A generalized product adoption model under random marketing conditions

Abstract

In marketing research, diffusion models are extensively utilized to predict the trend of new product adoption over time. These models are categorized based on their deterministic or stochastic characteristics. While deterministic models disregard the stochasticity of the adoption rate influenced by environmental and internal factors, we aim to address this limitation by proposing a generalized innovation diffusion model that accounts for such uncertainties. We validate our approach using the particle swarm optimization (PSO) technique on actual sales data from technological products. Our findings suggest that the proposed model outperforms existing diffusion models in forecasting accuracy.