By Susan Chiu
The authors current a pragmatic and hugely informative point of view at the components which are an important to the good fortune of a campaign. not like books which are both too theoretical to be of sensible use to practitioners, or too gentle to function good and measurable implementation guidance, this booklet makes a speciality of the combination of tested quantitative options into genuine existence case stories which are instantly appropriate to advertising and marketing practitioners. * offers a twin therapy of marketplace learn and knowledge mining * makes use of a how-to procedure for execs with illustrative case reports as well as idea * contains useful tips to create government stories, dashboards, and a industry intelligence infrastructure
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Additional resources for Data Mining and Market Intelligence for Optimal Marketing Returns
Models of this type must be able to capture the nonstationarity of the statistical properties of time series. Auto-regressive moving average models (ARMA) are examples of this type of models. Here we will limit the discussion to stationary markets. This is the situation we face when our marketing planning horizon, usually on a quarterly basis, is relatively short compared with the time evolution scales of economic or demographic effects. Static models A model can be very complex if the full interaction among variables is taken into consideration.
G. Econometric measurements of the duration of advertising effect on sales. Journal of Marketing Research, Chicago, Illinois, 13: 345–357, 1976. J. L. Schultz. Market Response Models, Econometric Time Series Analysis, 2nd ed. Kluwer, Massachusetts, 2001. M. Distributed Lags and Investment Analysis. Amsterdam, North-Holland, 1954. R. Wittink, M. A. Naert. Building Models for Marketing Decisions. Kluwer, Massachusetts, 2000. P. Generalizing what is known about temporal aggregation and advertising carryover.
Kluwer, Massachusetts, 2001. M. Distributed Lags and Investment Analysis. Amsterdam, North-Holland, 1954. R. Wittink, M. A. Naert. Building Models for Marketing Decisions. Kluwer, Massachusetts, 2000. P. Generalizing what is known about temporal aggregation and advertising carryover. Marketing Science, Hanover, Maryland, 14(3), 1995. G. Investment Science. Oxford University Press, New York, 1996. 37 This page intentionally left blank CHAPTER 3 Metrics Overview This page intentionally left blank In this chapter, we discuss the key metrics used for measuring and optimizing return on marketing investment.
Data Mining and Market Intelligence for Optimal Marketing Returns by Susan Chiu