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Marketing data science: Modelling techniques in predictive analytics with R and Python

By: Miller,Thomas W.
Material type: TextTextPublisher: New Delhi: Pearson; 2019Description: 458p.ISBN: 9789353065744.Subject(s): MARKETING - - DATA PROCESSING | R ( COMPUTER PROGRAM LANGUAGE) | PYTHON (COMPUTER PROGRAM LANGUAGE)DDC classification: 658.8:681.3.062 MIL/M
Contents:
Preface vii Figures xi Tables xv Exhibits xvii 1 Understanding Markets 1 2 Predicting Consumer Choice 13 3 Targeting Current Customers 27 4 Finding New Customers 49 5 Retaining Customers 65 6 Positioning Products 87 7 Developing New Products 111 8 Promoting Products 121 9 Recommending Products 139 10 Assessing Brands and Prices 159 11 Utilizing Social Networks 193 12 Watching Competitors 221 13 Predicting Sales 235 14 Redefining Marketing Research 247 A Data Science Methods 257 B Marketing Data Sources 291 C Case Studies 353 D Code and Utilities 397 Bibliography 415 Index 453
Summary: In Marketing Data Science, a top faculty member of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on his predictive analytics program at Northwestern, Miller covers segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where his widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R
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Item type Current location Call number Status Date due Barcode
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658.8:681.3.062 MIL/M (Browse shelf) Checked out to PROF. PRONOBESH BANERJEE (A233) 08/06/2020 36637

Preface vii
Figures xi
Tables xv
Exhibits xvii
1 Understanding Markets 1
2 Predicting Consumer Choice 13
3 Targeting Current Customers 27
4 Finding New Customers 49
5 Retaining Customers 65
6 Positioning Products 87
7 Developing New Products 111
8 Promoting Products 121
9 Recommending Products 139
10 Assessing Brands and Prices 159
11 Utilizing Social Networks 193
12 Watching Competitors 221
13 Predicting Sales 235
14 Redefining Marketing Research 247
A Data Science Methods 257
B Marketing Data Sources 291
C Case Studies 353
D Code and Utilities 397
Bibliography 415
Index 453

In Marketing Data Science, a top faculty member of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.

Building on his predictive analytics program at Northwestern, Miller covers segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.

Starting where his widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes:

The role of analytics in delivering effective messages on the web
Understanding the web by understanding its hidden structures
Being recognized on the web – and watching your own competitors
Visualizing networks and understanding communities within them
Measuring sentiment and making recommendations
Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics
Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R

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