FACULDADE DE
ENGENHARIA DA
UNIVERSIDADE DO
PORTO
Understanding Customer Willingness to
Wait (WTW) in the E-commerce
Grocery Retail
Beatriz Pereira da Silva
Master in Electrical and Computers Engineering Supervisor: Dr. Pedro Sanches Amorim Co-supervisor: Dr. José Luís Moura Borges Co-supervisor: Dr. Sara Sofia Baltazar Martins
Resumo
O retalho alimentar é conhecido por ser um negócio com baixas margens. O aparecimento do comércio eletrónico não alterou esse facto e os custos duma operação de e-commerce não são mais baixos do que aqueles duma loja de retalho física. Isto motiva os e-grocers (retalhistas alimentares com uma operação de e-commerce) a otimizar os seus processos, procurando oferecer aos seus clientes a qualidade de serviço que estes desejam. A compreensão das escolhas do cliente pode contribuir para esta otimização, ao fornecer informações importantes passíveis de serem utilizadas pelos e-grocers.
Um estudo aprofundado dos comportamentos dos consumidores, particularmente relaciona-dos com a sua disponibilidade para esperar por uma encomenda, é proposto neste projeto. Os dados utilizados para atingir este objetivo foram fornecidos por um importante e-grocer português e contêm informações que permitiram um processo de engenharia reversa para determinar quais as janelas temporais de entrega que o cliente visualizou no momento de escolha das mesmas. Os dois modelos propostos (modelo Logístico Multinomial e modelo Logístico Hierárquico), utilizando variáveis explicativas relacionadas com características da encomenda e da janela de entrega, ten-tam identificar o que influencia a disponibilidade para esperar. São avaliadas três hipóteses rela-cionadas com este tópico através da segmentação do conjunto de dados. A primeira hipótese é relativa à percentagem de frescos no basket do cliente e afirma que quanto maior for esta percent-agem, menor será a disponibilidade do cliente para esperar pela sua encomenda. Esta hipótese foi validada. Já a segunda diz respeito à percentagem de descontos: clientes cujo basket possua uma maior percentagem de descontos têm uma maior sensibilidade ao tempo de espera, causada, possivelmente, pela previsão de stock-out, dado o aumento da procura. Esta hipótese não foi con-firmada. A terceira e última hipótese afirma que um maior afastamento temporal do fim de semana se relaciona com uma maior disponibilidade para esperar. Verificou-se que encomendas realizadas no meio da semana (quarta, quinta e sexta-feira) têm, efetivamente, maior sensibilidade ao tempo de espera do que as realizadas durante o próprio fim-de-semana.
Concluindo, este projeto pode ser útil para estudos de gestão de janelas temporais de en-trega, uma vez que até ao momento não foram utilizados modelos comportamentais complexos do cliente, que constituem uma melhoria em relação àqueles que atualmente são postos em prática.
Palavras chave: modelos de escolha discreta, modelo multinomial logit, modelo nested logit, disponibilidade para esperar.
Abstract
Grocery retailing is known to be a business with low margins. E-grocery has not changed that fact and the costs of running an e-commerce operation are not lower than those of a brick-and-mortar store. This motivates e-grocers to optimize their processes whilst delivering the quality of service that customers desire. Understanding customer choices can contribute to this optimization, by providing important information that can be used by e-grocers.
A detailed analysis of customer behaviour, particularly concerning the willingness to wait for delivery, is proposed in this project. The dataset used to achieve it was supplied by a major por-tuguese e-grocer and contains information that allowed a reverse engineering process to determine which slots were visualized by the customer upon booking a delivery. The two proposed mod-els (Multinomial Logit model and Nested Logit model), through the use of explanatory variables relative to characteristics of the order and the delivery slot, attempt to identify what influences willingness to wait. Three different hypotheses concerning this topic are evaluated by segment-ing the dataset. The first hypothesis concerns the fresh products percentage in a customer basket, and states that the higher this percentage is, the lower will be the customer’s willingness to wait. This hypothesis was confirmed. The second hypothesis relates to the discount percentage: cus-tomers whose basket has a higher percentage of discounts are more sensitive to waiting time, due to, possibly, the predicted stock-outs caused by an increased demand. This hypothesis couldn’t be confirmed. The third and final hypothesis states that the farther from the weekend, the higher the willingness to wait. It was verified that orders placed on the middle of the week (Wednesday, Thursday or Friday) are, effectively, more sensitive to the waiting time than those placed during the weekend.
In conclusion, this project can be of use to time slot management studies, since a complex model of customer behaviour has not been used until this moment and is an improvement relative to the customer choice models currently in practice.
Keywords: discrete choice models, multinomial logit model, nested logit model, willingness to wait.
Acknowledgments
I would like to express my profound gratitude to my supervisor, Professor Pedro Amorim. For all suggestions, time and patience, thank you. My gratefulness extends to Professor José Luis Borges, for his help and valuable insights.
A very special and heartfelt thank you is due to Sara Martins. Without your dedication, avail-ability and constant reassurance, these months would have been a lot harder to endure.
To the teachers that inspired me throughout this academic journey: you will not be forgotten. I would especially like to thank my parents and Miguel. Your kind words of support we’re essential to keep me motivated.
To my friends, thanks for the laughs when they were most needed.
Beatriz Silva
“Without data you’re just another person with an opinion.”
