Adobe Digital Economy Project - March 2017

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

© 2017 Adobe Systems Incorporated. All Rights Reserved.© 2017 Adobe Systems Incorporated. All Rights Reserved.
Main takeaways: March
2
 The Adobe DPI shows March as the fourth consecutive month of inflation in the U.S. across DPI categories (0.3 percent in the all-items index and 0.5 percent in
the all-items less grocery index). This is the first time in the 40-month history of the DEP to show four or more consecutive months of inflation. The all-items
inflation figure is, however, lower in March than in February (0.4% in the all-items index in February) and prices in the U.S. remain down -0.7% YoY, led by year-
over-year price declines in grocery and durable goods
 Monthly inflation in March is driven by price increases in non-durable goods and select durable goods:
 Prices are up for flights (3.2% MoM), domestic hotels (2.3% MoM), computers (0.9% MoM), appliances (0.9% MoM), and groceries (0.1% MoM)
 Grocery inflation in March is small but represents the largest category of spending in the DPI. Price increases in the category in March are driven by price increases in cheese and related products (0.8% MoM),
sugar and sweets (0.6% MoM), fats and oils (0.4% MoM), and meats (0.3% MoM)
 Historically the DPI shows price increases for flights and hotels in March, but inflation this month is higher than expected for both categories
 The March DPI contains state-level inflation data for the second time. The state-level data show that inflation is consistent and affecting nearly all states
 35 of the 51 states and D.C. are showing 4 straight months of inflation. Only four states (West Virginia, Delaware, Ohio, and Louisiana) show MoM deflation in March. However, prices remain down year-over-
year: 47 out of 51 states and D.C. have lower prices compared to March 2016.
 Differences in inflation across states were smaller in March than differences in February because of a small but uniform rise in grocery prices that affected all states similarly. The states with the highest inflation
also had high inflation in flights and hotels
 The Adobe DPI for the U.K. shows cumulative inflation for groceries of 1.3% YoY (March 2016 to March 2017) with a sixth-straight month of inflation (0.1%
MoM)
 Prices for computers (down -0.5% MoM) and televisions (down -1.5% MoM) are down month over month following a short period of inflation in both categories. Prices in both categories remain down year-
over-year.

Adobe Digital Insights Background
Based on analysis ofaggregated and anonymous data from Adobe Marketing Cloud:
• Adobe Analytics and Adobe Mobile Services
Thousands of brands worldwide including two thirds of Fortune 50 companies rely on Adobe
Marketing Cloud:
• $7.50ofevery $10spent online with top 500 U.S.retailers go through Adobe Marketing Cloud **
• Adobe Marketing Cloud powers:
• 20 ofthe top 30 U.S.employers
• 9 of10largest hotel groups
• 7of10largest 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)
Job Seeking 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 supplies 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
Professional
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 supplies 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 a SKU)

State-by-state inflation: examples of sources of variance
14
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

Digital Price Index Findings
(DPI)

Consistent Inflation across the U.S., but prices down YoY
16
• Consistent inflation is occurring everywhere across the U.S: 35
out of the 51 states and D.C. showed 4 straight months of inflation
• Regional prices and consumer preferences are less significant
in March: Monthly inflation in March was more uniform than
previous months due to a small but uniform rise in grocery prices
across states
• Prices are still down compared to March 2016: 47 out of 51
states and D.C. are showing YoY deflation compared to March
2016

Travel driving price changes across states
17
Highest inflation by state
Lowest inflation by state
State March MoM Inflation Major Inflation Drivers
Massachusetts 0.8% Flights*, Hotels**
Alaska 0.8% Flights*, Hotels**
Washington 0.8% Flights*, Hotels**
Illinois 0.6% Hotels**, Flights*
New York 0.6% Hotels**, Flights*
State March MoM Inflation Major Deflation Drivers
Florida 0.0% Hotels**, Alcohol
Louisiana -0.1% Flights*, Hotels**
Ohio -0.1% Alcohol, Toys
Delaware -0.1% Alcohol, Appliances
West Virginia -0.2% Alcohol, Medical Equipment
* Flight prices are determined by the state of flight origin
** Hotel prices reflect hotel properties in a given state
*** New categories introduced in this month’s Adobe DPI

Adobe DPI Findings in the United Kingdom
18
• Adobe DPI for the U.K. shows cumulative inflation for groceries of
1.3% YoY (March 2016 to March 2017), with a sixth-straight month
of inflation and (0.1% 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 -0.5% deflation in March; the category is
still showing -0.2% deflation YoY
• After two straight months of inflation in televisions, computers saw a ---
-1.5% deflation in March, bringing the category to -15.7% YoY

