With eCommerce growth expected to slow, down 13% from 14.3% in 2018 to 12.4% this year we wanted to determine if there were regional/demographic/behavioral differences.

1. City Profiles: The Digital Divide

2. 2
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Profiling How American Cities Shop Online
Based on analysis of aggregate and anonymous data via:
• Adobe Analytics measures transactions at 80 of the top 100** retailers on the web in the U.S.
• Product and pricing insights based on analysis of sales of more than 55 million unique products
• American Community Survey used to profile US Cities; simplecmaps.com used to get location and population figures
• 4,.500+ cities used in analysis
Adobe Advertising Cloud
** Source: Adobe Analysis of Internet Retailer 2018
Adobe Experience Cloud Adobe Analytics Cloud
CITY PROFILES | 2019
Methodology

3. CITY PROFILES | 2019
Methodology Definitions
• Research Objective:
• With ecommerce growth expected to slow, down 13% from 14.3% in 2018 to 12.4% this year we wanted to
determine if there were regional/demographic/behavioral differences.
• Individual City Profiles:
• Big vs. Small: based on population of over/under 100,000 people
• High Income vs. Med/Low Income: based on per capita income above or below $50,000
• Rural vs. Urban: determined by county area defined as rural; 40% used as the line of demarcation
• Financially Stressed vs. OK: top ranked on composite of unemployment, no health insurance and below poverty
line
• Diverse vs. Homogeneous: top ranked on composite of race (non-white), other language than English, and not
native to state of residence
• High Revenue Growth vs. Low Growth: cities above/below 20% YoY growth in ecommerce
• Smartphone High Order Value vs. Smartphone Share of Revenue: top third on each metric, but not in both
3

4. CITY PROFILES | 2019
4
Key Findings
• Large cities are driving stronger e-commerce growth than smaller cities
• Affluent/High Income Cities are fueling the online economy, but they don’t convert easily are not able to stop
overall e-commerce growth from slowing
• Diverse Cities outperform homogenous cities, across practically every e-commerce growth metric
• Rural areas' infrastructure shortcomings are limiting ecommerce adoption and their contribution to the online
economy
• Desktops orders continue to boast higher AOVs, which should be considered as more users migrate to
smartphones, and lower income households make smartphones their sole purchasing device

5. 16.4%
12.6%
8.5% 8.1%
0.0%
10.0%
20.0%
Revenue Growth Visit Growth
Topline Growth
Population over 100k Population Under 100k
CITY PROFILES: Big vs. Small | 2019
Larger cities driving online retail revenue growth.
• Cities over 100k people contribute to ecommerce commensurate with their population
• Visit Index: 106 and Revenue Index: 98
• Larger cities exhibiting stronger Year over Year growth, with all drivers up year over year
• Smaller cities had an edge on conversion, RPV and AOV, larger cites had the edge on unit price
• With differential ecommerce growth there maybe a digital divide in the not too distant future
1.9x
1.6x
5
Method: Indices are defined as “share of {metric}” divided by “share of population”.
YoY Change in Key
Drivers
Population
over 100k
Population
Under 100k
RPV 1.6% 4.9%
Conversion 1.3% 2.8%
AOV -0.3% 1.9%
Unit Price 8.9% 7.0%

6. CITY PROFILES: Big vs. Small | 2019
Large market consumers look for different things than small market shoppers.
• Computers, Phones & Electronics along with
Baby & Toddler shows largest positive share
swing between segments
• Memberships* and Apparel & Accessories
account for smaller share of visits
6
Method: Indices are defined as “share of {category} among target segment” divided by “share of {category} among rest of cities”
Visits used instead of revenue due to the large variance in unit pricing across categories.
* Memberships refer to programs offered by retailer to gain access to products and or services.
More likely to buy
Less likely to buy
Similar shares.
(1.00) - 1.00
Computers, Phones & Electronics
Baby & Toddler
Hobbies, Toys & Sporting Goods
Gifts & Flowers
Personal Care & Medical Equip
Alcohol & Tobacco
Pet Products
Auto Parts & DIY
Toys & Sporting Goods
Home & Housekeeping
Media & Entertainment
Grocery
Office & Professional
Apparel & Accessories
Memberships
Difference in Shopping by Category
Small Market. Large Market

