Figure 5
A diagram of a data-driven framework for customer-based SPD showing six sequential stages. Stage 1 identifies consumer values using methods like surveys and sentiment analysis, highlighting six value dimensions. Stage 2 identifies perceived benefits through methods such as Means-End Chain laddering and behavioral analysis. Stage 3 determines product attributes using literature synthesis and expert review, listing seven attribute groups. Stage 4 weights and ranks attributes with methods like conjoint analysis and machine learning. Stage 5 maps attributes onto Mishra's design dimensions using A/B testing and pilot launches, identifying five design perception dimensions. Stage 6 focuses on customer valuation models and continuous improvement using predictive modeling and real-time sentiment analytics. A feedback loop indicates refining values, benefits, and attributes through continuous improvement.Data-driven framework for customer-based SPD. Source: Authors' own elaboration. Note(s): Six sequential stages move from identifying consumer values, through perceived benefits and product attributes, to attribute weighting, design-dimension mapping, and customer valuation modelling. Each stage names the key methods drawn from the SLR. Bordered chips indicate the substantive content of each stage: six consumer value dimensions (Stage 1), seven product attribute groups (Stage 3), and Mishra's (2016) five design perception dimensions (Stage 5). The dashed line on the left indicates that valuation results iterate back to refine values, benefits, and attributes through continuous improvement