The coefficients of the marketing
mix variables in the response models we have discussed so far are assumed to be
constant for the analysis period. It is a tenuous assumption considering that
many of the dynamic effects imply that these parameters do vary. Parameter
functions may be crafted to capture the impact of a variety of these effects,
including:
- The magnitude, frequency and recency of prior marketing
activities.
- Wear-in and wear-out of advertising.
- Quality of the creative material in advertising.
- Media effects (i.e., differences in ad response due to the choice of
TV programme, or section of newspaper).
For instance, consider quality of advertising. We
know that effectiveness of advertising is dependent greatly on the creative
material. One approach to capturing this effect is by means of a variable that
captures both quantity and quality, the Adfactor for instance. We could also
capture the ad quality effect by means of a process function for the
advertising coefficient, i.e., βadvt=f(ad campaign).
Similarly, the price-promotion elasticity (discount
elasticity) of products can change due to the magnitude and frequency of
previous discounts. If heavy promotions are repeated too frequently, their
impact begins to fade. Frequent, attractive promotions also tend to induce an
opportunistic behaviour — consumers lie in wait for the deals, resulting in the
lowering of the base line. The parameters for price discount, promotion (e.g.,
display, co-op advertising) and baseline (i.e., store intercept) are therefore a
function of historical levels of promotions.
One approach to capturing the dynamic nature of the
market is by means of multistage models where the coefficients of the mix
variables estimated at the first stage become the dependent variable in the
next stage. In the second stage, where the coefficients of the mix variables
are treated as the dependent variables, the independent variables are the
characteristics that influence the coefficient.
