This study has been performed to advance knowledge of short-term and long-term
effects of advertising in India. In this context, data on sales and marketing mix variables
(advertising, sales force, promotion, distribution and price) have been collected for two
brands due to unavailability of data on other brands in hair-oil product class in India.
It has been found that marketing mix variables are crucial factors in the realization of
sales, which has been confirmed by testing of hypothesis with respect to each effort
(Baidya and Basu, 2008; Linda, 2004). Further, advertising has short-term and long-term
effects are confirmed again by testing of hypotheses with respect to current period
advertising and previous periods advertising (Ataman et al., 2010; Newstead et al., 2009;
Zhou et al., 2003). In fact, current period advertising has much higher effect than
previous periods, this might be due to the lower levels of customer involvement in the
case of low-priced fast moving consumer goods. Though long-term effect persists for
pulsing advertising tactics, it is gradually decayed due to competition. Manager
should not devote his time, energy and resources in unproductive or marginally
productive efforts. To identify which effort is contributed to sales significantly, testing
of hypothesis approach is needed. Without testing the relative important of each effort
statistically, it is not wise to allocate budget to individual marketing efforts or trade-off
of investment to achieve between short-term and long-term objectives of advertising
even in the case of a single brand.
The findings of this study have some important implications for mangers. This work
takes a data-driven empirical approach to estimate the relative contribution of each
marketing mix variable, which will help manager to allocate a fixed marketing budget
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of advertising
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to individual efforts on the basis of the proportions of their relative contributions.
Moreover, these relative contributions will also navigate manager to reallocate budget
from low performing efforts to high performing ones to maximize sales in the case of a
fixed marketing budget. How does manager invest in advertising to maximize sales?
The answer lies in the findings of this study. Manager should invest in current and in
future periods on the basis of the short-term and long-term effects of advertising. Since
advertising has long-term effect on demand, manager could initiate an optimal pulsing
strategy to bunch all advertising activities in a few weeks in each period.
Although this work produces some interesting and meaningful findings, there are
some limitations need to be addressed. First, this work considers advertising as a cause
of sales, instead of sales as a cause of advertising, which is very common approach for
setting advertising budget in practice. Therefore, a future study by considering a
two-way causation between sales and advertising might provide interesting insights as
well. In this study, unit of analysis of is brand and aggregated data on sales and
marketing mix variables have been used to estimate relative contribution of each effort
as well as short-term and long-term effects of advertising, which is a supply side
approach. Therefore, a future study that includes customer-centric variables will
definitely enhance the credibility of this research. It may be that the magnitudes of
short-term and long-term of advertising are sensitive to the frequency of the data.
Researchers used medium frequency (quarterly) data hence expect lower long-term
effect than short-term. In future, higher frequency (weekly) data may be used to make the
findings free from data-interval-bias. The estimation system, however, is very
restrictive and it does not allow for competition in the models. Omitting competitive
variables is highly restrictive and limits the usefulness of the recommendations of
the findings. A substantial amount of data will be collected on competitors’ actions to
make the results more robust in future. Researchers used data on sales and marketing
efforts for two brands only and results are completely relevant for the brands in
question. Findings also vary from one brand to another, which is also added in
restriction of generalization of the findings for all the brands in the same product
category. Therefore, a study on multiple brands in same product category or in
multi-product-classes might provide a solid understanding in the dynamic effects of
advertising in future.