Is The Daily Deal Social Shopping?: An Empirical Analysis of

Is The Daily Deal Social Shopping?: An Empirical

Analysis of Purchase and Redemption Time of

Daily-Deal Coupons∗

Minjae Song† Eunho Park‡ Byungjoon Yoo§ Seongmin Jeon¶

September 2012

Abstract

Shortly after Groupon started its business in 2008, daily-deal sites became newshopping places for online shoppers. Daily deals are offered in a coupon format withhuge price discounts and have social shopping features that involve collective actions.In this paper we use individual-level panel data from one of the major daily-deal sites inKorea to analyze how online shoppers change their purchase and redemption behaviorsas they repeat purchases and redemptions. We find evidence that social shoppingfeatures deter inexperienced shoppers from buying deals early on. We also find evidencethat inexperienced shoppers drive congestion at participating businesses.

∗We are grateful to participants at the 2012 Marketing Science Conference for their comments. All errorsare ours.†Corresponding author. Assistant Professor of Economics and Marketing at the Simon Graduate School

of Business, University of Rochester, [email protected].‡Graduate Student, College of Business Administration, Seoul National University, [email protected]§Associate Professor of Management Information System, College of Business Administration, Seoul Na-

tional University, [email protected]¶Ph.D. Candidate, College of Business Administration, Seoul National University, [email protected]

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1 Introduction

In 2008 Groupon, an internet startup company, experienced an instant success by selling

daily deals with heavy discounts, and shortly afterwards daily-deal sites became new shop-

ping places for online shoppers. Daily deals are offered in a coupon format and have social

shopping features that involve collective actions among shoppers; many daily-deal sites in-

cluding Groupon require a certain number of shoppers to commit to purchase to make deals

active. If a deal does not attract the minimum number of shoppers, it does not go on sale

and those who are already committed receive their money back. If it succeeds in attracting

enough shoppers, the deal is “tipped,” which means it has reached a “tipping point” whereby

the minimum number of consumers have committed to purchase. For example, if the tipping

point is 100, the deal is not on sale until at least 100 consumers are committed to buy.1 Once

a deal has tipped, all buyers, whether they buy before or after it is on, enjoy the same price

discount and have the same time window to redeem it.

Daily-deal sites provide platforms that bring merchants, especially small businesses,

and online shoppers together. On its website Groupon describes its business model as follows.

“Groupon negotiates huge discounts—usually 50-90% off—with popular businesses. We send

the deals to thousands of subscribers in our free daily e-mail, and we send the businesses a

ton of new customers. That’s the Groupon magic.” Daily-deal sites make money by keeping

approximately half the money customers pay. For example, suppose a daily-deal site offers

an $80 massage at 50 percent discount and equally splits the revenue with the merchant.

The consumer pays $40 to the daily-deal site and gets a massage valued at $80 from the

massage shop, while the shop gets $20 from the daily-deal site.

Daily-deal sites, however, provide more than platforms for merchants and customers

to interact. Because of the minimum purchase feature, a consumer cannot buy a deal that

she likes unless other consumers also buy it. This means that consumers depend on one

1We use consumers, customers, and (online) shoppers interchangeably.

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another in their purchases, which makes buying daily deals social shopping.2 Wikipedia.org

categorizes the daily-deal site as one of the major social shopping methods, where social

shopping is defined as a method of e-commerce where shoppers’ friends become involved in

the shopping experience.3 This aspect distinguishes the daily-deal site from other discount

sites such as Restaurant.com that just function as platforms in the two-sided market context.

This social shopping or group shopping feature may result in, as a by-product, un-

pleasant redemption experiences as consumers try to redeem their coupons at the same time.

There are anecdotes about retailers complaining about dealing with too many customers right

after deals are sold and consumers complaining about receiving poor quality service due to

“too many” customers. For example, a person purchased a voucher for a spa from Groupon

and went to book the treatment, but was told that there were no more spots left as too many

people had already scheduled appointments. She could not use the voucher after all.4

One may argue that rational individuals should not care how many people are com-

mitted to buy when they make purchase decisions because they receive a full refund if deals

fail to attract the minimum number of purchases. On the other hand, consumers may enjoy

contributing to tipping deals. They may send a link to their friends or even buy deals that

they are not excited about otherwise. This would be what daily-deal sites expected to hap-

pen when they adopted the minimum purchase feature. It is also plausible, however, that

consumers may want to wait and see if deals are tipped to avoid wasting time, although it

only takes a few clicks to buy deals.

In this paper we use individual-level data from one of the major daily-deal sites

in Korea to analyze how online shoppers change their purchase and redemption behaviors

as they become more “experienced”. In particular, we test whether their purchase and

redemption times change as they buy more deals and redeem more coupons. For the purchase

time we analyze over 370,000 purchases on about 500 deals sold in a three month period.

2LivingSocial, another major daily-deal company, does not have the minimum purchase feature but givesdeals for free when consumers send a link to contacts and three of them buy the offer through the link.

3See http://en.wikipedia.org/wiki/Social shopping for details.4http://groupon-reviews.measuredup.com/Complaint-Voucher-Redemption-40928

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For the redemption time we analyze over 250,000 redemptions for the deals sold during that

three month period. For each individual we know how many deals she bought and redeemed

up to the sample period and what deals she bought during the sample period. For the deals

she bought during the sample period we observe at what time she bought them, whether

they were tipped at the time of purchase, and when she redeemed them.

Since about 40 percent of consumers bought multiple times during the sample period,

we can control for each consumer’s purchase and redemption experiences separately from

their characteristics (such as gender, age, location, etc.) and various deal characteristics.

Using variables that account for the number of past purchases and past redemptions, we

analyze whether and how online shoppers’ past experiences affect their current purchase and

redemption times.

We find that typical consumers in their first few purchase occasions tend to buy deals

that are already tipped. Experienced shoppers are more likely to buy deals before they are

tipped but we do not find evidence that they do so to contribute to tipping deals. They

rather tend to buy deals right after they are announced. We also find that typical consumers

redeem their first few coupons right after their purchases but tend to wait longer as they

redeem more coupons.

