Could Restaurants Improve Their Online Reputations By Offering
Deal Promotion? Evidence from Groupon
Extended abstract of a research-in-progress paper
Xitong Li
HEC Paris, France
lix@hec.fr
1. Introduction
The popularity of using daily deals as a new marketing vehicle has dramatically increased in
recent years (Dholakia 2012). As of April 2012, consumers in North America have spent
approximately $7 million a day (more than $2.5 billion a year) on daily deals1 and it is projected
to reach $5.5 billion a year by 2016.2 Groupon.com, the leading daily-deal site, has successfully
filed to go public within only three years since its launch in 2008 and gains a market value of
$4.5 billion by September 2014. Numerous restaurants have been offering deep-discount
vouchers through daily-deal sites (e.g., Groupon, LivingSocial, and Amazon Local) and thus it is
important to understand the impact of deal promotion on the long-term business performance of
the restaurants.
Despite the tremendous popularity of using daily deals, anecdotal evidences suggest that the
businesses of many local merchants have actually suffered because of offering deal promotion.
For example, the owner of a local food business wrote an online blog about her “terrible”
decision of offering Groupon promotion which literally had caused a financial loss of $8,000.3
Similar anecdotal stories from practitioners have spread wildly online. Researchers have also
started exploring the impact of offering deal promotion on merchants’ business performance.
Online reputation, as measured by the valence of user-generated online reviews (the average
review rating), is an important performance indicator of many businesses, such as restaurants
(Anderson and Magruder 2012), books (Chevalier and Mayzlin 2006), and video games (Zhu
and Zhang 2010). Based on a large-scale empirical analysis, Byers et al. (2012) find that on
average there is a sharp decline in Yelp ratings that coincides with offering Groupon promotion.
They also examine a number of possible explanations for the negative effect of Groupon
promotion. For example, reviews from Groupon users are more likely to be real and accurate,
whereas those from non-Groupon users may contain a higher fraction of inflated review ratings.
While in-depth empirical study is still lacking in the literature, by now it has been almost
accepted as a stylized fact that offering deal promotion would deteriorate local merchants’ online
reputations. Accordingly, many practitioners and researchers have questioned whether deal
promotion is a good marketing tool for local merchants and have concerned the validity of the
fundamental business model of daily-deal sites.
On the other hand, however, there are reasons to believe that deal users may post favorable
reviews about their post-consumption experiences. According to the consumer satisfaction model
(CSM) developed by Anderson and Sullivan (1993), a consumer’s post-consumption satisfaction
1
See http://savvr.com/2012/04/top-10-highest-grossing-daily-deals-of-all-time/
See http://streetfightmag.com/2012/09/17/forecast-consumer-daily-deals-spending-to-reach-5-5-billion-by-2016/
3
See http://posiescafe.com/wp/groupon-in-retrospect/ 2
1 is a function of the experienced net utility and the difference between the experienced net utility
and the expected utility. We believe the deep discount given by deal promotion would affect both
consumers’ experienced net utility and their expected utility. While the low price after the deep
discount may increase consumers’ experienced net utility (Li and Hitt 2008), it may also lower
consumers’ expected utility because in most cases consumers form a lower perception of product
quality when the price is low (Zeithaml 1988). As deal users have a higher experienced net
utility or a lower expected utility, they are likely to have a positive post-consumption satisfaction
and post favorable online reviews (Anderson and Sullivan 1993, Moe and Schweidel 2012). In
addition, for merchants with less favorable prior online reputation (relatively low prior average
review ratings), consumers would form a lower expectation about their products, which may also
result in an increased post-consumption satisfaction (Zeithaml 1988).
Therefore, whether deal promotion would deteriorate or improve merchants’ online
reputations (the average of online review ratings) is an empirical question. Given the significant
implications for practice and theory, we think it deserves to be investigated carefully.
Specifically, this study aims to answer two research questions:
(1) Could some merchants improve their online reputations by offering deal promotion?
(2) If so, could we characterize what type of merchants benefit the most?
2. Summary of Methodologies and Results
To answer the research questions, we first collect time-series data about the Yelp review
ratings of a sample of restaurants before and after they have offered a Groupon promotion. In
Study 1, we compare the monthly average Yelp ratings of the restaurants before and after
Groupon promotion and use the standard fixed-effect estimation model to measure the effect of
Groupon promotion. While time-invariant restaurant heterogeneity (e.g., restaurant location and
environment, food category) is reasonably controlled using the standard fixed-effect estimation
model, Study 1 has a critical limitation in establishing the causal effect of Groupon promotion.
