Commercial Data Mining: Processing, Analysis and Modeling for Predictive Analytics Projects (The Savvy Manager's Guides)
Format: PDF / Kindle (mobi) / ePub
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.
Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.
- Illustrates cost-benefit evaluation of potential projects
- Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools
- Approachable reference can be read from cover to cover by readers of all experience levels
- Includes practical examples and case studies as well as actionable business insights from author's own experience
or individuals who are the targets for a given product or service. Basic demographic data about individuals consists of such information as age, gender, ethnicity, type of employment, education, marital status, and so on. A data mining business objective requiring demographic data would be, for example, a customer segmentation model that adds a socioeconomic category and an education level category to the customer record. The national census is available at the United States Census Bureau, which
products and provides examples and functionality from a simple CRM application. Chapter 14, “Analysis of Data on the Internet I Website Analysis and Internet Search,” first discusses how to analyze transactional data from customer visits to a website and then discusses how Internet search can be used as a market research tool. Chapter 15, “Analysis of Data on the Internet II Search Experience Analysis,” Chapter 16, “Analysis of Data on the Internet III Online Social Network Analysis,” and Chapter
in the figures are not categorical (e.g., time as client and age, which are numerical). In order to consider them in the same format, the numerical variables must be categorized by calculating their quantiles. As an alternative, the ranges could be manually defined for these variables. For example, “time as client” could be categorized as follows: category 1, one to six months; category 2, six to twelve months; category 3, twelve to eighteen months, and so on. Figure 8.5 shows two types of
headlines from online news sources in 2013: Electronic Cigarettes “Help Nine out of Ten Smokers Quit Tobacco Completely” 1 in 10 U.S. Deaths Blamed on Salt (excessive salt consumption) New Survey (made by an IT company) Unveils Worldwide Innovation Gap: Only Five out of Ten People Are Satisfied with Innovations Currently Available In each of these cases, the article would have to be checked to see who did the study, what methods were used, and what the findings were compared to. For example, the
committed, such as defining a variable containing “zip code” as a numerical variable (it’s categorical), or giving the client address as an input to the model. If it isn’t right the first time, other modeling techniques, such as neural, induction, and regression, can be tried with the same data to see which one gives the best results. The technique used also depends on whether the priority is to create profiles (for which the most adequate technique is rule induction), or if predictive precision