Sales Mechanisms in Online Markets: What Happened to Internet Auctions?

Sales Mechanisms in Online Markets:
What Happened to Internet Auctions?
Liran Einav, Chiara Farronato,
Jonathan Levin, Neel Sundaresan
Advances in Price Theory Conference
BFI, University of Chicago
December 4, 2015
Decline of Online Auctions I
• Auction share of eBay active listings and gross revenue
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
Share of active listings
Share of revenues
0.1
0
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
2
Decline of Online Auctions II
• Evolution of internet search activity (from Google Trends)
100
Search volume index (Google generated)
90
80
Online auctions
70
Online prices
60
50
40
30
20
10
0
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
3
This paper
• Two (related) topics:
– Cross section: comparing auctions and posted prices as selling mechanisms
(eBay data useful as we see both mechanisms used for the same item side
by side)
– Time series: what may explain the decline in online auctions
• Based on multi-paper collaboration with eBay researchers to study
online commerce
– See what we can learn about consumer and firm behavior, competition,
market structure and platform design in large online marketplace
– Think about empirical strategies that might be useful for taking advantage
of internet data. Challenge: the market is huge and diverse and the data do
not arrive with an obvious structure to study economic mechanisms
4
Why the Decline in Online Auctions?
1. Composition: the composition of goods and sellers online has changed
over time, from geeky users selling their broken laser pointers and used
baby items toward retail goods sold by professional retailers.
This hypothesis looks plausible in cross-sectional data (here, for 2009):
• Auctions are favored by less professional sellers.
– “Occasional” sellers were 87% auctions versus 35% for “business” sellers.
• Auctions are favored for idiosyncratic goods.
– “Used” listings were 79% auction versus 44% for “new” goods.
5
Why the Decline in Online Auctions?
1. Composition: the composition of goods and sellers online has changed
over time, from geeky users selling their broken laser pointers and used
baby items toward retail goods sold by professional retailers.
This hypothesis looks plausible in cross-sectional data.
But can it account for the trend over time?
6
Changing Composition of Products / Sellers?
Share of posted price revenue (c category, s seller): 𝐹𝑃 =
In 2005: 𝑭𝑷 = 20.9%
𝐹𝑃′ − 𝐹𝑃 =
′ 𝜎′ −
𝐹𝑃𝑐,𝑠
𝑐,𝑠
𝑐,𝑠
𝑐,𝑠
In 2009: 𝑭𝑷′ = 53.9%
𝐹𝑃𝑐,𝑠 𝜎𝑐,𝑠
𝑐,𝑠
′
𝐹𝑃𝑐,𝑠 𝜎𝑠|𝑐
− 𝜎𝑠|𝑐 𝜎𝑐′ +
′
′
𝐹𝑃𝑐,𝑠
− 𝐹𝑃𝑐,𝑠 𝜎𝑐,𝑠
+
=
𝑐,𝑠
Within c,s
27.7%
𝑐,𝑠 𝐹𝑃𝑐,𝑠 𝜎𝑐,𝑠 .
Across s, Within c
2.8%
𝐹𝑃𝑐,𝑠 𝜎𝑠|𝑐 𝜎𝑐′ − 𝜎𝑐
𝑐,𝑠
Across c
2.5%
7
Changing Composition of Sellers / Buyers?
0.6
Sellers: Fraction of posted price revenue
0.6
Buyers: Fraction of posted price expenditure
0.5
0.5
2003 Cohort
2004 Cohort
2005 Cohort
2006 Cohort
2007 Cohort
2008 Cohort
2009 Cohort
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
2003
2004
2005
2003 Cohort
2004 Cohort
2005 Cohort
2006 Cohort
2007 Cohort
2008 Cohort
2009 Cohort
2006
2007
2008
2009
2003
2004
2005
2006
2007
2008
8
2009
Why the Decline in Online Auctions?
1. Composition: the composition of goods and sellers online has changed
over time, from geeky users selling their used baby items toward retail
goods sold by professional retailers.
The time trend does not appear to be driven by compositional changes.
 Suggests looking for change in incentives holding seller & product fixed.
9
Why the Decline in Online Auctions?
1. Composition: the composition of goods and sellers online has changed
over time, from geeky users selling their used baby items toward retail
goods sold by professional retailers.
The time trend does not appear to be driven by compositional changes.
 Suggests looking for change in incentives holding seller & product fixed.
2. Competition: more intense online retail competition led to lower seller
margins and less price uncertainty, which made auctions less attractive.