The key challenge lies in the fact that a restaurant’s decision of offering Groupon promotion is
endogenous (also called the restaurant’s self-selection). For example, a restaurant may decide to
offer Groupon promotion, because its online reputation has already suffered (i.e., the average
Yelp review rating has been decreasing over time). In such case, we would observe a decrease in
the average Yelp review ratings after Groupon promotion even if it has no real effect on
restaurants’ Yelp reviews. Without addressing the self-selection of a restaurant’s decision of
offering Groupon promotion, the estimation of the effect of Groupon promotion would be biased.
Therefore, in Study 2 we collect a new set of data and use the difference-in-differences (DID)
model combined with a preprocessing matching procedure to explicitly address the self-selection
issue and establish the causal effect of Groupon promotion. Specifically, we assemble a set of
“control” restaurants which have offered Groupon promotion but only later than the “treated”
restaurants (those are analyzed in Study 1). By using a preprocessing matching procedure, we
ensure the two groups of restaurants (treated vs. control) are comparable in terms of a number of
factors that may simultaneously influence a restaurant’s decision of offering Groupon promotion
and its Yelp review ratings. Accordingly, the DID model allows us to identify the causal effect of
Groupon promotion by estimating the difference in the change of monthly average Yelp ratings
of restaurants from the periods before to that after the deal promotion between the matched
treated and control restaurants (Angrist and Pischke 2008). The DID model combined with the
preprocessing matching procedure is arguably a more appropriate econometric method of
establishing the causal effect, compared to the traditional instrumental variable approach,
2 because no strong exclusion restriction assumption is required (Girma and Görg 2007). It has
been widely used in the recent literature for identifying the causal effects of an agent’s action
(Goh et al. 2013, Hosanagar et al. 2014, Huang et al. 2012, Rishika et al. 2013).
The results from Study 1 suggest that while the aggregate average effect of Groupon
promotion on the restaurants’ monthly Yelp review ratings is negative, certain type of restaurants
could actually improve their Yelp review ratings; For restaurants with a relatively low prior
average rating and a small number of reviews, their monthly average Yelp review ratings in fact
increase as a result of Groupon promotion. With the focus on addressing the endogeneity (selfselection) of restaurants’ decision of offering Groupon promotion, Study 2 confirms the findings
that the effect of Groupon promotion is positive for restaurants with a relatively low prior
average rating (≤3.67) and a relatively small number of reviews (≤30). This specific type of
restaurants, accounting for about 24% of all restaurants in our sample that have offered Groupon
promotion, has improved their Yelp review ratings the most by offering Groupon promotion. The
findings suggest that the stylized fact that offering deal promotion would deteriorate local
merchants’ online reputations may not be true in certain circumstances. Whether deal promotion
would deteriorate or improve a merchant’ online reputation would depend on the merchant’s
characteristics, such as the prior average rating and the review volume.
3. Study 1: Fixed-Effect Model
We find the public dataset provided by Byers et al. (2012) (named as BMZ). The BMZ
dataset contains a nationwide sample of deals distributed from 19 major cities across the US.4
The BMZ dataset includes the characteristics (e.g., vendor, discounted voucher price) and
accurate voucher sales of each deal. In the BMZ dataset, Groupon deals are collected between
January 3rd and July 3rd of 2011. We focus on restaurant deals offered via Groupon. For each
restaurant deal, we manually check if the restaurant has a profile on Yelp.com.5 We exclude
restaurants for which we could not confidently find their Yelp profiles6 and those with no
reviews on their Yelp profiles. For the remaining restaurants, we use a computer program to
automatically extract all individual Yelp reviews (including numeric ratings, textual contents,
and dates).7 Ultimately, there remain 2,294 restaurant deals of Groupon for Study 1, for each of
which we collect the monthly average Yelp review ratings for a time period covering three
months before and twelve months after the deal promotion.
We first conduct some descriptive analysis. Figure 1 plots the monthly average Yelp ratings
over time before and after the deal promotion for three different sets of restaurants, respectively.
