What is included with this book?
Now , a leader 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 Miller's pioneering program, Marketing Data Science thoroughly addresses 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 Miller's 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:
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.
Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.
THOMAS W. MILLER (Evanston, IL), faculty director of Northwestern University's Predictive Analytics program, has designed and taught courses in predictive analytics, predictive modeling, marketing analytics, and advanced modeling. Also owner of Research Publishers LLC, he has worked with predictive models for 30+ years, and consults on retail site selection, product positioning, segmentation, and pricing. He holds a Ph.D. in psychology (psychometrics); and M.S. degrees in statistics, business, and economics. His books include Data and Text Mining: A Business Applications Approach; Research and Information Services: An Integrated Approach for Business, and Without a Tout: How to Pick a Winning Team. He previously directed the A.C. Nielsen Center for Marketing Research in the School of Business, U. of Wisconsin-Madison.
1. Getting Technical
2. Delivering a Message on the Web
3. Being Recognized on the Web
4. Crawling the Web
5. Watching Competitors
6. Visualizing Networks
7. Understanding Communities
8. Measuring Sentiment
9. Making Recommendations
10. Playing the Network Game
11. What's Next for the Web?
A. Data Science Methods
A.1. Databases and Data Preparation
A.2. Classical and Bayesian Statistics
A.3. Regression and Classification
A.4. Machine Learning
A.5. Text Analytics
B. Primary Research Online
C. Case Studies
C.1. E-Mail or Spam?
C.2. ToutBay Begins
C.3. Anonymous Microsoft Web Data
C.4. Wikipedia Votes
C.5. Enron E-Mail Corpus and Network
C.6. Quake Talk
D. Code and Utilities
BibliographyDetailed, practical guidance on using analytics to improve all facets of marketing, from segmentation and targeting to developing products and estimating customer lifetime value – from the pioneers in marketing analytics at Northwestern University