3. Relative demand: auctions used to be considered “fun” and that
attracted bidders. Either because the fun faded over time or other “fun”
online activities have emerged, relative demand for auctions declined.
10
Auction vs. Posted Price
• A simple model can help distinguish the competition hypothesis from
the relative demand hypothesis.
• Consider a seller of a single item, with N potential buyers
–
–
–
–
Item costs c to the seller.
Buyers all have identical value 𝑣~𝐹 (identical not crucial).
Buyers have common reservation utility u.
Buying at auction has utility/hassle cost of .
• Seller’s decisions are:
– Decide whether to sell item in auction or via posted price.
– Set a price (if posted price) or start/reserve price (if auction).
11
Auction vs. Posted Price (cont.)
When are posted prices optimal?
 High auction disutility (high )
1
 Narrow margins (high u)
Posted Price “Demand” Curve
0.8
Price
Best choice of sales mechanism
0.6
Auction “Demand” Curve
Auction “Sales” Curve
0.4
0

𝑣~𝑈 0,1
𝑢 = 0.0
𝜆 = 0.2
0.2
0
0.2
𝑄𝐹 𝑝 = 1 − 𝑝
𝑄𝐴 𝑝 = 1 − 𝑝 − 𝜆
0.4
0.6
Probability of Sale
0.8
1
12
Empirical Strategy Using Matched Listings
Typical eBay listing page
Typical matched listings
13
Data Construction
• Start with universe of eBay listings (1 billion / year)
– Group listings with identical seller and item title, for each year.
– Majority of eBay listings have at least one match.
• Identify matched listings that include both auctions and fixed prices.
– Sets with ≥10 total listings, ≥4 by posted price and ≥4 by (pure) auction.
– Main data: 23k matched sets in 2003, 84k in 2009, each consisting of on
average 50 (in 2003) or 70 (in 2009) listings.
• Need to aggregate over many small sets, so mostly focus on normalized prices
(by average posted price of the item that year).
• Caveats and robustness: results stable for alternative matching criteria and
sample selection, although naturally data won’t perfectly represent all eBay or
online commerce and won’t capture “truly unique” items.
14
Auctions Listings are More Likely to Sell
0.5
Posted price sale rate
0.45
Health
45o
Line
PCs
0.4
Phones
Home
0.35
Music
Electronics
DVDs
Collectibles
Crafts
Sport
Books
Fan Shop
Jewelry
Clothing
0.3
0.3
0.35
0.4
0.45
Auction sale rate
0.5
0.55
0.6
Auction Sale Prices are Lower
Average sale price of posted price listings ($)
80
Sport
70
Electronics
60
Phones
PCs
50
40
Fan Shop
Collectibles Clothing
Jewelry
30
Books
Home
45o
Line
Health
Toys
20
Music Crafts
DVDs
10
10
20
30
40
50
Average sale price of auction listings ($)
60
70
Growth of the Auction Discount 2003-2009
3.4
Auction
Posted price
3.1
16.5%
Log(sale price)
(0.0005)
2.8
13.8%
(0.0008)
14.4%
(0.0006)
2.5
2.2
4.7%
(0.0012)
1.9
2003
2005
2007
2009
Estimating Listing-Level Demand
Next step: use variation in pricing within matched sets and within
format to estimate auction and fixed price demand curves in
different years.
• Fixed Price Listings
 
qij  i  f pijn   ij
• Auction Listings
 
qij  ai  g sijn  eij
 
pijn  bi  h sijn  ij
• There is a remarkable amount of within-set variation in the
reserve prices; variation in posted prices is not as large.
18
Demand Curves -- 2003
1.2
Price (normalized) conditional on sale
1.1
Auction
Posted price
1
0.9
0.8
0.7
0.6
0.5
0.1
0.2
0.3
0.4
0.5
Probability of sale
0.6
0.7
0.8
19
Demand Curves -- 2009
Price (normalized) conditional on sale
1.2
1.1
Auction
Posted price
1
0.9
0.8
0.7
0.6
0.5
0.1
0.2
0.3
0.4
0.5
0.6
Probability of sale
0.7
0.8
0.9
20
Connecting to the Model:  and u
1.2
1.1
Start price or Posted price
𝜆2003 = 0.076
1
0.9
𝜆2009 = 0.163
0.8
0.7
𝑢 = 0.144
0.6
Auction
Posted price
0.5
0.4
0.1
0.2
0.3
0.4
0.5
0.6
Probability of Sale
21
Explaining the Change in Sales Mechanisms
• Think about how changes in u and  affect seller profits.
