Incorporating e-commerce (home deliver and 'click and collect') in grocery sector retail location modelling

Lead Research Organisation: University of Leeds
Department Name: Sch of Geography


The UK retail grocery market is at the forefront of methodological advances in retail location-based decision making. Sophisticated Spatial Interaction Models (SIMs) are robust tools to capture interactions between retail supply and demand and are used to predict consumers' store-choice behaviours based on a trade-off between the accessibility and perceived attractiveness of individual stores. They have been able to capture consumer behaviours with considerable accuracy given the traditionally habitual nature of grocery shopping and store-choice behaviours. Consumer interactions are becoming more complex due to the increasing propensity for consumers to shop online, ordering groceries for home delivery or 'click and collect'. E-commerce consumers' choice of retailer (and store for 'click and collect') is driven by a different range of factors to those captured in a traditional grocery SIM. Brand attractiveness and the availability, reliability or price of home delivery may be more important than traditional indicators of store size or accessibility. There is thus a need to adapt SIMs to capture these complex multi-channel consumer interactions, recognising that e-commerce grocery demand and supply has its own unique set of consumer behaviours and supply side dynamics. Objectives and Method I have three interlinked objectives, each representing one year of the PhD programme: Understand the dynamics of grocery sector e-commerce demand and supply. I will analyse consumer e-commerce behaviours in the grocery sector (propensity to shop online, frequency and typical spend, channel usage) by location and geodemographic characteristics to build up an understanding of the demand for use in estimating e-commerce demand within a retail model. I'll assess current e-commerce supply (store networks, dark stores, click-and-collect facilities, home delivery catchment areas) to inform the representation of multi-channel supply within a modelling framework. Data sources will include: -Freely available secondary datasets such as the Internet Users Classification -YouGov consumer survey data (available as a secure dataset via application to the CDRC, at the University of Leeds) -The proposed supervisor is in discussion with a major grocery retailer (also a CDRC partner) who are likely to provide additional store and consumer level e-commerce transaction data. Develop a spatial interaction model to capture grocery sector multi-channel consumer behaviours. Calibrated to represent supply and demand dynamics observed in objective 1 and likely to include: - Propensity to shop online by consumer type, generating a nuanced set of small-area e-commerce demand estimations. - Specific drivers of channel, brand and store choice to model novel consumer interactions with the supply side. Apply my model to a range of case studies to demonstrate its potential value to the retail sector. I will undertake 'what if?' analysis to demonstrate the value of my SIM for location-based decision making under various e-commerce uptake growth scenarios: - Assessing which stores have 'capacity' to pick and pack e-commerce orders - Identify appropriate locations for new click and collect facilities - Location-planning for new 'dark stores' to service new areas or increase capacity - Defining 'service areas' for e-commerce home delivery, accounting for competition and proximity. Significance The UK online grocery market estimated to be worth £15Billion by 2020, the implications of this study go beyond traditional store-based grocery retailers. The project will provide new understanding of grocery e-commerce demand characteristics and supply dynamics to provide enhancement for incorporating mutli-channel consumer behaviour within a retail modelling framework. Applied case studies will demonstrate benefits to the commercial sector and the practical uses of the research in strategic location based decision making.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/P000401/1 30/09/2017 29/09/2024
1944840 Studentship ES/P000401/1 30/09/2017 31/01/2022 Ryan Urquhart
Description Data Partnership 
Organisation Sainsbury's
Country United Kingdom 
Sector Private 
PI Contribution This is a partnership through the Centre for Doctoral Training. This has helped shape the project to focus on the supply element of grocery e-commerce. Future work as part of this PhD will be addressing issues identified within Sainbury's.
Collaborator Contribution So far they have helped shape the plan for the project. They are also a data provider - we have received data through the LIDA IRC to facilitate further research.
Impact n/a
Start Year 2017
Description BeCurious 2019 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact Several of PhD cohort organised and ran a stall at the yearly BeCurious event at the University of Leeds. This is for members of the public and their children to see what research is going on at the university to inspire the children. This involved a series of posters and presentations for the general public audience as well as child friendly material to explain our research to the children. We talked around the theme of big data and smart cities with a number of discussions around the ethics of this with the adults, whilst making this fun for the children who were tasked with navigating a city (map) and thinking of how data could help them.
Year(s) Of Engagement Activity 2018,2019