The purpose of this study is to estimate the relative contributions of individual marketing mix variables to sales as well as short‐term and long‐term effects of advertising in India.
Time‐series data on sales and marketing mix variables have been collected for two brands. Two double‐log regression modes have been fitted on data to estimate the relative contribution of each effort as well as to isolate the amount of sales due to advertising only. In addition, a log‐linear partial‐adjustment model has been fitted on adjusted sales and advertising data to estimate both short‐term and long‐term effects of advertising.
Results reveal that all the marketing mix variables have significant relative contributions to sales in both the cases. It is also found that advertising does have significant short‐term and long‐term effects on adjusted sales for both the brands.
Findings provide a deep insight in dynamic perspective of advertising that make them eminently suitable in the process of allocation of budget to achieve both the short‐term and long‐term goals of advertising.
This research made a notable contribution to the literature due to lack of quantitative modeling works on marketing data reported in the field of advertising in India.
Baidya, M., Maity, B. and Ghose, K. (2012), "Measuring dynamic effects of advertising: a case study in India", Journal of Indian Business Research, Vol. 4 No. 3, pp. 158-169. https://doi.org/10.1108/17554191211252671Download as .RIS
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