The three sets are the full sample (N=2294), the subsample of restaurants with prior average
rating lower than the median of 3.7 (N=1137), and the subsample of restaurants with prior
average rating lower than 3.5 and no more than 20 reviews (N=399). In Figure 1, the x-axis is the
time span from three months before to nine months after the deal promotion. For example, -1 in
4
They are Atlanta, Boston, Chicago, Dallas, Detroit, Houston, Las Vegas, Los Angeles, Miami, New Orleans, New
York, Orlando, Philadelphia, San Diego, San Francisco, San Jose, Seattle, Tallahassee, and Washington DC.
5
We use the restaurant information (e.g., name, street address, zip code, and phone number) to search the
restaurants’ profiles on Yelp.com.
6
In order for a restaurant’s Yelp profile to be confidently identified, we require (a) the restaurant deal must have a
single physical location for redemption, and (b) the restaurant must only have one single Yelp profile.
7
In Study 1 we also exclude the deals from restaurants whose Yelp profiles indicate that they are closed by February
2013. Including those closed restaurants produces qualitatively similar results. We will include them in Study 2 as a
robustness check. 3 3.70 3.65 3.60 3.55 3.50 3.45 3.40 -­‐3 -­‐2 -­‐1 0 1 2 3 4 5 6 7 8 6 7 8 (a) Full sample (N=2294)
3.2 3.15 3.1 3.05 3 -­‐3 -­‐2 -­‐1 0 1 2 3 4 5 (b) Restaurants with prior average rating lower than 3.7 (N=1137)
3.2 3.15 3.1 3.05 3 2.95 2.9 2.85 -­‐3 -­‐2 -­‐1 0 1 2 3 4 5 6 7 8 (c) Restaurants with prior average rating lower than 3.5 and no more than 20 reviews (N=399)
Figure 1. Monthly average Yelp ratings before and after Groupon promotion
4 the x-axis means the first 30 days prior to the deal promotion, 0 means the first 30 days after the
deal promotion, and 1 means the second 30 days after the deal promotion. The two dashed
vertical lines in Figure 1 covers the 6-month period after deal promotion, because most of
Groupon deal vouchers are valid for six months (Byers, et al. 2012). Whereas in Figure 1(a) we
observe a sharp decline in the monthly average Yelp ratings after deal promotion, there seems to
be no decline after the deal promotion in Figure 1(b) where nearly 50% of the restaurants are
taken into account. More interestingly, Figure 1(c) seems to show that for the restaurants with
prior average rating lower than 3.5 and no more than 20 reviews, the monthly average Yelp
ratings plausibly increases for a few months after offering deal promotion. Therefore, Figure 1(c)
suggests that the average Yelp ratings may increase as a result of offering deal promotion for a
substantial proportion of restaurants (accounting for 17.4% of the full sample). Next, we conduct
an in-depth econometric analysis to examine this observation.
Given the panel structure of the data, we can use time-series variation in the monthly average
Yelp review ratings to identify the effect of deal promotion. The identification assumption in
Study 1 is that a restaurant’s decision of offering deal promotion is exogenous after controlling
time-invariant restaurant heterogeneity (e.g., restaurant location and environment, food category)
and some time-varying variables. Specifically, we use Equation (1) as the main econometric
model for Study 1.
MAVERi,t = α + β PROMi,t + ѲXi,t-1 + ηi + εi,t
(1)
In Equation (1), t denotes the index of a 30-day period and could be -3,-2,-1,0,1,...,11. t<0
denotes the time periods before the deal promotion and t≥0 indexes those after the deal
promotion. The dependent variable MAVERi,t is the monthly average Yelp review rating of
restaurant i at time t. PROMi,t is the key independent variable that equals 1 if t≥0 and 0 if t<0. ηi
is the restaurant-specific dummy variable and controls any time-invariant restaurant
heterogeneity, such as restaurant location and environment, food category. Moreover, the vector
Xi,t-1 includes time-varying control variables, such as the number of reviews in the previous
month (NREVi,t-1) or the numbers of different star ratings in the previous month (STAR1i,t-1,
STAR2i,t-1, STAR3i,t-1, STAR4i,t-1, and STAR5i,t-1). Therefore, the main parameter of interest is β
which estimates the effect of deal promotion on restaurants’ monthly average Yelp review
ratings.
First, we estimate the effect of deal promotion for all the restaurants in the sample and find
the aggregate average effect on Yelp review ratings is negative, consistent with the findings by
Byers et al. (2012). But we don’t find any statistically significant effect of deal promotion when
we estimate the model using the subsample of restaurants with prior average rating lower than
the median of 3.7 (N=1137); the results are not presented here but are available from the authors.
