Regression Analysis — Interaction Effects
Intended to achieve common imperatives, the elements of the marketing mix often interact synergistically to produce an effect greater than the sum of their individual effects.
Take consumer promotions for instance. Discounts, displays and co-op ads, often have a synergistic impact lifting sales more than the sum of their individual impact on sales.
Similarly, theme advertising strengthen a brand’s equity, resulting in the lowering of consumers’ sensitivity to changes in price.
These interaction effects where one element of the marketing mix affects the sensitivity of one or more other elements, can be captured in response functions by the inclusion of an additional term, x1 × x2, formed by the product of the two variables that interact.
$$ y = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_1 x_2 $$In this example, the overall effect of x1 for any value of x2 is equal to: b1-total = b1 + b3 x2.
To determine the presence of the moderator effect (b3x1x2), compare the unmoderated R2 with the moderated R2. A statistically significant difference would imply the existence of the interaction effect between the variables.
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