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Figure 4.3 illustrates Sainsbury’s weekly sales figures (total revenue) for the four stores of interest during 2010. The sales data are displayed relative to a base level which represents the lowest recorded weekly sales for each store during the same period and allows easy comparison between stores, removing the impact of store size on total revenue, whilst also preserving confidentiality. Figure 4.3 demonstrates that there are clear seasonal sales fluctuations. All four stores experience sales uplift during the Christmas and Easter periods, traditionally periods of sales uplift driven by increased household spend. At the Bodmin and Truro stores, sales roughly double (compared to their lowest recorded sales) during the Christmas period.

Figure ‎4.3 - Seasonal sales fluctuations at a store-level.

Sales increase shown relative to a base level of zero, representing the lowest weekly sales at each store.

The coastal resort stores in Newquay and Bude demonstrate very pronounced sales peaks during the summer, undoubtedly driven by visitor spend. During summer 2010, the Bude store experienced average weekly sales which at one point represented a threefold increase compared to their average January values. Smaller sales increases around the school half term holiday in October/ May and the late May bank holiday are also evident and would be expected since these are all key periods during the tourist season.

The provision of self-catering accommodation in these resorts, coupled with increases in visitor numbers at these times of year (see Chapter 3), suggest that this form of uplift is likely to be attributable to overnight visitors. Similarly, the lowest sales were recorded during January/February and November, which represents the low-season in terms of tourism within Cornwall. Bodmin and Truro demonstrate less seasonal fluctuation around key holiday periods. This is not unexpected, since these stores tend to serve more of a residential and workplace customer base in areas with a lower provision of visitor accommodation.

Based on these sales values, the trading intensity (sales per square foot) (Table 4.2), is seen to fluctuate considerably during the year. Trading intensity is commonly used as an indicator of store performance, with Sainsbury’s reporting that their UK estate trades at an average intensity of just over £20 per Sq Ft per week (J Sainsbury Plc, 2013). It is clear from Table 4.2 that these Cornish stores are trading well-below this intensity, particularly in the low-season, when trading intensity falls to less than £10 per Sq Ft per week in all but the Truro store. Nonetheless, in the peak summer season, trading intensity increases to its maximum value (at the Newquay and Bude stores), in line with company average, suggesting that peak season demand uplift contributes to the viability of these stores, which appear to trade well-below company average at certain times of year.

Table 4.2 - Trading intensity for Cornish study stores during 2010.

Sales per square foot based on recorded store revenue on a week-by-week basis.

Trading

Intensity

Newquay

Bude

Bodmin

Truro

Average £12.01 £11.72 £10.72 £15.03

Minimum £7.43 £7.86 £8.83 £12.15

Maximum £22.39 £20.34 £17.95 £25.60

In spite of the clear increase in sales at certain times of year, the average transaction value (as shown by Figure 4.4) tends to show little seasonal fluctuation, with the exception of a noticeable increase around Christmas and Easter. These fluctuations are likely to result from additional household expenditure on food and drink at these times of year and represent a demand uplift driven by the existing residential demand, rather than additional external demand inflow. Since average transaction values do not increase within Newquay and Bude in the summer months, sales uplift at this time of year must be driven by additional customer demand in the form of an increase in the number of customers and overall transactions,

rather than simply an increase in spending by existing consumers (which would be reflected in higher transaction values, as witnessed during the Christmas period).

The average transaction value at the Bude and Newquay stores is around half that of the larger Truro store, yet the pattern of fluctuation over the year is almost identical and, at an aggregate level, there is no recognisable impact of visitor demand on average transaction values. The Bodmin and Newquay stores are of a comparable size, thus the noticeably lower transaction value at the Newquay store (more in line with that of the smaller Bude store) may suggest that the Newquay store is trading below the levels that would be expected for a store of its size. This is addressed within the modelling reported in Chapter 7.

Sales figures presented from these Sainsbury’s stores clearly demonstrate the seasonal component to store-level sales and revenue, particularly at the Newquay and Bude stores. Visitor numbers have also been shown to exhibit a high degree of seasonality in resorts such as these (see Chapter 3), with documented impacts on business and services in these towns (for example see Gordon and Goodall, 2000; GVA Grimley, 2010). However, aside from key periods such as Easter and the school summer holidays, during which it is well documented that visitor numbers increase, it is difficult to correlate store sales with key indicators of the tourist sector.

Figure ‎4.4 - Seasonal variation in average transaction values

At a destination level, overall visitor numbers (or their seasonal fluctuations) are difficult to obtain, and therefore cannot be directly compared to observed store-level seasonal sales uplift. Nonetheless, surveyed occupancy rates (South West Tourism, 2010c) for visitor accommodation serve as a useful proxy to indicate variations in the number of overnight visitors that may be present within these resorts. The Newquay store, for example, demonstrates a clear link between seasonal sales uplift and accommodation occupancy, particularly when self-catering accommodation is considered. Most notably, the period with the highest recorded sales also represents the month with the highest self-catering

accommodation occupancy (August), whilst the lowest sales coincide with the period in which lowest occupancy rates are recorded (January). This relationship was tested using linear regression with self-catering occupancy rates as the independent variable, thus suggesting that accommodation occupancy, as a proxy for visitor numbers, drives recorded store sales. The coefficient of determination ) is 76.6% at the 95% confidence level for Newquay and 73.1% for Bude, suggesting that around three quarters of the total variation in store sales could be accounted for by the differences in self-catering occupancy rates.

At an aggregate level, there is thus clear indication that the seasonal sales variations experienced at these coastal resort stores are largely driven by visitor demand. The magnitude of demand uplift has been demonstrated to vary considerably on a week-by-week basis. This suggests that the use of simple up-scale factors to account for visitor demand within location-based modelling is potentially misleading, since no up-scale factor can account for the degree of variation evident on a week-by-week basis. In order to fully understand the nature and store-level impact of demand uplift (such that store revenue can be accurately estimated) it is important to consider the actual sales that make up that demand uplift. Section 4.3.3 begins by disaggregating overall store sales by product category, identifying the supply side impacts of seasonal demand uplift. Section 4.4 then focuses on the demand side, using loyalty card data to explore the characteristics and expenditure habits of external trade, including trade by consumers thought to represent visitors.