Adobe Digital Economy Project - February 2017

© 2017 Adobe Systems Incorporated. All Rights Reserved.
ADOBE DIGITAL ECONOMY PROJECT
FEBRUARY 2017 UPDATE

© 2017 Adobe Systems Incorporated. All Rights Reserved.© 2017 Adobe Systems Incorporated. All Rights Reserved.
Main takeaways: February
2
 The Adobe DPI shows a third-straight month of inflation across both indices in the U.S (0.4 percent in the all-items index and 0.4 percent in
the all-items less grocery index). This is first time in the 39 months of the Adobe DPI that both indices show three consecutive months of
inflation. February inflation is driven by price increases in nondurables including: groceries (0.4%), flights (5.3%), hotels (2.9%),
nonprescription drugs (0.3%) and alcohol (0.3%). Prices in the U.S. for DPI categories overall remain down -1.3% YoY, driven by price
declines in grocery over the last 12 months.
 Nondurables generally show price increases in the 2nd month of the year, but the DPI is showing higher than normal increases in flights,
groceries, and alcohol. Price increases in dairy and bakery products are driving grocery inflation in the DPI while rising fuel prices may be
leading to higher inflation in flights. Inflation is higher in every category of alcohol (beer, wine, and spirits) than last February. Inflation for
wine in February 2017 was twice (0.6%) than in February 2016 (0.3%). Inflation for hotels and nonprescription drugs in February is typical
of February inflation in years past.
 The February Adobe DPI contains first-ever state-level estimates of inflation across all U.S. states and D.C.. State level inflation numbers
provide critical new information about significant regional differences in consumer preferences and prices. The main factors driving
differences in inflation across states are tied to consumer behavior, such as different preferences for different product categories and
products, as well as price sensitivity.
 When it comes to price sensitivity, DPI data shows that in the latest holiday season, consumers in states with lower incomes were more
likely to purchase their Televisions during the Black Friday week (11/21-11/28) than consumers in states with higher incomes.
 The February state inflation rates show that monthly inflation is steady and widespread, but higher on average in the U.S. South.
 MoM inflation is occurring everywhere across the U.S: 40 out of the 51 states plus D.C. showed 3 straight months of inflation. West Virginia (1.01%), Oregon (0.88%),
and Virginia (0.81%) show the highest MoM inflation in February among all states.
 Clear regional differences exist: Monthly inflation in February was highest on average in the South and lowest in the Northeast, driven by higher inflation in groceries and
flights in the South.
 Prices are still down year-over-year, driven by lower grocery prices: Prices are down YoY in 47 out of the 51 states plus D.C. show cumulative inflation since February
2016. Most of this YoY is due to lower grocery prices, which are now seeing a rebound.
Adobe DPI for the U.K. shows cumulative inflation for groceries of 0.6% YoY (February 2016 to February 2017), with a fifth-straight month
of inflation and the largest MoM increase for the category (1.0% MoM inflation). Prices are on the rise for computers in the U.K. as well,

Adobe Digital Insights Background
Based on analysis of aggregated and anonymous data fromAdobe Marketing Cloud:
• Adobe Analytics and Adobe Mobile Services
Thousands of brands worldwide including two thirds of Fortune 50 companies rely on
Adobe Marketing Cloud:
• $7.50of every $10spent online with top 500 U.S. retailers go through Adobe Marketing Cloud **
• Adobe Marketing Cloud powers:
• 20 of the top 30 U.S. employers
• 9 of 10largest hotel groups
• 7of 10largest airlines
**Latest IR Top 500 Report 2015
3
Adobe
Analytics
Adobe
Mobile
Services

Economist Advisors
4
Austan Goolsbee, University of Chicago
 Robert P. Gwinn Professor of Economics at The University of Chicago's Booth School of Business
 Formerly served as President Obama’s Chairman of the Council of Economic Advisers
 Served as the youngest member of President Obama’s cabinet
Pete Klenow, Stanford University
 Professor, Department of Economics, Stanford University
 Currently Visiting Scholar, Federal Reserve Bank of San Francisco
 Member of editorial boards for Econometrica, American Economic Review, Quarterly Journal of Economics
and others

