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# The Value of Online Customer Reviews

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We study the effect of the volume of consumer reviews on the purchase likelihood (conversion rate) of users browsing a product page. We propose using the exponential learning curve model to study how conversion rates change with the number of reviews. We call the difference in conversion rate between having no reviews and an infinite number \textit{the value of reviews}. We find that, on average, the conversion rate of a product can increase by 142% as it accumulates reviews. To address the problem of simultaneity of increase of reviews and conversion rate, we explore the natural temporal trends throughout a product's lifecycle. We perform further controls by using user sessions where the reviews were not displayed. We also find diminishing marginal value as a product accumulates reviews, with the first five reviews driving the bulk of the aforementioned increase. Within categories, we find that the value of reviews is highest for Electronics (increase of 317%) followed by Home Living (increase of 182%) and Apparel (increase of 138%). We infer that the existence of reviews provides valuable signals to the customers, increasing their propensity to purchase. We also infer that users usually don't pay attention to the entire set of reviews, especially if there are a lot of them, but instead they focus on the first few available. Our approach can be extended and applied in a variety of settings to gain further insights.
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The Value of Online Customer Reviews
Northwestern University
Evanston, IL
Edward C. Malthouse
Northwestern University
Evanston, IL
ecm@northwestern.com
ABSTRACT
We study the eﬀect of the volume of consumer reviews on
the purchase likelihood (conversion rate) of users browsing
a product page. We propose using the exponential learning
curve model to study how conversion rates change with the
number of reviews. We call the diﬀerence in conversion rate
between having no reviews and an inﬁnite number the value
of reviews. We ﬁnd that, on average, the conversion rate of a
product can increase by as much as 270% as it accumulates
reviews, amongst the users that choose to display them. We
also ﬁnd diminishing marginal value as a product accumu-
lates reviews, with the ﬁrst ﬁve reviews driving the bulk of
the aforementioned increase. To address the problem of si-
multaneity of increase of reviews and conversion rate, we
use customer sessions in which reviews were not displayed
as a control for trends that would have happened regardless
of the increase in the review volume. Using our framework,
we further ﬁnd that high priced items have a higher value
for reviews than lower priced items. High priced items can
see their conversion rate increase by as much as 380% as
they accumulate reviews compared to 190% for low priced
items.We infer that the existence of reviews provides valu-
able signals to the customers, increasing their propensity to
purchase. We also infer that users usually don’t pay atten-
tion to the entire set of reviews, especially if there are a lot
of them, but instead they focus on the ﬁrst few available.
Our approach can be extended and applied in a variety of
settings to gain further insights.
Keywords
Marketing, Online Reviews, Word of Mouth
1. INTRODUCTION
Electronic Word of Mouth (eWOM) in the form of on-
line customer reviews, is omnipresent and part of many cus-
tomers’ purchase journey. Reviews are being collected, ag-
gregated and displayed to consumers in an easy-to-digest
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2016 ACM. ISBN 978-1-4503-4035-9/16/09. . . $15.00 DOI: http://dx.doi.org/10.1145/2959100.2959181 format in all types of settings: all of the top-10 U.S. on- line retailers (as well as most of the biggest retailers in the rest of the world) collect and display user reviews for their products. The same is true for all the major digital stores. Furthermore, companies like Yelp, Facebook, Google, IMDb and Rotten Tomatoes provide platforms for users to sub- mit reviews that are, in-turn, aggregated and displayed to other users. User reviews are also being used to build trust between customers in decentralized marketplaces like eBay, Airbnb and Uber. For online shoppers, reviews are not just an option any- more but an expectation. A recent survey1found that 30% of shoppers under the age of 45 consult reviews for every pur- chase they make, while 86% say that reviews are essential in making purchase decisions. In fact, after price, reviews are the factor with the most impact on purchases. An exten- sive literature has also showcased the economic importance of positive reviews for restaurants [14, 1], books [2], movies [6], mobile apps [8], and consumer goods [15]. In this work we aim to gain insights on a slightly more fundamental question. We ask, should retailers seek reviews for their products? Can the existence of reviews (without any assumptions on their valence or other characteristics) increase the likelihood of purchase? Our analysis suggests positive answers. We further explore the value of reviews for the three largest product categories of the studied retailer: electronics, apparel and home living. Within categories, we explore the value of reviews for high- and low-priced items. 