Our findings on the purchase time suggest that the social shopping feature of the daily

deal deters inexperienced shoppers from buying deals early on, rather than encouraging them

to do so. As they buy more deals, however, they seem to figure out that there is no cost

associated with buying deals before they are on and, hence, buy deals as early as they can.

Our findings on the redemption time provide evidence that inexperienced shoppers drive

congestion at participating businesses right after deals are sold but try to avoid crowds in

their next redemptions. When all these findings are put together, it is hard to claim that

the success of the daily-deal business is due to the social shopping feature. Rather, it causes

problems for merchants who have to handle an unusually large number of customers after

deals are sold.

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Our paper is the first to analyze daily-deal shoppers’ behaviors using individual-level

data and the very first paper to analyze their redemption behaviors. Existing studies on

daily deals use data compiled from monitoring daily-deal websites, so their analyses are

limited to purchase patterns across deals. Ye, Wang, Aperjis, Huberman, and Sandholm

(2011), for example, use data obtained from the websites of Groupon and LivingSocial and

compare the dynamics of purchasing times on these two deal sites. Byers, Mitzenmacher,

and Zervas (2011) use similar data to analyze the relationship between deal sales and deal

characteristics. An exception is Dholakia and Kimes (2011), who use survey data from daily-

deal users as well as non-users, but they focus on examining consumer perceptions of daily

deals. Subramanian (2012) develops an analytical model of the daily deal where real-time

information on ongoing coupon sales influences consumers’ belief about the quality of a deal

and consumers can strategically wait to check this information before they buy. He analyzes

a situation where setting up the tipping point hurts the daily-deal business.

There are also studies that focus on whether merchants benefit from selling goods

and services on daily-deal sites. Kumar and Rajan (2012) use an analytical model and data

from merchants to show that offering deals do not necessarily yield profits for merchants.

Edelman, Jaffe, and Kominers (2011) use an analytical model to show that offering deals is

more profitable for merchants who are relatively unknown or merchants with low marginal

costs. Dholakia (2011) examines the determinants of profitable Groupon promotions using

survey data from merchants.

The rest of the paper is organized as follows. Section 2 describes the data set. Section

3 analyzes purchase time and Section 4 analyzes redemption time. Section 5 concludes.

2 Industry and Data

While a number of new daily-deal sites such as WagJag and BuyWithMe have entered the

market since Groupon’s initial success, Groupon has been dominant in the US market and

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has expanded to more than 40 countries around the world. Groupon’s remarkable growth

continues today. In the first quarter of 2011, it made over $644 million revenue, which is

about 200 times larger than its revenue in the same quarter in 2009. Groupon’s subscriber

base grew from about 2 million in the beginning of 2010, to over 83 million by March 2011.5

The Groupon-like daily deal business did not exist in Korea until March 2010 when

a start-up company launched a daily-deal site following Groupon’s business model.6 Shortly

afterwards a series of daily-deal sites quickly sprang up with the identical business model.

As of December 2011 there are over 300 competing daily-deal sites in Korea with industry

annual sales reaching KW 500 billion ($470 million) by the end of 2011. The top three daily-

deal sites dominate the market with their combined sales reaching KW 45 billion ($42.21

million).7 According to a report by Rankey.com, these three sites are listed among the top

10 e-commerce websites in Korea as of August 2011.

We have detailed information on deals as well as registered customers from one of

the major daily-deal sites for the first year of its business. The data set includes over 3,000

deals that generated almost 3 million coupon sales in total from over 1 million registered

customers. From the entire data, we limit our attention to deals sold between October 1,

2010 and December 31, 2010. During this period 679 deals were sold to 253,151 customers

and the total number of coupons sold is 508,593. Since we have data from one company, we

avoided a period when the market is competitive. Our sample period is before the daily-deal

market exploded in Korea. In August 2010 there were only about 30 daily-deal sites and the

company we have data on was dominating the market. However, we also avoided the first

six months after the industry was born in order to obtain a large sample size of repeating

purchases and redemptions.

Table 1 summarizes the characteristics of the 253,151 consumers who bought deals

5See Groupon’s S-1 filing with the SEC at http://sec.gov. The article by Amy Lee on June 2, 2011 atthe Huffington Post provides a nice summary. See http://www.huffingtonpost.com/2011/06/02/groupon-ipo n 870652.html

6http://www.wikitree.co.kr/main/news view.php?id=38017http://www.cnngo.com/seoul/shop/social-commerce-sites-869074

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during the three-month period. About 65 percent are female and the median age is 30. The

age and gender distribution, not reported in the paper, shows that people in their twenties

and thirties are major customers of this site, and among those in their twenties the number

of females is almost twice as large as that of males. Over 50 percent of the shoppers in our

sample receive either mobile texts (SMS) or e-mails on daily deals from the site.8 Although

not reported in the table, we also know when they made an account on this deal site and

which regions are their preferred markets.9 The median number of purchases during the

sample period is 1, and about 40 percent of shoppers purchased more than 1 deal and about

10 percent purchased more than 3 deals.10 The distribution of purchase frequency is heavily

skewed and has a long right tail. Out of the 10 percent who purchased more than 3 deals,

about 40 percent bought more than 5 deals and about 7 percent bought more than 10 deals.11

In addition to knowing how many deals were purchased, we also know when they were

bought. Thus, given a deal, we know how many deals those shoppers bought from the time

they made accounts to the date of that deal, and we use this information to account for their

purchase experiences. In the beginning of the sample period about a quarter of consumers

bought at least once previously. We also know what time they bought a deal, so we can

determine whether they bought before a deal was tipped. In the following section we link

this information to consumers’ purchase experiences and test whether their purchase time

changes as they buy more deals. In addition, we have information on when they redeemed

the deals that they bought. Although each purchase almost always leads to redemption,

consumers have different patterns of redemption. For example, some redeem their coupons

immediately after their purchases, while others wait for several weeks before redeeming them.