7. CITY PROFILES: Big vs. Small | 2019
Conversion, not order value, separates big markets from small.
• In large cities, smartphones steal share from
both desktops and tablets.
• Smartphones account for $1 in $3 in large cities
• Are consumers taking advantage of more points of
distribution?
7
61%
32%
7%
66%
25%
9%
0%
10%
20%
30%
40%
50%
60%
70%
Share of Revenue by Device
Population over 100k Population Under 100k
• Higher conversion in smaller cities, 2.4% vs.
2.0%, raises the value (RPV) of those visitors
• Consumers in large cities place slightly bigger
orders (AOV) from higher priced items but
conversion dampens revenue per visit
Population
over 100k
Population
Under 100k
RPV $3.14 $3.64
Conversion 2.0% 2.4%
AOV $160 $154
Unit Price $39.77 $37.40
Basket Size 4.0 4.1

8. HOLIDAY PREDICTIONS | TALE OF TWO HOLIDAYS | 2019
Retailers See Traffic From Across the US
Markets profiled split into 3 segments and account for 180M people.
• 1,028 high income markets (median HHI $100K) tend to cluster around major metropolitan areas (7% of county is
rural)
• 965 low income markets (median HHI $33k) are spread across the country, often in outlying areas (40% of county is
rural)
High Income Market examples:
• Palm Beach, FL
• Atherton, CA
• Scottsdale, AZ
• Glencoe, IL
• Bronxville, NY
Low Income markets represented by:
• Coatesville, PA
• San Juan, TX
• Gatesville, TX
• Clinton, SC
• Gainesville, GA

9. HOLIDAY PREDICTIONS | TALE OF TWO HOLIDAYS | 2019
The Value of High-Income Markets is Traffic
Market commentary
• Relative to their share of the population, high income cities produce more retail traffic than low income.
• Improving visit performance vs. generating more visits represents a viable strategic distinction
+40% Visits
High vs. Low Income
Markets+40%

10. HOLIDAY PREDICTIONS | TALE OF TWO HOLIDAYS | 2019
Growth This Holiday Likely to Come from High Income Markets
Online retail shopping expanding among households with median HHI of $100k while it is contracting in
markets where households have median HHI of $33k in income.
Concentration of sales appears to be moving
to Higher Income Markets.
High Income markets showed slight
improvement in Revenue per Visit (RPV) of
+2% vs. no change year over year.

11. HOLIDAY PREDICTIONS | TALE OF TWO HOLIDAYS | 2019
Everyone, regardless of income, takes advantage of Holiday Weekend deals.
The value of a visit increased 74% during the five-day weekend regardless of where consumers’ income.
The gain comes from better conversion since there is only a small increase in average order value (AOV)
Key Metric Segment
6 Months
ending Aug
2018
Holiday
Weekend
2018 Holiday Lift
RPV
Low Income $3.81 $6.64 74%
High Income $3.89 $6.73 73%
AOV
Low Income $148 $163 10%
High Income $155 $157 1%
Conversion Low Income 2.6% 4.1% 59%
High Income 2.5% 4.3% 71%
There is a switch in AOV –
prior to the weekend higher
income markets placed larger
orders, that flipped during the
five-day weekend before
settling back down.

12. HOLIDAY PREDICTIONS | TALE OF TWO HOLIDAYS | 2019
Income Level Shifts the Types of Discretionary Products Consumers Buy Online.
75%+ of US e-commerce spend during the holidays is on personal items as opposed to more generic
items for the household.
• In low income markets spend shifts toward “something to do” with Media & Entertainment capturing
more share of wallet than in high income markets..
• High Income cities shift spending toward other types of discretionary items., including the large Apparel &
Accessories category.
High Income
markets spend
more on these
items.