W. Edwards Deming
Contents
1 Introduction 1 1.1 Motivation . . . 1 1.2 Research Problem . . . 2 1.3 Methodology . . . 3 1.4 Structure . . . 3 2 Literature Review 5 2.1 The Importance of Time . . . 52.2 Attended Home Delivery . . . 6
2.3 Assortment Planning Problems . . . 7
2.4 Multinomial and Nested Logit Model . . . 8
3 Online Booking of Home Delivery Groceries 9 3.1 Booking Process . . . 9
3.2 Retailers Transactional Data . . . 10
4 Methodology 13 4.1 Data Description . . . 13
4.2 Changes Performed on the Database . . . 14
4.2.1 Data removal . . . 14
4.2.2 Expiry date . . . 14
4.2.3 Characteristics of the order . . . 14
4.2.4 Coding the slots . . . 15
4.2.5 Determining the fee of all available delivery classes . . . 16
4.2.6 Reverse engineering . . . 16
4.2.7 Final dataset . . . 17
4.3 Applying a Multinomial Logit Model . . . 17
4.3.1 Data used in the MNL . . . 18
4.4 Applying a Nested Logit Model . . . 19
4.5 Segmentations . . . 21
5 Results 23 5.1 Descriptive Statistics . . . 23
5.2 Multinomial Logit Model . . . 25
5.3 Nested Logit Model . . . 25
5.3.1 Hypotheses . . . 26
5.4 Delivery Class Fee . . . 30
6 Conclusions 31
List of Figures
3.1 Distribution of orders across intervals between order and delivery . . . 10
3.2 Illustrative example of the available slots for two orders made at different times . 10 3.3 Distribution of orders across slots . . . 11
3.4 Distribution of orders across stores . . . 11
3.5 Histogram of orders by number of SKUs . . . 12
3.6 Histogram of orders by total basket cost . . . 12
4.1 Representation of the three main tables in the database . . . 13
4.2 Retailer’s product structure . . . 14
4.3 General hierarchy of a two level Nested Logit model . . . 20
4.4 Hierarchy in the Nested Logit model . . . 20
5.1 Histogram of type of delivery day . . . 24
5.2 Histogram of hours between order and delivery . . . 24
5.3 Confidence intervals for the discount percentage segmentation at 95% confidence level . . . 26
5.4 Confidence intervals for the fresh percentage segmentation at 95% confidence level 27 5.5 Confidence intervals for the type of order day segmentation at 95% confidence level 28 5.6 Confidence intervals for the type of order day segmentation at 90% confidence level 29 5.7 Confidence intervals of the fee variable for all segmentations at 95% confidence level . . . 30
List of Tables
5.1 Descriptive statistics . . . 23
5.2 Results from the Multinomial Logit model . . . 25
5.3 Results from the Nested Logit model . . . 26
5.4 Nested Logit model for discount percentage segmentation . . . 27
5.5 Nested Logit model for fresh percentage segmentation . . . 28
5.6 Nested Logit model for type of order day segmentation . . . 29
Abreviaturas e Símbolos
AHD Attended Home Delivery CI Confidence Interval
IIA Independence of Irrelevant Alternatives MNL Multinomial Logit
NL Nested Logit SKU Stock Keeping Unit
Chapter 1
Introduction
In the past years, e-commerce has grown steadily and the number of companies selling online has also increased. As reported byEcommerce Foundation(2017), 6% of Europeans shop online everyday. The convenience inherent to online shopping is extremely alluring for consumers, es-pecially because it allows them to save time. However, just because they crave convenience, that does not mean they are willing to sacrifice product quality or good delivery/pickup arrangements. According toAccenture Consulting(2016), 91% of shoppers indicate the assignment of a delivery time as the most important shipping feature for them and 74% said they would shop online more often if they had more control over delivery. Evidently, customers are expecting more quality of service and retailers need to correspond to their expectations (Yang and Strauss,2017). Though customers demand all this, they do not wish to pay an excessive amount for the quality of service they desire. Yang and Strauss(2017) show that even small differences in the delivery fees can impact the choice of slots and that clients are not prepared to pay much for peak time delivery. Hence, besides making an effort not to loose clients, retailers also need to balance the costs of providing everything that is expected of them. E-fulfillment, i.e. delivering physical goods to the customer, is frequently cited as one of the most important and costly operations of Internet sellers (Agatz et al., 2008b). Due to the complexity present in the delivery of e-commerce goods, it is very important to improve the availability and affordability of deliveries in order to further stim-ulate e-commerce growth, whether this is achieved through dynamic slot pricing policies or more efficient and less costly routes.
The goal of this dissertation is to study customer behaviour in an e-grocery setting, relative to the waiting time between order and delivery. We expect that by shedding light on this critical dimension, retailers will be more prone to make better fulfillment decisions.
1.1
Motivation
Back in the Dot Com era, several new pure play Internet grocers thought they could revolutionize the industry and offer customers grocery prices lower or at least equal to those seen on brick-and-mortar grocery stores. However, they failed to keep those intentions and collapsed, since
2 Introduction
delivering their promises was considerably harder than making them (Boyer et al.,2003). After the burst of the Internet bubble, e-tailers have tried to improve the profitability of their online distribution channels (Agatz et al.,2013). Nowadays, there are companies that sell grocery goods online, that have a successful business model and a lucrative Internet channel, such as Tesco (a British grocer) (Lawson,2014).
In this work, the focus is placed on an e-grocer that practices attended home delivery (AHD). AHD implies that customers need to be in a reception point within a selected time frame in order to receive a delivery. There are disadvantages to this process for all participants: on one side, the customer is constrained to wait for his order, and on the other, customer’s time restrictions complicate vehicle routing. Retailer’s objectives typically lean towards the maximization of ve-hicle utilization and minimization of transportation costs, all while keeping the customer satisfied (Hübner et al.,2016). In the case of low value items, such as groceries, transportation costs are an important driver to the success of the business. The width of delivery time-windows and delivery location can also influence the overall cost of transport.
As the customers’ time slot selection impacts the routing planning and, therefore, the costs, retailers should decide very carefully which time slots to show to the customer and at what price. This type of decisions are analyzed under the time slot management research stream. Several ap-proaches can be used, such as: (statically/dynamically) assigning delivery time slots as new orders are placed, (statically/dynamically) pricing time slots and dynamically creating and adjusting de-livery routes (Agatz et al.,2013;Yang et al.,2016). These methodologies aim to skew customer choices towards more convenient slots. However, in order to do so, customers’ needs and desires need to be taken into consideration. Currently, either customer preferences are being ignored or the models that describe them are simple and could benefit from improvements and added complexity (seeYang et al.(2016)).
The goal of this project is to make a thorough study of customer behaviour, that can, in the future, be useful for time slot management studies. Such an empirical study on customer behavior has not been conducted so far and constitutes an improvement on the simpler models currently in practice. To the best of the author’s knowledge, it is the first time anyone has tried to estimate how much a customer is willing to wait for a delivery, particularly using historical e-commerce records, since it is not common to have access to real data that encapsulates the slot options visualized by the customer upon booking a delivery. We’ll use exactly this data from an e-grocery case-study to explore such information.
