Disaggregate production-constrained SIM

6.3.1 Examples within the literature

The literature suggests that, with appropriate calibration, the aggregate level production- constrained SIM can predict consumer flows to an acceptable level of accuracy and thus achieve robust revenue predictions (e.g. see Birkin et al., 2002). As shown in section 6.2, the use of this production-constrained SIM has enabled revenue prediction to within 10% of reality at some of the study stores used. This is in-line with the current performance of Sainsbury’s in-house spatial interaction model, which is able to predict revenue to within 10% of reality around 70% of the time (Wright, 2011). Nonetheless, the Sainsbury’s board require all forecasts to be consistently within 5% of the actual trading patterns (Wright, 2011). Whilst the incorporation of visitor demand in a similar modelling framework has improved the accuracy of predictions (particularly in comparison to up-scaling on a monthly basis), there clearly remains scope to improve the predictive capacity of the SIM, especially to meet the accuracy levels expected by major retailers.

There are a number of obvious limitations inherent in the application of the aggregate level SIM, most notably its handling of all consumers as one homogeneous group that are thought to exhibit similar decision making behaviour. Whilst it is accepted and acknowledged that no SIM will ever be able to capture all possible consumer behaviours, there is scope to improve the ability of the SIM in order to capture greater consumer decision making behaviour in terms of how and where they interact with supply. It is realistic to assume that, based on factors such as age, geodemographic or socio-economic status and income, consumers will exhibit more individualised behaviours. In particular the literature suggests that certain groups of consumers may have a higher propensity to travel further to the store of choice, and that certain retail brands may be more attractive to certain types of consumer based on their income (e.g. see Fotheringham and Trew, 1993).

As such the aggregate level spatial interaction model often requires disaggregation on the demand or supply side in order to estimate flows based on more specific characteristics of demand, interaction or supply and thus to accurately handle the complex behaviour of different groups of consumers (Benoit and Clarke, 1997; Birkin et al., 2010a; Birkin et al., 2004; Wilson, 1971; Wilson, 2010). This section outlines how key parameters and constraints within a SIM can be disaggregated by consumer and store type, allowing the model to handle some of the more complex and individualised behaviour of different groups of consumers, and to take account of key socio-economic characteristics that drive expenditure and store choice.

It is recognised that the characteristics of demand and the attractiveness of supply will vary according to income, age, ethnicity or other socio-economic characteristics of the consumer, and may also vary depending on the type of product in question. Disaggregation may thus be as straightforward as applying different values for different groups of consumers to account for the fact that a single value is unlikely to be able to represent all the different complex consumer flows that exist. For example, in an application of a SIM to estimate the impacts of the new Silverburn regional shopping centre near Glasgow, Scotland, Khawaldah (2012) applied different values for consumers in each postal area, recognising that those residents in geographically remote postal areas were less likely to be over-sensitive to the impact of distance due to the inevitable longer journeys involved in accessing key shopping centres.

There remains, however, a considerable gap in the literature which explores fully the development and calibration of disaggregated spatial interaction models for real-world commercial applications and, whilst it is acknowledged that many such applications have been carried out, these are simply not represented in the literature (Birkin et al., 2010a; Wilson, 2010). As such, much of the discussion that follows is based on the experience of a group of geographic modellers based at the University of Leeds, and who, since the mid- 1980s, have developed considerable knowledge and practical experience building such models for commercial application, many carried out through GMAP Ltd. Some of their experiences and insights are documented through review articles, of which Birkin et al. (2010a) and Birkin et al. (2010b) provide an excellent overview from an applied modelling perspective. Birkin et al. (2010a) is drawn upon heavily here because these authors probably have more experience than anyone in developing and calibrating SIM that actually work for business applications.

Birkin et al. (2010a) suggest that retail brand is increasingly important in determining consumer flows. One particular study of stores in Leeds, West Yorkshire, recognised that, at the time, Sainsbury’s and Tesco were considered more attractive than other grocery retailers, and, assuming all other things being equal, a square foot of a Sainsbury’s or Tesco was relatively more attractive to consumers than a square foot of a competing retailer (Benoit and Clarke, 1997). They also suggested that should vary to reflect the mobility of individual

consumers, and to reflect consumer willingness to travel further to reach certain brands. Benoit and Clarke therefore made use of a SIM disaggregated by consumer type ( ) and store brand . The SIM is shown in equation 6.3. The relative attractiveness of different stores was controlled using two variables, representing the overall attractiveness of store j, measured using traditional store floorspace, and , the additional attractiveness of store j, measured via brand market share. The demand side was also disaggregated, handling different consumer types within the demand estimation through the term , representing the purchasing power of different consumers based on their geodemographic or socio-economic characteristics.

_(6.3)

Where: represents the expenditure flow between zone i and retail destination j,

by consumer of type for store brand .

_{is a competition factor which ensures that all demand is allocated to} stores in the region. It is calculated as:

∑ (6.4)

is the demand or expenditure available in residential zone by consumer of type .

accounts for the attractiveness of centre/store

is the additional attractiveness of retailer at centre

is the distance term and includes the travel time between zone

and centre , and the distance deterrence parameter , which reflects the willingness or ability of consumer of type in zone to travel in order to purchase goods.

