The Effect of Grocery Delivery Services on Last Mile Emissions

Delivery or Not: The Effect of Grocery Delivery Services on Last Mile Emissions

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Citation Kunkel, Chadwick E. 2020. Delivery or Not: The Effect of Grocery Delivery Services on Last Mile Emissions. Master's thesis, Harvard Extension School.

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Delivery or Not:

The Effect of Grocery Delivery Services on Last Mile Emissions

Chadwick E. Kunkel

A Thesis in the Field of Sustainability and Environmental Management

for the Degree of Master of Liberal Arts in Extension Studies

Harvard University

May 2020

Abstract

Many millions of Americans shop for groceries every day and make thousands of choices about which groceries to buy and how to bring them home. Each one of these choices shapes the total greenhouse gas (GHG) emissions directly attributed to food consumption, and the total GHG emissions for millions of shoppers adds up quickly. To understand how each decision impacts their GHG emissions, shoppers must search and glean information from a multitude of resources. Ideally, they would focus on a small set of decisions impacting GHG emissions.

This research focused on the last mile delivery methods used for groceries and how it impacts GHG emissions. The research quantified last mile GHG emissions totals by evaluating the difference in carbon emissions if a grocery shopper switched from regularly using self-deliver methods to include grocery delivery services. I used grocery shopper customer personas based on consumer marketing data to compare GHG emissions. Each persona represented different US adult grocery shoppers’ habits and demographics.

The research quantified GHG emission totals for each customer persona and answered the question if making changes to their last mile behaviors significantly impacted monthly pounds of CO2e. I predicted using delivery services each week decreases the CO2e GHG last mile emissions by 50% when compared to self-delivery, and total pounds of CO2e GHG emissions is reduced by 20% if grocery delivery service replaces 33% of monthly self-delivery trips.

This research design identified which variables determine monthly carbon emissions and created personas based on demographics and grocery shopping marketing research. Data were collected in a spreadsheet tracker that represented each persona based on their vehicle, fuel type, and delivery density variables. Together, the personas and the research inform consumers what last mile footprint they typically embody. The personas’ last mile footprints were used to do “what-if” analysis that determined the impact of GHG reductions in carbon equivalents.

The results indicated that if 10% of the urban and suburban populations used grocery delivery service for 33% of their monthly trips, this would save the equivalent of

1096 US households’ yearly carbon footprint each month. Drilling down further, if 10% of the Suburban Millennial consumers used monthly grocery delivery services 33% of the time, they would reduce the equivalent of 288 US households’ carbon footprints each month. Collectively, 30 million daily US shoppers can, on average, each save 0.40 pound of CO2e a day if they incorporated delivery service into their routines. On any given day, this is equivalent to 122 US households yearly carbon footprint.

Using this information, marketing can be generated to target specific customer types and drive change in their last mile behaviors. The reduction of GHG emissions for large customer groups can impact last mile delivery options in a clear way. This clarity can be used to inform grocery consumers how their collective actions make a difference.

Acknowledgements

There are many different people to thank for their generous time and support during this endeavor. I am extremely grateful to each one of you and every nugget of support and encouragement you passed my way. This definitely includes Dr. Mark

Leighton’s support developing the thesis proposal and Dr. Brad Allen’s guidance and mentorship as I worked to complete the thesis.

Specifically, I want to say thank you to my mother and father because without their support, I would not be where I am today. My daughters, their concern for their future and the environment guided this work; my family in Boston for all their support, interest, and dinners; and Judi and Anne for taking such an interest that you were willing and able to read multiple copies and offer advice. Finally, to Lauren, we did it!

Table of Contents

Acknowledgements…………………………………...……………………....…………...v

List of Tables…………..……………………………..……………………....…………viii

List of Figures…………………………….……..…………....……………....…………..ix

Definition of Terms…………………………….…….…..…..…………....………………x

I. Introduction…………….…………….……………..…....………………………..1

Grocery Customer Personas……………..……..……………………….....2

Research Significance and Objectives………………..…..…………………….....5

Background……………………………………………..……..…………………..5

GHG Emissions by LCA Stages and Meal Types………..…..….……...... 6

Decreasing GHG Emissions…….………………………………………...8

Last Mile Transportation………………………..……..…………………10

Grocery Delivery Business.…….………..…….………………………...10

Delivery Density.…….…………..…………..………...………………...12

Relationship of Data Variables……..…………….……………………...13

Research Questions, Hypothesis and Specific Aims………....………………….14

Specific Aims……………………………….……...…………….………14

II. Methods……………………………………………....…………………………..16

GHG Emissions from Last Mile Delivery………..…....……...…..………..……16

Delivery Trucks…..………..……….………….…………..…………….19

Delivery Density Variables………..……………………..………………20

Market Research to Characterize Grocery Shoppers Market………….……..….21

Customer Persona Demographics..………...…..…...…………………....23

III. Results…………..…………………………..……….…………………….……..28

Test of First Hypothesis…………………….…….……………………...28

Test of Second Hypothesis………………….…….……………………...29

Urban and Rural Customers……...... …..………..………………………30

Key Customer Indicators………….....…..……….………..…….………31

IV. Discussion……………………………………….……...….…….………………33

Impacts on Delivery Services……..….…………..………...…………....34

Demographic Changes………………...... …..…….……..………………36

Conclusions………………………………………….……..…..……………...…38

Purpose Driven Marketing…………..…..…….………….………...…....38

Future Trends in Grocery Marketing…………...……….……………….39

Optimizing Data on the Last Mile to Influence Change……..…..………42

Appendix Last Mile Emission Calculator ………………….…..……….……..….…....44

References…………………………………………….………..……………...…………45

vii

List of Tables

Table 1 Vehicle carbon emissions from annual fuel use in lb of CO2e per gal…..18

Table 2 Summary of delivery density research..…….…….…..…….………....…21

Table 3 Grocery shopper types analysis.…….…….………….…....…….….…...22

Table 4 US grocery shoppers' population summary statistics…….…….….….…23

Table 5 Hypothesis One and Two results in % reduction of GHG emissions……28

viii

List of Figures

Figure 1 US Trend of weekly grocery shopping trips………………………………2

Figure 2 Trends in the US regularly using online only grocery shopping……...…..3

Figure 3 Grocery shopper types………………………………………………….....4

Figure 4 General stages of food production in an LCA analysis……………….…..6

Figure 5 Share of GHG emissions per food production stage………………….…...7

Figure 6 Peapod and FreshDirect trucks………………………………….……….11

Figure 7 Illustration of self-delivery compared to grocery delivery service..……..13

Figure 8 Relationship of annualized pounds of CO2e emissions to mpg………….18

Figure 9 Expected lb of CO2 emissions from diesel fuel………..….……….…….20

Figure 10 Average Suburban and Urban US Adult customer profiles…….………..24

Figure 11 Suburban Baby Boomer customer profile...……………………….……..25

Figure 12 Rural Baby Boomer customer profile..……………………………..…....25

Figure 13 Suburban Millennial customer profile..………..….……..…….….……..26

Figure 14 Urban Millennial customer profile..…………..…………..………...…....26

Figure 15 Urban Gen X customer profile..……..……..……..………………..…….27

Figure 16 Suburban Gen X customer profile..………………..…………………….27

Figure 17 Percent of personas achieving monthly hypothesized CO2e reductions....30

Figure 18 Weekly percent of millennials using online grocery shopping…………..34

Figure 19 Monthly online grocery consideration trends………………………..…..41

Figure 20 US online grocery sales in billions………………………………………42

Definition of Terms

Carbon footprint: Amount of carbon dioxide and carbon compounds emitted due to

the consumption of fossil fuels by a particular person, group, etc.