1.2
Research Problem
This dissertation intends to model customer choice behavior of delivery slots and complement the work done so far regarding this subject. It aims to help retailers make informed decisions, mostly about dynamic slot pricing, although it can also be of use to other problems, such as inventory management. Using characteristics of the order, this work intends to explore and validate different dimensions of customer preferences. According toUPS(2014), customers give as a reason to ask
1.3 Methodology 3
for faster delivery the fact that they have purchased perishable products. Furthermore, customers also acknowledge that time of day/week is a driver for their willingness to wait. In preliminary discussions with the management team of the e-grocer that supplied the data, it was mentioned that discounts could also influence the customer’s delivery slot preferences. Hence, this work attempts to shed light upon the three following hypothesis:
• HD: Baskets with more promotional products yield less customer willingness to wait;
Customers might predict there will be a lower service level, due to the possible increase of demand on products with discount. Since the products are picked on the day of delivery, this might lead to undesired stock-outs, with increasing probability the farther away the delivery day is from the beginning of the discount.
• HF:Baskets with more perishable products yield less customer willingness to wait;
Fresh products are usually bought to be immediately consumed and, typically, customers do not order them predicting they will need them after a few days. The hypothesis assumes that they buy them to satisfy an immediate need.
• HW:The farthest to the weekend the higher the customer willingness to wait.
Since most people’s availability is larger on the weekends, they might be willing to wait until Saturday or Sunday to receive their orders, instead of choosing an undesired slot during the week, which might not be as convenient for them.
1.3
Methodology
The first step to reach the goal of this project was to alter the database in a way that all the necessary information could be extracted from it. In order to model customer choice behavior, a Multinomial Logit model (MNL) was applied to the data. An MNL is a discrete choice model where the decision maker is assumed to choose the option with the most utility to him (Yang et al.,
2016). This problem is approximated as an assortment problem, a common application of the MNL, in which the delivery slots correspond to the product that a customer picks from the shelf in a brick-and-mortar store. However, this model failed an important assumption (Independence of Irrelevant Alternatives) and its conclusions could not be taken into consideration. To overcome this problem, a Nested Logit model was applied to the data. The hypothesis presented above were evaluated by segmenting the data and performing further analysis, using the Nested Logit model.
1.4
Structure
Besides the Introduction, this dissertation has 5 other chapters. On Chapter2, the relevant liter-ature for this project is described, namely: assortment problems, value of time, attended home delivery and the Nested Logit model. Chapter3details the booking process and presents possible
4 Introduction
analysis with data extracted from this process, that can be relevant for a retailer. In Chapter4are explained the changes performed to the database and the resulting final dataset, as well as the the-oretical background of the MNL and NL models. Their application to the data is also discussed. Chapter 5 presents the results of the application of the methodology and provides a graphical interpretation of the models, accompanied by their explanation. The last chapter presents the conclusions of the elaborated project and suggests future implementations.
Chapter 2
Literature Review
This chapter is divided into four main streams considered pertinent for the development of this project. The relevance of time, both in industry as attended home delivery is addressed, and, after-wards, a special focus is placed onto AHD. The following section is about assortment problems, which commonly use the same consumer choice model (MNL) as the one that is used in this project. The last section reviews the application of Nested Logit models to different areas.
2.1
The Importance of Time
Estimating the relevance of time in consumers’ decisions is crucial for firms to make informed decisions on inventories, warehouse processes, delivery routes, among others. Literature on the value of time dates back to the early nineties. In 1991,Lindsley et al. (1991) elaborated a study about the book distribution industry and put forward one of the first attempts to estimate the value of time for a supply chain. The authors performed an analysis on industry survey data to assess the particular rewards one might expect from competitive actions set in motion. They managed to capture the importance that time, variety and efficiency have, especially for new players in the market.
Later, Taylor (1994) presented a model of the "wait experience", where the author hypoth-esized actions that affected an overall evaluation of service. This model was tested on delayed airline passengers and it was declared that the lack of punctuality affected negatively the evalua-tion of service.
The work ofAllon et al.(2011) presented an example in the fast food drive-through industry and proposed an approach to estimate the influence that prices and waiting time of other service providers have on the sales of a certain organization. In that study, it was mentioned that consumers attribute a very high cost to their waiting time and that both price and time affect consumers’ decisions. However, their waiting time sensitivity was also influenced by other factors, such as time of day and store location.
In a restaurant setting and using data collected over a period of 12 months, De Vries et al.
(2018) studied the impact of waiting time on customer loyalty, dining duration and reneging. 5
6 Literature Review
The authors used empirical models to test the hypothesis they developed and a discrete event-simulation model to evaluate the consequences of the effects of waiting time. They also explored the effect of hypothetical changes in the restaurant policy.
Ray and Jewkes(2003) presented an analytic approach for a firm to maximize its profit by optimally selecting a guaranteed delivery time. The authors modeled a relationship between price and delivery times and stated that customers may be willing to pay premium for a shorter delivery lead time. In addition, they considered that there were two types of customers: lead-time-sensitive and price-sensitive, and suggested different approaches for companies with high percentages of each type.
InKremer and Debo(2013), waiting was considered an indicator of the quality of the good customers wait to acquire. By dividing consumers in informed (about the quality of an item) and uninformed, the authors concluded that the purchasing probability of an uninformed consumer might increase as waiting time also increases, under certain conditions. On another hand, the pres-ence of informed customers made uninformed customers less likely to purchase at short waiting times, which is analogue to the "empty restaurant syndrome".
Fisher et al.(2017) made an experiment to evaluate the effects of faster delivery on revenue. They studied a U.S. apparel retailer that, by opening a new distribution center, reduced signifi-cantly the lead time for customers in 11 states. They found that the reduced delivery time mainly impacted infrequent and newly acquired customers and that it resulted in a 2.2 % increase in the retailer’s net profit.
Evidently, there are articles that focus on the value of time and how waiting can influence it, but so far there is nothing connected to the customer’s willingness to wait, especially in an e-commerce setting. Regarding attended home delivery and online groceries, there are studies about willingness to pay (e.g. Agatz et al.(2013)), but willingness to wait is a novelty, and has not been done so far.
2.2
Attended Home Delivery
The statements above seem to remain adequate for e-commerce customers, specifically when con-sidering AHD. AHD is viewed as an attractive alternative for consumers who are incapable of going shopping by themselves, whether it is due to physical disability, lack of a mean of transport, need to care for children, busy lifestyle, or quite simply convenience (Klein et al.,2017). However, for online grocery services, the cost of home delivery operations is critical (Asdemir et al.,2009).
