Insights from Dynamic Pricing Scenarios for Multiple-generation Product Lines with an Agent-based Model using Text Mining and Sentiment Analysis

Abstract

Corporations must constantly upgrade and improve their offerings due to changes in customer preferences. It is a common strategy for firms in technology-intensive markets to use online reviews as a source of product information to inform such changes. This user-generated information is valuable since it provides companies with valuable and low-cost input. In this paper, we propose an agent-based model for simulating potential cannibalization situations with respect to customer satisfaction throughout consecutive generations of a product line. The level of customer satisfaction is regarded as a parameter in the model, which is conceptualized to affect the product price. The proposed model provides insights into different pricing strategies regarding customer satisfaction levels affect the total lifecycle profitability of multiple-generation product lines, and how they can be used to assist organizations in developing appropriate dynamic pricing strategies.