Conclusion

This chapter sought to situate and contextualise this thesis within the tourist sector, identifying the role of visitors in driving local seasonal demand uplift within grocery stores. Section 3.2 introduced tourism as a demand side concept, recognising the importance of domestic holidaymakers in driving a highly seasonal and spatial distribution of trips, particularly within coastal resorts. Section 3.3 outlined a series of key national surveys which provide detailed and timely information on the tourist sector, but noted the lack of data collection at the local level. As such, it is acknowledged that very little is known about the volume or seasonal distribution of visitor expenditure at the local level, and even less can be inferred about the localised impacts on specific services such as grocery stores.

Nevertheless, a series of economic impact models are commonly applied within the tourist sector. These attempt to estimate sub-regional visitor numbers and associated spend and highlight a number of important considerations. These must be addressed when building

seasonal and spatial estimates of small area visitor populations and their associated spend. In particular they note the role of visitor accommodation in driving the spatial and seasonal distribution of visitor spend, which was explored, based on a handful of industry and academic studies in section 3.5. This chapter identifies a number of data sources and modelling tools for exploring tourist consumption, but concludes that very little is known about seasonal and spatial patterns of visitor grocery expenditure at the small-area level. Using observations from the grocery industry, Chapter 2 identified that grocery stores in popular tourist resorts (especially those in coastal areas) exhibit pronounced seasonal demand uplift. The seasonal nature of visitor demand identified in this chapter, and the high propensity for certain types of visitor to purchase groceries, supports the notion that visitors are driving the demand uplift experienced around stores in tourist resorts. This is explored further in Chapter 4, making use of consumer loyalty card data from the Nectar scheme, allowing consumption by visitors to be identified. Much of the insight outlined in this chapter is applied further in developing small-area seasonal and spatial expenditure estimates in Chapters 5 and 8.

4

Chapter 4: Visitor grocery expenditure in Cornwall -

analysis of store and loyalty card data

4.1 Introduction

Chapter 3 identified that the UK tourism sector is experiencing a period of growth, with increasing numbers of domestic holidaymakers enjoying breaks within the UK. Self-catered accommodation in the form of rented cottages, apartments, static caravans, lodges and camping and caravanning have enjoyed much of this growth in visitor numbers (Johns and Lynch, 2007). These forms of accommodation generate visitor expenditure on food and drink which is purchased from a variety of sources including supermarkets and other grocery stores (Dudding and Ryan, 2000). This type of consumption is often neglected within the tourism literature and retail modelling, yet retailers note that visitor demand may make up a considerable proportion of store-level trade in certain destinations (see Chapter 2). Chapter 3 has identified that tourist resorts in South West England are important destinations for domestic tourism, and a number of resorts in the county of Cornwall, South West England, form the basis for this chapter and for subsequent modelling. Section 4.2 briefly introduces the county of Cornwall, with specific resorts introduced further throughout the discussion that follows and in subsequent Chapters.

The aims of this chapter are threefold. First, and building on the discussion from Chapter 2, section 4.3 seeks to identify the store-level impact of seasonal sales fluctuations, using trading data obtained from Sainsbury’s stores in Cornwall, including stores in the popular Cornish coastal resorts of Newquay and Bude. Having identified that store-level seasonal sales uplift is evident, this chapter secondly seeks to demonstrate that this uplift is attributable to expenditure inflow driven by visitors. Consumer level loyalty card data is used and allows consumers’ characteristics, including their residential origin, to be identified. Finally, this loyalty card data is used in order to understand the nature of visitor demand, considering actual consumer expenditure attributable to visitors whilst away from home and drawing comparisons with local resident spend in the same stores (and with visitors usual home consumption habits).

The majority of studies of visitor spend (or indeed of visitor characteristics more generally) take place at the aggregate level (considering all forms of expenditure) for example Craggs and Schofield (2009). Other studies consider only a subset of visitors (i.e. Downward and Lumsdon (2000) who consider visitors within only one destination, or Algere and Magdalena (2010) who consider only repeat visitors) or focus explicitly on visitor spend associated with particular short term events (e.g. Barquet et al., 2011; Bracalente et al., 2011; Young et al., 2010). Spending categories such as ‘food and drink’ are used frequently within destination specific visitor surveys (often referred to as ‘destination benchmarking’), yet are

predominantly concerned with eating out, such that spending on food and drink purchased from grocery stores is not uniquely identifiable. There are consequently very few studies that explicitly consider destination level spend on individual expenditure categories such as groceries, to which this chapter makes a contribution.

The use of customer loyalty card data from the Nectar scheme affords a unique opportunity for analysis. Chapter 2 identified that there are around 12 million active Nectar cards in use at Sainsbury’s stores, representing a valuable dataset to understand consumer level purchasing. This form of data is not usually made available for academic investigations and allows visitors and their associated expenditure to be inferred (based on their loyalty card being registered to an address outside the store catchment). Using loyalty card data allows customer level visitor spend to be identified without the need for surveys, and gives an insight into the characteristics of consumer demand, as observed on the supply side.

Section 4.2 introduces Cornwall as a popular destination for highly seasonal domestic tourism, with tourism concentrated on a series of major resorts, explored further in this chapter and subsequent modelling. Section 4.3 introduces the study stores and store-level trading data that has been made available for this thesis and uses this data to identify store- level seasonal sales variations, which are also broken down by product category. Sections 4.4 and 4.5 explore seasonal trade at four study stores using loyalty card data. Specifically, visitor spend is identified and comparisons are drawn with local resident expenditure and visitors’ usual home consumption. This is an important step in unpicking some of the characteristics of visitors and their associated expenditure and is used to inform the modelling approach developed in Chapters 5 and 6.