#Art: Artists’ Instagram Presence and Professional Success
Vincent Bivona, Akshat Podar, Megan Gutter, Laura Knebel
Duke University
Econ 321S: Art and Markets
April 26, 2017
Abstract
This study explores the relationship between levels of user engagement and overall
Instagram presence for artists’ Instagram accounts and artists’ sales performance at auction. This
relationship is assessed through comparing total number of Instagram posts, total number of
followers, total number of likes, total number of comments, and total number of #hashtags
containing or associated with artists’ names, in years after their emergence on Instagram with the
sales price of works (USD) and the number of sales at auction. All Instagram data was scraped
programmatically from Websta.com and all sales data on artist’s work included in the study was
scraped from ArtNet. This study investigates the degree to which the aforementioned factors
related to Instagram presence and engagement can be and are currently being used as a tool to
promote and drive sales of art works. Through this analysis, we aim to provide key insights into
how the relationship between artists and buyers is transforming with technological advances and
the degree to which new platforms on the web such as Instagram are being successfully
leveraged by artists and potentially coming to shape the future of the art market by shifting the
channels by which art is sold.
Introduction
“It is hype for sure, which has negative and positive effects. But if your artwork isn’t
represented on Instagram these days, do you exist?” – NY Art Collector.1
In November of 2016, Brett Gorvy, one of Christie’s top art dealers posted a picture of a
Jean-Michel Basquiat painting of boxing champion Sugar Ray Robinson on his Instagram page.
Two days after the fact, the painting was purchased for $24 million, more than tripling the $7.3
1 Soboleva, Elena. "How Collectors Use Instagram to Buy Art." Artsy. April 19, 2015. Accessed April 2, 2017. https://www.artsy.net/article/elena-soboleva-how-collectors-use-instagram-to-buy-art.
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million the work sold for at auction in 2007.2 This and other stories alike continue to raise
questions of the power of social media and the transformation of how people are buying art.
Industry experts’ continued curiosity about how the art market is faring in the midst of a digital
revolution, beg the question, how are mobile platforms, like Instagram shaping the future of the
art market?
When Kevin Systrom and Mike Krieger launched Instagram in 2010, they both hoped to
facilitate communication and discourse through the sharing of pictures and images. Leveraging
its arsenal of in-app features, such as filters, hashtags, and geotags, the Instagram platform
appealed to a diverse user base, rapidly scaling to 500 million total users and 300 daily active
users by the end of 2016.3 Given Instagram’s substantial user base, the social media platform
offered a novel marketing channel for various institutions, corporations, and artists to engage
with their followers and advertise their respective events, products, and services. Thus, Instagram
grew beyond the scope of a plain-vanilla social media company; instead, Instagram’s size and
ubiquity gave the platform the power to drastically influence sales in various economic
marketplaces, ranging from the cosmetic industry to the athletic industry. As Instagram’s user
base and active engagement levels continue to grow, many project the platform to increase sales
in multiple economic markets by the site’s offering its users a medium for direct contact with
followers and additional opportunities for exposure.
According to the 2016 Hiscox Online Art Trade Report, surveys drawn from a sample of
existing art buyers indicate that 31% of people in 2016 acknowledged that social media
influenced their art purchases while 38% of new collectors reported that social media impacted 2 Kazakina, Katya. "Want to Sell a $24 Million Painting Fast? Instagram for the Win." Bloomberg.com. December 21, 2016. Accessed April 2, 2017. https://www.bloomberg.com/news/articles/2016-12-21/want-to-sell-a-24-million-painting-fast-instagram-for-the-win.3 Hutchinson, Andrew. "139 Facts and Stats About Instagram You Should Be Aware of in 2017 [Infographic]." Social Media Today. January 25, 2017. Accessed April 10, 2017. http://www.socialmediatoday.com/social-networks/139-facts-and-stats-about-instagram-you-should-be-aware-2017-infographic.
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their collecting habits and their decision on when and what to buy. 4 Of the social media
platforms preferred by buyers and used by galleries to adapt their marketing strategies and
generate sales, Instagram remains at the top of the list. Consistent reports indicating the power of
Instagram as a discovery tool for collectors and an effective marketing tool to drive sales have
ignited conversation around whether Instagram is on track to become the next major sales
channel for art.
