Specific purpose of the project

The effects of atmospherics have been measured on a wide variety of different dependent variables over the last 30 years of research. Sales, time spent in the environment and approach-avoidance behaviour have been the most widely studied dependent variables in experimental studies of retail atmosphere. Some leading studies, such as those by Baker et al. (1994) and Verhoef et al. (2009), are focused on service quality and the impact of in-store atmosphere on customers’ satisfaction. This approach, however, is not fully aligned with retail market trends involving a complexity of elements of the in-store experience impacting customer satisfaction and spending. A review of the existing literature has identified that the focus of

research is mainly on elements of the retail environment that are under the retailer’s control (e.g., lighting, layout, colour, music, in-store visualisation). Although a substantial body of literature describes how retailers can influence observable customer behaviours by manipulating enduring and transient aspects of their store environments, few researchers have investigated how consumers experience these different aspects, particularly in a grocery retailing environment. The impact of the in-store experience on customers is not fully explored and there are many further research opportunities (Appendix B). In addition, existing studies do not provide information for practitioners concerning the guidelines for selecting the appropriate arousal level for a store environment with a specific layout (Kaltcheva & Weitz, 2006). Furthermore, to know exactly what drives customer behaviour, in terms of attitudes and feelings, research cannot be based on customers’ memories alone, as they fade rapidly. There is a need for an additional research context to understand how the physical and social environment impacts customer satisfaction and shopping spending in a real, not simulated retail environment (Lam, 2001). The relationship between the perceptions of the in-store environment, service, product, and customer behaviour, should also be researched in greater depth. There is a need for a study that links travel patterns, purchase behaviour and customer feedback concerning shopping satisfaction.

When examining the opportunities in the research domains concerning retail atmospherics more closely, I could observe that the field of retail atmospherics provides a framework from which to explore potential antecedents and consequences of consumer behaviour and spending. According to Kotler (1973), atmospherics, itself, represents an attempt to manipulate the physical retail environment to create specific emotional reactions among store patrons (Kotler, 1973). That is why, conceptual and empirical studies are attempting to prove, that there is systematic covariance between store environments and consumer behaviours (Babin & Darden, 1996). The data suggest that any change in the environment may be noticed and evaluated similarly by everyone, but responded to differently (Grossbart et al., 1975). Furthermore, it is widely known that one tends to buy more things and to spend more money when one is in a positive rather than in a negative mood state (Spies et al., 1997). In addition, I identified that traditional in-store measurement techniques overlook critical factors that go into shaping

insights, however they did not fully capture what is required to succeed in today’s competitive retail environment. There is also a need to remember, that many previous studies were experimental, empirical or declarative in nature. Baker et al. (1992) described several methods of testing the effects of the store environment: using a prototype store, asking participants to respond to verbal descriptions of a store or creating a simulated store environment. These methods generally use small sample sizes and because they are based on a single instance rather than a continuous and objective measure, and the results serve as reliable benchmarks. However, with a bigger sample size and real in-store environment experiments, these results could serve as more meaningful measurements of change.

I could observe that the use of customer insight in marketing decisions could be better understood, partially due to difficulties in obtaining research access (Said

et al., 2015). All of this constitutes an important gap in previous research, overall.

Few studies have investigated the direct effects of the in-store experience and the mediating role of physiological states in the relationship between the store environment and shopping behaviours concerning spending. In this context, an issue which deserves attention is defining what constitutes delightful and unpleasant shopping experiences (Arnold et al., 2005) and how it may influence customers’ shopping plans and behaviour, impacting their spending and satisfaction. All the gaps identified in academic literature I described and presented in my literature review and summarised in Appendix B helped me create the detailed research model that will contribute to the existing knowledge. The purpose of this study was also to provide a clear answer regarding the manner in which in-store experience cues influence shoppers through the focus on their shopping plans. The greatest value would be achieved by obtaining not only declarative findings, but also using customers’ behavioural data.

Thus, the purpose of my research was to use a robust model in a real in-store environment, including detailed shopping spending data provided by Dunnhumby. The model was based on an extensive amount of data, which in my case represented big and secondary data. Big data usually are rich in trends and patterns but in order to identify them, the data require strong computational techniques. The insights received from this kind of extractions, can be of great value for official statistics, surveys and archival data sources. In my case, the data were directly linked to each of 30,696 customers who responded to the survey. The details of

spending on different category levels helped me reach conclusions on the impact of in-store experience on the performance of individual categories. Till data, not declarative data, helped to ensure that the findings were not impacted by mistakes regarding what customers were declaring they bought.

Based on the findings presented in Chapter 3, I created a simple table focusing on key studies concerning sales and customer spending (Appendix A). What is interesting is that nobody had previously researched the impact of key in-store experience constructs (e.g., assortment, service, in-store environment) on customers simultaneously. Knowing all the gaps and future research opportunities described in my literature review (Appendix B) helped me define the purpose of my research project. It aimed to identify which elements of the in-store experience have the greatest impact on customer satisfaction and which ones influence customer behaviour. It also aimed to more closely examine what might impact the number of visits of individual customers. My objective was to achieve a very large research sample and till data linked to individual customers. This approach had a significant advantage over prior studies, as it was neither declarative nor experimental, and provided a very high level of credibility. To achieve this, I needed to first create my conceptual model, which formed the basis for my research and data collection. It included key determinants that shaped customers’ journeys and influenced their behaviour. The model’s various components allowed the identification of the most important factors with the greatest impact on overall shopping satisfaction and behaviour of customers. I used spending data, which is an aspect that also substantially constitutes new information not captured by demographics (Otto et al., 2009). Through my research I also aimed to assess whether the in-store experience is the main driver for changes in customer behaviour. Even finding factors that have a minor impact on behaviour or spending can be extremely important for retailers, considering the very high competitiveness of the retail sector. This led me to develop my detailed research question:

What is the impact of product, service and in-store environment perceptions on customer satisfaction and behaviour?

also be able to give retailers a clear indication in terms of which elements of the in-store environment cues are impacting their customers’ behaviour most and where they could expect the highest return from one unit investment in the researched factors. This is very important for the industry, as retailers can control many in-store experience factors, and in different markets in different formats, different retailers invest in different in-store experience determinants. As it was mentioned earlier, the greatest challenge is to measure which in-store experience construct is the most effective and which strategy brings about the highest and most sustainable benefits. There is ongoing debate in the industry regarding the importance of price, range, in-store environment and customer service. That is why, in my research, I addressed all those factors and I aimed to determine which particular one creates the greatest value for customers as well as retailers, which creates loyalty from increased shopping experience and which is driving retailers’ sales from increased customer spending.

All this information together should help me indicate the right balance regarding the in-store experience factors in which retailers should invest. Considering the high capital spending by retailers to refit old stores, open new ones, create different store experiments and also investments into marketing, this work can lead to many financial benefits for operators. Finding even a small relationship between one of the researched elements and customers’ spending, the benefits considering the scale of some of the retailers (Tesco: $91 billion in sales in 2015; Carrefour: $98 billion in sales in 2014 (Deloitte, 2016)) can be enormous. Significant financial benefits can stem from even the smallest correlation of even 1% between in-store experience elements and customer spending. Therefore, knowing the gravity of the challenge and the possible benefits, I approached my research project using a real in-store environment for the study and robust till data in order to create models that would answer my research question, contributing to existing knowledge, as well as helping retailers to grow and invest in what really matters to their customers and business.