Agatz et al. (2008a) presented issues and solving approaches for AHD, with focus on e-grocers. The authors examined the tactical planning issues related to the design of a time slot schedule, considering dynamic slot pricing (dynamically changing the delivery fee) and dynamic time slotting (displaying a reduced set of options to the customer).Agatz et al.(2011) introduced a time slot management problem particularly relevant for e-grocery that, given service requirements and average weekly demands for each zip code in the service region, selected a set of time slots to offer in each zip code, minimizing the expected delivery costs.
2.3 Assortment Planning Problems 7
Hungerländer et al.(2017) produced a similar work to the one above, but while the first was concerned with the strategic question of which time slots to offer in general, the second dealt with the operational question of which time slots to offer to a certain customer for a specific schedule.
Yang et al.(2016) were the first to use a Multinomial Logit to model customer choice in the study of time slot management, using real e-grocer data. The authors proposed a framework for dynamic slot pricing and demonstrated that, by using their model, it was possible to increase profits. In their estimation of delivery costs they considered not only the orders that were already placed but also the ones expected to come. Yang and Strauss(2017) also developed a dynamic delivery slot pricing policy, but contrary toYang et al.(2016), they set low delivery fees in areas with lower demand, in order to stimulate purchases.Klein et al.(2017) were the first to introduce an e-grocer’s differentiated time slot pricing problem as a mathematical optimization problem, in which delivery costs are anticipated by incorporating routing constraints, and customer behavior is modeled by a general non-parametric rank-based choice model.
The target for this project is to present a more complex model of customer behavior, providing more information about it in an e-grocery setting. The conclusions derived from this work can be applied, for example, at a tactical level, by helping to define which slots to offer the customer. Additionally, the suggested model can be the starting point for other models to be developed and integrated into time slot management methodologies.
2.3
Assortment Planning Problems
One of the models used in this project to understand customer behaviour (Multinomial Logit model) is commonly applied to assortment problems. Thus, this section describes relevant lit-erature related to assortment, highlighting only a few papers, since the litlit-erature in this field is so vast.
According to Kök et al.(2008), a retailer’s assortment is determined by the set of products carried in each store at each point in time. Assortment planning intends to define an assortment that boosts sales or gross margins, subject to various constraints, such as a limited budget for purchase of products, restricted shelf space for exhibiting products or even wanting to have at least two vendors for each type of product.
The first study of assortment planning dates back to 1999, whenvan Ryzin and Mahajan(1999) used an MNL model to describe the consumer choice process. In this paper, the substitution con-cept is static, which means that the inventory level of the items is not considered in the model and the sales would be lost if the first choice of the customers was out of stock. However, substitution was possible if their favourite variant was not carried. The authors demonstrated that the optimal assortment always consisted of a certain number of the most popular products.
The research ofMiller et al.(2010) used a MNL model to capture the heterogeneity of con-sumer preferences, relative to an assortment of infrequently purchased products, resulting in good predictions of the product sales shares for a national retailer’s assortment of DVD players.
8 Literature Review
InRusmevichientong et al.(2010) both the static and the dynamic optimization problems were approached, and the goal was to choose an assortment of products that maximized profit, subject to a capacity constraint. The MNL model was used to represent demand.
Assortment problems relate to this work since it’s possible to compare the choice that a cus-tomer makes of a product contained in a certain assortment with the choice he makes of a delivery slot for an order he placed online.
2.4
Multinomial and Nested Logit Model
The second model applied to the data was the Nested Logit model. The areas of application of the NL model can vary greatly, as can be seen in the review below. Also, some articles refer to both MNL and NL, trying to evaluate which one performs better on a specific situation.
InSmith et al.(2018), a comparison of the NL and the MNL models was done in order to eval-uate which one best described how patients choose their hospital, considering there were several within a reasonable distance to them. The authors presented a two-level Nested Logit model in which the patients first chose the city or group of hospitals and, afterwards, individual hospitals. In the MNL model patients chose only the hospital they deemed most suitable from a full list within their region. The conclusion was that the MNL model was the one that best fit the data and the authors drew conclusions from it.
InKoppleman and Wen(1998), was estimated the number of passengers expected to travel in a given itinerary between a pair of airports. Several models, including the MNL and variations of the NL (two and three-level models), were applied to data. The authors reached the conclusion that competition among air-travel itineraries could be almost completely defined by nesting itineraries by time of day and carrier dimensions.
Siriwardena et al.(2012) implemented a Nested Logit model to estimate the impact that a marketing campaign had in the purchases of green vehicles. In the two-level Nested Logit model implemented, it was considered that first the customer chose if the car was eco-friendly or not and, secondly, the brand and model of the car. The authors concluded that eco-marketing cam-paigns could have a positive effect on the purchases of eco-friendly vehicles, as long as these were repeated over time.
In this project, the Nested Logit model is used to model customers’ slot choices, assuming that they first choose the day on which they want the delivery and, afterwards, the type of delivery window.
Chapter 3
Online Booking of Home Delivery
Groceries
The context of the developed empirical study is presented in this chapter, where the slot booking process is described, as well as the type of data that a retailer can obtain through online operations.
3.1
Booking Process
The usual transaction in e-grocery services goes as follows: first the customer who is willing to make an order logs into the website. Secondly, he selects the products he wishes to purchase, adding them into the cart. Afterwards, when he does not wish to add anything else, he clicks "Buy" and chooses the delivery option: either pick up in store or home delivery. Focusing on home delivery, the customer then selects a delivery slot from the ones available according to his location and slot availability, since some might be missing due to capacity restrictions. For the delivery, a fee is charged, which is not the same for all slots or all days. The customer can opt to choose a delivery slot and proceed with the order or leave the site without booking. Notice the latter case is very rare.
It is pertinent to add that, through the analysis of Figure 3.1, the great majority of orders is delivered within 7 days of its placement, although customers can select a delivery slot up to 60 days in our case study. For this reason, only the orders delivered within 7 days are considered in this study.
The delivery slots viewed by the customers vary due to three main reasons: capacity con-straints, cut-off time and delivery area.
• Capacity constraints: There is an associated capacity to each slot, existing a limited number of orders that can be accepted for each slot in the corresponding delivery area. Once that number is reached, no further orders can be placed and the slot can no longer be selected.
