A brand share prediction model based on disparate sources of data from the Mumbai market
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.
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