Considerers: Type of grocery shopper who primarily shops in stores but

considers using online services more frequently

Curbside pickup: Grocery service where the customer picks up the groceries in a

designated area near the store

Customer persona: is model used to represent key traits of a large segment of your

consumers inspired by data collected from research

Delivery density: The amount of deliveries spaced in one route; the closer the

deliveries, the higher the delivery density

FMI: The food industry association that works with and on behalf of the

entire industry to advance a safer, healthier and more efficient

consumer food supply chain

FreshDirect: Is an online grocer that delivers to (sub)urban areas

Grocery delivery: A service where a grocery company delivers to a customer

GHG: Greenhouse gases reflect heat energy onto the Earth’s surface

Gig-economy: Labor market defined by short-term or freelance work

GRI: Global Reporting Initiative that standardizes business impact on

climate change, human rights, and corruption

ICSC: The global trade association of the shopping center and retail real

estate industry

Interchangers: Type of grocery shopper who regularly switches between online

and in person shopping

Last mile: The movement of goods from a hub/store to their final destination

LCA: Life Cycle Analysis is a technique to assess environmental impacts

associated with production and disposal of a product

Peapod: Is an online grocer that delivers to (sub)urban areas

Self-delivery: The movement of goods to their final destination by the consumer

Shipt: An internet-based delivery service company

Chapter I

Introduction

In the past two decades, the number of options consumers have to purchase and deliver groceries has multiplied (Martinez & Elitzak, 2018; Kuijpers, Simmons, &

Wamelen, 2018). What hasn’t changed is that convenience is still a main preference that drives grocery purchasing choices (Scholderer & Grunert, 2005; MarketWatch, 2019;

Shahbandeh, 2020). Consumers decide every day between how to optimize convenience for their grocery shopping based on the marketing available to them (Zwiebach, 2016).

Home delivered grocery services as a matter of convenience is a growing trend for grocery shoppers (Dumont, 2019a). During 2017-2019, over 25% of primary grocery shoppers in the US used delivery services instead of self-delivery at least once (ICSC,

2018) versus under 10% the years prior (ICSC, 2018). As more consumers select delivery services, new choices and options become available for shoppers (Rohm & Swaminathan,

2004). These options are solutions for the last mile of delivering groceries. “Last mile” is a term used to describe the logistics of delivering products from a store or hub to consumers’ homes, which vary by location and distance (Dolan, 2018).

Consumers are making decisions based on what is convenient to them with minimal information about the environmental costs of their last mile alternatives (Heard,

Bandekar, Vassar, & Miller, 2019). Collectively, millions of daily shoppers’ choices greatly impact the GHG emissions from last mile options. GHG emission research can determine what percent of emissions each food category accounts for (Pelletier, 2015) but

extra research is required for the analysis of every food product available. Research on transportation options for shoppers’ last mile avoids complexity of hundreds of categories of food and their GHG emissions for each item. Focusing on GHG emissions between last mile options shoppers have a simpler set of choices (Brown & Guiffrida, 2014).

Grocery Customer Personas

Modeling monthly last mile GHG emissions using customer personas is a specific way to identify how to market to consumers preferences. Research has shown that consumers respond favorably to marketing that “exhibited high levels of both environmental beliefs and perceived usefulness compared with other marketing” (Schill

& Godefroit-Winkel, 2019, p. 317). The personas create and guide targeted marketing.

Each persona represents the total of GHG emissions from shoppers’ choices based on their different transportation choices. These form the base emission levels that determine how marketing strategies impact consumers last mile choices.

Figure 1. US Trend of weekly grocery shopping trips (FMI, 2017).

The average domestic US consumer monthly grocery trips are calculated by using weekly average of grocery shopping trips (FMI, 2017) (Figure 1). Using data from FMI

(2017) to calculate monthly shopping trends concludes that shoppers self-deliver

groceries seven times a month in 2017. This frequency has held constant since 2014 through 2019 (Bedford, 2019). These trends denote average trips to the grocery store peaked in 2012 and has held steady since 2014.

Figure 2. Trends in the US regularly using online only grocery shopping (FMI, 2019).

During the same time grocery store trips held steady, the use of online only grocery stores has been on a steady upward trend (FMI, 2019) (Figure 2). Since 2015, this trend doubled from 16% to 33% in 2019. Taking together, steadfast monthly grocery store visits and growing demand for online grocery services, infers a developing demand for grocery delivery services for US consumers.

This research correlates trends with marketing research from ICSC (2018) to categorize four types of grocery consumers. The four types have been used to explain grocery shopping behaviors, which predict last mile transportation choices.

• In-Store Regulars essentially buy most of their groceries in physical stores and

plan to continue doing so in the near future.

• Considerers do most of their grocery shopping in store but would consider

increasingly taking advantage of buying groceries online.

• Interchangers regularly switch between grocery shopping in stores and online.

• Online Regulars prefer buying groceries online and rarely going to physical stores

to purchase items.

Figure 3. Grocery shopper types percent of US adults based on online shopping adoption (ICSC, 2018).

Examining these four shopper types trends in Figure 3 suggests millennials select online delivery services at a higher percentage than their generational peers. They are twice as likely to shop online only and interchange between self-shopping and online at almost twice the rate. Convenience and cost savings are two central reasons given for higher adoption rate for millennials (Koch, 2019; Melton, 2019).

A quarter of Gen X and millennials shoppers are considerers, which is almost

10% more than Baby Boomers. Baby Boomers lead the way as in-store regulars but are almost twice as likely to be interchangers. This could indicate a growing trend toward consistent online shopping due to changes as they age (ICSC, 2018).

Research Significance & Objectives

I used the four grocery shopper types to define separate personas by predicting the amount of their monthly grocery trips, how often they use online shopping, and frequency of use of grocery deliver services. The shopping types effect personas profile statistics that determine monthly last emissions.