10 Online Booking of Home Delivery Groceries
Figure 3.1: Distribution of orders across intervals between order and delivery
• Delivery area: Each area is constituted by a group of postal codes. Slots can vary across areas. The delivery areas with the most slots are the ones corresponding to the high demand zone.
• Cut-off time: A certain delivery slot can only be selected before the corresponding cut-off time. Figure3.2exemplifies graphically this constraint.
Figure 3.2: Illustrative example of the available slots for two orders made at different times
3.2
Retailers Transactional Data
Transactional data results from customer actions on a retailer’s web page. It captures information about orders and customers, that can be analyzed and used to better understand their needs. This type of information can also be utilized to adapt marketing campaigns to customer profiles, in order to incentive growth and customer retention. Contrary to retail stores, in online grocery pur-chases data is generated about the order and about the delivery, since they happen in independent moments.
Besides the typical information about the basket that can also be obtained in brick-and-mortar stores, there is information about the delivery as well. Customer preferences relative to time slots
3.2 Retailers Transactional Data 11
and to the interval between order and delivery are depicted in Figure3.3and3.1, respectively. Fig-ure3.1shows that the great majority of orders are delivered within the first three days, evidencing customers’ favouritism, and Figure 3.3 shows that the preferred slots are very well distributed across the day, considering that the first five occur in different times of day (morning, afternoon and night). From these figures is possible to extract which slots and intervals were the most used. A possible way of helping the retailer understand where to focus his efforts in improving adhesion to online services and in determining the location of new strategic picking points is explicited in Figure3.4, which represents the relative quantity of orders picked in each store, reflecting not only adhesion to online orders, but also populational density.
Figure 3.3: Distribution of orders across slots
12 Online Booking of Home Delivery Groceries
There is also information that is more deeply connected to the order, for example: the numbers of SKUs (Figure 3.5) and the total basket cost across orders, present in Figure 3.6, where it’s possible to verify that the cost of most orders did not surpass 200 euros.
Figure 3.5: Histogram of orders by number of SKUs
Chapter 4
Methodology
This chapter consists of a description of the data used, the changes performed to the database, as well as the influence they had on the Multinomial Logit model used. Moreover, the application of the MNL and NL models to the data is explained.
4.1
Data Description
The data were provided by a major e-grocer in Portugal. The dataset contains anonymized cus-tomer orders made in the company’s website for over 1 year, from October 2016 to September 2017. It includes characteristics of the order, such as the customer that places it, the purchased products (price, quantity and discount), the delivery slot chosen and corresponding fee, the order-ing time and type of delivery (home or pickup in store). It also contains characteristics of the slots, such as the period on which it is available, cut-off time (time until which that slot can be booked), capacity (number of orders that can be booked in that slot) and delivery area. There is also infor-mation about the customer’s postal code, which may be used to obtain the purchasing power by city; for example, this particular parameter was retrieved from the 2015 database "Pordata: Base de Dados de Portugal Contemporâneo". A summary of this information is presented in Figure4.1.
Figure 4.1: Representation of the three main tables in the database
14 Methodology
4.2
Changes Performed on the Database
Some data present in the database were incongruous. Therefore, it had to be eliminated. Addi-tionally, the database also did not possess all the information this project required. Hence, it was necessary to employ mechanisms to estimate/determine whatever variables were deemed neces-sary. The software used to store and alter the database was SQL Server 2017.
4.2.1 Data removal
All orders that had a delivery date prior to the order date, as well as the orders that had one product with requested quantity 0, were deleted. One order that had an unfeasible delivery cost was also excluded.
4.2.2 Expiry date
To obtain the percentage of fresh products in an order it was necessary to determine the expiry date of all existing products. To provide a clearer understanding of this process, the retailer’s product structure is represented in Figure4.2.
Some SKUs did not have an associated validity period. Hence, this parameter was estimated based on the median of the validity period of products in the same base unit. If there weren’t any products in the same base unit, this procedure was executed for products in the same sub-category or category. Afterwards, it was verified that all products that were supposed to have an associated shelf life, already had, and this step was finalized.
Figure 4.2: Retailer’s product structure
4.2.3 Characteristics of the order
To further characterize the orders and, since they were necessary for the customer choice model applied, the following parameters were calculated and added to the database:
• Total cost of the order (discounts are contemplated in this value); • Number of different SKUs purchased;
4.2 Changes Performed on the Database 15
• Percentage of fresh products: fresh products are those whose shelf life is less than 7 days:
Fresh products percentage = Number of fresh products Total number of products × 100; • Percentage of discount:
Discount percentage = Value of discount
Total cost of the order + Discount× 100.
4.2.4 Coding the slots
In the Multinomial and Nested Logit models employed, the time-slots are the output variable. However, as to not have so many different outputs, since customers can select a wide variety of time-slots for their delivery, an aggregation of slots into delivery classes (new output variable) was made based on:
• Delivery time width:
– S: if the width of the delivery slot is less than 3 hours; – M: if the width of the delivery slot is between 3 and 6 hours; – L: if the width of the delivery slot is more than 6 hours. • Delivery time of day:
– 1: if it overlaps the morning period; – 2: if it overlaps the afternoon period; – 3: if it overlaps the evening period;
– 4: if it overlaps the morning and the afternoon period. • Days between purchase and delivery:
– N: if the order is delivered in the same day of purchase; – N1: if the order is delivered in the following day; – N2: if the order is delivered after 2 days;
– NN: if the order is delivered between 3 and 7 days.
These three codes were concatenated in order to provide a delivery class of the type, e.g.: - S2N: slot that lasts less than three hours, occurs during the afternoon and in the same day of the order.
In total, these combination of factors should yield a total of 48 delivery classes. However, if the delivery class chosen is in the same day as the order, due to time restrictions (cut-off time), most delivery classes, especially those that occur during the morning and early afternoon, do not
16 Methodology
appear available to the customer. There are also combinations of variables that simply do not exist, especially those with width "L". In conclusion, depending on the area, these 48 delivery classes can be significantly reduced.
4.2.5 Determining the fee of all available delivery classes
When booking a delivery, all available slots appear to the customer with an associated fee. Un-fortunately, in the database, were only saved the fees of the slots that were effectively chosen and not the fees of all slots that were shown to the customer. After discussing with the practitioners, the method chosen to circumvent the situation was to attribute to a certain delivery class that was not chosen, the weekly average fee of all delivery classes of the same type. If that type of deliv-ery class was not chosen during a certain week, the fee attributed was the average fee of delivdeliv-ery classes during the year.