2. THE EFFECT OF REVIEW VOLUME ON PURCHASE Prior research on review volume has produced mixed re- sults. Volume is believed to exert positive eﬀects by increas- ing review credibility and/or signaling product popularity. Volume has been shown to have a positive eﬀect on box of- ﬁce sales [7, 13] and sales rank of electronic products [5, 11]. Moreover, it inﬂuences other outcomes such as consumer at- tention [9, 13], product evaluation [4], product popularity [18] and purchase intention [17]. However, [10] ﬁnds that volume does not impact the sales of cell phones, and [3] shows that volume has no signiﬁcant impact on box oﬃce performance. There is no evidence for a negative eﬀect. In order to understand the eﬀect of volume on purchase we propose the following assumptions: Assumption 1: Each product has an inherent con- 1http://www.powerreviews.com/blog/survey-conﬁrms-the- value-of-reviews version rate, in the absence of any reviews. Assumption 2: Each product has an upper bound on its conversion rate that is less than 1. Assumption 3: As a product accumulates more re- views, their marginal value decreases. Consider a product (or service). When the product is ﬁrst introduced it will not have any reviews, and there will be an initial probability of purchase, which we will call the inter- cept. Over time the product will attract reviews. The accu- mulating reviews should cause the probability of purchase to increase and approach an asymptote. This asymptote can theoretically be 1, i.e., given an inﬁnite number of reviews every product view can lead to purchase, but in reality we expect it to be much lower. The exact location of the asymp- tote will depend on other factors, such as the price, product category, quality, and the level of marketing support. We furthermore assume that as a product accumulates reviews, the value of each additional review will decrease, i.e., the marginal value of the 5th review will be higher than that of the 50th review. If these assumptions are accepted then one possible function for the value of reviews is the exponential learning curve [16, equation 13.9 and §13.5]: π(x) = γ0γ1eγ2x, x 0, γ2>0,(1) where 0 π(x)1 is the probability that a product with x reviews is purchased. Parameter γ0speciﬁes the asymptote because limx→∞ π(x) = γ0. The intercept is π(0) = γ0γ1, and so γ1gives the distance between the intercept and the asymptote. Thus, γ1gives the value of reviews, since it is the diﬀerence between the conversion rate with no reviews and when there are inﬁnite reviews. We do not constraint γ10, and γ1<0 would indicate that the conversion rate decreases as reviews are added. The other parameter, γ2, characterizes the steepness of the function, where large val- ues indicate π(x) approaches γ0quickly and smaller values indicate a more gradual approach. This model has been used extensively for modeling learning curves and memory [12]. 3. METHODS We have data for the entire year 2015 from an online re- tailer that sells high-end specialty gifts. The data record ev- ery visit to the site for each user id, including the date and time, what products were viewed, whether the user viewed reviews and whether the user purchased the product. We also know the number of reviews that the user was exposed to, where the number increases over the year. Thus, we can study the conversion rate as a function of the number of reviews. Our dataset consists of around 15.5 million page views for 1800 unique products, from 7.8 million users over the course of one year (January 4, 2015 – January 2, 2016). For each product, we track the number of its page views and sales as it accumulates reviews. Hence, for each product and num- ber of reviews, n, we calculate the product’s conversion rate while it had nreviews. We then average the conversion rate over all products for each n, to calculate the average conver- sion rate as a function of the number of reviews. When we want to study the value of reviews for products of a speciﬁc category or characteristic (e.g., price) we repeat the above approach but restrict our attention to the relevant products. We then estimate the parameters of Equation 1 by using the curve ﬁt function of the scipy module for Python2. Our approach has a built-in control group, since we are tracking the conversion rate of a ﬁxed set of products while they accumulate more reviews. Since we are comparing the conversion rate of the products to their past selves, many eﬀects due to the products’ characteristics are controlled for within our model. What our model doesn’t inherently con- trol for are temporal trends related to exogenous time re- lated variables. To address this issue we use a control set of observations. Observations regarding page views in which the user did not display reviews. We use these sessions as a control for time and other exogenous trends. We adjust our exponential function to incorporate these distinction be- tween page views where the user did and did not display the reviews. π(x) = (γ0+γ1disp) (γ2+γ3disp)e(γ4+γ5disp)x, x0, γ2>0(2) where disp is a binary indicator of whether a display of re- views happened. By ﬁtting Equation 1 on our data, we isolate the eﬀect of the display of reviews while controlling for all other exogenous trends. To further remove any noise from our data, we focus only on products whose ﬁrst page view was observed after March 1, 2015. This is to ensure that our data cover the beginning of a product’s life cycle, and we don’t use data for products that have been on the market already for some time in the beginning of 2015. 4. RESULTS Overall, the conversion rate of a product increases by 270% as it accumulates reviews. This increase is calculated as the diﬀerence between the asymptote (γ1) and the inter- cept (γ0), divided by the intercept. Moreover, over the bulk of that eﬀect happens within the ﬁrst 10 reviews the product receives. Figure 1 displays the ﬁtted curve. Note that this eﬀect is isolated for when the display of reviews happened, i.e., when disp equals 1 in Equation 1. Figure 2 compares the value of reviews for high- and low- priced items. Products were categorized into high and low price depending whether their price was above or below the median price amongst the studied products. The median price was$79.99. We notice that low-priced items have con-
sistently higher conversion rates than the high-priced prod-
ucts. The value of reviews is around 190% for low-priced
items but much higher, at 380% for high-priced items. It
seems that users gain conﬁdence in their purchase when
they see other users have bought a high-priced item. For
low-priced items, the monetary risk is lower and perhaps
that’s why the value of reviews is lower.