Table 2 summarizes the characteristics of 679 deals in our sample. Product Sales is

the number of coupons sold per each deal. The mean is 1,590 with the standard deviation

8The company sends emails at 7 o’clock in the morning.9Consumers indicate which region is their preferred market but do not always buy in their preferred

markets.10If a consumer buys two different deals in two different markets, we count it as two purchases. But if she

purchases multiple coupons of the same deal, we count it as one purchase.11For the 1 year we have data on the median number of purchases is 4 and the mean is 6.67.

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3,588. The deal with the highest sales sold 49,995 coupons. Original Price is the original

price of deals and ranges from 700 KW to 1 million KW with the median 35,000 KW.12

This large variation in prices is mainly due to having very different “products”. Discount

Rate refers to the discount rate and varies widely, but over 95 percent of deals offer at least

50 percent discount. The price consumers actually pay (the original price multiplied by the

discount rate) ranges from 0 to 599,000 KW with the median price 14,900 KW. Sale Period

denotes the number of days that a deal is available. A majority of deals are on sale for 24

hours but some deals, mainly those that are put up on Fridays, are sold for multiple days.

Tipping Point is the number of sales required to turn on a deal. The median tipping point is

100 but as low as 1 and as high as 5,000. Maximum Sale is the number of coupons available

for each deal and ranges from 30 to 60,000 with the median 1,500. Both the tipping point

and the maximum sale are predetermined and revealed to consumers when deals appear on

the site. Redemption Period refers to the number of days that purchasers have for redeeming

coupons they bought. This period is usually 60 or 90 days. Deals with zero redemption days

are concerts or performances on fixed dates. Soldout indicates whether a deal was sold out,

meaning the number of coupons sold reached the maximum sale. About 30 percent of the

deals in our sample were sold out. Figure 1 shows deal types in a pie chart. Restaurants

make up the largest category, followed by beauty/healthcare, concert/exhibition and so on.

The deal site sets the discount rate, the tipping point, and the redemption period,

presumably jointly with vendors, to sell all coupons available. A simple correlation among

them, however, does not show any clear pattern in how they set these values. The most

correlated characteristics are the tipping point and the maximum sale. They are positively

correlated with the correlation coefficient 0.60. The original price is positively correlated

with the discount rate with the correlation coefficient 0.31, suggesting that more expensive

products are usually more heavily discounted. The tipping point is negatively but weakly

correlated with the discount rate with the correlation coefficient -0.11. Although it is not

121,200 Korean Won is approximately equal to one US dollar.

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high, this suggests that a heavier price discount tends to accompany a lower tipping point.

The correlation between the tipping point and the original price is even weaker with the

correlation coefficient -0.04.

To see whether or how these attributes affect coupon sales, we run a simple OLS with

the log of sales as the dependent variable and all other attributes as covariates. In addition

to the variables reported in Table 2, we include the dummy variables for each day of the

week, 6 category dummy variables, and 13 region dummy variables. We first run OLS for all

deals, and then selected two subsamples and run OLS for each sample. The first subsample

excludes 173 deals that lasted for longer than 24 hours, and the second subsample excludes

206 deals that were sold out.

Table 3 reports results for these three regressions. Results are very similar across the

three samples. The adjusted R-squared is 0.61 for all deals, which is high considering the

fact that every deal is unique. The log of the (original) price variable has a negative and

statistically significant coefficient, suggesting that more expensive products sell less. The

discount rate is positive and statistically significant, suggesting that a deal with a heavier

discount is more popular. The tipping point is also positive and statistically significant,

suggesting that a deal with a higher tipping point is more popular. The maximum sale is

positive and significant, indicating the deal site sets a higher maximum sale for more popular

deals. The positive coefficients for the tipping point and the maximum sale suggest that the

deal site sets them at higher values for deals that they expect to be popular. The redemption

period variable has a positive and significant coefficient, showing that a deal with a longer

redemption period is more popular. D salesday is a dummy variable for deals that lasted

longer than a day, and has a positive and significant coefficient, showing that a deal that

lasted longer sold more coupons.

The day variables do not show any significant effects. Deals sold on Wednesdays seem

more popular, but its effect is not significant. The insignificant day effects are consistent

with what the deal site describes about their business during the sample period: they did

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not have a large inventory of deals to select for each day. They admit that the hardest

part in an earlier period was to convince vendors to sell their products as the daily deal

business was new and the vendor’s margin was extremely low.13 This means that the deal

site did not have much freedom to select more appropriate days for each deal. The category

variables do not show much significant effects other than positive effects for restaurants and

concerts/exhibitions.

In analyzing purchase time in the following section we use deals that were sold for 24

hours but do not exclude deals that were sold out. 506 deals satisfy these criteria, and the

number of consumers goes down to 195,553 but the consumer characteristics hardly change.

The results in Table 3 show that the relationship between the coupon sales and the deal

characteristics does not change substantially with these criteria. The most notable change is

seen in the category dummy variables; three category dummy variables including restaurants,

beauty/health care, and concerts/exhibitions have larger coefficients, and the pub and cafe

category variables now have positive yet insignificant coefficients.

3 Purchase Time

The feature of the daily-deal site that makes it social commerce or social shopping is the

(predetermined) minimum number of purchase commitments, which is called the tipping

point. If a deal does not attract a certain number of committed buyers, consumers cannot

buy it no matter how much they like it. One of our main research questions is how consumers

behave towards the tipping point. Given that consumers find deals that they like, are they

more likely to purchase before deals have tipped? In particular, we link consumers’ purchase

behaviors to their purchase experiences and test if their purchase time changes as they buy

more deals. For this we analyze 370,616 purchases made by 195,553 consumers on 506 deals.

To control for purchase experiences we create five dummy variables for the first pur-

chase through the fifth purchase. Given a deal the dummy variable for the first purchase

13Due to a confidentiality agreement we cannot disclose the names of the people we talked to.