4.2.6 Reverse engineering
It was necessary to only consider the orders that could choose all available delivery classes, avoid-ing to estimate substitution effects. In order to attain such goal, and as the data supplied did not contain which slots customers could choose in the moment of their order, an algorithm of reverse engineering had to be implemented. The data available consisted of information about the slots, including their capacity, and information about the orders, their details and the chosen slot, as seen in Figure4.1.
To execute the algorithm, it was necessary to make one assumption: no order was placed before the earliest order in the table and, therefore, the first order had all slots, with capacity > 0 available.
Algorithm 1 Determining the available slots at the time of order
1: Input: Set of slots S; Set of orders O; Set of delivery areas A, Set of delivery classes C
2: Group the slots in S into subsets Sc, representing the slots of delivery class c according to the slot coding
3: for each delivery area a ∈ A and delivery class c ∈ C do 4: Calculate ClassCapacityc,a= ∑s∈ScSlotCapacitys,a
5: end for
6: Sort orders in O chronologically by ordering date 7: i = first order placed
8: repeat
9: a= delivery area of order i
10: Save for order i all delivery classes with positive capacity (ACi⊂ c ∈ C : ClassCapacityc,a> 0)
11: c= delivery class of slot selected in order i 12: ClassCapacityc,a= ClassCapacityc,a− 1
13: move i to next order 14: until i reaches last order
4.3 Applying a Multinomial Logit Model 17
4.2.7 Final dataset
The only orders used in this analysis are those that, when the customer was booking a delivery slot, were not affected by capacity constraints and were in the high demand zone. This is due to the fact that substitution effects are undesired and, therefore, only the orders whose associated customer was capable of choosing all delivery classes were considered. Furthermore, only customers who paid for delivery were included: those who bought a delivery pass or made use of a promotion and did not pay for delivery were excluded from the data. Also, the orders picked up in store were filtered and rejected. The final dataset was composed by 73596 orders.
4.3
Applying a Multinomial Logit Model
The Multinomial Logit (MNL) was used, in this project, to model customer behavior. The MNL model is a discrete choice model, used in economics and marketing literature. It’s commonly applied to assortment problems. The MNL is based on the principle that every customer associates a utility Ujwith each option j ∈ N and chooses the one that offers him the highest utility (Fisher
and Vaidyanathan, 2014). For consumers that choose not to select any option j = 0, with an associated utility of U0.
The MNL assumes that utility Ujcan be decomposed in two parts: a deterministic component
ujand a random component εj.
Uj= uj+ εj uj= β0 j+ β 0 Xj β 0
is a vector of weighs, Xj is a vector of attribute values for the alternative j and β0 j is a
constant specific to each alternative.
The εjare modeled as Gumbel random variables, assumed to be independent, with mean zero
and scale parameter µ (Kök et al.,2008;Karampatsa et al.,2017). If µ increases, this implies that there is a higher level of heterogeneity among customers. Consequently, the realized utility will be different for different customers for each product, despite the expected utility being the same; this might be caused by the heterogeneity of preferences across consumers or unobservable factors in the utility of the product. The Gumbel distribution possesses the property of being closed under the operation of maximization.
Therefore, the probability that a customer chooses option j ∈ N is
pj(S) = e u j µ ∑k∈Ne uk µ
18 Methodology
According toCroissant(2018), there are three types of variables that can be considered when using a MNL:
• alternative specific variables xi jwith a generic coefficient β ,
• individual specific variables zi with an alternative specific coefficients γj
• alternative specific variables wi j with an alternative specific coefficient σj
The main disadvantage of using the MNL model is the Independence of Irrelevant Alternatives (IIA). MNL models are only valid under this assumption, which states that characteristics of one particular choice do not impact the relative probabilities of choosing other alternatives (Vijverberg,
2011). Formally, this property corresponds to: for all R ⊂ N, T ⊂ N, R ⊂ T, for all j ∈ R, k ∈ R
pj(R)
pk(R)
= pj(T ) pk(T )
This expression entails that the ratio between the choice probabilities of R and T does not depend on the set that contains R and T . This means that IIA would not hold on cases where options in a subgroup are more similar between themselves than across subgroups. A very well known example is the "blue bus/red bus" paradox. Suppose that an individual needs to get to work and has the same chance of doing so by bus or driving his own car: Pr{car} = Pr{bus} = 1/2. Now suppose that there are two buses, only different in color: one is red and the other is blue. Assuming that the individual is indifferent to the color, one can assume that Pr{car} = 1/2 and Pr{red bus} = Pr{blue bus} = 1/4. However, the MNL model implies that Pr{car} = Pr{red bus} = Pr{blue bus} = 1/3 (Kök et al.,2008). One way to circumvent the IIA property, is to use a Nested Logit model, which is an hierarchical model that will be explained in Section4.4.
4.3.1 Data used in the MNL
Datasets used for Multinomial Logit estimation deal with several individuals, who make one or a sequential choice of one alternative among a plethora of alternatives. There are variables that influence these choices and they can be alternative specific or individual specific (Croissant,2018). Being alternative specific implies that the variable only relates to the choice (output). In this case, the choice is a delivery class and the alternative specific variables with a generic coefficient β are:
• Fee of the slot (γ);
• Hours between the order and the delivery (τ): This variable was divided into groups of 10 hours. • Delivery day (δ );
4.4 Applying a Nested Logit Model 19
– Beginning: Monday and Tuesday (δB);
– Middle: Wednesday, Thursday and Friday (δM);
– Weekend: Saturday and Sunday (δW).
The individual specific variables with an alternative specific coefficient βi j depend, as the
name suggests, on the individual that makes the choice. In this context, these variables relate to the order. They are:
• Percentage of fresh products (α); • Percentage of discount (ϕ); • Basket value (θ );
• Number of SKUs (ϑ ); • Socioeconomic index (ς ).
Customers assign a utility value to each delivery class, as well as to the no-choice option, which is specified as a function of the variables above. The selected delivery class is the one that the customer attributed the highest utility to. This utility is modeled as:
ui j= β0 j+ β1· τ + β2· δ + β3· γ + β4 j· α + β5 j· ϕ + β6 j· θ + β7 j· ϑ + β8 j· ς .
