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

Peer-Olaf Siebers Uwe Aickelin University of Nottingham, School of Computer Science Nottingham, NG8 1BB, UK pos@cs.nott.ac.uk

Helen Celia Chris W. Clegg University of Leeds, Centre for Organisational Strategy, Learning & Change (LUBS) Leeds, LS2 9JT, UK

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.

Keywords: agent-based modeling, agent-based simulation, retail performance, management practices, shopping behavior, customer satisfaction, word of mouth

1. Introduction The retail sector has been identified as one of the biggest contributors to the productivity gap, whereby the productivity of the UK lags behind that of France, Germany and the USA [1, 2]. A recent report into UK productivity asserted that, ‘…the key to productivity remains what happens inside the firm and this is something of a ‘black box’…’ [3]. A recent literature review of management practices and organizational productivity concluded that management practices are multidimensional constructs that generally do not demonstrate a straightforward relationship with productivity variables [4], and that both management practices and productivity measures must be context specific to be (respectively) effective and meaningful. Many attempts have been made to link management practices to an organization’s productivity and performance (for a review, see [5]), however research findings have been so far been mixed, creating the opportunity for the application of different techniques to advance our understanding.

Simulation can be used to analyze the operation of dynamic and stochastic systems showing their development over time. There are many different types of simulation, each of which has its specific field of application. Agent-Based Simulation (ABS) is particularly useful when complex interactions between system entities exist such as autonomous decision making or proactive behavior. Agent-Based Modeling

2 (ABM) shows how micro-level processes affect macro level outcomes; macro level behavior is not explicitly modeled, it emerges from the micro-decisions made by the individual entities [6].

There has been a fair amount of modeling and simulation of operational management practices, but people management practices have often been neglected although research suggests that they crucially impact upon an organization’s performance [7]. One reason for this relates to the key component of people management practices, an organization’s people, who may often be unpredictable in their individual behavior. Previous research into retail productivity has typically focused on consumer behavior and efficiency evaluation (e.g. [8, 9]), and we seek to build on this work and address the neglected area of people management practices in retail [10].

The overall aim of our project is to investigate the link between different management practices and productivity. This strand of work focuses on simulating various in-store scenarios grounded in empirical case studies with a leading UK retailer. In this paper we aim to understand and predict how Agent-Based Modeling and Simulation (ABMS) can assess and optimize the impact of people management practices on customer satisfaction and Word-Of-Mouth (WOM) in relation to the performance of a service-oriented retail department. To achieve this aim we have adopted a case study approach using applied research methods to collect both qualitative and quantitative data. In summary, we have worked with a leading UK retail organization to conduct four weeks’ of informal participant observations in four departments across two retail stores, forty semi-struct

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