This research compared six specific and two average US consumers’ monthly emissions totals measured in pounds of CO2e for self-delivery and grocery delivery services. The consumer data used to develop the personas are vehicle types and mpg, trips to grocery store, types of online grocery shopping behaviors, and age and geography demographics (FMI, 2017, 2019; ICSC, 2017, 2018). Together, they establish monthly

GHG emissions contributed to last mile delivery preferences.

Using the personas monthly GHG emission totals, what-if scenarios were used to determine the impact on GHG emissions reductions if customers changed last mile self- delivery behaviors. The what-if comparisons were one way to measure potential changes of over 30 million daily grocery shoppers (FMI, 2014; Lake, 2019). The last mile behavior changes have measurable impact when total reductions are aggregated via the personas. These comparisons are core to forming market strategies, deciding which persona group to target, and the types of messages that lead to adopting more grocery delivery services.

Background

The US food sector contributes about 25% of GHG emissions of final consumption of all goods purchased (Hanssen et al. 2017; Heard et al. 2019). Increasing

population in the US and ever-expanding demand for convenient food options continue to drive energy demand in food production (Lizunkov, Politsinskaya, Malushko, Kindaev,

& Minin, 2018). Food production includes every stage and process involved in cultivating, processing, packaging, delivering, consuming, and end-of-life phases for any type of food.

Food production is dependent on fossil fuels and growth in demand for food directly correlates to increased GHG emissions. The GHG emissions food production stages are measured using a life cycle analysis methodology. Life cycle analysis (LCA) tabulates total energy produced from multiple stages to produce goods (Brown &

Guiffrida, 2014; Fenton, 2017; Hanssen et al. 2017; Heard et al. 2019).

GHG Emissions by LCA Stages and Meal Types

The LCA model details the GHG emissions of each food production stage as food moves from farm to home (Fenton, 2017) (Figure 4). The first four stages identified in gray, generate GHG emissions from the demand to provide food products and prepare them for resale. The stages identified in blue are directly related to each step of consumption for food products.

Figure 4. General stages of food production in an LCA analysis (Fenton, 2017).

This research and analysis from food production LCA defines emissions attributed to each stage. The largest producer of GHG emissions is due to the current requirement of fossil fuels to perform each stage of the LCA. The US dependency on fossil fuels to produce and transport food from farm to home is the largest driver of GHG emissions (Canning, Rehkamp, Etemadnia, & Waters, 2017).

Using multiple meal types to investigate a food production LCA, clarifies the percentages of GHG emissions attributed to each stage (Heard et al. 2019). Quantifying the emissions of an LCA identifies the impact each stage has in consumers carbon footprints. The analysis reviewed used five different meal types that were purchased as a meal kit [MK] or at a grocery store [GS] to examine the LCA for food production (Heard et al. 2019) (Figure 5).

Figure 5. Share of GHG emissions per food production stage of five meal types and two delivery options (Heard et al. 2019).

Each bar graph section represents the percent of GHG emissions per stage for each meal type. The food section is the main contributor of GHG emissions and is ~50% or more of GHG emissions for meal kits and grocery store meals (Heard et al. 2019). This is true for all meals except chicken and salmon. This is due to controlling for safe handling conditions and food waste practices (Heard et al. 2019).

The second highest contributor of GHG emissions for the meal types is attributed to last mile transportation from the store to home (Heard et al. 2019) (Figure 5). This is

~25% for every meal type analyzed. Combining packaging and processing stages accounts for ~10% of the GHG emissions. The remaining four stages of the analysis explain the remaining GHG emissions. On average, every meal kit type or grocery-based meal, food production plus transportation accounted for at least 60% of GHG emissions

(Heard et al. 2019).

Decreasing GHG Emissions

Disconnecting food production’s dependency on fossil fuels would require national changes in behavior, technology, and logistics. This is a complicated, expensive, and difficult task that requires marketing and a large amount of data (Poinski, 2018).

Large quantities of research to drive policy changes and update supply channels to change consumption patterns has been stymied in the current US political environment.

Looking past fuel requirements, the next step is to review the variables associated in the types of food, packaging, processing and transportation, and to identify ways to reduce GHG emissions totals in each LCA stage. The food stage would be ideal area to make changes. It would also be very complicated and expensive to influence consumers

preference for food and food type and update all the supply chains involved in food production (Hanssen et al. 2017).

For example, the type of food determines the amount of GHG emissions totals.

Reviewing the meal types detailed in Figure 5, hamburgers’ percentage of GHG per meal is two to three times greater than all other meals emissions (Heard et al. 2019). Switching to plant-based meat products or cutting out hamburgers from an average diet would greatly reduce GHG emissions. Current market trends provide hope that this could be done as Beyond Meat has shown since becoming a publicly traded company

(Damodaran, 2019) but current trends show meat consumption continues to grow

(USDA, 2019).

Food processing and packaging makes up ~10% of GHG emissions per meal, and packaging materials could be manufactured so that they could be recycled. Currently, estimates put ~30% of food packaging produced is recyclable (Rosane, 2019) and US trends of recycling are consistent at about 35% overall for all material types (EPA, 2019).

Both of these trends have work ahead of them to move towards improving rates of recycled material and recycling as a whole to lower emissions in food production.

Examining last mile transportation, the per meal emissions account for ~25% of the LCA. Additionally, consumers have two basic options to bring their food home: self- delivery or delivery services. As grocery delivery options increase and adoption of delivery service increases (Dumont, 2019a; Melton, 2019; Poinski, 2018), the last mile of food production has the most potential for making changes in consumer behaviors.

Last Mile Transportation

Last mile transportation is complicated, polluting, and an expensive element of

US food production. The last mile delivery for consumers accounts for ~28% of all transportation costs (Brown & Guiffrida, 2014). For service providers, last mile expenses often exceed 50% of logistics costs and are time-consuming to solve because of the many variables involved based on geography and local policies (Yuan, Cattaruzza, Ogier, &

Semet, 2018).

Directly influencing consumers last mile behaviors can create reductions in expenses and carbon emission pollution. Changing behaviors today could collectively reduce the amount of GHG emissions tomorrow. More online shopping could reduce personal shopping trips because delivery services remove thirty times the travel than consumers selecting self-delivery for the same purchases (Cortright, 2019).

Last mile delivery services are projected to grow 60% from now through 2022.

This growth is projected to increase sales to over $51 billion in the US from 2018 levels

(Statista, 2018). The projected growth and the reductions in GHG emissions of last mile delivery services provides an achievable target for effective behavior change in consumers.

Grocery Delivery Business

FreshDirect utilizes a fleet of hundreds of vehicles. The fleet ranges from box trucks to tractor trailers using diesel and gasoline fuel with multi-variable mpg for each truck depending on route, driver, and maintenance. They have invested in a variety of

technologies to offset traditional fuel consumption: hybrid diesel-electric trucks, fully electric trucks, and trucks with battery powered refrigeration (Gager, 2018).

