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Analysis of Sales Promotion Effects on Household Purchase Behavior

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The reacti<strong>on</strong> mechanisms that can occur during the promoti<strong>on</strong>al period are brand switching,<br />

purchase timing (buying so<strong>on</strong>er or later), and purchase quantity (buying more or less). This<br />

sec<strong>on</strong>d angle provides detailed insights in households’ reacti<strong>on</strong> to a promoti<strong>on</strong> during the<br />

promoti<strong>on</strong>al shopping trip itself, but does not take the intertemporal dynamics into<br />

c<strong>on</strong>siderati<strong>on</strong>.<br />

The two-step approach presented in this secti<strong>on</strong> is used throughout the remainder<br />

<str<strong>on</strong>g>of</str<strong>on</strong>g> this dissertati<strong>on</strong>. In the next secti<strong>on</strong>, we will develop two research models, <strong>on</strong>e for each<br />

step.<br />

5.4 Research Models<br />

5.4.1 <str<strong>on</strong>g>Promoti<strong>on</strong></str<strong>on</strong>g> Resp<strong>on</strong>se Model<br />

We want to predict whether a household will make use <str<strong>on</strong>g>of</str<strong>on</strong>g> a specific promoti<strong>on</strong> or not.<br />

There is a variety <str<strong>on</strong>g>of</str<strong>on</strong>g> multivariate statistical techniques that can be used to predict a<br />

dependent variable from a set <str<strong>on</strong>g>of</str<strong>on</strong>g> independent variables. For example, multiple regressi<strong>on</strong><br />

analysis and discriminant analysis are two techniques that quickly come to mind. However,<br />

linear regressi<strong>on</strong> analysis poses difficulties when the dependent variable can have <strong>on</strong>ly two<br />

values. For such a binary variable, the assumpti<strong>on</strong>s for hypothesis testing in regressi<strong>on</strong><br />

analysis are violated. For example, the distributi<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> errors can never be normal. Another<br />

difficulty with multiple regressi<strong>on</strong> analysis is that predicted values cannot be interpreted as<br />

probabilities. They are not c<strong>on</strong>strained to fall in the interval between 0 and 1. Linear<br />

discriminant analysis does allow direct predicti<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> group membership, but the assumpti<strong>on</strong><br />

<str<strong>on</strong>g>of</str<strong>on</strong>g> multivariate normality <str<strong>on</strong>g>of</str<strong>on</strong>g> the independent variables is not appropriate here.<br />

The logistic regressi<strong>on</strong> model requires far less assumpti<strong>on</strong>s. In logistic regressi<strong>on</strong>,<br />

<strong>on</strong>e directly estimates the probability <str<strong>on</strong>g>of</str<strong>on</strong>g> an event occurring. For more than <strong>on</strong>e independent<br />

variable the model can be written as<br />

z<br />

e<br />

Pr(event) =<br />

1+<br />

e<br />

or equivalently<br />

z<br />

89

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