© 2017 Adobe Systems Incorporated. All Rights Reserved.© 2017 Adobe Systems Incorporated. All Rights Reserved. 5
Digital Price
Index
(DPI)
JobSeeking
Index
(JSI)
Digital Housing
Index
(DHI)

Digital Price Index Methodology
(DPI)

Methodology – U.K.
7
Based on analysis of aggregated and anonymous data
• Fisher Ideal Price Index
• $6 of every $10 spent online with top 100 European retailers go through Adobe Marketing Cloud **
• Based on analysis 1 billion website visits and 100k products sold online
ONS: Consumer Price Index Adobe: Digital Price Index
Quantities Sold No Yes
Products/quantities updated Annually Daily
Number of Products 180,000
100k
(currently covered categories)
Data Collection Frequency Monthly Daily
Data Availability Monthly Daily
Offline Prices Yes No
Long history Yes No
All categories of spending Yes No
** Latest IR U.K. Top 500 Report 2015

Methodology – U.S.
8
Based on analysis of aggregated and anonymous data
• Fisher Ideal Price Index
• Adobe measures 80% of all online transactions from top 100 U.S. retailers **
• $7.50 of every $10 spent online with top 500 U.S. retailers go through Adobe Marketing Cloud **
• Based on analysis of 15 billion website visits and 2.2 million products sold online
BLS: Consumer Price Index
BEA: Personal Consumption
Expenditures
Adobe: Digital Price Index
Quantities Sold No No Yes
Products/quantities updated Every 2 years Monthly Daily
Number of Products 83 thousand (all categories) 83 thousand (all categories)
2.5 million
(currently covered categories)
Data Collection Frequency Bi-Monthly Monthly Daily
Data Availability Monthly Monthly Daily
Offline Prices Yes Yes No
Long history Yes Yes No
All categories of spending Yes Yes No
Index Type Laspeyeres Fisher Ideal Fisher Ideal
** Latest IR Top 500 Report 2015

DPI Coverage of U.S. CPI Categories
9
DPI Category
February 2017 DPI Weights
All-Items All-Items Minus Groceries
Grocery 54.00% N/A
Furniture and bedding 5.16% 11.22%
Hotels 4.99% 10.85%
Personal care products 4.79% 10.40%
Tools & Home Improvement 4.61% 10.03%
Flights 4.27% 9.27%
Pet Products 4.01% 8.72%
Alcohol 3.99% 8.68%
Sporting goods 2.95% 6.40%
Auto Parts 2.62% 5.71%
Nonprescription drugs 2.45% 5.33%
Toys 2.00% 4.35%
Computers 1.87% 4.07%
Appliances 1.21% 2.63%
Televisions 0.66% 1.44%
Medical equipment and 0.41% 0.89%
Shelter
Food
Away
From
Home
Vehicles
Apparel
Electricity,
gas etc.
Grocery
Furniture
Lodging
Personal Care
Np. DrugsCurrent DPI
To be added to DPI
Unlikely
Profession
al services
Hospital
services

Big data in DPI decreases uncertainty
10
DPI CPI
Appliances 0.15% 0.46%
Airfare 0.08% 0.51%
Computers 0.28% 0.49%
Furniture and bedding 0.07% 0.31%
Grocery 0.10% 0.12%
Hotels: domestic 0.04% 2.30%
Medical equipment and 0.20% 0.39%
Nonprescription Drugs 0.18% 0.47%
Sporting Goods 0.12% 0.31%
Televisions 0.24% 0.60%
Toys 0.25% 0.59%

Inflation rates by price ranges
11
• Televisions have seen higher-ticket
items deflate faster than lower-ticket
items in 2015
• For computers, mid-range models
priced between $700 and $1,200
showed the highest deflation last year
*Price bins are determined based on the price the product had when originally
launched (first price).

