Establishing Design Consensus toward Next-Generation Retail: Data-Enabled Design Exploration and Participatory Analysis

Document Title

Establishing Design Consensus toward Next-Generation Retail: Data-Enabled Design Exploration and Participatory Analysis

Document Information

• Subject Area: Human-Computer Interaction (HCI) Design, Data-Driven Design, Retail Industry
• Keywords: Data-Driven Design, Participatory Data Analysis, Shopping Behavior Understanding, Retail Design, Multi-Stakeholder, Consumer Data Visualization

Research Background and Problem

• This study addresses the challenges posed by the integration of online and offline retail, including technology adoption, evolving interaction modes, and changes in customer behavior.
• The research highlights that retail design involves three distinct stakeholder groups—consumers, retail employees, and retail companies—making it difficult to establish a unified design consensus.
• Traditional retail design focuses on profitability but neglects the genuine needs of consumers (e.g., political and environmental beliefs, diverse dietary requirements, and cultural backgrounds).
• Retailers lack the skills and knowledge to understand customer behavior and fail to effectively use user experience design strategies to enhance customer experience.
• Design blind spots exist in retail environments (e.g., spatial layout, traffic flow analysis), and obtaining customer feedback is challenging.

Solution

• Method: Proposes a method combining "data-driven design" and "participatory data analysis" by collecting shopping paths, customer behavior data, and collaborative multi-stakeholder analysis to achieve design consensus.
• Innovations:
  • Treating data as a design material, generating design insights through qualitative and quantitative analysis;
  • Utilizing participatory discussions among stakeholders, integrating diverse perspectives and expertise to form design recommendations;
  • Introducing a three-step design experience model (contextual step, informed step, awareness step).
• Implementation Steps:
  1. Data-Driven Process:
     • Collect consumer shopping behavior data (paths, dwell times, gender differences, etc.), spatial layout data, and product purchase records;
     • Use UWB technology for precise positioning, combined with surveys/receipt analysis to study consumer habits.
  2. Visualization and Problem Identification:
     • Present data using visualization tools (Python, Tableau) to uncover issues in spatial layout, product selection, and customer interaction.
  3. Participatory Workshops:
     • Invite 13 stakeholders (consumers, retail employees, retail company management) to share and discuss data, propose design suggestions, and reach consensus.

Research Results

• Specific Results:
  1. Data revealed issues in shopping routes, customer consumption habits, and spatial utilization (e.g., overcrowding in popular areas, shelf shape affecting pass-through rates, gender differences).
  2. Stakeholders formed four categories of design suggestions through workshops: spatial layout optimization, product selection efficiency improvement, interactive experience innovation, and management functionality enhancement.
• Advantages Compared to Existing Solutions:
  • Provides a design process driven by real-world scenario data;
  • Simultaneously incorporates quantitative and qualitative data insights, combined with multi-stakeholder participation, resulting in highly actionable solutions.
• Experiments and Evaluation:
  • Data analysis uncovered design blind spots in spatial layout, product selection, and interaction methods;
  • Workshops further validated stakeholder recognition and suggestions for the data-driven approach.
• Limitations and Future Directions:
  • Limitations: Data collection was constrained by time and funding, limiting coverage of a broader population; privacy and ethical risks require further resolution.
  • Future Directions:
    1. Expand the scope and scale of data collection to establish a long-term consumer behavior database;
    2. Apply the three-step model to other complex retail scenarios (e.g., malls, multifunctional commercial environments);
    3. Explore lightweight interactive designs to reduce customer interaction burden while improving shopping efficiency.

References

Provides an extensive literature review covering the latest developments in HCI research, retail data analysis, and participatory design, contributing to theoretical innovation and practical validation.