The regression coefficients of independent variables for each output level are interpreted in comparison with the reference level; this was arbitrated to be S2N1, since it was thought to be more relevant to compare delivery classes of the first days.
4.4
Applying a Nested Logit Model
The NL model groups similar alternatives into nests or hierarchies. Suppose that, from the top level, there are J limbs to choose from. The jth limb has Kjbranches to choose from, which are
numbered jl,..., jk,..., jK j. This is illustrated in Figure4.3. The utility for the alternative in the jth
of J limbs and kth of K branches is the following:
Ujk= ujk+ εjk, k= 1, 2, ..., Kj, j= 1, 2, ..., J
It is possible to have several levels, but for simplicity it is presented only a two level model. For models with nesting, the joint probability of being on limb j and branch k (pjk) can be
factored as the multiplication of the probability of electing limb j (pj) and the probability of
electing branch k conditional on being on limb j (pk| j).
20 Methodology
The deterministic utility function for this model is:
ujk= z
0
jω + x
0
jkβj k= 1, ..., Kj, j= 1, ..., J,
where zjvaries over limbs and xjkvaries over limbs and branches. ω and βjare called
regres-sion parameters (Cameron and Trivedi,2005).
According toKök et al.(2008), the IIA property applies within nests, but not for two alterna-tives in different nests. The main limitation of the Nested Logit model is the inherent difficulty, in some problems, of choosing the nesting structure. It may be obvious in some cases, but not in others.
Figure 4.3: General hierarchy of a two level Nested Logit model
In this application of the Nested Logit model, the hierarchy followed is depicted in Figure4.4. The first limb corresponds to the days between order and delivery and the branch is the width of the time window along with the time of day. As reported byXing et al.(2010), both customers with online experience and no online experience give more importance to specifying the delivery day than the delivery time slot, even though both are considered important.
4.5 Segmentations 21
4.5
Segmentations
Besides applying the Nested Logit model to the final dataset, to answer the hypothesis presented in Section 1.2, it was necessary to segment the data into different datasets. The segmentation variables were the percentage of fresh products, the percentage of discount and the type of day on which the order was made. The threshold to divide the set was based on the average value of the full set, in the case of the first two variables. Since the third variable is categorical, the set was divided according to each level. Each segmentation was then modeled with a NL regression. Using this method, it was expected that differences would be observed in the coefficient of the variable that translates the importance of time between order and delivery (τ).
A possibility discussed was using a product term to improve the explanatory power of the model. However, according toBerry et al.(2010), a statistically significant product term is neither necessary nor sufficient to conclude that there is substantively meaningful interaction among inde-pendent variables in their influence on the probability of the event. Furthermore, using interaction terms would not allow to evaluate the effect of other independent variables on τ (time between order and delivery), which segmentations do.
Chapter 5
Results
In this chapter are presented the results achieved through the implementation of the methodol-ogy described in the previous chapter. These results were obtained using the statistical software RStudio and the package MLogit v3-0 (Croissant,2018).
5.1
Descriptive Statistics
The descriptive statistics of the variables used in the Multinomial Logit model are shown in Table
5.1. Since "type of delivery day" and "hours between order and delivery"are categorical variables, they are described in Figures5.1 and5.2, respectively. The collinearity between variables was evaluated and no association was found.
Table 5.1: Descriptive statistics
Min Max Mean Median Std. Deviation Basket Value 0.74 1790.03 111.14 88.13 90.75 Fresh Percentage 0.00 50.00 9.75 7.41 9.96 Discount Percentage 0.00 95.25 16.92 15.75 10.90 Socioeconomic Index 57.30 214.50 173.40 214.50 47.02 Number of SKUs 1.00 298.00 30.30 27.00 18.82 Fee 4.21 7.96 6.22 6.24 0.40 23
24 Results
Figure 5.1: Histogram of type of delivery day
5.2 Multinomial Logit Model 25
5.2
Multinomial Logit Model
Applying the Multinomial Logit model to the data resulted in Table5.2. The coefficient of the vari-able that represents the time between order and delivery (τ) is negative, as expected, which means that the utility of the delivery classes diminishes when the waiting time increases. The individual specific variables increase the explanatory power of the model, but since the coefficients vary for each slot, we opted for only presenting the fact that the impact of the variable was "confirmed".
Table 5.2: Results from the Multinomial Logit model Predictors β eβ Std. Error Pr(>|z|) τ (Hours) - 0.24 0.78 0.02 <0.001 δB(Beginning) 1.16 3.18 0.02 <0.001 δW (Weekend) 0.56 1.75 0.02 <0.001 γ (Fee) 0.08 1.08 0.02 <0.001 θ (Value) confirmed ϑ (SKUs) confirmed α (Fresh) confirmed ϕ (Discount) confirmed ς (Index) confirmed McFadden R2 0.04
However, through the Hausman-McFadden test, it was verified that the IIA assumption was violated in this model. The implication is that one particular choice alternative does impact the relative probabilities of choosing other alternatives. Therefore, an alternative had to be considered and implemented. In order to relax the IIA assumption, a Nested Logit model was executed.
5.3
Nested Logit Model
The Nested Logit model was applied to the data. The nesting structure was already referred in Section4.4, Figure4.4. The first limb corresponds to the days between order and delivery and the branch is the width of the time window and the time of day.
Table5.3 presents the results achieved. From these results it is possible to infer that the fee of the delivery class (γ) and the time between order and delivery (τ) have a negative impact on the overall utility of the delivery classes, which meets the expectations: the longer the time until delivery and the higher the fee associated with a delivery class the less utility it will have.
26 Results
Table 5.3: Results from the Nested Logit model
Predictors β eβ Std. Error Pr(>|z|) τ (Hours) - 1.83 0.16 0.06 <0.001 δB(Beginning) 1.21 3.35 0.02 <0.001 δW (Weekend) 0.62 1.86 0.02 <0.001 γ (Fee) -1.62 0.20 0.05 <0.001 θ (Value) confirmed ϑ (SKUs) confirmed α (Fresh) confirmed ϕ (Discount) confirmed ς (Index) confirmed McFadden R2 0.04 5.3.1 Hypotheses
The hypotheses introduced in Section1.2were confirmed or reported inconclusive, according to the results below. Both fee (γ) and time between order and delivery (τ) are statistically signifi-cant, with p-values of approximately 0, in all segmentations presented below. All hypotheses are analyzed next.