• Posted prices
Π𝐹 𝜆, 𝑢 = 𝑚𝑎𝑥𝑞 𝑞 𝑝𝐹 𝑞; 𝜆, 𝑢 − 𝑐
By the envelope theorem,
𝜕Π𝐹
𝜕𝑢
=
𝜕Π𝐹
−𝑞𝐹 and
𝜕𝜆
∗
=0
• Auction
Π𝐴 𝜆, 𝑢 = 𝑚𝑎𝑥𝑞 𝑞 𝑝𝐴 𝑞; 𝜆, 𝑢 − 𝑐
By the envelope theorem,
𝜕Π𝐴
𝜕𝑢
= −𝑞𝐴 ∗ and
𝜕Π𝐴
𝜕𝜆
= −𝑞𝐴 ∗
22
Explaining the Change in Sales Mechanisms
• Approximating effects of u and 
On Π𝐴
On Π𝐹
On Π𝐹 − Π𝐴
Effect of u
−𝑞𝐴 ∗ ∙ ∆𝑢
−𝑞𝐹 ∗ ∙ ∆𝑢
(𝑞𝐴 ∗ − 𝑞𝐹 ∗ )∆𝑢
Effect of 
−𝑞𝐴 ∗ ∙ ∆𝜆
0
𝑞𝐴 ∗ ∙ ∆𝜆
• Use 2003 values: 𝑞𝐴 =0.48 and 𝑞𝐹 =0.40, plus ∆=8.7%, ∆𝑢 = 14.4%.
On Π𝐴
On Π𝐹
On Π𝐹 − Π𝐴
Effect of u
−6.9%
−5.8%
1.1%
Effect of 
−4.2%
0
4.2%
• Relative importance of  versus u: 3.8
23
Shifting Demand: Idiosyncratic Categories
1.4
Auction 2003
1.2
(Normalized) Sale Price
Posted Price 2003
Auction 2009
1
Posted Price 2009
0.8
0.6
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Sale Probability
Idiosyncratic: stamps, pottery, collectibles, dolls, clothes
24
Shifting Demand: Commodity Categories
1.4
Auction 2003
(Normalized) Sale Price
1.2
Posted Price 2003
Auction 2009
1
Posted Price 2009
0.8
0.6
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Probability of Sale
Commodities: PCs, video games, electronics, phones
25
Idiosyncratic and Commodity Categories
• Idiosyncratic categories: ∆=22.6%, ∆𝑢 = 9.9%
Effect of u
On Π𝐹
On Π𝐴
On Π𝐹 -Π𝐴
3.16%
3.66%
0.50%
8.36%
8.36%
Effect of 
• Commodity Categories: ∆=3.9%, ∆𝑢 = 17.2%
Effect of u
Effect of 
On Π𝐹
On Π𝐴
On Π𝐹 -Π𝐴
8.08%
9.63%
1.55%
2.18%
2.18%
26
Do eBay sellers agree?
• E-mailed survey to 50 established eBay sellers. 14 responded, almost all
were active since 2001
• Respondents match the trend: today 8 of them use only posted price, 5
use both, and only one use only auctions.
• General agreement that competition has become fierce relative to 2001
(11-2-1 on eBay; 12-0-2 across platforms). Only two of the 14 sold on
other platforms in 2001, now there are 7.
• Do eBay buyers prefer to buy via auctions: 10-2-2 in 2001, down to 5-5-4
today.
• Explanation for the trend: most mention that today’s buyers want speed
and convenience and don’t enjoy the auctions as much, few also
mention the importance to protect profit margin.
27
Selected quotes …
• “Buyers just want to buy at a good price and be done.”
• “Customers don't want to wait for the auction to end, they want
immediate gratification.”
• “I think the newness of the "auction" has worn off. People now just
want items immediately at a good price. They don't want to wait to see
if they get [the item] and then wait for another to come along and start
over. Being able to see completed items on eBay gives you an idea of
what they will bring so as long as your price is in the average that’s what
they want.”
28
Summary (long) quote …
“10 years ago, buyers were shopping online as a novelty and for fun, not
for things they necessarily needed, so the auctions were appealing. Now,
they are shopping for particular items, mostly things that they need (ex. a
birthday present for a grandson, a book for their child's book report, a
summer workbook for their child), so they want to just buy it while they're
thinking about it, not have to come back in a few days to see if they actually
got it or not.
