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

将电子商务（送货上门和“点击取货”）纳入杂货行业零售地点建模

• 批准号: 1944840
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1944840
• 关键词: Incorporating; commerce; home; deliver; click

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.

英国零售杂货市场处于基于零售地点的决策方法进步的前沿。复杂的空间相互作用模型(SIMS)是捕捉零售供需之间相互作用的强大工具，用于根据单个商店的可及性和感知吸引力之间的权衡来预测消费者的商店选择行为。考虑到杂货店购物和商店选择行为的传统习惯性，它们能够相当准确地捕捉消费者的行为。消费者之间的互动变得越来越复杂，因为消费者越来越倾向于在网上购物，订购送货上门的食品杂货，或者“点击提货”。与传统杂货店SIM中的因素不同，电子商务消费者选择零售商(以及点击提货的商店)受到一系列不同因素的影响。品牌吸引力和送货上门的可用性、可靠性或价格可能比传统的商店规模或可及性指标更重要。因此，有必要调整SIMs，以捕捉这些复杂的多渠道消费者互动，认识到电子商务杂货需求和供应有自己独特的一套消费者行为和供应方动态。目标和方法I有三个相互关联的目标，每个目标代表博士课程的一年：了解食品杂货部门电子商务需求和供应的动态。我将按地点和地理人口特征分析杂货行业的消费者电子商务行为(网上购物倾向、频率和典型消费、渠道使用)，以建立对在零售模型中估计电子商务需求的需求的理解。我将评估当前的电子商务供应(商店网络、暗店、点击提货设施、送货上门的区域)，以在建模框架内告知多渠道供应的表示。数据来源将包括：-免费提供的二级数据集，如互联网用户分类-YouGov消费者调查数据(通过向利兹大学CDRC申请以安全数据集的形式提供)-拟议的监督者正在与一家主要杂货零售商(也是CDRC的合作伙伴)进行讨论，后者可能会提供额外的商店和消费者层面的电子商务交易数据。开发一个空间交互模型，以捕捉杂货行业多渠道的消费者行为。经校准以代表目标1中观察到的供需动态，并可能包括：-按消费者类型在线购物的倾向，生成一组细微差别的小区域电子商务需求估计。-渠道、品牌和门店选择的特定驱动因素，以模拟消费者与供应方的新型互动。将我的模型应用于一系列案例研究，以展示其对零售业的潜在价值。我将承担‘如果？’分析以展示My SIM在各种电子商务应用增长场景下基于位置的决策的价值：-评估哪些商店具有挑选和打包电子商务订单的能力-为新的点击和收集设施确定合适的位置-位置-规划新的“暗店”以服务于新的区域或增加容量-定义电子商务送货上门的“服务区”，考虑竞争和邻近。意义英国在线杂货市场估计到2020年价值150亿GB，这项研究的影响超出了传统的以商店为基础的杂货零售商。该项目将提供对杂货电子商务需求特征和供应动态的新理解，以加强将多渠道消费者行为纳入零售建模框架。应用案例研究将展示商业部门的好处和研究在基于战略位置的决策中的实际用途。