The Importance of detail: Black Friday discount comparisons
12
• DPI captures the effect of major discount
holidays like Black Friday and Cyber Monday
because it measures quantities and does so
in real-time
• Prices online for Televisions dropped later in
November 2016 than in 2015, with more
discounts during the Black Friday week
(Monday 11/21 through Cyber Monday),
which accounted for more than half of all
Televisions units purchased between
November and December
• Television prices then increased more in
December 2016 than they did in December
2015, a sign that retailers were able to
capitalize on later shopping

Measuring rapidly shifting consumer preferences
13
• Electronics see an average of 77% of
online spend on new* products**
• 17% of monthly online spend for
groceries on new products
* Products that have been available for 1 year or less
** A product is defined as an item for purchase which has an unique identifier (such as

Digital Price Index Findings
(DPI)

State-by-state inflation: examples of sources of variance
15
1. Different baskets across states
• Consumers in western states buy more on organic fruits and vegetables than other
regions
• Consumers in southern states buy more meats, poultry and fish than other regions
2. Different shopping attitudes, preferences across states
• Consumers in states with lower median incomes tend to shop more on heavy-promotion
days
3. Different prices for same products across states
• Price-only variances across states tend to be bigger in groceries and smaller in consumer
electronics

MoM Inflation is occurring across the U.S., but prices down
YoY
16
• Inflation is occurring everywhere across the U.S: 40
out of the 51 states plus D.C. showed 3 straight months
of inflation
• Regional prices and consumer preferences are
significant: Monthly inflation in February was highest on
average in Southern states and lowest in Northeastern
states
• Prices are still down compared to February 2016:
Prices are down YoY in 47 out of the 51 states plus D.C.
Only Oklahoma (0.5% YoY), Idaho (0.4%), Delaware
(0.2%) and Alaska (0.1%) show cumulative inflation since
last February

Grocery, flights, hotels, alcohol and furniture driving variance
17
Highest inflation by state
Lowest inflation by state
State MoM Inflation Major Drivers
West Virginia 1.01% Grocery, Flights, Hotels
Oregon 0.88% Grocery, Flights, Hotels
Virginia 0.81% Grocery, Flights, Hotels
District of Columbia 0.78% Flights, Grocery, Hotels
Illinois 0.77% Flights, Grocery, Hotels
State MoM Inflation Major Drivers
New Hampshire -0.16% Furniture and Bedding, Toys, Grocery
Wyoming -0.39% Furniture and Bedding, Flights, Toys
North Dakota -0.43% Furniture and Bedding, Flights, Auto Parts
Montana -0.79% Alcohol, Furniture and Bedding, Grocery
Connecticut -1.26% Grocery, Furniture and Bedding, Toys

States with lower incomes more reliant on deep discounts
18
$30,000
$35,000
$40,000
$45,000
$50,000
$55,000
$60,000
$65,000
$70,000
$75,000
$80,000
35% 40% 45% 50% 55% 60%
MedianIncome(2015)
Percent of TVs Bought During Black Friday Week 2016
State median income vs. Percent of televisions bought on
Black Friday week
• Across the U.S., more than half of all TVs purchased in
November and December were purchased during Black
Friday week in 2016
• The Adobe DPI shows that for states with lower median
incomes took more advantage of TV sales during Black
Friday week in 2016
West Virginia
Washington, D.C.

Inflation levels differ significantly by region
19
Topline Inflation Topline Inflation Less Grocery
Geography
February 2017
MoM (Average)
February 2016
February 2017
YoY (Average)
February 2017
MoM (Average)
February 2016
February 2017
YoY (Average)
Midwest Region 0.3% -1.6% 0.1% -2.1%
Northeast
Region
0.0% -2.3% 0.4% -2.2%
South Region 0.5% -1.2% 0.3% -1.6%
West Region 0.3% -0.9% 0.1% -1.3%
United States 0.4% -1.3% 0.4% -1.6%
• Geography plays an important role: Monthly inflation
in February was highest on average in Southern states
and lowest in Northeastern states
• Southern states on average saw the highest levels of
monthly inflation in flights and groceries among Census
regions
• Northeast states saw on average monthly declines in
grocery prices