13. 4.1%
6.0%
13.0% 13.4%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
Revenue Growth Visit Growth
Topline Growth
City In Rural Counties City In Urban Counties
CITY PROFILES: Rural vs. Urban | 2019
Rural area ecommerce growth trails urban areas.
• Consumers in rural areas shop less online than those in urban areas
• Visit Index: 72 and Revenue Index: 79
• Cities in rural counties exhibiting weak Year over Year growth
• Rural markets trending positive on all four drivers; driving traffic may be the objective
3.1x
2.2x
13
Method: Indices are defined as “share of {metric}” divided by “share of population”.
YoY Change in Key
Drivers
City In Rural
Counties
City In Urban
Counties
RPV 7.5% 3.8%
Conversion 5.1% 2.0%
AOV 2.2% 1.7%
Unit Price 4.7% 7.9%

14. CITY PROFILES: Rural vs. Urban | 2019
Strong rural conversion suggests intent when they do shop.
• In towns in rural areas, smartphones less
prevalent than in more urban areas.
• Smartphones account for $1 in $4 in rural areas
14
• Higher conversion in rural cities, 2.7% vs. 2.1%,
raises the value (RPV) of those visitors
• Despite consumers in rural cities placing
smaller orders (AOV) consisting of less
expensive items.
• More intent, less “tire kicking”
City In Rural
Counties
City In Urban
Counties
RPV $3.71 $3.39
Conversion 2.7% 2.1%
AOV $139 $158
Unit Price $33.02 $38.86
Basket Size 4.2 4.1
65%
25%
10%
63%
29%
8%
0%
10%
20%
30%
40%
50%
60%
70%
Desktop Smartphone Tablet
Share of Revenue by Device
City In Rural Counties City In Urban Counties

15. CITY PROFILES: Rural vs. Urban | 2019
Cities in rural counties found everywhere east of the Mississippi
• Description: Small towns that are economically below national averages
• Less educated in general
• Shift toward from manufacturing, transportation and agriculture occupations
• Location: sparser in the West, some may be too small to be included in ACS.
• Nearly absent in California – agricultural counties still have very large cities in them
15
Data source: American Community Survey, Census Bureau and Zillow
National figures: $59k for Median HH Income and $227k for home value.
City In Rural
Counties
City In Urban
Counties
No. of Cities in Segment 1,073 3,452
Avg Population 10,890 49,298
Median HH Income $42,789 $65,950
House Value $127,183 $260,403
Rural Percent 56.7 12.4
Mfr/Trans/Wholesale/AG Industry 23.8 19.9
Prof /Tech Industry 12.7 19.5
Enrolled In Bach+ Program 24.6 26.6
Have Bach+ Degree 21.1 32.1

16. 15.1%
21.7%
9.1%
7.9%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Revenue Growth Visit Growth
Topline Growth
Diverse Cities Homogeneous Cities
CITY PROFILES: Diverse vs. Homogeneous | 2019
Cities defined by diversity outperform more uniform markets
• Diverse cities contribute more to retailer visits and revenue than their population alone would suggest
• Visit Index: 114 and Revenue Index: 107
• Diverse cities exhibited stronger Year over Year growth, particularly in terms of visits.,
• They led more homogeneous cities on all but conversion, but only lagged by a small amount.
1.7x
2.8x
16
Method: Indices are defined as “share of {metric}” divided by “share of population”.
YoY Change in Key
Drivers Diverse Cities
Homogeneous
Cities
RPV 5.5% 4.4%
Conversion 2.6% 2.7%
AOV 2.2% 1.6%
Unit Price 8.4% 6.7%

