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Books > Business & Economics > Economics > Econometrics
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Econometric Models in Marketing (Hardcover)
Loot Price: R4,109
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Econometric Models in Marketing (Hardcover)
Series: Advances in Econometrics
Expected to ship within 12 - 19 working days
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In the 16th Edition of "Advances in Econometrics", we present
twelve papers discussing the current interface between Marketing
and Econometrics. The authors are leading scholars in the fields
and introduce the latest models for analysing marketing data. The
papers are representative of the types of problems and methods that
are used within the field of marketing. Marketing focuses on the
interaction between the firm and the consumer. Economics
encompasses this interaction as well as many others. Economics,
along with psychology and sociology, provides a theoretical
foundation for marketing. Given the applied nature of marketing
research, measurement and quantitative issues arise frequently.
Quantitative marketing tends to rely heavily upon statistics and
econometrics. However, quantitative marketing can place a different
emphasis upon the problem than econometrics, even when using the
same techniques. A basic difference between quantitative marketing
research and econometrics tends to be the pragmatism that is found
in many marketing studies. Another important motivating factor in
marketing research is the type of data that is available. Applied
econometrics tends to rely heavily on data collected by
governmental organizations. In contrast, marketing often uses data
collected by private firms or marketing research firms.
Observational and survey data are quite similar to those used in
econometrics. However, the remaining types of data, panel and
transactional, can look quite different from what may be familiar
to econometricians. The automation and computerization of much of
the sales transaction process leaves an audit trail that results in
huge quantities of data. A popular area of study is the use of
scanner data collected at the checkout stand using bar code
readers. Methods that work for small data sets may not work well in
these larger data sets. In addition, new sources of data, such as
clickstream data from a web site, will offer new challenges. This
volume addresses these and related issues.
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