The majority of the fleet used in the NYC area are diesel vehicles (FreshDirect,

2020) (Figure 6) that are 2010 or newer and that incorporate clean diesel technology and particulate filters to operate more cleanly. FreshDirect will continue to pursue technology opportunities to make their fleet of greener (Gager, 2018).

Figure 6. Peapod and FreshDirect trucks (Salazar, 2018; Welcome 2 the Bronx, 2019)

Peapod utilizes data to manage their fleet of grocery delivery trucks. This is similar to FreshDirect as they both use data to inform each driver on how many miles they will drive a day, how long it takes to deliver for each customer, and the time to travel to each stop (Doering, 2018; Peapod, 2020). Drivers have the autonomy to reach out to customers to inquire about delivering earlier or later than scheduled to optimize travel time and distance.

Ultimately, customers’ requests and delivery windows determine trip distances and routes. Drivers make many stops in one area, drive to another city far away, only to return back to the first to make just a single delivery. From a Peapod driver perspective, the daily requests and updates from customers disadvantages the company with more miles on the road and higher expenses (Doering, 2018).

Delivery Density

Together, delivery trip distance and average mpg of the delivery vehicle are two variables determining amount of carbon emissions for each delivery. Dividing total trip emissions by the total number of deliveries calculates the last mile emissions for each customer. (GRI Index, 2019; SLS Consulting, 2008).

Multiple studies confirm that two key variables determine the GHG emission efficiency of delivery: CO2e emissions ratio and customer density on delivery routes

(Goodchild, Wygonik, & Mayes, 2017; Hardi & Wagner, 2019; Wygonik & Goodchild,

2012). Emissions ratios represent the difference in mpg of the delivery vehicles, and customer density is defined by how closely each delivery is located to each other.

Increasing delivery density for deliveries is one example on how to reduce GHG emissions of delivery trucks. High density routes have more customers spaced closer to each other than low density routes (Siikavirta et al. 2003) (Figure 7). These higher density routes use less fuel because the truck is traveling less miles and has more deliveries on the same stop, minimizing idle time. The more stops a delivery truck has per mile and the more deliveries per stop, the lower the emission and economical cost of

deliveries. When delivery volumes increase, delivery becomes progressively more efficient (Cotright, 2019).

Figure 7. Illustration of self-delivery compared to grocery delivery service (Siikavirta et al. 2003).

Relationship of Data Variables

Modeling the last mile GHG grocery emissions for the average U.S. adult, we can assume the following variables are important and these relationships must logically hold.

These relationships are used to guide the research and development of personas.

• Round trip distance to grocery stores is positively related to GHG emissions

• Vehicle mph averages are inversely related to GHG emissions

• Higher delivery density metrics reduce GHG emissions per grocery order

• Frequency of all shopping trips increases overall GHG emissions

Collective changes US consumers make to their own last mile behaviors add up quickly. Creating clarity for consumers to choose options that reduce their own GHG footprints simplifies the process to make collective changes. These changes are not just a physical means of transportation, but also displace the typical grocery shopping

experience for U.S. consumers (Heard et al. 2019). This displacement is an opportunity for grocery companies.

Research Questions, Hypotheses and Specific Aims

My research questions were guided by comparing the monthly GHG emissions total for an US adult to transport groceries using multiple last mile options:

● What is the difference in a month’s total of GHG emissions if a grocery shopper

switched from only using self-deliver methods to only grocery delivery options?

● Can an average US grocery shopper modify their last mile grocery delivery

behaviors and significantly lower their monthly GHE emissions?

I hypothesized that using grocery delivery services exclusively decreases the total amount of pounds of CO2e GHG emissions for a US adult month grocery by 50% when compared to using self-delivered only. Secondly, I hypothesized that the total pounds of

CO2e GHG emissions will be reduced by 20% if they replace ~33% of their self- delivered trips each month with grocery delivery services.

Specific Aims

To conduct the research, these steps were executed to complete the analysis and create customer personas:

• Create a table of pound of CO2e per mile for each vehicle and fuel type by using

the EPA average vehicle mileage report (EPA, 2015) and EPA emission

calculator (EPA, 2018a) to record the emission per vehicle and mpg, and then

convert to carbon emissions per mile.

• Chart the pound of CO2e for annualized fuel use by vehicle type to incrementally

visualize how mpg and vehicle type impacts the amount of GHG emissions.

• Graph the pound of CO2e per mile for different grocery delivery trucks to

showcase the expected monthly emissions for grocery delivery services.

• Use customer information from marketing reports (FMI, 2017, 2019; ICSC, 2017,

2018) to generate multiple consumer types based on last mile delivery options,

and frequency of use of options based on geographical and age demographics.

• Use comparison graphics to showcase the difference in monthly totals of

emissions for each of the customer types.

• Model what-ifs scenarios using the monthly GHG emissions as baselines and

changing last mile behaviors to compare impact in carbon equivalent scenarios.

Chapter II

Methods

Calculators were created to compile and compare total pounds of CO2e emissions for eight consumer types. Each type was created using US adult grocery shopping behaviors from marketing research. The research segmented the data into multiple demographic groups based on generational groups, residential geography, and delivery preferences formed by grocery shopper types (FMI, 2017, 2019; ICSC, 2017, 2018).

These calculators are used to measure likely last mile GHG emission baselines of the eight consumers and explore delivery options that could alter their carbon output (Butler,

Henderson, & Raiborn, 2011; Rohm & Swaminathan, 2004).

GHG Emissions from Last Mile Delivery

The collected data was applied in a spreadsheet tracker that calculated monthly emission totals for each customer persona and tested both hypothesizes. To summarize, the following list of US adults’ variables were utilized in the calculator:

• Monthly self-delivery trips

• Monthly grocery delivery usage

• Vehicle fuel type

• Vehicle mpg

• Round trip miles to grocery store

• Average miles per delivery

Self-delivery and delivery services use an array of vehicles and delivery trucks to transport groceries. Average US consumer vehicle types was determined using the

Transportation Energy publication by the Oak Ridge National Laboratory (Davis,

Williams, & Boundy, 2019). Miles per gallon of the vehicles was sourced from US

Department of Energy (DOE, 2018). These data were used to examine different vehicle types and average mpg to calculate monthly last mile emissions.

Last mile emissions of delivery services vehicles were calculated using published vehicle type and mpg from multiple sources for FreshDirect, Peapod, and UPS (Cortright,

2019; DOE, 2009; Isuzu, 2019a; Lammert, 2009). The emission calculations were based on the number of times the customer utilized grocery delivery service each month, the mpg of the delivery vehicles, and average trip lengths of each delivery.

