A predictive model approach to forecast consumers’ cluster membership in the green fast moving consumer goods sector

Abstract

Predictive models are increasingly crucial in navigating heterogeneous markets. This study develops a predictive model approach to forecast consumer cluster membership in the green fast-moving consumer goods sector, focusing on bio-based products like adhesives and plasters. Through two online surveys in Germany, we identified key factors acting as drivers and barriers, demonstrating their effectiveness in distinguishing similar consumer segments across both product categories. Utilizing multinomial logistic regression, we crafted a prediction model that accurately forecasts cluster membership, providing novel insights into consumer behavior towards non-food bio-based products. This facilitates the development of targeted business and marketing strategies, optimizing resource allocation in market research activities. Our findings offer significant contributions to understanding the dynamics influencing consumer choices in the bio-based product market.

Keywords

• Consumer segments
• Bio-based products
• Green fast moving consumer goods (FMCG)