Also, it is harder to find auctions starting at a low price, and so there is less
chance of scoring a fabulous deal. Ten years ago I started all my auctions at
99 cents, and most sold at a much higher price than I expected. Then traffic
to my auctions fell off (more competition, Buy.com's diamond seller deal
where they got free listings and most of the search exposure) and the
selling price and number of bids on the auctions plummeted, so for the past
5 years I never start an auction at a price I do not want to sell the item at.
The buyers are continuing to move away from auctions most likely because
of the high start prices. Yes, it is a self-perpetuating downward spiral. In
my category (children's books) I believe I have more success with auctions
29
than most sellers. “
Summary
• The internet has made auctions and related dynamic pricing
mechanisms more attractive from a transaction cost perspective.
–
–
–
–
e-commerce (eBay for auctions, Groupon and others for group discounts)
consumer loans and business financing (Prosper, Kickstarter)
on demand services (Freelancer, TaskRabbit).
advertising (Google, Bing, RightMedia, DoubleClick, Facebook, etc.)
• Do these clever mechanisms ultimately have low enough transaction
costs relative to the value they create?
• In the case of the consumer e-commerce auctions, competition
among sellers and for consumer attention seems to have squeezed
out auctions in favor of posted prices.
30
Extra Slides
Distribution of Auction Prices (2003)
0.25
0.2
0.15
0.1
0.05
0
Auction Sale price / Average posted sale price
Auction prices were not that much lower than posted prices in 2003.
32
Decline in Distribution of Auction Prices (2009)
0.25
0.2
0.15
0.1
0.05
0
Auction sale price / Average posted sale price
Auction prices were significantly lower relative to posted prices in 2009.
33
Not Auction Design: Start Prices Increased
0.30
2009 sold auctions
2009 unsold auctions
0.25
2003 sold auctions
2003 unsold auctions
0.20
0.15
0.10
0.05
0.00
[0-0.1] (0.1-0.2] (0.2-0.3] (0.3-0.4] (0.4-0.5] (0.5-0.6] (0.6-0.7] (0.7-0.8] (0.8-0.9] (0.9-1]
Start price / reference value
(1-1.1] (1.1-1.2]
>1.2
34
Buyer Heterogeneity and Segmentation
• There is a great deal of heterogeneity across buyers.
– Experienced buyers tend to buy more at auction.
– When they do buy at auction, they get better prices.
• Perhaps despite the general shift toward posted prices, both selling
mechanisms may persist for many goods. Sellers might use both
auctions and posted prices to reach different types of buyers.
– Posted prices to sell at a high price to buyers seeking convenience,
– Auction to sell at a lower price to buyers who don’t mind bidding.
• We can capture this in an extended version of the model.
– Buyers differ in their willingness to pay (v-u) and in their hassle cost λ.
– Using A + FP is appealing if high value buyers also are relatively impatient.
35
Figure 4: Posted Price Use, by Seller Cohort
0.6
Fraction of revenues from posted price sales
2003 Cohort
0.5
2004 Cohort
2005 Cohort
2006 Cohort
0.4
2007 Cohort
0.3
0.2
0.1
0
2003
2004
2005
2006
2007
2008
2009
Figure presents the evolution of the annual fraction of eBay sellers’ revenue from posted price listings (out of revenues from posted price and "pure"
auction listings), separately for each cohort of sellers. A seller is assigned to a cohort based on the calendar year in which the seller's first ever listing
was posted on eBay. The thick black line presents the overall platform average for each year.
Figure 7(b): Trends in Sale Rate Based on Seller
0.50
Experiments
0.45
Sale rate
0.40
0.35
0.30
Auction
Posted Price
0.25
0.20
2003
2005
2007
2009
Figure is based on the seller experiments sample described in Section 4. The lines represent the predicted value from a linear probability regression
of a sale indicator on an indicator that is equal to one for an auction listing and on seller experiment fixed effects. The regression is estimated for each
year separately.
Figure 12: Buyer Preferences for Sale Formats
8
7
Probability density function
6
2003
5
2009
4
3
2
1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Probability of purchasing at posted price
0.8
0.9
1
Figure presents the distribution of buyers’ preferences in 2003 and 2009, assuming that in a given year, the expected fraction of purchases that a
buyer would make by posted price, pk, is drawn from a beta distribution on [0,1]. An observation is a buyer active in every year between 2003 and
2009, with weights corresponding to their yearly expenditures.