February 2017 U.S. Recap
19
MoM: January
to February
MoM: January to
February
Difference
(percentage)
Same
Sign?
YoY: Cumulative February
2016-2017
YoY: Cumulative February
2016-2017
Difference
(percentage)
Same
Sign?
DPI CPI DPI vs. CPI DPI vs. CPI DPI CPI DPI vs. CPI DPI vs. CPI
Alcohol 0.3% -0.1% 0.4% No 0.3% 0.1% 0.2% Yes
Appliances -1.3% 0.8% -2.1% No -4.5% -3.8% -0.7% Yes
Auto Parts -0.2% 0.2% -0.4% No -1.3% -0.9% -0.4% Yes
Computers -0.6% -0.2% -0.4% Yes -7.9% -4.2% -3.7% Yes
Flights 5.3% 4.2% 1.1% Yes 3.7% -1.3% 5.0% No
Furniture and bedding -1.1% -0.1% -1.0% Yes -3.6% -1.4% -2.2% Yes
Grocery 0.4% 0.2% 0.2% Yes -1.0% -1.7% 0.7% Yes
Hotels 2.9% 4.3% -1.4% Yes 3.2% 1.3% 1.9% Yes
Medical equipment and supplies -0.3% 0.8% -1.1% No -1.0% 0.1% -1.1% No
Nonprescription drugs 0.3% 0.1% 0.2% Yes 0.3% 0.3% 0.0% Yes
Personal care products -0.6% 0.1% -0.7% No -3.6% -0.8% -2.8% Yes
Pet Products -0.1% -0.2% 0.1% Yes -0.1% -0.2% 0.1% Yes
Sporting goods -0.9% 0.2% -1.1% No -6.1% -1.2% -4.9% Yes
Televisions -1.0% 1.4% -2.4% No -19.1% -20.1% 1.0% Yes
Tools & Home Improvement -0.1% 0.0% -0.1% No -2.0% -0.8% -1.2% Yes
Toys -1.8% 0.2% -2.0% No -7.1% -7.9% 0.8% Yes
Weighted Average Inflation* 0.4% 0.5% -0.2% Yes -1.3% -1.3% 0.0% Yes
Weighted Aggregate Inflation, Less
Grocery** 0.4% 0.8% -0.2% Yes -1.6% 0.0% -1.5% 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.

March 2017 U.S. Summary
20
• Main MoM inflation in March:
• Flights, hotels, computers, appliances
• Main MoM deflation in March:
• Tablets, medical equipment and supplies, toys
YoY MoM
Alcohol -0.9% -0.2%
Appliances -4.5% 0.9%
Auto Parts -1.3% 0.0%
Computers -4.9% 0.9%
Electronics -9.2% 0.2%
Flights 5.0% 3.2%
Domestic Flights 4.8% 3.1%
Intl Flights 4.7% 3.6%
Furniture and bedding -4.2% -0.5%
Grocery -0.4% 0.1%
Domestic Hotels 3.7% 2.3%
Intl Hotels -1.4% 1.0%
Medical Equipment and Supplies -1.3% -0.7%
Nonprescription Drugs 0.6% -0.1%
Personal Care Products -1.3% 0.5%
Pet Products -0.7% -0.1%
Sporting Goods -5.2% 0.0%
Tablet -15.7% -2.5%
Televisions -17.5% 0.6%
Tools & Home Improvement -2.2% -0.2%
Toys -4.9% -0.6%
Weighted Average Inflation* -0.7% 0.3%
Weighted Aggregate Inflation, Less Grocery** -1.1% 0.5%
* 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.

Alcohol
21
• Adobe DPI shows cumulative deflation of -0.9% YoY (March
2016 to March 2017)
• Prices for Beer show cumulative inflation of 3.4% YoY
• Prices for Spirits show cumulative inflation of 0.4% YoY
• Prices for Wine show cumulative deflation of -3.2% YoY
• For March 2017, prices decreased -0.2% vs February 2017
MoM
METHODOLOGY:
• The data used reflect transactions for ~120 K products

Appliances
22
2016 to March 2017)
• Main YoY price decreases for Appliances priced between $200
and $400
• For March 2017, prices increased 0.9% vs February 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
23
2016 to March 2017)
• Main YoY price decreases for Auto Parts priced greater than
$200
• For March 2017, prices increased 0.0% vs February 2017
MoM
METHODOLOGY:
• The data used reflect transactions for ~300 K products

Airfare
24
• Adobe DPI shows cumulative inflation of 5.0% YoY
(March 2016 to March 2017)
MoM
METHODOLOGY:
• The data used contains transactions for ~370 K flight routes
• Includes associated fees, where applicable