The emissions data were collected from the EPA GHG calculations (EPA, 2018b) for gasoline and diesel fuel. The data from the EPA is reported in grams of carbon per gallon of fuel type. Gasoline is 8,887 grams of carbon per gallon and diesel is 10,180 grams of carbon per gallon. Grams were converted to pounds by dividing the total amount of grams of carbon emitted by 453.59. This calculation was built into the last mile emission calculators to create output in both grams and pounds for verification. All of the emission data is labeled as pounds of CO2e.

The US vehicles data confirms the relationship between mpg and annualized pounds of CO2e (Table 1). Analysis indicates an indirect relationship: as mpg increases, annualized pounds of CO2e decreases. This relationship is true for both delivery options: self-delivery and group delivery. Improving group delivery vehicle mpg is a validated

method to decrease last mile emissions (Goodchild, Wygonik, & Mayes, 2017; Hardi &

Wagner, 2019; Siikavirta, Punakivi, Karkkainen, & Linnaen, 2003).

Graphing the relationship between mpg and annualized pounds of CO2e shows a decrease after 17-mpg is achieved (Figure 8). After 20-mpg, the amount reduced decreases at a slower rate. The area of the graph between 10-mpg to 20-mpg represents the average grocery delivery service vehicles mpg.

Table 1. Vehicle carbon emissions from annual fuel use in lb of CO2e per gallon. Vehicle type mpg Fuel use Annual lb CO2e per gal Transit bus 2.7 11,242 220,258 Delivery truck 6.7 1,754 34,365 Light truck/van 17.4 689 13,499 Light-duty vehicle 22 522 10,227 Car 24 475 9,306 Motorcycle 43.9 54 1,058 Data collected from EPA (2015) for gasoline fuel type.

Figure 8. Relationship of annualized pounds of CO2e emissions to mpg for gasoline from Table 1.

Delivery Trucks

Grocery delivery companies use various types of delivery trucks to optimize their delivery range and capacity. There is a high degree of variability in the types of trucks used based on business objectives and planning. Primary factors considered are pricing of vehicle, operational costs, size of truck, neighborhoods serviced, and local policies requiring fuel efficiencies and idle time (Doering, 2018; Gager, 2016; Tracey, 2015;

UPS, 2017).

I used FreshDirect and Peapod as proxies for (sub)urban customers to calculate last mile emissions for grocery delivery services. Both companies utilize an Isuzu NPR diesel class delivery truck as a primary delivery truck (FreshDirect, 2020; Peapod, 2020).

This delivery truck has a fuel tank of 30 gallons (Isuzu, 2019b) and 9.5-mpg for gasoline and 16-mpg for diesel (Isuzu, 2019c).

UPS delivery data were used as proxy for rural customers for delivery service.

UPS has a very diverse delivery area and their universal P70 model is used widely in all delivery regions in the US (UPS, 2019a). This model uses diesel and in some cases is hybrid diesel/electric (Lammert, 2009). UPS P70 model has an average fuel economy of

10.2-mpg for their diesel engines and when using low-emission hybrid diesel/electric is

13.1-mpg (Lammert, 2009). Like the Isuzu NPR truck, the P70 also has a 30-gallon fuel tank (UPS, 2019b).

Using the standard 30-gallon fuel tank for grocery delivery trucks, we can compare Isuzu and UPS delivery trucks mpg with the US average mpg for light truck/van and delivery trucks (DOE, 2018). Calculating pounds of CO2e of diesel emissions each truck emits per mile (Figure 9) shows that FreshDirect, Peapod, and UPS delivery trucks

compare closely to the light truck/van category for the Department of Energy published mpg (DOE, 2018). This confirms a close relationship between tracked averages of mpg light trucks/vans across the US with the standard trucks being used today to delivery groceries for FreshDirect and Peapod.

Figure 9. Expected lb of CO2 emissions from diesel fuel per mile between grocery delivery trucks with a standard 30-gallon fuel tank.

Delivery Density Variables

To establish and verify delivery density variables, multiple papers were examined on round trip self-delivery and grocery delivery services (CDC, 2015; Fenton, 2017;

Goodchild, Wygonik, & Mayes, 2017; Hardi & Wagner, 2019; Martinez, & Elitzak,

2018; Siikavirta et al. 2003; Wygonik & Goodchild 2012). These papers built on delivery density research and utilized updated survey results from adult grocery shoppers. The authors statistically modeled and analyzed the survey data to establish average delivery density for suburban, urban, and rural customers. The review of these variables is

summarized in Table 2 and used as proxies to establish last mile variables for each customer persona.

Table 2. Summary of delivery density research. Variables Sub(urban) Rural Self-delivery: grocery store round trip mileage 3.8 25 Delivery service: delivery miles per customer 0.6 1 Delivery service: customer per delivery 54 62

Truck size is one factor that optimizes value to delivery companies and a method to establish the average amount of rural customers per delivery that the research review did not cover (Wygonik & Goodchild, 2012). UPS P70 truck is ~15% larger than the

Isuzu truck used a proxy for suburban and urban deliveries (Isuzu, 2019a; Morgan Olson,

2019; UPS, 2019a). The rural density of 62 customers is the result of increasing the

(sub)urban customer per delivery variable. UPS data establishes rural delivery service variables, since Peapod and FreshDirect currently do not delivery to any of the rural zip codes used in the personas.

Market Research to Characterize Grocery Shoppers

Research by two consumer marketing firms FMI (2017, 2019) and ICSC (2017,

2018) was leveraged to understand and describe the average US adult grocery shopper.

They both used a sampling method to survey 1000+ adult primary shoppers. The surveys and sample group were selected as representatives of urban, suburban, and rural zip codes across the US to determine the grocery shopping habits across multiple generations: Baby

Boomers, Gen X, Millennials, and the Silent Generation. This research utilized the geography demographics and age demographics sans Silent Generation data.

These survey samples tracked age, income, shopping and delivery behaviors and compared these with sales trends in the grocery industry. The survey samples were extrapolated further by the marketing firms to develop grocery shopping types (FMI,

2017, 2019; ICSC, 2017, 2018) (Table 3).

Table 3. Grocery shopper types analysis. Shopper Types Baby Boomer Gen X Millennial AVG US Adult In Store Regular 77% 70% 65% 68% Considerers 17% 26% 25% 22% Interchangers 5% 3% 8% 6% Online Regulars 1% 1% 2% 2% Online Deliver to Home 43% 39% 29% 35% Online Pick up at Store 43% 43% 56% 49% 2017 Online Orders % 12% 24% 43% 10% 2019 Online Orders % 18% 40% 45% 25% Online orders are the percentage of each group who made at least one online grocery order a month (FMI, 2017, 2019; ICSC, 2017, 2018)

The analysis was used to create the customers personas in this research. A review of the analysis indicates these primary characteristics for the average US adult grocery shopper:

• They travel seven times a month to the store

• They do the primary shopping for their household

• 70% of them only self-deliver groceries

• All groups use multiple stores to source their groceries

• Age demographics indicate:

o Different preferences to self-deliver versus grocery delivery services

o How often they consider changing between these two options

Customer Persona Demographics

Customer personas that mirror marketing research and grocery shoppers’ demographics determine last mile emissions and informs marketing strategies. Different relationships between the variables can infer if a customer is able or willing to switch from self-delivery to grocery delivery. For example, if a customer’s home is located in a rural area compared to an urban area, they will need to drive more miles to the store and have less options (if any options at all) for grocery delivery services.

