Business Analytics & Quantitative Models for Marketing & Internet - 2nd Edition

The aim of the book is to provide the reader with the knowledge required to design and develop models and to use data analysis tools and techniques, optimization methods, predictive analysis and correlation tools, and knowledge extraction methods.

The book consists of thirteen chapters. Its structure follows a logical approach, from the first to the last chapter, in order to achieve a progressive understanding of the theory and the individual concepts and methods. However, each chapter is relatively autonomous and can be studied separately and independently of the others.

The 2nd improved edition includes 4 additional chapters dealing with network analysis, categorization methods and machine learning, text analytics and natural language processing, and web analytics and clickstream data analysis through the measurement of critical performance indicators.

It is intended for undergraduate and postgraduate students of business administration schools and, more generally, for students of related disciplines in social and economic sciences. At the same time, it is aimed at business executives who wish to acquire, enrich and broaden their knowledge and skills related to business analytics tools and data analysis techniques with the aim of designing, developing and practically applying models in decision-making.

It includes:

• Time series and demand forecasting.
• Design of experiments.
• Classic regression models as well as advanced econometric models.
• Consumer behavior models.
• Preference determination models.
• Models for customer clustering and market segmentation.
• Categorization models and application of machine learning methods.
• Demand curves, price elasticity, pricing and discounts.
• Models for the optimal design of new products and services.
• Models for optimizing the offered variety.
• Purchasing strategies for seasonal products with a short life cycle.
• Analysis of the structure and evolution of social networks.
• Text analytics and knowledge extraction from texts.
• Sentiment analysis in data from the internet (e.g., comments and tweets).
• Recommendation systems with collaborative filtering methods.
• Models for measuring the effectiveness of online advertising.
• Shopping cart analysis.
• Clickstream data analysis and measurement of various indicators to monitor traffic and visitor behavior on websites.