Adobe DPI Findings in the United Kingdom
20
• Adobe DPI for the U.K. shows cumulative inflation for
groceries of 0.6% YoY (February 2016 to February 2017),
with a fifth-straight month of inflation and the largest MoM
increase in the DPI (1.0% MoM inflation)
• U.K. computers and television inflation have diverged in
recent months, though both saw MoM inflation in January
• Computers in the U.K. saw a 0.3% inflation in February; the
category is still showing -1.3% deflation YoY
• Televisions in the U.K. saw a large 5.0% MoM inflation, the
second straight month of inflation following several months
of deflation

January 2017 U.S. Recap
21
MoM:
December to
January
MoM:
December to
January
Difference
(percentage)
Same
Sign?
YoY: Cumulative
2016-2017
YoY: Cumulative
2016-2017
Difference
(percentage)
Same
Sign?
DPI CPI DPI vs. CPI
DPI vs.
CPI DPI CPI DPI vs. CPI
DPI vs.
CPI
Alcohol 2.6% 0.7% 1.9% Yes -0.1% 0.2% -0.3% No
Appliances 1.7% 1.9% -0.2% Yes -5.1% -4.5% -0.5% Yes
Auto Parts 0.8% 0.2% 0.6% Yes -1.4% -1.1% -0.3% Yes
Computers -0.5% 0.2% -0.7% No -8.0% -4.0% -4.0% Yes
Flights -3.5% 1.4% -4.9% No 1.1% -5.3% 6.3% No
Furniture and bedding 0.6% 0.9% -0.3% Yes -3.5% -1.3% -2.2% Yes
Grocery 0.1% 0.4% -0.3% Yes -1.2% -1.9% 0.7% Yes
Hotels 4.3% 4.7% -0.4% Yes 3.1% -2.9% 6.0% No
Medical equipment and supplies 0.1% 0.0% 0.1% No -1.5% -0.6% -0.9% Yes
Nonprescription Drugs -0.1% 0.0% -0.1% No 0.0% -0.1% 0.1% No
Personal Care Products 1.1% -0.1% 1.2% No -3.0% -0.9% -2.1% Yes
Pet Products 1.1% -0.2% 1.3% No 0.2% 0.0% 0.2% No
Sporting Goods 1.2% -0.2% 1.4% No -5.4% -1.4% -4.0% Yes
Televisions 1.1% 2.8% -1.7% Yes -19.5% -21.2% 1.7% Yes
Tools & Home Improvement 0.6% 0.1% 0.5% Yes -1.9% -0.8% -1.1% Yes
Toys 3.4% 1.3% 2.1% Yes -6.7% -8.1% 1.4% Yes
Weighted Average Inflation* 0.5% 0.6% -0.1% Yes -1.4% -2.0% 0.6% Yes
Weighted Aggregate Inflation, Less
Grocery** 1.0% 0.9% 0.1% Yes -1.8% -2.2% 0.5% Yes
* Weighted average inflation is calculated using CPI-U relative component weights (2014-2015) across the shared categories in the DPI and the CPI
** Grocery is a large component of the current CPI weights, so changes in grocery prices drive the majority of weighted average inflation. As we add more categories (like
housing, auto etc.), these aggregate numbers will change.
*** New categories introduced in this month’s Adobe DPI

February 2017 U.S. Summary
22
• Main MoM inflation in February:
• Flights, hotels, grocery
• Main MoM deflation in February:
• Tablets, toys, appliances
YoY MoM
Alcohol 0.3% 0.3%
Appliances -4.5% -1.3%
Auto Parts -1.3% -0.2%
Computers -7.9% -0.6%
Electronics -10.0% -0.9%
Flights 3.7% 5.3%
Domestic Flights 3.4% 6.0%
Intl Flights 2.9% 3.3%
Furniture and bedding -3.6% -1.1%
Grocery -1.0% 0.4%
Domestic Hotels 3.2% 2.9%
Intl Hotels -1.1% 2.9%
Medical Equipment and Supplies -1.0% -0.3%
Nonprescription Drugs 0.3% 0.3%
Personal Care Products -3.6% -0.6%
Pet Products -0.1% -0.1%
Sporting Goods -6.1% -0.9%
Tablet -14.3% -3.8%
Televisions -19.1% -1.0%
Tools & Home Improvement -2.0% -0.1%
Toys -7.1% -1.8%
Weighted Average Inflation* -1.3% 0.4%
Weighted Aggregate Inflation, Less Grocery** -1.6% 0.4%
* Weighted average inflation is calculated using CPI-U relative component weights (2014-2015) across the shared categories in the DPI and the CPI
** Grocery is a large component of the current CPI weights, so changes in grocery prices drive the majority of weighted average inflation. As we add more categories (like housing,
auto etc.), these aggregate numbers will change.
*** New categories introduced in this month’s Adobe DPI