Comparing the demographic data between where an average US adult lives and what generation they represent helps recognize where new customers might exist for grocery delivery services. The majority of the US population lives in a suburban geography with about half each of the Baby Boomer, Gen X, and Millennial population calling it home (Zillow, 2016) (Table 4). This information is valuable to know who and where to target messages that impact last mile behaviors and reduce GHG emissions.

Table 4. US grocery shoppers' population (pop.) summary statistics. Variable Suburban Urban Rural Percent of US pop. 55 31 14 Percent of Baby Boomer represent 53 20 23 Percent of Gen X represents 54 23 27 Percent of Millennials represent 47 33 20 2020 US pop. ~329 million. Data sourced from Parker et al. 2018 and Zillow, 2016

Each persona separates total customer populations into groups that could help target marketing. The Suburban Millennial profile provides details on a large proportion of the US population and that over 40% of them shop online for their groceries. In contrast, Suburban Baby Boomer customer type outnumber millennials population in the suburbs and have more economic resources to try new services. As they continue to age, preferences could change due to decreases in their mobility and limited access to grocery stores. As of 2018, only 17% of Baby Boomers have used online grocery shopping as compared to 40% of millennials. As a group, they have large growth potential for online shopping. These and other trends from the persona’s demographics could be used to create specific groups for focused marketing about delivery service value. Details for each of the eight personas are presented in Figure 10 through Figure 16.

Figure 10. Average Suburban and Urban US Adult customer profiles.

Figure 11. Suburban Baby Boomer customer profile.

Figure 12. Rural Baby Boomer customer profile.

Figure 13. Suburban Millennial customer profile.

Figure 14. Urban Millennial customer profile.

Figure 15. Urban Gen X customer profile.

Figure 16. Suburban Gen X customer profile.

Chapter III

Results

The test results are summarized in Table 5 to highlight comparisons between the results based on age and geography demographics. The results are represented in percentages and indicate the amount of monthly last mile emissions reduced by altering grocery delivery behaviors. All tests that did not reduce last mile emissions at or beyond the test thresholds are highlighted in red for clarity.

Table 5. Hypothesis One and Two results in % reduction of GHG emissions. Customer Personas Test One Test One Test Two Test Two Suburban Baby Boomer 85.79% 90.34% 75.49% 74.19% Rural Baby Boomer 93.84% 94.51% 81.23% 81.10% Suburban Millennial 68.42% 78.53% 74.32% 70.53% Urban Millennial NA* NA* NA* NA* Urban Gen X -7.38% 27.01% 101.74% 93.71% Suburban Gen X 73.46% 81.97% 75.51% 72.86% Suburban AVG US adult 60.13% 98.16% 82.78% 79.14% Rural AVG US adult 92.35% 93.18% 73.61% 73.37% Gasoline fuel in yellow & diesel fuel in green. Percentages in red did not achieve hypothesized results. Asterisk indicates a preference to self-delivery by walking.

Test of First Hypothesis

The first hypothesis specified that a customer could reduce 50% or more of their emissions by switching completely to a delivery service. Any percentage above 50% passed the test. The larger the percentage, the greater the reduction in monthly last mile emissions. For example, in Test One, a Suburban Baby Boomer achieved a ~85%

reduction and a Suburban Millennial achieved a ~69% reduction in monthly last mile gasoline emissions using only delivery service as compared to using only self-delivery.

Any persona near 0% indicates the monthly amount of carbon emitted is about equal for either monthly delivery behaviors. Any percentages that are negative implies emissions were greater than using only self-delivery. For example, the Urban Gen X consumer at -7.38% increased emissions by using delivery services.

It is not surprising that 75% of the customers achieved 50% or more reductions for both fuel types. This result endorses the value of combining multiple groceries deliveries in one vehicle. This verifies that increasing mpg of self-delivery vehicles will not reduce emissions at the same rate as utilizing more delivery services a month. It also endorses the correlation of lower emissions for delivery services: deliver density ratios determine the relationship between mpg and emission reductions.

Test of Second Hypothesis

The second test specified that customers could reduce 20% of their emissions by switching ~33% of the self-delivery trips with a delivery service. Test Two gasoline and diesel results equal or lower than 80% indicate a customer achieved the stated reductions

(Table 5). The lower the percentage, the greater the reduction. For example, a Baby

Boomer living in the suburbs, had a percentage score of ~75%. This means they reduced more than 25% of their monthly emissions using grocery delivery services a third of the time.

All the customers that passed the Test Two are in the range between 70% to 79%.

This confirms using delivery services at least one-third of the time each month resulted in

measurable and significant difference in last mile emissions. Having results this close to the test threshold indicates there are opportunities to target specific customer characteristics to reduce last mile emissions. Specifically, targeting variables that have a higher determination of reducing emissions and increased value to consumers. Targeting messages that provide both services to consumers could impact last mile behaviors.

Urban and Rural Customers

The overall Test One results indicated 75% of the personas reduced their monthly last mile emissions (Figure 17). It is significant to note that both types of the urban personas did not to pass the test. Additionally, both urban persona types did not pass Test

Two. Together, the two urban personas accounted for 72% of the failed tests. The two rural persona types did not pass Test Two and all together these four persona types accounted for 90% of the failed tests and are represented in red (Figure 17).

Figure 17. Percent of personas achieved monthly hypothesized CO2e reductions (in blue).

Having the urban demographic type fail each test threshold is an important result.

This clear outcome of the research signifies that urban demographics share important characteristics of last mile delivery options that determine GHG emissions. Primarily,

round trip mileage is smaller than other personas and they also have an option to walk their groceries home. Having the only customer with rural demographic characteristics fail Test Two indicates there is more to explore to understand the degree of elasticity these customer types have in in altering their monthly last mile emissions. The initial review might indicate that incremental changes to behaviors could lead to different results. Both of these findings should be taken into consideration when selecting groups for marketing.

Key Customer Indicators

For both hypothesizes, none of the urban consumers achieved measurable reductions in last mile emissions. The Urban Millennial consumer has a preference to walk to and from the grocery store. This is a primary attribute of this urban grocery shopper and likely the preference of many more. The amount of CO2e emitted while walking is not a measurable factor for last mile emissions. This is the reason why all the results are NA in Table 5.

The Urban Gen X consumer is unique compared to the other personas. Their delivery service mileage is about equal to their self-delivery mileage. The research used

0.6 miles per grocery delivery and their primary grocery store is a one-mile round trip.

