Data Mining and Market Intelligence for Optimal Marketing by Susan Chiu

By Susan Chiu

The authors current a pragmatic and hugely informative standpoint at the parts which are an important to the luck of a campaign. in contrast to books which are both too theoretical to be of functional use to practitioners, or too gentle to function strong and measurable implementation guidance, this publication specializes in the mixing of verified quantitative suggestions into genuine lifestyles case reports which are instantly suitable to advertising practitioners. * presents a twin therapy of industry study and knowledge mining * makes use of a how-to procedure for execs with illustrative case experiences as well as conception * contains sensible find out how to create government studies, dashboards, and a industry intelligence infrastructure

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Since there are four seasons, if we take one of them as a reference we need only three dummy variables to represent the changes due to the remaining seasons. Assume the reference season is indicated by the index 1, we can extend Eq. 43) to handle seasonality as follows. 44) The dummy variable Zi is one within season i and zero elsewhere. The dummy variable Z1 does not appear in this expression because index 1 identifies the reference season. It is possible to extend the same idea to the case of multiple independent variables and to the nonlinear functional forms.

If function f(X) is linear, the model is referred to as a linear model. Notice that this notion of linearity is not the same as the concept of linearity we encounter in estimation problems. In estimation, linearity refers to the way in which coefficient c0 and c1 enter in the functional form of the model. In estimation problems, the formulation is called linear if the estimation coefficients enter linearly, even if the dependent variables appear in a nonlinear form. The important distinction in this regard is that as long as the estimation coefficients appear linearly in the model they can be estimated by linear regression.

Case study two A dollar spent on marketing activities today drives not only the sales today, but also sales in the future. The impact of advertising on generating marketing returns has a residual effect over time. In the next case study, we incorporate such residual effects in a time series (Leeflang, Wittink, Wedel, and Naert 2000). We assume that the residual effect is t at time t and that the simply compounded discount rate per unit time period is i. 23) Using the sum of a geometric series, this expression can be simplified as the following when n is large.

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