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:
- 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.
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.
