Network Analysis of Two-Stage Customer Decisions with Preference-Guided Market Segmentation

Abstract

Abstract Network-based analyses have shown their effectiveness in understanding customer preferences through interactions and relationships between customers and products, particularly for tailored product design. There is limited research on applying this analysis to diverse customers with varied preferences. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers' two-stage decision-making process: consideration-then-choice. In heterogeneous markets, like household appliances, even customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, In this method, we segment customers into groups based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using Joint Correspondence Analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer-product networks and apply the Exponential Random Graph Model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how a deeper understanding of customer preferences at different decision stages can inform and refine design strategies, ensuring products are more closely aligned with the dynamic needs and tastes of diverse markets.