adobe digital economy project - february 2017

© 2017 Adobe Systems Incorporated. All Rights Reserved.

ADOBE DIGITAL ECONOMY PROJECTFEBRUARY 2017 UPDATE

© 2017 Adobe Systems Incorporated. All Rights Reserved.© 2017 Adobe Systems Incorporated. All Rights Reserved.

Main takeaways: February

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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, where inflation in the category has been rising for the past six months.


Adobe Digital Insights BackgroundBased on analysis of aggregated and anonymous data from Adobe Marketing Cloud:• Adobe Analytics and Adobe Mobile ServicesThousands of brands worldwide including two thirds of Fortune 50 companies rely on Adobe Marketing Cloud:• $7.50 of every $10 spent 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 10 largest hotel groups• 7 of 10 largest airlines

** Latest IR Top 500 Report 2015

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Adobe Analytics

AdobeMobile Services


Economist Advisors

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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.

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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 YesProducts/quantities updated Annually DailyNumber of Products 180,000 100k

(currently covered categories)Data Collection Frequency Monthly DailyData Availability Monthly DailyOffline Prices Yes NoLong history Yes NoAll categories of spending Yes No

** Latest IR U.K. Top 500 Report 2015


Methodology – U.S.

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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 YesProducts/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 DailyData Availability Monthly Monthly DailyOffline Prices Yes Yes NoLong history Yes Yes NoAll categories of spending Yes Yes NoIndex Type Laspeyeres Fisher Ideal Fisher Ideal** Latest IR Top 500 Report 2015


DPI Coverage of U.S. CPI Categories

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DPI Category February 2017 DPI WeightsAll-Items All-Items Minus Groceries

Grocery 54.00% N/AFurniture 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.

GroceryFurniture

LodgingPersonal Care

Np. DrugsCurrent DPI

To be added to DPI

Unlikely

Professional

services

Hospital services


Big data in DPI decreases uncertainty

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DPI CPIAppliances 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

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• 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

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• 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

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• 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)


Digital Price Index Findings(DPI)


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

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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

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• 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

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Highest inflation by state

Lowest inflation by state

State MoM Inflation Major DriversWest Virginia 1.01% Grocery, Flights, Hotels

Oregon 0.88% Grocery, Flights, HotelsVirginia 0.81% Grocery, Flights, Hotels

District of Columbia 0.78% Flights, Grocery, HotelsIllinois 0.77% Flights, Grocery, Hotels

State MoM Inflation Major DriversNew Hampshire -0.16% Furniture and Bedding, Toys, Grocery

Wyoming -0.39% Furniture and Bedding, Flights, ToysNorth Dakota -0.43% Furniture and Bedding, Flights, Auto Parts

Montana -0.79% Alcohol, Furniture and Bedding, GroceryConnecticut -1.26% Grocery, Furniture and Bedding, Toys


States with lower incomes more reliant on deep discounts

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35% 40% 45% 50% 55% 60% $30,000

$35,000

$40,000

$45,000

$50,000

$55,000

$60,000

$65,000

$70,000

$75,000

$80,000

State median income vs. Percent of televisions bought on Black Friday week

Percent of TVs Bought During Black Friday Week 2016

Medi

an In

com

e (2

015)

• 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

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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

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• 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

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MoM: December to January

MoM: December to January

Difference (percentage

)Same Sign?

YoY: Cumulative January 2016-2017

YoY: Cumulative January 2016-2017

Difference (percentage)

Same Sign?

DPI CPI DPI vs. CPIDPI vs.

CPI DPI CPI DPI vs. CPIDPI vs.

CPIAlcohol 2.6% 0.7% 1.9% Yes -0.1% 0.2% -0.3% NoAppliances 1.7% 1.9% -0.2% Yes -5.1% -4.5% -0.5% YesAuto Parts 0.8% 0.2% 0.6% Yes -1.4% -1.1% -0.3% YesComputers -0.5% 0.2% -0.7% No -8.0% -4.0% -4.0% YesFlights -3.5% 1.4% -4.9% No 1.1% -5.3% 6.3% NoFurniture and bedding 0.6% 0.9% -0.3% Yes -3.5% -1.3% -2.2% YesGrocery 0.1% 0.4% -0.3% Yes -1.2% -1.9% 0.7% YesHotels 4.3% 4.7% -0.4% Yes 3.1% -2.9% 6.0% NoMedical equipment and supplies 0.1% 0.0% 0.1% No -1.5% -0.6% -0.9% YesNonprescription Drugs -0.1% 0.0% -0.1% No 0.0% -0.1% 0.1% NoPersonal Care Products 1.1% -0.1% 1.2% No -3.0% -0.9% -2.1% YesPet Products 1.1% -0.2% 1.3% No 0.2% 0.0% 0.2% NoSporting Goods 1.2% -0.2% 1.4% No -5.4% -1.4% -4.0% YesTelevisions 1.1% 2.8% -1.7% Yes -19.5% -21.2% 1.7% YesTools & Home Improvement 0.6% 0.1% 0.5% Yes -1.9% -0.8% -1.1% YesToys 3.4% 1.3% 2.1% Yes -6.7% -8.1% 1.4% YesWeighted Average Inflation* 0.5% 0.6% -0.1% Yes -1.4% -2.0% 0.6% YesWeighted 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

