Book Image

Data Analysis Using SQL and Excel - Second Edition

By : Gordon S. S. Linoff
Book Image

Data Analysis Using SQL and Excel - Second Edition

By: Gordon S. S. Linoff

Overview of this book

Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.
Table of Contents (18 chapters)
Free Chapter
1
Foreword
17
EULA

Summarizing Customer Behaviors

The customer signature has been presented as a place to put lots of data elements and basic summaries. It is also a place to put more complex measures of customer behaviors that rise to being customer-centric business metrics.

This section has three examples. The first is calculating the slope, the beta value, for series of transactions. The second is identifying weekend shoppers, and the third is applying metrics to identify customers whose usage is decreasing.

Calculating Slope for Time Series

Pivoting numeric values creates a time series, such as the dollar amount of purchases in a series of months. Using the ideas from Chapter 12, we can calculate the slope for these numbers.

Most households in the purchases data have one order, which does not provide a good example for finding a trend. Instead, let’s look at the zip-code level: Which zip codes have seen an increase in customers in the years before the cutoff date? Notice that this question is...