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Marketing Data Science: Modeling techniques in predictive analytics with R and python

By: Material type: TextPublisher number: Allied Informatics, Jaipur | 2019-20Publication details: Noida Pearson India Education Services Pvt. Ltd. 2019Description: 458ISBN:
  • 978-93-530-6574-4
Subject(s): DDC classification:
  • 658.800285 MIL
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Holdings
Item type Current library Call number Status Barcode
Books BSDU Knowledge Resource Center, Jaipur General Stacks 658.800285 MIL (Browse shelf(Opens below)) Available 018005


Table of Content

"Preface
Figures
Tables
Exhibits
1 Understanding Markets
2 Predicting Consumer Choice
3 Targeting Current Customers
4 Finding New Customers
5 Retaining Customers
6 Positioning Products
7 Developing New Products
8 Promoting Products
9 Recommending Products
10 Assessing Brands and Prices
11 Utilizing Social Networks
12 Watching Competitors
13 Predicting Sales
14 Redefining Marketing Research
A Data Science Methods
B Marketing Data Sources
C Case Studies
D Code and Utilities
Bibliography
Index


Salient Features

The fully-integrated, expert, hands-on guide to predictive analytics and data science for marketing

Fully integrates everything you need to know to address real marketing challenges - including all relevant web analytics, network science, information technology, and programming techniques
Covers analytics for segmentation, targeting, positioning, pricing, product development, site selection, recommender systems, forecasting, retention, lifetime value analysis, and much more
Includes multiple examples demonstrated with Python and R
By Thomas W. Miller, leader of Northwestern's pioneering predictive analytics program, and author of Modeling Techniques in Predictive Analytics"

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