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takes 1 if the number of previous purchases is 0, the dummy variable for the second purchase

takes 1 if the number of previous purchases is 1, and so on. These five dummy variables

cover about 84 percent of all observations. Note that these dummy variables control for

the number of past purchases from the date that consumers made an account. For previous

redemptions we create three dummy variables for the first two redemptions. Given a deal

the dummy variable for no redemption takes 1 if a consumer did not redeem any coupon up

to that point of time, the dummy for one redemption takes 1 if she redeemed one coupon

before, and so on. These three dummies cover about 90 percent of all observations.

Figure 2 shows an empirical distribution of purchase times for all deals that were sold

for 24 hours. The figure shows that 9 to 10 am has the highest frequency, about 11 percent,

closely followed by midnight to 1 am. 10 to 11 am has the third highest peak followed by 11

am to the noon. The frequency is quite evenly distributed in the afternoon hours and is lower

in the evening. Not surprisingly, 3 to 8 am has the lowest frequencies. In order to see who

buys at what time and if purchase times change as consumers buy more deals, we first run the

multinomial logit regression where we divide 24 hours into 4 equal length intervals (6 hour

intervals) and use the second interval (from 6 am to the noon) as the base. Table 4 reports

results. In addition to the variables reported in the table, we include the deal characteristics

reported in Table 2, deal categories, weekday dummies, 13 dummies for market locations,

12 dummies for weeks that deals were sold, and 32 dummies for weeks that consumers made

accounts. The 12 week dummies control for any time trend in purchase behaviors during

the sample period, and the 32 week dummies control for consumer heterogeneity associated

with the timing of becoming customers at the deal site.

Results show that most of the purchase/redemption experience dummies are statis-

tically significant. To interpret these estimates we compute the predicted probability of

buying the “average” deal in each time interval for the “average” consumer. For the average

buyer who buys a deal for the first time the probability of buying it in each time interval

is 0.0000052 for the first 6 hours of the day (from midnight to 6 am), 0.67 for the next 6

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hours, 0.12 for between noon and 6 pm, and 0.22 for the last 6 hours. When this consumer is

buying the second deal, the probability goes up by 21 percent for the first time interval and

11 percent for the second time interval, while it goes down by 17 percent for the third interval

and 25 percent for the fourth interval. In other words, given that the average consumer is

buying the second deal, she is more likely to buy it in the first 12 hours of the day (between

midnight and the noon) and less likely to buy it in the last 12 hours of the day compared

to her last purchase. A purchase-time change is more dramatic when she is buying her sixth

(or higher) deal. The probability of buying a deal in the first 6 hours of the day is three

times higher compared to her first purchase while the probability of buying it in the last 12

hours is more than 40 percent lower.

One concern here is, however, that we do not perfectly control for individual differ-

ences other than with a few customer characteristics and the 32 dummies for weeks that

they made an account at this deal site. It is possible that the result above is mainly driven

by those who bought no more than one or two deals by the end of the sample period; if

those who bought only one deal bought it in the afternoon, the first purchase dummy may

not distinguish the first purchase effect from the effects of (unobserved) individual charac-

teristics that made them buy only once. Because of this concern, we also run the fixed-effect

regression with purchase times as the dependent variable. In particular, we divide 24 hours

into 2,400 units and transform purchase times into these units.14 In Table 5 we compare the

fixed effect regression results with the OLS results. The OLS results show that the purchase

time of the first purchase is about one hour later than that of the second purchase and about

one hour and thirty minutes later than that of the third purchase. When consumers make

their sixth (or higher) purchase, they are likely to buy deals three hours earlier than their

first purchase. The fixed effect results show that the effects of previous purchases become

smaller but are still consistent with those of the OLS results. Now the purchase time of the

14This linear regression may not be the best choice because the dependent variable is bounded and thereare a lot of observations near the bounds, but it may still show how results change with the individual fixedeffects.

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first purchase is about 40 minutes later than that of the second purchase and 1 hour and

30 minutes later than that of the sixth purchase, but the results still suggest that the more

deals consumers have purchased in the past, the earlier they buy the next deal.

Why would consumers buy earlier as they buy more deals? Put differently, why

would those who buy for the first time or the second time buy in the afternoon or later? One

implication from the purchase time results is that consumers who are buying for the first

time or who have only bought a couple of deals want to wait until deals have tipped. Figure

3 shows when deals reach the tipping point. The figure shows that about 40 percent of deals

reach the tipping point in the first hour, but about 20 percent of deals reach it between

7 and 10 o’clock in the morning and over 10 percent of deals reach it after 10 o’clock in

the morning. It may well be that those consumers do not want to buy deals that have not

attracted enough consumers.

We first test if the purchase time around the tipping point is associated with the

purchase experience. In particular, we estimate the probability of purchasing a deal before

it has tipped with the same regressors as in the multinomial logit regression above. Table 6

reports results from the binary logistic regression as well as the fixed effect logit regression.

Note that the number of observations goes down from 370,616 to 71,402 in the fixed effect

logit regression as it only uses consumers that change their purchase times over different deal

purchases. With the fixed effect logit regression we can control for unobserved individual

heterogeneity, although we cannot learn much about the average partial effect.

In the binary logistic regression all five of the previous purchase dummies are statisti-

cally significant and negative and their absolute magnitude goes down with more purchases,

indicating that the more deals consumers bought previously, the more likely they are to buy

before deals reach the tipping point. In particular, compared to those who are buying their

first deal, the probability of buying a deal before it reaches the tipping point is about 30

percent higher for those who bought one deal before, about 68 percent higher for those who

bought two deals before, and about three times higher for those who bought five deals be-

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fore.1516 These effects are much larger than the effects of any consumer characteristics such

as age, sex, and whether they are receiving emails. In the fixed effect logit regression the

coefficients of the previous purchase dummies are lower in the absolute term but the relative

magnitude among the purchase experience dummies does not change much, indicating that

individual differences do matter but the main implication still holds.

