A brand share prediction model based on several disparate sources of data: an empirical model of detergent choice in Mumbai, India

TLDR: In this article, the authors describe the application of a nested logit function for modelling consumer brand choice using household transaction data from the Indian market and test the usefulness of the model for forecasting brand market share in the premium detergents market in Mumbai, India.

Abstract: We describe the application of a nested logit function for modelling consumer brand choice using household transaction data from the Indian market. This is unique since it is one of the first attempts to integrate disparate consumer information sources available at various levels of aggregation towards developing a prediction model for brand market share in India. We test the usefulness of the model for forecasting brand market share in the premium detergents market in Mumbai, India. The results of the model building exercise reveal the importance of advertising, specifically the role of ad message in influencing brand choice. It is concluded that such modelling initiatives show significant returns for market planning exercises in developing markets. However, the need for streamlining the collection of market data and its subsequent organization in a form that can help develop more portent prediction models is apparent.