Alcohol
23
• Adobe DPI shows cumulative inflation of 0.3% YoY
(February 2016 to February 2017)
• Prices for Beer show cumulative inflation of 3.3% YoY
• Prices for Spirits show cumulative inflation of 1.2% YoY
• Prices for Wine show cumulative deflation of -1.4%
YoY
• For February 2017, prices increased 0.3% vs
January 2017 MoM
METHODOLOGY:
• The data used reflect transactions for ~120 K products

Appliances
24
• Adobe DPI shows cumulative deflation of -4.5% YoY
• Main YoY price decreases for appliances priced
between $200 and $400
• For February 2017, prices decreased -1.3% vs
January 2017 MoM
METHODOLOGY:
• The data used contains transactions for ~110 K products
• Products include large appliances such as fridges, ovens, washers, dryers as well as smaller appliances such as vacuums, mixers, blenders etc

Auto Parts
25
• Main YoY price decreases for auto parts priced
greater than $200
January 2017 MoM
METHODOLOGY:
• The data used reflect transactions for ~300 K products

Airfare
26
• Adobe DPI shows cumulative inflation of 3.7%
YoY (February 2016 to February 2017)
January 2017 MoM
METHODOLOGY:
• The data used contains transactions for ~370 K flight routes
• Includes associated fees, where applicable

Airfare: domestic
27
January 2017 MoM
METHODOLOGY:

Airfare: international
28
• Largest price increases for airfare were to South
America, with inflation of 19.6% YoY
• Largest price decreases are for airfare to Europe with
deflation of -9.4% YoY
• For February 2017, prices increased 3.3% vs January
2017 MoM
METHODOLOGY:

Electronics
29
• Adobe DPI shows cumulative deflation of -10.0%
• Main YoY price decreases for electronics priced
greater than $1500
January 2017 MoM
METHODOLOGY:
• The data used contains transactions for ~1 M products
• Products span across dozens of diﬀerent categories such as televisions, laptops, Blu-ray players, desktops, tablets, wearables, headphones, video
games, printersetc..

Electronics: computers
30
• Main YoY price decreases for computers priced
• Main YoY price increases for computers priced
between $0 and $300
January 2017 MoM
METHODOLOGY:

Electronics: televisions
31
• Main YoY price decreases for televisions priced
greater than $2000
January 2017 MoM
METHODOLOGY:

Electronics: tablets
32
• Main YoY price decreases for tablets priced between
$350 and $550
January 2017 MoM
METHODOLOGY:

Furniture and Bedding
33
• Main YoY price decreases for furniture and bedding
priced between $100 and $150
January 2017 MoM
METHODOLOGY:
• Products include interior and exterior furniture, mattresses and other bedding products

Groceries
34
• Main YoY deflation for eggs (-22.7%),
beverages (-3.2%) and cheese (-2.2%)
January 2017 MoM
• Main price increases for dairy products
including ice cream (inflation 1.3% MoM) and
cheese (1.0% MoM). Meats and poultry
products also saw a 0.6% MoM increase
METHODOLOGY:
• Our dataset covers an estimated 30-40% of all online grocery purchases. It contains transactions for ~195k products
• The mix of grocery products bought online is not the same as oﬄine – the top category for online shopping is drinks (nonalcoholic), while for oﬄine
shopping it is meat

Hotels: domestic
35
METHODOLOGY:
• The data used contains transactions for ~250 K hotel properties
January 2017 MoM

Hotels: international
36
METHODOLOGY:
• The data used contains transactions for ~250 K hotel properties
• For February 2017, prices increased 2.9% vs January
2017 MoM

