pivot

Published: 2015-10-09

Total Pages: 0

ISBN-13: 9781349489886

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Over the past few decades marketing practices have shifted with the sudden growth of social media and the proliferation of devices, platforms, and applications. This rapidly changing environment presents new opportunities and challenges for marketers, who need to stay up to date with the development of e-marketing. Viglia instructs readers in the theories and practices of online marketing;, detailing the characteristics, consumer behaviors, and differences between platforms, analytics, and pricing strategies of new media. Pricing, Online Marketing Behavior, and Analytics covers many different aspects of how online marketing works and its continuous evolution. Case studies and examples are used throughout the book to outline theories and explain e-marketing characteristics in a practical way.

Marketing Analytics
Marketing Analytics

Author: José Marcos Carvalho de Mesquita

Publisher: Mastering Business Analytics

Published: 2021-11-02

Total Pages: 200

ISBN-13: 9781032052182

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Marketing Analytics provides guidelines in the application of statistics using IBM SPSS Statistics Software (SPSS) for students and professionals using quantitative methods in marketing and consumer behavior. With simple language and a practical, screenshot-led approach, the book presents 11 multivariate techniques and the steps required to perform analysis. Each chapter contains a brief description of the technique, followed by the possible marketing research applications. One of these applications is then used in detail to illustrate its applicability in a research context, including the needed SPSS commands and illustrations. Each chapter also includes practical exercises that require the readers to perform the technique and interpret the results, equipping students with the necessary skills to apply statistics by means of SPSS in marketing and consumer research. Finally, there is a list of articles employing the technique, which can be used for further reading. This textbook provides introductory material for advanced undergraduate and postgraduate students studying marketing and consumer analytics, teaching methods along with practical software-applied training using SPSS. Support material includes two real data sets to illustrate the techniques' applications and PowerPoint slides providing a step-by-step guide to the analysis and commented outcomes. Professionals are invited to use the book to select and use the appropriate analytics for their specific context.

Data-Driven Modelling and Predictive Analytics in Business and Finance
Data-Driven Modelling and Predictive Analytics in Business and Finance

Author: Alex Khang

Publisher: CRC Press

Published: 2024-07-24

Total Pages: 443

ISBN-13: 1040088465

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Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.