More importantly, the estimated effect of deal promotion turns to be positive when we focus
on analyzing the subsample of restaurants with prior average rating lower than 3.5 and no more
than 20 reviews (N=399). Table 1 reports the results. In Columns (1)-(3), the estimated
coefficients of PROMi,t are all positive and statistically significant at the 0.05 level when the
model adds no controls (the fixed effects are included), while a 6-month, 9-month or 12-month
period after deal promotion is considered, respectively. In Columns (4)-(6), the estimated
coefficients of PROMi,t continue to be positive and statistically significant when NREVi,t-1 is
added as controls. In Columns (7)-(9), the estimated coefficients of PROMi,t are still positive and
significant when STAR1i,t-1, STAR2i,t-1, STAR3i,t-1, STAR4i,t-1, and STAR5i,t-1 are added as controls.
5 It is worth noting that the point estimates of PROMi,t across Columns (1)-(9) are also relatively
stable, suggesting the estimates are robust and consistent. According to the stable estimates in
Columns (1)-(9), the monthly average Yelp ratings of a restaurant with prior average rating
lower than 3.5 and no more than 20 reviews would on average increase by 0.13 to 0.16,
depending on the estimation specification.
Table 1. Effect of Deal Promotion on Monthly Average Ratings for
Restaurants with Prior Average Rating Lower than 3.5 and No More than 20 Reviews (Study 1)
PROMi,t
(1)
t∈[-3,5]
0.127**
(0.065)
(2)
t∈[-3,8]
0.133**
(0.062)
(3)
t∈[-3,11]
0.132**
(0.061)
NREVi,t-1
(4)
t∈[-3,5]
0.152*
(0.078)
0.030*
(0.017)
(5)
t∈[-3,8]
0.143*
(0.075)
0.016
(0.015)
(6)
t∈[-3,11]
0.158**
(0.072)
0.016
(0.013)
STAR1i,t-1
STAR2i,t-1
STAR3i,t-1
STAR4i,t-1
STAR5i,t-1
Intercept
2.98***
(0.047)
2.97***
(0.050)
2.96***
(0.051)
2.92***
(0.061)
2.93***
(0.062)
2.92***
(0.063)
(7)
t∈[-3,5]
0.164**
(0.079)
(8)
t∈[-3,8]
0.146*
(0.075)
(9)
t∈[-3,11]
0.161**
(0.073)
0.109**
(0.047)
0.164***
(0.049)
-0.028
(0.048)
-0.076**
(0.037)
-0.016
(0.058)
2.92**
(0.047)
0.085**
(0.043)
0.101**
(0.044)
-0.016
(0.039)
-0.052
(0.033)
-0.017
(0.052)
2.94***
(0.062)
0.097**
(0.042)
0.071*
(0.039)
-0.027
(0.037)
-0.025
(0.028)
-0.013
(0.042)
2.92***
(0.063)
Restaurant
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
fixed effects
# of Obs.
383
389
390
382
389
390
382
389
390
R2
0.003
0.003
0.002
0.007
0.003
0.003
0.021
0.009
0.007
Notes: Dependent variable is MAVERi,t. Fixed-effect estimates are shown. All standard errors are clustered at the
restaurant deal level and reported in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
4. Study 2: DID in Combination with PSM
Because we aim to explore if a restaurant could improve its online reputation by offering deal
promotion, Study 2 will specifically examine if the findings from Study 1 about the positive
effect of deal promotion are robust when the endogeneity (self-selection) of a restaurant’s
decision of offering deal promotion is explicitly considered. To address the possible endogeneity
(self-selection) issue, Study 2 uses the difference-in-differences (DID) analysis in combination
with propensity score matching (PSM). DID in combination with PSM is a econometric
technique recommended by the recent literature (Goh, et al. 2013, Hosanagar, et al. 2014, Huang,
et al. 2012, Rishika, et al. 2013) to address the endogeneity of an agent’s action in identifying the
causal impact of such action. For example, Rishika, et al. (2013) use this technique to identify
the effect of customer participation in a firm’s social media site on their visit frequency.
We would be happy to show the details of Study 2 and appreciate the feedback and
comments from WISE audiences.
5. References
References are available upon request.
6