A choice modelling approach to evaluate effectiveness of brand development initiatives

Banerjee, Arindam; Awasthy, Dheeraj; Gupta, Vivek

doi:10.1504/ijmdm.2005.006031

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models can be applied as simulators of market response to the marketing strategies of firms in order to suggest appropriate alternative decisions on price promoting, policies of companies, and so on. Thus, development of a proper model with high accuracy on the prediction of customer behavior in the market can be of help to companies as a strategic tool to improve the adoption of decision-making policies in the scope of marketing (Banerjee et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

Shahrabi¹,

Khameneh²

2016

Industrial Engineering and Management Systems

View full text Add to dashboard Cite

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

show abstract

Section: Introductionmentioning

confidence: 99%