Short answer

Designers should consider the dynamic, multi-faceted nature of customer journeys in physical retail spaces and explore simulation tools to test and optimize spatial configurations and experiential elements.

Field: User-Centred Design
Source: Journal of Economic Interaction and Coordination (2022)
Method: Literature Review and Conceptual Framework Development
Evidence: Moderate effect

Agent-based modeling can simulate complex customer behaviors and decision-making processes on retail high streets, offering valuable insights for urban planning and retail design. This user-centred design research insight is drawn from a 2022 study published in Journal of Economic Interaction and Coordination. Using Literature review and conceptual framework development, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Designers should consider the dynamic, multi-faceted nature of customer journeys in physical retail spaces and explore simulation tools to test and optimize spatial configurations and experiential elements.

Study

User-Centred DesignHigh ImpactModerate effect

Simulating Customer Journeys on Retail High Streets Enhances Understanding of Spatial Decision-Making

Agent-based modeling can simulate complex customer behaviors and decision-making processes on retail high streets, offering valuable insights for urban planning and retail design.

Journal of Economic Interaction and Coordination · 2022

Key Findings

01Agent-based models can represent high-resolution and high-fidelity simulations of dynamic phenomena on urban high streets.
02Customer journeys involve complex interactions with geography, perception, cognition, and dynamic environmental factors.
03Modeling both high-level agency (shop choice) and low-level agency (perception) is crucial for realistic simulations.
04The temporal aspect of customer journeys and their adaptation to experiences are important considerations.

Application

Design takeaway

How to apply

When designing or redesigning a retail street or a shopping center, use agent-based modeling to simulate potential customer flows and identify areas for improvement in layout, signage, and the placement of amenities.

Project actions

01When defining your agents, consider what factors influence their decisions (e.g., time of day, weather, promotions, proximity to other stores).
02Think about how to represent the 'journey' – is it just moving from A to B, or does it involve browsing, stopping, and changing direction?

Method & Evidence

AimHow can agent-based modeling frameworks be developed to simulate dynamic customer journeys on retail high streets, considering factors like perception, cognition, and environmental interactions?

MethodLiterature Review and Conceptual Framework Development

ProcedureThe research reviews existing agent-based modeling approaches and insights from indoor shopping environments to propose a framework for simulating outdoor retail high street customer journeys. It considers customer typologies, retailing abstractions, path planning, crowd dynamics, and the influence of atmospherics and services.

ContextUrban retail environments, high streets, customer behavior, spatial planning

Variables

IV["Agent rules (e.g., pathfinding algorithms, decision-making logic for shop choice)","Environmental parameters (e.g., store density, street layout, presence of amenities)"]

DV["Customer journey paths","Time spent in different zones","Number of shops visited","Congestion levels"]

CV["Agent's initial starting point","Overall simulation duration","Basic movement speed"]

Strengths & Limitations

Strengths

+Provides a framework for simulating complex spatial interactions.
+Integrates multiple factors influencing customer behavior (cognition, environment, social dynamics).

Limitations

Real-world data collection for calibrating agent behavior can be difficult and time-consuming. Simplifying human behavior for modeling can lead to inaccuracies.

Reliability & validity

Reliability could be assessed by running the same simulation multiple times to check for consistent outputs. Validity would be a challenge, requiring comparison of simulation results against real-world observational data of customer journeys.

Think critically

To what extent can simplified agent behaviors truly capture the complexity and unpredictability of human decision-making in a dynamic retail environment?

Design Principles

"Design interventions in retail environments should account for the emergent behaviors and decision-making processes of individual users within a complex spatial context."

By modeling individual customer agents with varying motivations and behaviors, designers and planners can better understand how people navigate, interact with, and make choices within the physical retail environment. This allows for more informed decisions regarding store placement, street layout, and the integration of services to optimize the customer experience.

What This Means for Your Design

Imagine you're designing a shopping street. This research shows how computer models can pretend to be shoppers, moving around and making choices, to help you figure out the best way to lay out the shops and streets to make people happy and encourage them to buy things.

How to use in your project

1.Use this research to justify the use of simulation or spatial analysis in your design project, especially if your project involves optimizing user flow or experience in a public or commercial space.

Add to My Project

Quick Cite

Paragraph starter

This research by Torrens (2022) highlights the utility of agent-based modeling for simulating customer journeys on retail high streets. By abstracting individual customer behaviors and decision-making processes, such models can provide valuable insights into spatial dynamics, helping designers to optimize layouts and understand the impact of environmental factors on user experience.

Source

Journal of Economic Interaction and Coordination

Agent models of customer journeys on retail high streets

journal · 2022

View source

Questions About This Research

What does the research say about simulating customer journeys on retail high streets enhances understanding of spatial decision-making?: Designers should consider the dynamic, multi-faceted nature of customer journeys in physical retail spaces and explore simulation tools to test and optimize spatial configurations and experiential elements. Evidence: Journal of Economic Interaction and Coordination (2022).
Why does "Simulating Customer Journeys on Retail High Streets Enhances Understanding of Spatial Decision-Making" matter for design?: By modeling individual customer agents with varying motivations and behaviors, designers and planners can better understand how people navigate, interact with, and make choices within the physical retail environment. This allows for more informed decisions regarding store placement, street layout, and the integration of services to optimize the customer experience.
How can designers apply this research?: Designers should consider the dynamic, multi-faceted nature of customer journeys in physical retail spaces and explore simulation tools to test and optimize spatial configurations and experiential elements.
What were the main findings?: Agent-based models can represent high-resolution and high-fidelity simulations of dynamic phenomena on urban high streets.. Customer journeys involve complex interactions with geography, perception, cognition, and dynamic environmental factors.. Modeling both high-level agency (shop choice) and low-level agency (perception) is crucial for realistic simulations.. The temporal aspect of customer journeys and their adaptation to experiences are important considerations.
What research method was used?: Literature Review and Conceptual Framework Development.
How strong is the evidence?: Evidence strength is rated Moderate effect, based on a 2022 journal from Journal of Economic Interaction and Coordination.
What should I do differently in my next project?: When designing or redesigning a retail street or a shopping center, use agent-based modeling to simulate potential customer flows and identify areas for improvement in layout, signage, and the placement of amenities.
What are the limitations?: The proposed models are conceptual and require empirical data for validation. The complexity of human behavior and external factors can be challenging to fully capture.