Source: Adapted from Benoit and Clarke (1997)

Benoit and Clarke (1997) demonstrate, with reference to ASDA store turnover in West Yorkshire, that the use of a disaggregate SIM of the form shown in equation 6.3 can produce revenue predictions that are considerably more accurate than ‘off-the-shelf’ aggregate level models. Their study remains one of the only examples in the literature where model predictions have been calibrated against empirical data from a major grocery retailer. Their use of empirical data provides clear evidence that disaggregation by both consumer type ( ) and store brand ( ) afforded great potential to improve revenue predictions by capturing the additional attractiveness associated with certain brands, and the spending power of different consumer groups. The model applied in this thesis is based on the same principle as the grocery model used by Benoit and Clarke (1997), but seeks to develop further the link between consumer type and retailer type/brand, incorporating ideas about the relative

attractiveness of different retailers or brands to different consumer types, as explored in section 6.3.2.

6.3.2 Disaggregate SIM for this study

Chapter 4 noted that household level grocery demand is often habitualised, with consumers exhibiting brand loyalty through routine, habit, in-store promotions and perceptions of quality. Chapter 4 also identified that visitors are likely to display some form of brand loyalty when away from home, often motivated to shop with their existing retailer through familiarity or routine. As a result, certain consumer groups (e.g. those with the highest income) may view certain stores (such as those operated by M&S or Waitrose) as relatively more attractive than others, even where floorspace and distance may suggest otherwise in the model. Drawing on the disaggregate model used by Benoit and Clarke (1997), a disaggregated SIM should thus be developed in order to reflect:

a) the relative attractiveness of different stores, brands or fascias to different groups of consumers; for example based on their affluence or age, and

b) the ability or willingness of different consumer groups to travel further to access the store, brand or fascia of choice, which is also likely to be based on affluence, car ownership and other similar factors.

The disaggregate model used is shown in equation 6.5. Unlike Benoit and Clarke (1997), this model does not introduce in order to reflect additional brand attractiveness. Instead, a power function ( _{) is incorporated within the attractiveness term in order to apply a} measure of relative brand attractiveness to the existing attractiveness term on a consumer-by- consumer basis, as explored in section 6.4.2.

Where: represents the predicted expenditure flow between zone i and store j (of

brand ) by consumer of type .

is a balancing factor which takes account of competition and ensures that all demand from zone i by consumer type is allocated to stores within the

modelled region. The balancing factor thus ensures that:

∑ _(6.6)

It is calculated as:

∑ (6.7)

is a measure of the demand or expenditure available in demand zone by consumer of type .

reflects the overall attractiveness of store , whilst _{represents the} additional or perceived relative attractiveness of store j for consumer type and by store type (brand) .

is the distance (although in this application, travel time is used) between zone and store , and incorporates the distance deterrence/decay parameter

for consumers of type .

(Source: Developed with reference to Birkin et al. (2010a); Clarke (2011))

The model takes the same form as the classic production-constrained SIM, yet the balancing factor ( ) demand ( ) supply ( ) and distance deterrence ( _{) terms have been}

modified to incorporate different consumer types (k). An additional parameter, termed alpha ( , has also been incorporated on the supply side. modifies the attractiveness term ( ) to reflect the relative attractiveness of one store type, fascia or brand ( over another, by consumer type. The inclusion of these terms allows both supply and demand to be disaggregated independently, yet the links between them maintained through the recurrence of consumer type (k) on both the demand and supply side.

The SIM has been developed separately by two parallel research projects in the School of Geography, University of Leeds. On-going work (see Thompson, 2013; Thompson et al., 2012; Thompson et al., 2010) aims to develop and validate a similar model to replicate flows of household grocery expenditure in West Yorkshire. In particular, Thompson’s work makes use of Axciom consumer survey data to identify reported grocery consumption habits by household characteristics, a crucial step in understanding how different household types (as a proxy for consumer type) interact with grocery supply. His study has been used here to develop an understanding of the relative attractiveness of different brands to different types of consumer and informs the application of different values for different household types. The disaggregation by both consumer type and retailer type affords tremendous potential for the model to incorporate flows between different consumer types and different retailers, through modified attractiveness and distance terms. The attractiveness term ( ), allows the relative attractiveness of different store types to vary by consumer type and can be visualised in the matrix shown in Figure 6.1. Figure 6.1 attempts to illustrate that the attractiveness of a particular store to a given consumer (household) is a product of both household and store characteristics, hence the need to disaggregate by both supply and demand. The conceptual illustration is based on the premise that discount retailers will be relatively more attractive to low income households and, with increased affluence, the attractiveness of discount retailers will fall, whilst the relative attractiveness of high end retailers (i.e. Sainsbury’s, Waitrose and M&S) will increase.

– Store/Retailer Type - Household Type Discount Premium 1 ++ + - - - 2 + - 3 - + 4 -- - + ++

Key: More Attractive Less Attractive

Figure

‎6

1

Whilst Thompson is able to incorporate observed consumer behaviour into the assumptions that drive flows, the dataset he is using doesn’t allow store-level revenue and inflow (on the supply side) to be validated against recorded store or consumer level data (and thus relies on reported shopping habits). This thesis plays a major part in the development of the model not only through the addition of visitor demand, previously omitted from all forms of spatial interaction modelling, but also by calibration and validation against store and consumer data supplied by Sainsbury’s. This study also develops the model further via application to two study areas, Cornwall and then (in Chapter 8) for East Kent.

This thesis calibrates and validates the model against empirical store and loyalty card data from Sainsbury’s. The use of genuine commercial data, and the notion that Sainsbury’s are the intended end-user also addresses Birkin et al.’s (2010a) assertion that models produced for specific private sector applications are able to replicate consumer behaviours with some accuracy. This work forms a very important component in the development and validation of this form of model, particularly for use in investigations of expenditure flows for groceries. Section 6.4 considers the input demand and supply side data, including incorporation of visitor demand, before section 6.5 addresses model calibration and validation.

6.4 SIM development for modelling consumer demand and supply