These results show that “inexperienced” shoppers tend to wait until deals have tipped

while “experienced” ones buy before they reach the tipping point. One may interpret these

results as evidence that there is not much group shopping or social shopping going on with

inexperienced shoppers. The idea of having the tipping point is that it may trigger social

interaction among online shoppers; if I see a deal that I like, I may call up my friends to join

me buying it. However, the results do not seem consistent with such a behavior at least for

inexperienced shoppers.

These results also suggest that experienced buyers are the main driving force behind

tipping daily deals; they may receive so much utility from turning deals on that they even

buy deals that they would not normally purchase if they have already tipped. Instead of the

dummy variables, we also use the number of previous purchases and the number of previous

purchases squared in the binary logistic regression and find that this effect persists up to 42

previous purchases which covers almost everyone in the sample. However, one may interpret

the same results as consumers “learning” that they do not lose a penny even if a deal they

are committed to buy fails to be on; all they lose is a few minutes spent on the deal site.

In this interpretation experienced shoppers’ early-purchase behavior is not associated with

social shopping.

In order to shed more light on motives behind the early purchase, we analyze purchases

before deals reach the tipping point. In particular, we estimate the probability of being part

of the last 10 percent of purchases before deals have tipped as well as the probability of being

15As above we compute the probability of the average consumer buying the average deal.16The probability of buying a deal before it reaches the tipping point is 0.049 for consumers buying their

first deal.

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part of the very first 10 percent of purchases. Suppose a deal needs 100 purchases to be on,

and when a customer visits the deal site, only 5 customers are committed to buy.17 If she

receives a utility from tipping a deal, her purchase has a marginal impact as compared to

when 95 or 99 customers are committed to buy. On the other hand, if she does not receive

such a utility and does not want to buy a deal that is unlikely to tip, she is more likely to

buy when 95 customers are committed to buy.

We use the binary logistic regression as well as the fixed effect logit regression with

the same regressors as in Table 6. Because we only use purchases before deals have tipped,

the number of observations goes down to 32,076. Results are reported in Table 7. On the

left panel (under Last 10% ) we estimate the probability that purchases are part of the last

10 percent before deals reach the tipping point, and on the right panel (under First 10% )

we estimate the probability that purchases are part of the first 10 percent after deals appear

on the site. The binary logistic regression results show that those who bought many deals

in the past are less likely to buy right before deals have tipped but are more likely to buy

right after deals appear than those who bought no deal or a couple of deals in the past. In

the fixed effect logit regression most of the past purchase dummies are no longer statistically

significant, but the dummy for the first purchase is still significant and even has a larger

coefficient than in the binary logistic regression, implying that the difference between those

who bought many deals in the past and those who bought no deal in the past does not stem

from individual heterogeneity.

These results show that the more deals consumers buy, not only are they more likely

to buy before deals are turned on but also more likely to buy right after deals start. This

implies that those frequent buyers buy deals that they like without paying much attention

to whether deals are going to be on. The “typical” consumer implied by our results tends to

wait until a deal has tipped in their first few purchases but after that she pays less attention

to the deal status and buys deals that she likes. It is important to note that our results

17The deal site we obtain data from shows the number of purchase in real time, while Groupon in the USshows a rough status like “over 530”.

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show strong state dependence in purchase behaviors. In other words, our results suggest

that consumers do change their purchase behaviors as they buy more deals.

One may interpret these results in the context of observational learning (Banerjee,

1992; Cai, Chen and Fang, 2009); shoppers learn about the quality of deals from others. In

particular, Subramanian (2012) shows that in such a situation shoppers are willing to wait

until a “sufficient” number of coupons are sold. In this perspective our results suggest that

inexperienced buyers are those who wait and learn from others while experienced ones do

not care much about whether other shoppers like a deal or not. However, the observational

learning motivation does not explain why experienced buyers no longer care about other

shoppers’ decisions. The deal site offers different deals every day, so buying deals in the past

does not necessarily provide more information on a deal offered today.

4 Redemption Time

Once a deal has tipped, everyone who buys it has the same amount of time to redeem their

coupons. The redemption period ranges from 50 to 100 days for about 60 percent of deals in

our sample and the median is 90 days. Because of this limited time window, businesses who

“successfully” sold many coupons on the daily-deal site serve an unusually large number of

customers, and sometimes have difficulty in satisfying customers’ expectation. For example,

consumers may have to wait for hours to be seated at restaurants. This “congestion” is an

inevitable cost of daily deals and presents a challenge to the industry unless it finds a way

to coordinate consumers’ redemption times.18

In this aspect it is important to know when consumers redeem the coupons and if

their redemption time changes as they redeem more coupons. In Figure 4 we select 121

deals that have a 90 day redemption period and show the frequency of redemption time

18Margie Fishman writes in her blog at www.openforum.com on September 19, 2011 that Living-Social “staffers assist small business owners during peak redemption periods, such as right after acoupon is released or just before its expiration date”, but does not explain how they do it. Seehttp://www.openforum.com/articles/the-top-5-group-buying-sites-love-em-or-hate-em

16

for consumers who bought those deals. The figure shows that a large number of customers

redeem their coupons right after purchases and right before they are expired. 28 percent of

coupons were redeemed in the first 10 days, 44 percent were redeemed in the first 20 days,

and 14 percent were redeemed in the last 10 days.

For the redemption-time analysis we select deals whose redemption period is at least

20 days long and track redemptions for those deals. With this criterion deals that can

be redeemed only on fixed dates such as concerts or plays are excluded. The new sample

includes 267,143 observations and their redemption times range from 1 to 183 days where

1 day means redeeming a coupon on the next day of purchase. Also, note that redemption

periods for many deals in our sample, at least for deals sold in the last 20 days of our sample

period, end later than the end of our sample period.

We first estimate the probability of redeeming coupons in the first 10 days of the

redemption period using the binary logistic regression and the fixed effect logit regression,

and report results on the left panel of Table 8. We use the same regressors as in the previous

section, but the dummy variables for days now represent days that coupons were redeemed,

not days that deals were purchased. Also, the dummy variable for the first purchase is taken

out because all consumers in this sample inevitably made at least one purchase in the past.