Airfare: domestic
25
• Adobe DPI shows cumulative inflation of 4.8% YoY (March
2016 to March 2017)
MoM
METHODOLOGY:

Airfare: international
26
2016 to March 2017)
• Largest price increases for airfare were to South America, with
inflation of 19.2% YoY
• Largest price decreases are for airfare to Europe with deflation of -
6.2% YoY
METHODOLOGY:

Electronics
27
2016 to March 2017)
• Main YoY price decreases for Electronics priced greater than
$1500
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
28
• Adobe DPI shows cumulative deflation of -4.9% YoY
• Main YoY price decreases for Computers priced between $700
and $1200
• Main YoY price increases for Computers priced between $0 and
$300
MoM
METHODOLOGY:

Electronics: televisions
29
• Main YoY price decreases for Televisions priced greater than
$2000
MoM
METHODOLOGY:

Electronics: tablets
30
• Main YoY price decreases for Tablets priced between $350 and
$550
MoM
METHODOLOGY:

Furniture and Bedding
31
• Main YoY price decreases for Furniture and bedding priced
between $150 and $250
MoM
METHODOLOGY:
• Products include interior and exterior furniture, mattresses and other bedding products

Groceries
32
• Main YoY deflation for eggs (-20.6%), beverages (-2.8%)
and sugar and sweets (-1.8%)
MoM
• Main price increases for cheese (inflation 0.8% MoM),
sugar and sweets (0.6% MoM). Fats and oils (0.4%
inflation MoM) and meat products (0.3% inflation
MoM) also contributed to the rise in grocery prices in
March
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
33
METHODOLOGY:
• The data used contains transactions for ~250 K hotel properties
2016 to March 2017)
MoM

Hotels: international
34
METHODOLOGY:
• The data used contains transactions for ~250 K hotel properties
2016 to March 2017)
• Largest price increases were for hotels in South America
(4.1% inflation YoY)
• Largest price decreases were for hotels in Asia (-1.5
deflation YoY)

Medical Equipment and Supplies
35
2016 to March 2017)
• 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
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
36
2016 to March 2017)
• Main YoY price decreases for Nonprescription Drugs priced
between $0 and $10
• Main YoY price increases for Nonprescription Drugs priced
between $25 and $40
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
37
2016 to March 2017)
• Main YoY price decreases for Personal care products priced
between $10 and $30
• Main YoY price increases for Personal care products priced
greater than $150
MoM
METHODOLOGY:
• Includes cosmetics, lotion, hair (shampoo, conditioner) and nail care

Pet products
38
2016 to March 2017)
• Main YoY price decreases for Pet Products priced between $10
and $20
• Main YoY price increases for Pet Products priced between $0
and $10
MoM
METHODOLOGY:
• Includes pet food & treats, kitty litter and accessories (leashes, pet beds)

Sporting Goods
39
• Main YoY price decreases for Sporting Goods priced between
$200 and $350
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
40
• Main YoY price decreases for Tools & Home Improvement priced
greater than $600
MoM
METHODOLOGY:

Toys
41
• Main YoY price decreases for Toys priced between $70 and $200
MoM
METHODOLOGY:
• Products include toys, games as well as playground equipment

Job Seeking Index
(JSI)

Online job seeking continues YoY declines
43
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 -19.3% YoY in March

Digital Housing Index
(DHI)

Online housing search grows YoY
45
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 13.0% YoY
in February.

Appendix
46

Inflation rates by price ranges, cont’d (tablets)
47
• 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
(first price).

Inflation rates by price ranges, cont’d (televisions)
48
• 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
(first price).

Inflation rates by price ranges, cont’d (televisions)
49
• 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
(first price).

Digital Price Index: Fisher Ideal Price Index
∑𝑖𝑖=1
𝑛𝑛
𝑃𝑃𝑖𝑖𝑖𝑖 𝑈𝑈𝑖𝑖𝑖𝑖−1
∑𝑖𝑖=1
𝑛𝑛
𝑃𝑃𝑖𝑖𝑖𝑖−1 𝑈𝑈𝑖𝑖𝑖𝑖−1
∗
∑𝑖𝑖=1
𝑛𝑛
𝑃𝑃𝑖𝑖𝑖𝑖 𝑈𝑈𝑖𝑖𝑖𝑖
∑𝑖𝑖=1
𝑛𝑛
𝑃𝑃𝑖𝑖𝑖𝑖−1 𝑈𝑈𝑖𝑖𝑖𝑖
• 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.
𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝐹𝐹𝐹 𝜋𝜋𝑡𝑡 =

Methodology Comparison: Fisher, Laspeyres and Paasche

Adobe Digital Economy Project - March 2017

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