This customer uses a 17-mpg vehicle to self-deliver, which is better than a delivery vehicle average of 9.5-mpg. All of these characteristics created comparable delivery calculations. These equivalent delivery density variables make it difficult to reduce monthly emissions.

The customer would possibly have to drive an electric car or make more trips a month to reduce their emissions. If either of these two urban demographics was targeted with specific marketing for grocery delivery services, the focus should be to identify preferences they’d be willing to pay for, like low-cost deliveries, sustainable food products, and time-savings.

The Rural Baby Boomer consumer missed the 20% reduction by less than 2% for gasoline grocery delivery and by <1% for diesel. Delivery density analysis established that delivery service would reduce CO2e emissions at higher rate due to an improved

CO2e emissions ratio and higher customer density. The longer round-trip distance for rural customers would be a direct indication of this result. In this case the emissions saved by grouping grocery delivery was offset by longer distance between deliveries and lower mpg of UPS delivery trucks.

Rural Baby Boomers shop five times a month, which is a smaller number of trips to the grocery than other personas. If their shopping behaviors were closer to suburban personas of eight trips a month, this would increase their monthly shopping trips available to test. Thus, when measuring for Test Two, the number of monthly trips saved by using delivery would increase from one to two and reduce their total monthly emissions by 27% instead of 19%. Although adding additional trips are most likely unrealistic. Rural customers have higher opportunity costs driving 25+ miles round-trip for groceries.

Chapter IV

Discussion

The final product of the research described multiple U.S. grocery shoppers’ monthly last mile emissions for self-delivery and delivery service. Comparing monthly reductions in emissions for each persona, the carbon amount reduced is insignificant as compared to monthly US household carbon footprints. However, by aggregating monthly total reductions of last mile emissions and comparing against carbon equivalents, the significance of the research is revealed.

Over 85% of the US population lives either in an urban or suburban area (Parker et al. 2018). If 10% of this group committed to using grocery delivery service for 33% of their monthly trips, they could reduce their last mile emissions by 105 million pounds of

CO2e. This is equivalent to 1096 US households yearly carbon footprint. A US household emits approximately 48 tons of CO2e per year, as of 2018 (Center for Sustainable

Systems, 2019).

There are 30 million daily US shoppers (FMI, 2019) and on average, they could each save 0.40 pound of CO2e a day if they incorporated delivery service 33% of the time into their monthly routines. On any given day, this reduction could sum to over 11 million pounds of CO2e. This is equivalent to the yearly carbon footprint of 122 US households.

The Suburban Millennial consumer has the most potential to generate changes and reduce in emissions. In the aggregate, they represent ~10% of the US population or

over 32 million US adults (Statista, 2019c & 2019d; Zillow, 2016). They are the most likely to be using or start using grocery delivery services (Farhangi, 2019; Statista,

2019c) (Figure 18). They will continue to age, have families, and increasingly move to the suburbs (Parker et al. 2018). If 10% of this sample group of millennials used monthly grocery delivery services, they would reduce the equivalent of 288 US households carbon footprints each month.

Figure 18. Weekly percent of millennials using online grocery shopping compared to all US adult average in 2017 (Farhangi, 2019; Statista, 2019c).

Impact on Delivery Services

Consumers choose between a myriad of services to deliver food directly to their homes. Included in this array of options is the decision to skip the entire grocery shopping experience. One type of grocery delivery service that has risen quickly is using gig-economy business model to finish the last mile. Which means using contracted

workers to deliver goods and services. Primarily this means deliveries made using personal vehicles.

One example of this is Shipt, which was purchased by Target and employs independent contractors. These contractors are called pickers and they select groceries for multiple orders and then deliver. They are free to flex their schedule to shop one or multiple orders at a time. Shoppers are zoned to customers whose addresses are closest to store they service (Shipt, 2019a, 2019b).

Shipt provided the following guidelines about their contracted workers:

• They are able to determine which areas in the city they shop and usually shop

for members near them.

• Workers with more experience take on multiple orders at a time than workers

just starting out.

• The drivers are required to drive a 2000 model or newer personal vehicle.

• Workers typically do not drive father than 20 minutes from a store to a

member's home.

Another method to skip the grocery shopping experience is having meal kits shipped directly to your door. Meal kits are marketed as another option for consumers to remove a trip to the grocery store and still provide fresh, convenient, and ready to cook meals. There are still many gaps in this option: packaging and recycling of meal kit logistics (Hanssen et al. 2017; Heard et al. 2019), purchases of non-meal items (Wygonik

& Goodchild, 2012), and the actuality of decreasing the number of monthly trips for groceries (Carins, 2005).

Regardless of these gaps, the meal kit market has expected annual growth of 17% for the next five years and is expected to reach USD 8.94 billion kits by 2025 (Hexa,

2019). These trends are a positive sign for grocery delivery services because the meal kit market is constructed on a delivery service model.

Even though a majority of consumers still prefer to self-select their groceries,

(Morganosky & Cude, 2000; Rohm & Swaminathan, 2004) global meal kits sales and gig-economy last mile models have grown exponentially since their arrival in the marketplace. How this convenience and service is marketed becomes a very interesting use case for this research.

Demographic Changes

The future of grocery store services will be determined by the millennial and the upcoming Gen Z generations as they demand more online services (Alberts & Lahad,

2018; Farhangi, 2019). As they age into careers and increase their purchasing power, their comfort utilizing online shopping will influence the grocery marketplace (Statista,

2019a).

Millennials are a major factor of growth in suburbs with 47% of the generation owning homes (Zillow, 2016), increasing by 16% since 2012 (Parker et al. 2018).

Linking home ownership with millennial and Gen Z higher rate of demand for online shopping options confirms online grocery shopping is here to stay (Alberts & Lahad,

2018; Farhangi, 2019; Statista, 2019c).

Rural communities are shrinking and aging faster than (sub)urban communities

(Parker et al. 2018). Baby Boomers and Gen X make up over 50% of the rural population

(Zillow, 2016) and they have not adopted online shopping nearly at the same rates as millennials (ICSC, 2017, 2018).

This impacts rural communities in two specific ways: grocery delivery services like Peapod and FreshDirect do not have the same plans to grow service rurally as they do in the suburbs (FreshDirect, 2020; Peapod, 2020), and rural delivery density is much lower than (sub)urban deliveries. Low delivery density increases last mile emissions

(Goodchild, Wygonik & Mayes, 2015; Hardi & Wagner, 2019; Wygonik & Goodchild,

2012).

But don’t count out the Baby Boomers just yet. Online grocery shopping percentages for older cohorts are lower, but competitive. About one-fifth to one-sixth of the Baby Boomer generation shop for groceries online and have valid reasons to continue to do so. They are high-income consumers who prefer convenience and shopping digitally is an indispensable tool to meet this demand. These consumers may not drive or have easy access to public transportation, and additionally, have trouble carrying their groceries home. Older consumers taking advantage of online grocery delivery is a logical conclusion (Garcia, 2018).