To avoid confusion, we re-label the previous purchase dummies as “One Purchase” instead of

“Second Purchase”, “Two Purchases” instead of“Third Purchase”, etc. “One Purchase”means

that consumers made one purchase up to that point, “Two Purchases” means that consumers

made two purchases up to that point, etc. We also re-label the previous redemption dummies

as “First Redemption” instead of “No Redemption”, “Second Redemption” instead of “One

Redemption”, etc.

Results show that consumers are less likely to redeem in the first 10 days as they

redeem more. In the binary logistic regression the probability of redeeming the average

coupon in the first 10 days is 0.32 for the average consumer who is redeeming for the first time.

This probability goes down to 0.17 for those redeeming for the second time and 0.09 for those

17

redeeming for the fourth time.19 In the fixed effect logit regression the number of observations

goes down to 96,892 because consumers who always redeemed in the first 10 days or always

redeemed later than that are not used. Results show that the redemption experience effects

are now much larger. One implication of these results is that experienced consumers try to

avoid the “crowds” that made their visits unpleasant in the previous redemption occasions.

Similarly, we also estimate the probability of redeeming coupons in the last 10 days

of the redemption period and report results on the right panel of Table 8. Results show

that consumers are more likely to redeem their coupons in the last 10 days as they redeem

more, and the effects become larger in the fixed effect logit regression. We may interpret

this result as experienced consumers waiting to redeem their coupons as much as possible,

but it is also possible to interpret it as their “procrastinating” using coupons or having “too

many” coupons to redeem on time.20

In addition to estimating the probability model, we also run linear regressions with

the redemption time as the dependent variable. Table 9 shows both the OLS and the FE

regression results. The OLS results show that consumers who are redeeming for the first time

redeem about 12 days earlier than those who are redeeming for the second time and about

30 days earlier than those who redeemed more than five times. These differences become

much larger in the fixed effect regression. The differences are more than 20 days between

those who are redeeming for the first time and those who are redeeming for the second time

and more than 90 days between the former and those who redeemed more than five times.

These results are consistent with those presented in Table 8.

These results imply that “typical” consumers in our data set redeem their coupons

right away after the first purchase but then wait longer as they redeem more. Although

19To be more specific the former probability is for those who bought two deals and are redeeming theirsecond coupon and the latter probability is for those who bought four deals and are redeeming their fourthcoupon.

20Using redemption data for deals that have at least 30 days as the redemption period, we divide it intothree periods: the first 10 days, the last 10 days, and the rest, and use the multinomial logit model toestimate the probability of choosing a redemption time out of these three periods. Results and implicationsare similar as those of Table 8.

18

we can only speculate why they are changing their redemption time this way, it may very

well be that they do so to avoid the congestion they experienced in the previous redemption

occasions. Considering the fact that small businesses are the main vendors on the daily-

deal sites, smoothing out the redemption time is a crucial for the success of the daily-deal

industry.

5 Conclusions

In this paper we empirically analyze consumers’ purchase and redemption time of daily-deal

coupons using individual-level data from one of the major daily-deal sites in Korea. The

daily-deal site we analyze is a social shopping site where each deal requires a certain number

of committed purchases to be on sale. We find that typical consumers in their first few

purchase experiences buy deals that are already on but tend to buy earlier as they buy more

deals. Experienced shoppers are more likely to buy before deals are on but we do not find

evidence that they do so to turn on deals. Rather they tend to buy deals as soon as deals

start. We also find that typical consumers redeem their first few coupons right after their

purchases but tend to wait longer as they redeem more coupons.

Our findings on the purchase time suggest that the social shopping feature of the

daily deal does not encourage inexperienced shoppers from buying deals early on. As they

buy more deals, however, they seem to learn that there is no cost of buying deals before they

are on. Our findings on the redemption time provide evidence that inexperienced shoppers

drive congestion right after deals are sold and that they soon try to avoid crowds in their

subsequent redemptions.

When all these findings are put together, it is hard to claim that the success of the

daily-deal industry is due to the group shopping feature. Rather it causes problems for small

businesses that have to handle an unusually large number of customers after deals are sold.

19

References

[1] Banerjee, A. (1992), “A Simple Model of Herd Behavior”, Quarterly Journal of Eco-nomics, 107, 797-817.

[2] Byers, J. W., M. Mitzenmacher, and G. Zervas (2011), “Daily Deals: Prediction, SocialDiffusion, and Reputational Ramifications”, Working Paper, Boston University.

[3] Cai, H., Y. Chen, and H. Fang (2009), “Observational Learning: Evidence from a Ran-domized Natural Field Experiment”, American Economic Review, 99, 864–82.

[4] Dholakia, U. M. (2011), “What Makes Groupon Promotion Profitable For Businesses?”,Working Paper, Rice University.

[5] Dholakia, U. M. and S. E. Kimes (2011), “Daily Deal Fatigue or Unabated Enthusiasm?A Study of Consumer Perceptions of Daily Deal Promotions”, Working Paper, Rice Uni-versity.

[6] Edelman, B., S. Jaffe, S. D. Kominers (2011), “To Groupon or Not to Groupon: TheProfitability of Deep Discounts”, Working Paper, Harvard Business School.

[7] Kumar, V. and B. Rajan (2012), “Social Coupon as a Marketing Strategy: A MultifacetedPerspective”, Journal of the Academic Marketing Science, 40, 120-136.

[8] Subramanian, U. (2012), “A Theory of Social Coupons”, Working Paper, University ofTexas at Dallas.

[9] Ye, M., C. Wang, C. Aperjis, B. A. Huberman, and T. Sandholm, (2011) “CollectiveAttention and the Dynamics of Group Deals”, Working Paper, Pennsylvania State Uni-versity.