Auction Discount for Different Auction Subsamples
0.2
0.18
An identical posted price listing is
available on the auction end date
No identical posted price listing is
available on the auction end date
18.6%
18.2%
17.3%
0.16
14.4%
0.14
0.12
11.3%
10.9%
11.3%
0.1
0.08
0.06
0.04
2.2%
0.02
0
2003
2005
2007
2009
Figure C.2: Distribution of Auction Reserve Prices
0.30
2009 sold auctions
2009 unsold auctions
0.25
2003 sold auctions
2003 unsold auctions
Fraction
0.20
0.15
0.10
0.05
0.00
[0-0.1]
(0.1-0.2]
(0.2-0.3]
(0.3-0.4]
(0.4-0.5]
(0.5-0.6] (0.6-0.7] (0.7-0.8] (0.8-0.9]
Start price / reference value
(0.9-1]
(1-1.1]
(1.1-1.2]
>1.2
Figure presents the distribution of the normalized reserve prices of the auction listings from the 2003 and 2009 experiment data. Each column is
divided in two separate groups, corresponding to the auction listings that did not sell (bottom), and those that resulted in a sale (top).
Figure C.3: Distribution of Auction Sale Prices
0.3
2003
0.25
Fraction
0.2
0.15
0.1
0.05
0
Sale price / reference value
Figure presents the distribution of the normalized sale prices of the successful auction listings from the 2003 and 2009 experiment data.
2009
Decline in Distribution of Auction Prices
0.25
0.2
0.15
0.1
0.05
0
Auction sale price / Average posted sale price
Auction prices were significantly lower relative to posted prices in 2009.
47
Figure C.4: Distribution of Number of Bidders per
0.5
Auction
0.45
2003
2009
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
1
2
3
4
5
6
7
8
9
10
Number of Unique Bidders
11
12
13
14
15
Figure presents the distribution of the number of unique bidders of the successful auction listings from the 2003 and 2009 experiment data.
>15
Figure 9(a): The Relative Demand Curve of
1.2
Idiosyncratic
Categories
Auction 2003
Posted price 2003
Auction 2009
Posted price 2009
Price (normalized) conditional on sale
1.1
1
0.9
0.8
0.7
0.6
0.5
0.1
0.2
0.3
0.4
0.5
0.6
Probability of sale
0.7
0.8
0.9
Figure is based on the 2003 and 2009 seller experiments sample described in Section 4, restricting estimation to five idiosyncratic categories: Stamps,
Pottery, Collectibles, Dolls, and Clothes. It is otherwise identical to Figure 8(a) and Figure 8(b) combined.
Figure 9(b): The Relative Demand Curve of
1.2
Commodity
Categories
Auction 2003
Posted price 2003
Auction 2009
Posted price 2009
Price (normalized) conditional on sale
1.1
1
0.9
0.8
0.7
0.6
0.5
0.1
0.2
0.3
0.4
0.5
0.6
Probability of sale
0.7
0.8
Figure is based on the 2003 and 2009 seller experiments sample described in Section 4, restricting estimation to five commodity categories:
Computers, Video Games, Electronics, Phones, and Cameras. It is otherwise identical to Figure 8(a) and Figure 8(b) combined.
0.9
Table 2: Calibration Results
All categories
Idiosyncratic
Commodity
l 2003
u 2003
l 2009
u 2009
0.076
-0.050
0.208
0
0
0
0.163
0.176
0.247
0.144
0.099
0.172
Posted price Auction Success
success (2003)
(2003)
All categories
Idiosyncratic
Commodity
All categories
Idiosyncratic
Commodity
0.40
0.32
0.47
0.48
0.37
0.56
Change in λ
Change in u
0.087
0.226
0.039
0.144
0.099
0.172
Posted price Auction Success
success (2009)
(2009)
0.25
0.24
0.27
0.37
0.30
0.40
Relative
Relative
importance of λ importance of λ
v. u (2009
v. u (2003
quantities)
quantities)
1.92
11.07
0.73
3.60
16.95
1.35
Table shows results based on the calibration exercise from Figure 10. The top panel presents the calibration of u and λ from the estimated demand
curves for auction and posted price listings in 2003 and 2009. It presents calibrated values for the entire seller experiments sample, for idiosyncratic
categories, and for commodity categories. The middle panel shows success probabilities estimated from the linear probability regression explained in
Section 4.1, again for the entire seller experiment sample and separately for the five most and least idiosyncratic categories. Finally, for the same three
experiment samples, the bottom panel derives the profit differential between auctions and posted prices due to the calibrated increase in u and λ, as
explained in Section 4.3.