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• Main MoM inflation in February:• Flights, hotels, grocery

• Main MoM deflation in February:• Tablets, toys, appliances

YoY MoMAlcohol 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

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• 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

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• Adobe DPI shows cumulative deflation of -4.5% YoY (February 2016 to February 2017)• 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

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• Adobe DPI shows cumulative deflation of -1.3% YoY (February 2016 to February 2017)

• Main YoY price decreases for auto parts priced greater than $200


METHODOLOGY:

• The data used reflect transactions for ~300 K products


Airfare

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• Adobe DPI shows cumulative inflation of 3.7% YoY (February 2016 to February 2017)


METHODOLOGY:

• The data used contains transactions for ~370 K flight routes• Includes associated fees, where applicable


Airfare: domestic

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METHODOLOGY:



Airfare: international

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• 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


METHODOLOGY:



Electronics

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• Main YoY price decreases for electronics priced greater than $1500


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, printers etc..


Electronics: computers

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• Main YoY price decreases for computers priced between $700 and $1200

• Main YoY price increases for computers priced between $0 and $300


METHODOLOGY:

• The data used contains transactions for ~63 K products• Products span across dozens of different categories such as televisions, laptops, Blu-ray players, desktops, tablets, wearables,



Electronics: televisions

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• Main YoY price decreases for televisions priced greater than $2000


METHODOLOGY:




Electronics: tablets

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• Main YoY price decreases for tablets priced between $350 and $550


METHODOLOGY:




Furniture and Bedding

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• Main YoY price decreases for furniture and bedding priced between $100 and $150


METHODOLOGY:

• The data used contains transactions for ~173 K products• Products include interior and exterior furniture, mattresses and other bedding products


Groceries

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• Main YoY deflation for eggs (-22.7%), beverages (-3.2%) and cheese (-2.2%)


• 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

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METHODOLOGY:

• The data used contains transactions for ~250 K hotel properties• Includes associated fees, where applicable




Hotels: international

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METHODOLOGY:

• The data used contains transactions for ~250 K hotel properties• Includes associated fees, where applicable




Medical Equipment and Supplies

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• 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


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

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• Main YoY price decreases for nonprescription drugs priced between $10 and $15

• Main YoY price increases for nonprescription drugs priced between $15 and $25


METHODOLOGY:

• The data used contains transactions for ~16 K products• Includes all non-prescription medicines taken by swallowing, applying on your skin (such as topicals), inhaling, as suppositories,

or enemas.


Personal care products

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• Main YoY price decreases for personal care products priced greater than $150


METHODOLOGY:

• The data used contains transactions for ~110 K products• Includes cosmetics, lotion, hair (shampoo, conditioner) and nail care


Pet products

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• Main YoY price decreases for pet products priced between $10 and $20

• Main YoY price increases for pet products priced between $0 and $10


METHODOLOGY:

• The data used contains transactions for ~70 K products• Includes pet food & treats, kitty litter and accessories (leashes, pet beds)


Sporting Goods

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• Main YoY price decreases for sporting goods priced between $40 and $100


METHODOLOGY:

• The data used contains transactions for ~300 K products• Products include small equipment and accessories (balls, gloves, uniforms) as well as large equipment such as treadmills,

weightlifting tools etc.


Tools & Home Improvement

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• Main YoY price decreases for tools & home improvement priced greater than $600


METHODOLOGY:

• The data used contains transactions for ~225 K products


Toys

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• Main YoY price decreases for toys priced between $200 and $300


METHODOLOGY:

• The data used contains transactions for ~249 K products• Products include toys, games as well as playground equipment


Job Seeking Index(JSI)


Online job seeking continues YoY declines

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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

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Inflation rates by price ranges, cont’d (tablets)

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• 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

𝑃 𝑖𝑡𝑈 𝑖𝑡− 1

∑𝑖=1

𝑛

𝑃 𝑖𝑡 −1𝑈 𝑖𝑡 −1

∗√ ∑𝑖=1

𝑛

𝑃 𝑖𝑡𝑈 𝑖𝑡

∑𝑖=1

𝑛

𝑃 𝑖𝑡− 1𝑈 𝑖𝑡

• 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.

=


Methodology Comparison: Fisher, Laspeyres and Paasche

adobe digital economy project - february 2017

Economy & Finance