17. CITY PROFILES: Diverse vs. Homogeneous | 2019
Conversion separates Diverse Cities from Homogenous Cities.
• In diverse cities, smartphones garner a higher
share stealing from both desktops and tablets.
• Smartphones account for $1 in $3 in large cities
17
61%
32%
7%
66%
25%
9%
0%
10%
20%
30%
40%
50%
60%
70%
Share of Revenue by Device
Population over 100k Population Under 100k
• Lower conversion in diverse cities, 1.9% vs.
2.6%, lessens the value (RPV) of those visitors
• Consumers in diverse cities placing bigger
orders (AOV) consisting of higher priced items
illustrating the standard relationship.
Diverse Cities
Homogeneous
Cities
RPV $3.19 $3.76
Conversion 1.9% 2.6%
AOV $166 $ 147
Unit Price $40.43 $36.13
Basket Size 4.1 4.1

18. CITY PROFILES: Higher vs. Lower Revenue Growth | 2019
Revenue growth comes from smaller markets
• Description: While slightly smaller than low growth cities, all other demos are remarkably similar
• Demographics don’t cause growth, but they can help explain it.
• Easier to grow a small number than a big one.
• Location: fairly well distributed throughout the country
18
Data source: American Community Survey
High
Revenue
Growth
Low Revenue
Growth
No. of Cities in Segment 963 3,562
Avg Population 26,146 43,987
Median HH Income $61,510 $ 60,174
House Value $244,244 $224,641
Rural Percent 23.1 22.8
Mfr/Trans/Wholesale/AG Industry 21.8 20.6
Prof /Tech Industry 17.6 18.0
Enrolled In Bach+ Program 24.4 26.6
Have Bach+ Degree 28.0 29.9

19. 13.5%
10.5%11.2%
19.3%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Revenue Growth Visit Growth
Topline Growth
High on Smartphone AOV High on Smartphone Share of Rev
CITY PROFILES: Smartphone AOV vs. Share of Revenue | 2019
Judging smartphone shopping depends on how you measure it.
• Smartphones can be viewed in two ways:
• Consumers who place large orders on their phones
• They have stronger revenue than visit growth
• Consumers who use smartphones for a large portion of their spend
• They have stronger visit growth than revenue growth
• Both groups exhibit strong YoY metrics – much higher than overall totals
1.2x .5x
19
Method: Indices are defined as “share of {metric}” divided by “share of population”
These are non-overlapping groups, some cities could be high on both metrics..
YoY Change in Key
Drivers
High on
Smartphone
AOV
High on
Smartphone
Share of Rev
RPV 17.6% 14.7%
Conversion 13.7% 25.3%
AOV 16.0% 21.1%
Unit Price 12.5% 20.4%

20. (1.00) - 1.00 2.00
Toys & Sporting Goods
Personal Care & Medical Equip
Alcohol & Tobacco
Memberships
Pet Products
Gifts & Flowers
Baby & Toddler
Office & Professional
Auto Parts & DIY
Grocery
Home & Housekeeping
Computers, Phones & Electronics
Hobbies, Toys & Sporting Goods
Media & Entertainment
Apparel & Accessories
Difference in Shopping by Category
CITY PROFILES: Smartphone AOV vs. Share of Revenue | 2019
Consumers adopting these behaviors focus on different categories.
• Consumers who use smartphones for a large
proportion of their shopping spread their
shopping around
• Toys & Sporting Goods, Personal Care &
Medial Equip, Alcohol & Tobacco command a
larger share of visits than markets with high
AOV.
• Consumers with highest value for smartphone
orders
• Apparel & Accessories and Media &
Entertainment capture higher share of eyeballs
than their counterparts.
20
Method: Indices are defined as “share of {category} among target segment” divided by “share of {category} among rest of cities”
Visits used instead of revenue due to the large variance in unit pricing across categories. .
High Share more
likely to visit
High AOV more
likely to visit
Similar shares.
AOV Share of Revenue

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