Connecting demographic trends with marketing creates opportunities for delivery companies to create messages for their services. For example, they can target groups within a five-mile radius from a primary grocery store and draw comparisons between last mile emissions of self-delivery and using their services. For example, this would apply to Suburban Baby Boomers with mobility concerns and Suburban Millennial customers with families who would benefit from saving time or reducing effort to shop for groceries.

Conclusions

My research focused on the last mile impact on GHG emissions of eight grocery customer personas. Last mile options were evaluated to avoid the complexity of consumer preferences on countless food categories and their GHG emissions. This research design limited the focus to measure and compare how last mile choices impact carbon footprints.

The research applied behaviors of online shopping and related them to the growing development and adoption of last mile delivery options. Linking these variables with US adult customer data compared differences in a month’s total of GHG emissions.

Specifically, the research clarified the impact of consumers switching to delivery service.

Together, the research and testing answered both hypothesis with clear results, addressing the extent an average US grocery shopper’s last mile delivery behavior impacted monthly

GHG emissions.

Last mile changes customers make have an impact on carbon footprints. The research confirms last mile GHG emission reductions are possible by switching to delivery services. This research and future growth trends of online grocery shopping are credible data for marketing. Using this research would provide companies a way to meaningfully engage consumers to take action and substantially reduce personal and collective carbon footprints.

Purpose Driven Marketing

Future opportunities to apply these conclusions are in influencing marketing strategies for grocery delivery companies. Personal connections to the environment have

been an ongoing trend in retail marketplaces (Ottman, 2017) and should be leveraged in grocery delivery marketing.

Specifically, companies can provide personal reasons to consumers that connect to the value of using delivery services. Aligning marketing strategies tied to personal needs and values of customer that also reduce GHG emissions can have a positive impact

(Schill & Godefroit-Winkel, 2019). Together, they can influence more US adults to try or continue to use grocery delivery services to positively impact their own carbon footprints.

Another strategy could focus on what consumers can do now to adopt cleaner last mile to prevent their own carbon footprints from increasing in the future. For example, companies like Whole Foods or HEB could extrapolate their customer data on grocery delivery and customer types to create messages that resonate with customer who also want to positively impact the environment today and tomorrow, or generally, leave their community in a better place for future generations.

Future Trends in Grocery Marketing

Collectively, millions of daily shoppers’ choices greatly impact GHG emissions, whether from last mile behaviors, online shopping growth, or preferences in food products. The GHG emissions for millions of shoppers adds up quickly. Online retailers and delivery services need to position their services with carbon reduction goals of consumers and market to the growing trends of younger online shoppers.

Several reports have predicted that at least 70% of all US adults will have used online grocery shopping at least once a month in 2021 (Dumont, 2019b; Farhangi, 2019).

This is up from 2017-2019 when 25% of primary grocery shoppers in the US tried online

grocery services at least once (ICSC, 2018). If this prediction holds true, the grocery delivery service market will experience significant growth. The multiple strategies outlined in this research are intended to capture specific customer’s attention with targeted messaging. Specifically, these messages should focus on positively and confidently impacting the environment by reducing carbon footprints.

In the last five years online grocery shopping trends for all US adults has stayed relativity flat. When millennials are separated (red line in Figure 19), their adoption and growth out pace all other age generations. Current research has also started including Gen

Z behaviors as they age into the workforce and become a growing influence over the grocery shopping experience. For the first year tracked, Gen Z are already on par with millennials adoption of online grocery shopping (Dumont, 2019b; Farhangi, 2019; FMI

2017, 2019; ICSC 2018; Statista, 2019a) (Figure 19). These are the specific customers’ attention that marketing messages need to capture.

Figure 19. Monthly online grocery consideration trends. Millennial and Gen Z compared to US adults (Dumont, 2019b; Farhangi, 2019; FMI 2017, 2019; ICSC 2018; Statista, 2019a).

Online grocery sales are growing and by several reports, expectations are for growth to $100 billion by 2025 (MarketWatch, 2019; Melton, 2019; Nielsen, 2018). This is the online future US adults are experiencing today and will continue to experience according to sales trends (Statista, 2017, 2020a) (Figure 20).

Retailers want to promote a simple online shopping experience and capture their share of the anticipated sales growth. Online channels are driving growth in grocery sales

(Koch, 2019), and the 2025 projection is equivalent to every US household purchasing

$850 online to for their food and beverage consumption (Nielsen, 2018). This is up from

$250 in 2019 (Statista, 2017, 2020).

Figure 20. US online grocery sales in billions reported in blue and projected in light blue (Statista, 2017, 2020a). 2025 reported sales in purple by Nielson (2018). Trendline of projected sales does not incorporate 2025 forecast sales. The time series 2014 – 2023 has a R2 score of .84 and further reduced to .69 if the 2025 prediction is included.

During the same time frame of growth for online grocery shopping, overall grocery sales are practically stagnant. This has motivated grocers to optimize their

marketing strategies to drive more consumers online to take advantage of new growth opportunities. Kroger, which controls ~10% of the US market share (Statista, 2020c) has a strategy called Restock Kroger. One of the primary goals of this strategy is to expand

Kroger’s digital and ecommerce efforts to make clearer use of tailored customer data. At the same time, other grocery store chains are investing in new experiences and services directed to keep current customers and attract digitally motivated shoppers (Melton

2019).

Optimizing Data on the Last Mile to Influence Change

Together, the online grocery adoption and sales projections are validation of using grocery personas to deliver purpose driven marketing. Optimizing shopper insights into a comprehensive marketing strategy positions retailers and consumers to take full advantage of the value of online grocery shopping (Nielson, 2018), and capture the most tech-savvy users who are able and willing to pay for grocery delivery services

(MarketWatch, 2019). As of 2018, 49% of US adults were willing to pay between $1 and

$15 to have groceries delivered (Conway, 2018). In 2019, ~33% of US adults identified sustainably packaged and produced services as valuable and are willing to pay premium prices (Shahbandeh, 2020).

Consumers are motivated by convenience, variety in products, and a simplified shopping experience. Helping them choose online grocery shopping achieves all three.

Furthermore, motivating customers to change their last mile grocery behaviors add personal value quickly: consumers have a clearer tracking of food expenses, add time back to their schedules, and minimize commuting times (MarketWatch, 2019).

The 2025 projections may be over-indexed on the high end of growth, but this creates motivation for retailers to generate impactful marketing messaging to reduce

GHG emissions. Customer last mile persona analysis combined with increased adoption and growth of online grocery sales confirms the value of purpose driven marketing to reduce carbon footprints.

Appendix

Last Mile Emission Calculator

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