20

Table 1: Consumer Summary StatisticsMean Stdev Median Min Max

Gender (Male=1) 0.36 0.48 0 0 1

Age 31.4 7.04 30 8 97

SMS (Yes=1) 0.56 0.50 1 0 1

e-mail (Yes=1) 0.52 0.50 1 0 1

Purchase† 1.96 1.94 1 1 158

Volume of Order‡ 2.01 1.47 2 1 40

Note: The summary statistics on 253,151 consumers who bought at least one deal from October

1, 2010 to December 31, 2010.†Purchase indicates the number of deals that these consumers bought during the sample period.‡Volume of Order indicates the number of coupons consumers bought per deal.

21

Table 2: Product Summary StatisticsMean Stdev Median Min Max

Product Sales 1,590 3,588 909 2 49,995

Original Price (in KW†) 69,384 120,110 35,000 700 1,000,000

Discount Rate (%) 56.50 8.45 54 15 81

Sale Periods (in days) 1.52 0.90 1 1 5

Tipping Point 112 292 100 1 5,000

Maximum Sale 2,984 6,249 1,500 30 60,000

Redemption Periods (in days) 83.85 46.59 90 0 366

Soldout‡ 0.302 0.459 0 0 1

Note: The summary statistics on 696 deals that were sold from October 1, 2010 to December 31,2010.†KW denotes Korean Won.‡Soldout indicates whether the number of coupons sold reached the maximum number of coupons

available.

22

Table 3: Sales Analysis: OLSAll One-day No Soldout

Discount Rate 0.021** 0.032** 0.026**

(0.004) (0.005) (0.005)

log(Price) -0.613** -0.553** -0.669**

(0.038) (0.042) (0.047)

Tipping Point (in 100) 0.023** 0.024** 0.029*

(0.011) (0.011) (0.016)

Max. Sale (in 1,000) 0.060** 0.068** 0.057**

(0.006) (0.007) (0.007)

Redemption Periods 0.004** 0.004** 0.004**

(0.001) (0.001) (0.001)

D salesday 0.346** - 0.220**

(0.069) - (0.087)

Monday -0.024 -0.068 -0.013

(0.084) (0.087) (0.107)

Tuesday -0.065 -0.141 -0.015

(0.087) (0.088) (0.109)

Wednesday 0.097 0.068 0.140

(0.086) (0.088) (0.112)

Thursday -0.121 -0.091 -0.160

(0.084) (0.087) (0.108)

Restaurant 0.408** 0.693** 0.412**

(0.109) (0.119) (0.131)

Pub -0.300** 0.055 -0.265*

(0.131) (0.137) (0.151)

Cafe -0.235* 0.117 -0.313*

(0.142) (0.146) (0.175)

Beauty/Health Care 0.119 0.248* 0.110

(0.116) (0.127) (0.141)

Leisure 0.104 0.190 0.232

(0.142) (0.174) (0.168)

Concert/Exhibition 0.201* 0.475** 0.220

(0.122) (0.139) (0.137)

Adj. R-squared 0.614 0.611 0.629

N 679 506 473

* p-value < 0.1, ** p-value < 0.05

Note: The dependent variable is log(Sales). We also included the dummy variables for weeks that

deals were sold and market locations.

23

Table 4: Purchase Time: Multinomial Logit0 - 6 12 - 18 18 - 24

First Purchase -0.632** 0.653** 0.792**

(0.021) (0.022) (0.028)

Second Purchase -0.547** 0.361** 0.401**

(0.021) (0.022) (0.027)

Third Purchase -0.417** 0.237** 0.237**

(0.021) (0.022) (0.028)

Fourth Purchase -0.336** 0.169** 0.155**

(0.022) (0.023) (0.029)

Fifth Purchase -0.240** 0.094** 0.080**

(0.023) (0.025) (0.032)

No Redemption -0.256** 0.064** -0.028

(0.022) (0.024) (0.030)

One Redemption -0.302** 0.079** -0.034

(0.021) (0.023) (0.029)

Two Redemptions -0.282** 0.076** -0.064**

(0.021) (0.024) (0.031)

Volume of Order -0.025** 0.017** 0.012**

(0.005) (0.004) (0.004)

Age -0.040** -0.004** -0.028**

(0.0008) (0.0006) (0.0008)

Male 0.451** 0.127** 0.370**

(0.010) (0.009) (0.011)

e-mail -0.319** -0.075** -0.194**

(0.015) (0.014) (0.016)

sms -0.156** 0.036** -0.015

(0.014) (0.013) (0.015)

e-mail·SMS 0.268** 0.019 0.145**

(0.020) (0.018) (0.022)

Preference Area -0.176** -0.042** -0.106**

(0.011) (0.010) (0.012)

* p-value < 0.1, ** p-value < 0.05

Note: We also control for the deal characteristics reported in Table 2, days of the week, weeks that

deals were sold, deal categories, market locations, and weeks that consumers made accounts.

24

Table 5: Purchase Time: Linear RegressionOLS Fixed Effect

First Purchase 3.150** 1.513**

(0.050) (0.076)

Second Purchase 2.053** 0.890**

(0.048) (0.067)

Third Purchase 1.439** 0.560**

(0.049) (0.062)

Fourth Purchase 1.105** 0.398**

(0.051) (0.059)

Fifth Purchase 0.748** 0.179**

(0.055) (0.058)

No Redemption 0.584** 0.106

(0.052) (0.078)

One Redemption 0.662** 0.095

(0.050) (0.067)

Two Redemptions 0.587** 0.042

(0.052) (0.063)

Volume of Order 0.056** -0.029**

(0.009) (0.013)

Age 0.022** -

(0.002)

Male -0.184** -

(0.021)

e-mail 0.216** -

(0.032)

sms 0.312** -

(0.030)

e-mail·SMS -0.247** -

(0.043)

Preference Area 0.089** -0.091**

(0.023) (0.036)

Adj. R-squared 0.147 0.119

N 370,616 370,616

* p-value < 0.1, ** p-value < 0.05



25

Table 6: Purchase Before Deals are OnBinary Logistic FE Logit

First Purchase -0.987** -0.487**

(0.028) (0.054 )

Second Purchase -0.697** -0.307**

(0.027) (0.048)

Third Purchase -0.465** -0.187**

(0.027) (0.044)

