۱- Introduction

۲- Literature and research questions

۳- Method: RTLS technologies

۴- First output of RTLS

۵- Conclusions and future research

References

Abstract

Over the last few years, shopper behaviour analysis in the retail environment has become an interesting topic both for managers who want to see the tangible impact of their trade marketing activities and researchers who are trying to identify new patterns or confirm known trends in this field. In such a context, technologies today play a central role, because of the possibility of implicitly observing how shoppers move inside the store, and collecting a wide data-set, through an unbiased approach, free from distortion. In this paper, we will describe the major outcomes from a study based on data collected through an innovative technology, Real Time Locating System (RTLS). We base our conclusions on a data-set, collected over three months of observations, composed of more than 18 million records transmitted by RTLS tags, monitoring the entire path of each shopper throughout the entire store area. The outcomes of our study are 1) the identification of the store’s best performing areas based on traffic and dwell time metrics, 2) the development of a novel method to estimate the probability of in-store shopper paths and 3) a preliminary shopping trip segmentation.

Introduction

Shopper behavioural analytics has been receiving increasing attention over the last few years. Observing how shoppers behave within different store and shelf layouts provides a fundamental insight for industries and retailers who want to optimize the revenue/cost equation by enriching the in-store experience of their shoppers. Despite the growth in e-commerce and digital retailing, the physical store still maintains a central role in the shopping journey. However, even the physical store needs to be adapted to shopper dynamics and emerging desires. A set of proved strategies has to be activated at the point of sale, with the ultimate goal of satisfying the value equation, attracting more shoppers more frequently and with bigger basket sizes. The modern retail sector still considers retailers, manufacturers, and shoppers as held apart like three different actors who operate independently of each other. Manufacturers produce goods to sell, retailers manage the stores to sell the goods, and shoppers enter the store to buy things. For decades, i) the retailer’s belief that profits come from brand promotions rather than from the shoppers themselves has led to placing a greater emphasis on price strategy than on the customers themselves; ii) brand manufacturers have invested a great deal in analysing shopping behaviour in order to understand consumer outside the store. Only in the last few years has there been a change in paradigm: now both retailers and manufacturers have understood the great opportunities for improving sales and profits arising from understanding shoppers’ behaviour inside the store. In the meantime, the bar to accessing analytic solutions has been lowered, allowing small and medium-sized companies to exploit the value of data and increasing competition. Analytical solutions that, up to a decade ago, constituted a key benefit and added value for a few market players, are today accessible also to smaller players, who can effectively have access and apply those insights to their decisionmaking processes. In this direction, the use of technologies has improved relentlessly and revolutionised a way to generate insights to answer the key business questions of the industry and the retailers. Since the retail sector has increased in complexity (multiple formats across a range of countries), numerous studies have been carried out with the aim of investigating how shoppers behave and how the information collected inside the store can be useful in creating the best strategies to improve sales and profits.