Simulating Customer Experience and Word Of Mouth in Retail - A Case Study

TL;DR

This paper presents an agent-based simulation model to analyze how retail management practices influence customer experience and word of mouth, with experiments demonstrating the impact of various parameters on retail performance.

Contribution

It introduces new features in simulation models to track customer evolution and evaluates their effects on retail performance, advancing understanding of management practices.

Findings

Customer pool size affects overall satisfaction.

Noise reduction improves model stability.

Word of mouth significantly influences customer flow.

Abstract

Agents offer a new and exciting way of understanding the world of work. In this paper we describe the development of agent-based simulation models, designed to help to understand the relationship between people management practices and retail performance. We report on the current development of our simulation models which includes new features concerning the evolution of customers over time. To test the features we have conducted a series of experiments dealing with customer pool sizes, standard and noise reduction modes, and the spread of customers' word of mouth. To validate and evaluate our model, we introduce new performance measure specific to retail operations. We show that by varying different parameters in our model we can simulate a range of customer experiences leading to significant differences in performance measures. Ultimately, we are interested in better understanding the impact of changes in staff behavior due to changes in store management practices. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents offer potential for fostering sustainable organizational capabilities in the future.