Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer RelationshipsRoutledge, 23 mrt 2016 - 352 pagina's While the definition of database marketing hasn’t changed, its meaning has become more vivid, versatile and exciting than ever before. Advanced Database Marketing provides a state-of-the-art guide to the methods and applications that define this new era in database marketing, including advances in areas such as text mining, recommendation systems, internet marketing, and dynamic customer management. An impressive list of contributors including many of the thought-leaders in database marketing from across the world bring together chapters that combine the best academic research and business applications. The result is a definitive guide and reference for marketing and brand analysts, masters students, teachers and researchers in marketing analytics. The proliferation of marketing platforms and channels and the complexity of customer interactions create an urgent need for a multidisciplinary and analytical toolkit. Advanced Database Marketing is a resource to enable marketers to achieve insights and increased financial performance; to provide them with the capability to implement and evaluate approaches to marketing that will meet, in equal measure, the changing needs of customers and the businesses that serve them. |
Inhoudsopgave
Textual Customer Data Handling for Quantitative Marketing | |
Index | |
Bayesian Networks and Applications in Direct Marketing | |
Pedagogical Rule Extraction Algorithms | |
Hybrid Models for Recommender Systems | |
Marketing in the New Mobile Economy | |
Targeting Display Advertising | |
Paid Search Advertising | |
A Shortterm Perspective Paid Search as a Direct Marketing Tool | |
A Longterm Perspective Indirect Effects of Paid Search | |
Beyond Keywords | |
Bayesian Network Classifiers | |
Learning Bayesian Networks from Incomplete Databases | |
Direct Marketing Modeling | |
The Evolutionary Bayesian Network EBN Algorithm | |
Application in Direct Marketing Modeling | |
Conclusion Acknowledgments References | |
Methods and Applications Dries F Benoit and Dirk Van Den Poel 1 Introduction | |
Methodological Background | |
Case Studies | |
Summary | |
References | |
Ensemble Learning in Database Marketing Koen W De Bock and Kristof Coussement 1 Introduction | |
Basics of Ensemble Learning | |
Algorithms | |
Applications in Database Marketing | |
Advanced Topics | |
Software | |
Summary | |
References | |
Active Learning Rule Extraction and Incorporating Domain Knowledge Thomas Verbraken Véronique Van Vlasselaer Wouter Verbeke David Marte... | |
Decompositional Rule Extraction from Artificial Neural Networks | |
Decompositional Rule Extraction from Support Vector Machines | |
Emerging Topics | |
Conclusion | |
References | |
Introduction | |
The Why and What? of Social Media | |
Social Media Metrics and Data Collection | |
The Firms Management of Social Interactions and Social Media | |
References | |
Dynamic Customer Optimization Models Scott A Neslin 1 Introduction 2 The Impetus for Dynamic Customer Optimization 3 The Elements of Dyna... | |
The Development of the Dynamic Customer Optimization Field | |
Applications | |
Summary Key Challenges and Future Research | |
References | |
Direct Marketing in the Nonprofit Sector Griet Verhaert 1 Introduction | |
Different Aspects of the Donor Lifecycle | |
Multichannel Approach | |
Database and Methods to Optimize Direct Marketing in Fundraising | |
Campaign Evaluation | |
Conclusion Challenges and Opportunities for the Future | |
References | |
Overige edities - Alles bekijken
Advanced Database Marketing: Innovative Methodologies and Applications for ... Koen W. De Bock Gedeeltelijke weergave - 2016 |
Advanced Database Marketing: Innovative Methodologies and Applications for ... Kristof Coussement,Koen W. de Bock,Scott A. Neslin Geen voorbeeld beschikbaar - 2013 |
Veelvoorkomende woorden en zinsdelen
accuracy AdaBoost algorithms analysis applications approach attributes Bayesian classifier Bayesian network behavioral targeting binary Boosting brand campaign chapter click-through collaborative filtering consumer Coussement covariates customer lifetime value data mining data set database marketing decision trees direct marketing display ads distribution document donors dynamic effects ensemble learning ensemble members estimate evaluation example firm function Ghose individual interactions Internet Journal of Marketing keywords learners lifetime value Machine Learning Management Marketing Research Marketing Science matrix factorization measure metrics missing values mobile apps naïve Bayesian classifier neural networks offline optimal paid search parameters percent Poel prediction method predictive modeling problem purchase quantile regression Random Forests recommender systems regression model response model rule extraction sampling score search advertising search engines social media statistical strategies structure support vector machines Table techniques text mining training set