Medical Equipment and Supplies
37
• Main YoY price decreases for medical equipment and
supplies priced between $45 and $135
• Main YoY price increases for medical equipment and
supplies priced greater than $135
January 2017 MoM
METHODOLOGY:
• The data used contains transactions for ~6,000 products
• Includes supportive and convalescent equipment, dressings and first aid kits, and medical equipment for general use

Nonprescription drugs
38
• Main YoY price decreases for nonprescription drugs
• Main YoY price increases for nonprescription drugs
January 2017 MoM
METHODOLOGY:
• Includes all non-prescription medicines taken by swallowing, applying on your skin (such as topicals), inhaling, as suppositories, or enemas.

Personal care products
39
• Main YoY price decreases for personal care products
priced greater than $150
January 2017 MoM
METHODOLOGY:
• Includes cosmetics, lotion, hair (shampoo, conditioner) and nail care

Pet products
40
• Main YoY price decreases for pet products priced
between $10 and $20
• Main YoY price increases for pet products priced
between $0 and $10
January 2017 MoM
METHODOLOGY:
• Includes pet food & treats, kitty litter and accessories (leashes, pet beds)

Sporting Goods
41
• Main YoY price decreases for sporting goods priced
January 2017 MoM
METHODOLOGY:
• Products include small equipment and accessories (balls, gloves, uniforms) as well as large equipment such as treadmills, weightlifting tools etc.

Tools & Home Improvement
42
• Main YoY price decreases for tools & home
improvement priced greater than $600
January 2017 MoM
METHODOLOGY:

Toys
43
• Main YoY price decreases for toys priced between
$200 and $300
January 2017 MoM
METHODOLOGY:
• Products include toys, games as well as playground equipment

JobSeeking
Index
(JSI)

Online job seeking continues YoY declines
45
METHODOLOGY:
• Based on analysis of aggregated and anonymous data from more than 1 billion visits to U.S. employment-search websites and top employer career pages
• Adobe Marketing Cloud powers 20 of the top 30 U.S. employers**
US online job seeking is down -17.3% YoY in
February

Digital Housing
Index
(DHI)

Online housing search grows YoY
47
METHODOLOGY:
• Based on analysis of aggregated and anonymous data from more than 2 billion visits to U.S. housing search websites
Online search for purchases and rentals is up
10.0% YoY in February.

Appendix
48

Inflation rates by price ranges, cont’d (tablets)
49
• Tablets have seen higher-ticket items
deflate faster than lower-ticket items
• The greatest price decrease and
increase in November and December
were for tablets priced between $350
and $600

Inflation rates by price ranges, cont’d (televisions)
50
• The highest priced televisions have
have seen faster deflation than lower
priced items
• The greatest price decrease in
November and increase in December
were for TVs priced between $600 and
$1,000

Inflation rates by price ranges, cont’d (televisions)
51
• Mid-range computers have seen faster
deflation than lower and higher priced
models
• The greatest price decrease in
November were for mid range
computers between $700 and $1,200
while the greatest price increase in
December were for lower priced
models between $300 and $700

Digital Price Index: Fisher Ideal Price Index
𝑖=1
𝑛
𝑃𝑖𝑡 𝑈𝑖𝑡−1
𝑖=1
𝑛
𝑃𝑖𝑡−1 𝑈𝑖𝑡−1
∗ 𝑖=1
𝑛
𝑃𝑖𝑡 𝑈𝑖𝑡
𝑖=1
𝑛
𝑃𝑖𝑡−1 𝑈𝑖𝑡
• The Fisher Ideal Price Index uses quantities purchased on the current period (month) and a previous
period (previous month) to determine actual consumer’s preferences. Instead of assuming that
consumers preferences for what goods to buy are constant over a certain number of years, it is able
to account for the fact that they are constantly changing what they buy.
𝐹𝑖𝑠ℎ𝑒𝑟 𝜋 𝑡 =

Methodology Comparison: Fisher, Laspeyres and Paasche

Adobe Digital Economy Project - February 2017

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Editor's Notes