HD: Baskets with more promotional products yield less customer willingness to wait
At a 95% confidence level (Figure5.3), it is not possible to evaluate this segmentation, since the confidence interval (CI) of the two models (segmentation with discount> 17% and segmentation with discount <17%) overlap each other. The same result holds for 90% confidence level. There-fore, the hypothesis that baskets with more promotional products yield less customer willingness cannot be confirmed. The coefficients relative to this segmentation are present in Table5.4.
5.3 Nested Logit Model 27
Table 5.4: Nested Logit model for discount percentage segmentation *** means p-value < 0.001 <17% > 17% Predictors β eβ β eβ τ (Hours) -1.70 *** 0.18 -1.86 *** 0.16 δB(Beginning) 1.35 *** 3.85 1.06 *** 2.88 δW (Weekend) 0.61 *** 1.85 0.61 *** 1.84 γ (Fee) -1.81 *** 0.16 -1.48 *** 0.23 θ (Value) confirmed confirmed ϑ (SKUs) confirmed confirmed α (Fresh) confirmed confirmed
ϕ (Discount) NA NA
ς (Index) confirmed confirmed
McFadden R2 0.05 0.03
HF: Baskets with more perishable products yield less customer willingness to wait
In Table5.5, are shown the coefficients for the fresh percentage segmentation in the Nested Logit model. In Figure5.4, with a 95% CI, it is possible to verify that the segmentation of customers who bought more fresh products (fresh products percentage> 10%) presents more sensitivity to time than the segmentation with customers who bought fewer fresh products (fresh percentage < 10%). The coefficient of the variable that translates time between order and delivery (τ) for the segmentation with fresh percentage> 10% is more negative than the coefficient of the same variable (τ) with fresh < 10%. The implication of this is that the content of the basket, specifically the presence of fresh products, does affect the choice of the slot and, consequently, the eagerness of the customer to receive its order. The results presented are in agreement with hypothesis HF,
that customer buying baskets with more perishable products have less willingness to wait. HF is,
therefore, confirmed.
28 Results
Table 5.5: Nested Logit model for fresh percentage segmentation *** means p-value < 0.001 <10% > 10% Predictors β eβ β eβ τ (Hours) -1.61 *** 0.20 -2.04 *** 0.13 δB(Beginning) 1.21 *** 3.36 1.23 *** 3.43 δW (Weekend) 0.57 *** 1.77 0.70 *** 2.01 γ (Fee) -1.52 *** 0.22 -1.91 *** 0.15 θ (Value) confirmed confirmed ϑ (SKUs) confirmed confirmed
α (Fresh) NA NA
ϕ (Discount) confirmed confirmed ς (Index) confirmed confirmed
McFadden R2 0.05 0.04
HW: The farthest to the weekend the higher the customer willingness to wait
In Table 5.6, are shown the coefficients for the ordering day segmentation. In Figure 5.5, with a 95% CI it is not possible to reach a conclusion since all CI overlap. However, with a 90% CI (Figure5.6), customers who made a purchase in the Middle of the week (Wednesday, Thursday and Friday) present more sensitivity to waiting than customers who ordered during the Weekend. That is, the order day causes an impact on the celerity with which the customer desires to receive its order. This is in agreement with the hypothesis presented.
5.3 Nested Logit Model 29
Figure 5.6: Confidence intervals for the type of order day segmentation at 90% confidence level
Table 5.6: Nested Logit model for type of order day segmentation *** means p-value < 0.001
Beginning Middle Weekend
Predictors β eβ β eβ β eβ τ (Hours) -1.75 *** 0.17 -2.01 *** 0.13 -1.70 *** 0.18 δB(Beginning) - - 0.32 *** 1.38 -0.12 *** 0.88 δW (Weekend) - - 0.18 *** 0.83 - -δM(Middle) -1.24 *** 0.29 - - - -γ (Fee) -1.46 *** 0.23 -2.50 *** 0.08 -2.11 *** 0.12 θ (Value) confirmed confirmed confirmed ϑ (SKUs) confirmed confirmed confirmed α (Fresh) confirmed confirmed confirmed ϕ (Discount) confirmed confirmed confirmed ς (Index) confirmed confirmed confirmed
30 Results
5.4
Delivery Class Fee
Is also possible to evaluate the effect of the fee on the utility of the delivery classes. In the complete model, as well as in every segment, the fee has a negative impact on the overall utility (see Figure
5.7). Customers that order more fresh products seem to be more sensitive to the influence of fees and customers that have more discounted value on their baskets seem willing to pay more for a delivery class, probably because they already paid less for their basket than anticipated. Also, customers that order in the beginning of the week are much less sensitive to fees than customers that order in the weekend or middle of the week. The most sensitive to fees are customers that order in the middle of the week.
Chapter 6
Conclusions
This work assessed the problem of understanding customer willingness to wait, in an e-grocery set-ting. The goal was to develop a model that allowed further comprehension of customer behaviour and that consisted of an improvement of the models currently adopted in time slot management studies.
The methodology adopted was, first, to model the slot choice using a Multinomial Logit model. However, this model did not comply to the assumption of IIA, since it failed the Hausman-McFadden test. Thus, a Nested Logit model was implemented, in order to relax this assumption. By segmenting the data using the percentage of fresh products, the percentage of discounts and the order day, conclusions were derived from the τ variable, which corresponds to the time between order and delivery. Through this, it was possible to conclude that the bigger the percentage of fresh products the lower the customer willingness to wait and that customers that made a purchased in the middle of the week were more sensitive to waiting than those who ordered during the weekend. Summarizing, two of the three hypotheses presented were confirmed.
We propose several ways of extending this research. The dataset used in this project had its limitations, since it only contained data that regarded online shopping. Other explanatory variables could be included in the model. These include, but are not limited to, the offline purchase history of the customer, the stock-outs he might have experienced in the past and the geographical proximity to brick-and-mortar stores. There is also the possibility that a connection exists between the ordering time and the time of the chosen delivery slot, since, even to make an order, a customer has to be available. This dimension was not explored in this project.
One of the limitations of this project is that customers are considered to be homogeneous, which means they do not present significant differences in their preferences. To improve this, customers could be separated according to their sensitivities to time and price (Ray and Jewkes,
2003).
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