Finally, we also explored the value of reviews for high- and
low-quality products. We approximate a product’s quality
by observing all the reviews it accumulated over its (ob-
erage below 3.5 were classiﬁed as low quality and products
with a lifetime average above 3.5 stars were classiﬁed as
high quality. We notice that high-quality products have
consistently higher conversion rate than lower-quality prod-
ucts. But, perhaps surprisingly, low-quality products have a
higher value for reviews, at 324% compared to high-quality
2Python Documentation, http://bit.ly/1XDwA76
products, at 135%. This could be because of the lower base-
line for low-quality products as well as that social signals are
more important and valuable when the product in question
is of lower quality.
Figure 1: Eﬀect of Number of Displayed Reviews on the
Conversion Rate
5. CONCLUSION AND FUTURE WORK
Our work provides strong evidence for a positive value of
reviews, i.e., causal eﬀect of the existence of reviews towards
the purchase likelihood of a browsing customer. This is with
no assumptions on the characteristics of the reviews. Practi-
tioners should take this to mean that it’s beneﬁcial for their
platform to solicit reviews, even if they have no control over
the reviews they will get (e.g., their valence).
We explore the value of reviews for various product char-
acteristics such as price and category, but our results are
still conﬁned under the speciﬁc retailer we study. The exact
value may change depending the speciﬁc characteristics of
the retailer. We believe our approach, and model, can help
guide future studies for other settings and retailers.
Many interesting directions are available for future work,
and our dataset can be utilized to help explore them. First,
better understanding of the value of reviews for various types
of products as well as users can be important. Are reviews
more important for returning or new users? How about users
that have submitted a review themselves? Insights here,
can help a retailer identify products and users for whom the
value of reviews is high and act accordingly, e.g., by more
prominent displays.
Moreover, the eﬀect of valence can be better studied and
understood. What is the value of higher ratings versus lower
ratings? Studying the interaction between volume and va-
lence can also help with understanding the way that cus-
tomers interpret consumer reviews. For example, all else
being equal, is it better for a product to have a 4.5 star av-
erage rating based on 5 reviews or a 4 star average based on
50 reviews?
6. ACKNOWLEDGMENTS
We thank the Spiegel Center for Digital and Database
Marketing at Northwestern University for support.
Figure 2: Eﬀect of Number of Displayed Reviews on Various
Price
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Table 1: Learning Curve Parameter Estimates
Category ˆγ0ˆγ1ˆγ2ˆγ3ˆγ4ˆγ5
Overall 0.07 0.053 0.042 0.03 0.078 0.27
(0.008) (0.008) (0.008) (0.015) (0.04) (0.7)
High Price 0.073 0.063 0.06 0.05 0.046 0.3
(0.0025) (0.0064) (0.01) (0.014) (3.6·106) (3.6·106)
Low Price 0.059 0.037 0.0028 0.017 13.1 -12.79
(0.002) (0.0038) (0.011) (0.014) (3.2·105) (3.2·105)
High Rated 0.051 0.03 0.001 0.013 13.58 13.37
(0.002) (0.006) (0.01) (0.014) (3.6·106) (3.6·106)
Low Rated 0.036 0.013 0.03 0.047 0.33 -0.29
(0.006) (0.16) (0.014) (0.15) (0.35) (0.64)
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