Fourth Purchase -0.361** -0.143**

(0.028) (0.041)

Fifth Purchase -0.280** -0.113**

(0.030) (0.034)

No Redemption -0.201** -0.097*

(0.028) (0.054)

One Redemption -0.234** -0.052

(0.026) (0.046)

Two Redemptions -0.169** 0.010

(0.027) (0.042)

Volume of Order -0.051** -0.037**

(0.006) (0.010)

Age -0.019**

(0.001)

Male 0.219**

(0.013)

e-mail -0.118**

(0.020)

sms -0.083**

(0.018)

e-mail·SMS 0.107**

(0.026)

Preference Area -0.009 0.081**

(0.014) (0.026)

N 370,616 71,402

* p-value < 0.1, ** p-value < 0.05



26

Table 7: First and Last 10% of Sales before Deals are OnLast 10% First 10%

Binary Logit FE Logit Binary Logit FE Logit

First Purchase 0.334** 0.554** -0.720** -0.510**

(0.090) (0.275) (0.091) (0.237)

Second Purchase 0.208** 0.298 -0.394** -0.297

(0.087) (0.250) (0.085) (0.211)

Third Purchase 0.164* 0.078 -0.388* -0.263

(0.087) (0.225) (0.086) (0.193)

Fourth Purchase 0.034 0.038 -0.232** -0.308*

(0.093) (0.211) (0.087) (0.176)

Fifth Purchase 0.160* 0.331 -0.212** -0.136

(0.095) (0.202) (0.084) (0.167)

No Redemption -0.044 -0.507* 0.002 -0.017

(0.090) (0.263) (0.084) (0.230)

One Redemption -0.015 -0.097 -0.062 -0.085

(0.084) (0.223) (0.078) (0.184)

Two Redemptions -0.136 -0.041 -0.103 -0.017

(0.089) (0.206) (0.080) (0.168)

Volume of Order 0.100** 0.078* -0.111** -0.128**

(0.017) (0.046) (0.022) (0.049)

Age 0.002 -0.005

(0.003) (0.003)

Male -0.022 -0.004

(0.039) (0.041)

e-mail 0.017 0.006

(0.061) (0.063)

sms 0.014 -0.050

(0.057) (0.061)

e-mail·SMS 0.007 0.085

(0.081) (0.085)

Preference Area -0.032 0.022 0.029 0.183*

(0.044) (0.135) (0.046) (0.110)

N 32,076 2,720 32,076 3,715

* p-value < 0.1, ** p-value < 0.05



27

Table 8: Redemption TimeFirst 10 Days Last 10 Days

Binary Logistic FE Logit Binary Logistic FE Logit

One Purchase 0.205** 2.284** 0.682** -4.287**

(0.023) (0.091) (0.031) (0.254)

Two Purchases -0.070** 1.121** 0.664** -2.542**

(0.022) (0.070) (0.028) (0.196)

Three Purchases -0.161** 0.557** 0.583** -1.539**

(0.022) (0.059) (0.027) (0.162)

Four Purchases -0.168** 0.279** 0.455** -0.908**

(0.023) (0.052) (0.026) (0.138)

Five Purchases -0.162** 0.074 0.319** -0.545**

(0.024) (0.048) (0.027) (0.116)

First Redemption 1.898** 7.253** -2.094** -7.890**

(0.027) (0.072) (0.031) (0.094)

Second Redemption 1.334** 5.561** -1.468** -5.788**

(0.026) (0.062) (0.028) (0.077)

Third Redemption 0.966** 4.280** -1.027** -4.273**

(0.026) (0.055) (0.026) (0.065)

Fourth Redemption 0.666** 3.198** -0.668** -2.993**

(0.028) (0.051) (0.026) (0.057)

Fifth Redemption 0.481** 2.300** -0.482** -1.973**

(0.032) (0.050) (0.028) (0.050)

Volume of Order 0.138** 0.129** -0.169** -0.214**

(0.004) (0.010) (0.006) (0.015)

Age -0.008** 0.007**

(0.001) (0.001)

Male 0.198** -0.123**

(0.010) (0.012)

e-mail -0.046** 0.101**

(0.015) (0.019)

sms 0.015 0.061**

(0.014) (0.017)

e-mail·SMS 0.022 -0.056**

(0.020) (0.024)

Preference Area 0.087** 0.024 -0.176** -0.121**

(0.011) (0.030) (0.013) (0.034)

N 267,143 96,892 267,143 68,867

* p-value < 0.1, ** p-value < 0.05



28

Table 9: Redemption Time: Linear RegressionOLS Fixed Effect

One Purchase 8.376** -6.399**

(0.296) (0.535)

Two Purchases 9.764** 0.113

(0.275) (0.446)

Three Purchases 9.052** 3.163**

(0.269) (0.394)

Four Purchases 7.383** 3.759**

(0.272) (0.362)

Five Purchases 5.710** 3.187**

(0.282) (0.345)

First Redemption -39.816** -85.460**

(0.308) (0.320)

Second Redemption -28.918** -64.943**

(0.288) (0.286)

Third Redemption -20.694** -48.577**

(0.282) (0.266)

Fourth Redemption -14.176** -35.131**

(0.292) (0.261)

Fifth Redemption -9.754** -24.175**

(0.323) (0.270)

Volume of Order -2.388** -1.784**

(0.054) (0.067)

Age 0.113**

(0.009)

Male -2.769**

(0.126)

e-mail 1.163**

(0.191)

sms 0.072

(0.177)

e-mail·SMS -0.332

(0.250)

Preference Area -2.466** -1.185**

(0.136) (0.181)

Adj. R-squared 0.199 0.106

N 267,143 267,143

* p-value < 0.1, ** p-value < 0.05



29

Figure 1: Product Category

30

Figure 2: At What Time Consumers Buy Deals

31

Figure 3: When Deals are Tipped On

32

Figure 4: When Coupons are Redeemed

33

Documents

Is The Daily Deal Social Shopping?: An Empirical Analysis of