Survey reports show that Instagram impacts collectors’ purchase decisions. According to
the results of an Artsy survey report sampling the usage and buying habits of collectors active on
Instagram, collectors rely on Instagram heavily as a tool for discovering and researching art
trends.5 The ease of discovery that the platform offers continues to be a major strength. 74% of
collectors surveyed in Hiscox’s Report declaring it the primary advantage to making online
purchases.6 Around 61% of surveyed collectors reported consistently looking at an artist’s
hashtags before making a purchase decision, while 30% post the works they are considering
acquiring for their collection. More than half the collectors surveyed reported posting on
Instagram multiple times a week. The report finds that 87% of collectors surveyed check
Instagram more than twice a day and 55% open the app 5 or more times a day.7 Thus, collectors
active on Instagram are not only consuming but also actively engaging with content and are
doing so frequently. Furthermore, Artsy reports that 51.5% of surveyed collectors have
purchased work from artists that they originally discovered through Instagram. Almost a third
(31%) of the collectors have purchased specific works discovered on Instagram and of those who
4 Reid, Robert. The Hiscox Online Art Trade Report 2016. Report. London: Hiscox, 2016.5 Soboleva, Elena. "How Collectors Use Instagram to Buy Art." Artsy. April 19, 2015. Accessed April 2, 2017. https://www.artsy.net/article/elena-soboleva-how-collectors-use-instagram-to-buy-art.6 Reid, Robert. The Hiscox Online Art Trade Report 2016. Report. London: Hiscox, 2016.7 Soboleva, Elena. "How Collectors Use Instagram to Buy Art." Artsy. April 19, 2015. Accessed April 2, 2017. https://www.artsy.net/article/elena-soboleva-how-collectors-use-instagram-to-buy-art.
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did, they did so four times on average.8 Reports such as the ones described above support claims
about Instagram’s being a powerful marketing tool, capable of driving user engagement that
leads to art sales.
The question that remains and serves as the main point of investigation in this paper is,
does increased engagement enhance Instagram’s potential to drive overall art auction sales
performance and if so, how is the platform’s potential to perform as such correlated to particular
types or measures of engagement? Artsy suggests that accounts fueling personal interaction with
and between potential clients, generate feedback, and often present opportunities to start a
conversation are most effective in capturing collectors’ attention.9 Experts indicate that profiles
showing effective and often use of relevant hashtags that enable collectors to instantly aggregate
artists’ content and gauge public support for artists, along with higher levels of generated likes
and comments have the potential to increase awareness that could lead to sales.10 Several studies
attempting to determine the significance of engagement levels as a social media metric show that
the amount of social interactions on a brand’s social media posts often correlates with the
number of visits to the brand’s website, suggesting that measures of engagement can be
indicators of higher conversion rates.11 Engagement levels, while they may not be the best
predictors for every business goal, are the most complete social media metric. As supported by
many reports, sites whose content has high levels of social engagement also tend to have higher
levels of organic traffic. Thus, evaluating the level and type of social engagement does hold
value in determining the potential to drive larger exposure to content, leading to higher discovery
8 Ibid.9 Soboleva, Elena. "7 Ways to Win Over Collectors on Instagram." Artsy. May 15, 2015. Accessed April 2, 2017. https://www.artsy.net/article/elena-soboleva-7-ways-to-win-over-collectors-on-instagram.10 Ibid.11 Traphagan, Mark. "Why Engagement DOES Matter As A Social Media Metric." Marketing Land. January 22, 2015. Accessed April 7, 2017. http://marketingland.com/engagement-matter-social-media-metric-114497.
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and perhaps more sales.12 Ultimately, it is social media’s purpose to move people along to a
location where potential conversion happens. Social engagement levels can be an indicator of the
health of an Instagram profile and its content and therefore, its overall ability to serve its
purpose.13
As such, the analysis presented in this paper explores the relationship between artists’
Instagram presence and their performance at art auctions through one main hypothesis: There
exists a statistically significant relationship between an artist’s Instagram engagement or
frequency of use (measured by the number of accumulated comments, likes, followers, posts,
and #hashtags) and the overall performance of the artist at auctions (measured by number of total
sales and median sales price).
Methodology
Data Sources
ArtNet is “the leading online resource for the international art market, and the destination
to buy, sell, and research art online.” Though it includes many products, the “Price Database”
was the primary tool used in this project. It is a comprehensive archive of over 10 million auction
results from the past 30 years, with 1,700 auction houses and 320,000 artists catalogued.14 Sales
data were collected for each artist in our study, numbering over 10,000 works total.
The majority of our Instagram data was collected through a website called Websta.
Websta started as a web viewer for Instagram in 2011 (known then as Webstagram), but since
has evolved into an analytics website for Instagram users.15 Users can still view, like and
12 Ibid.13 Ibid.14 “About Index.” Artnet. http://www.artnet.com/about/aboutindex.asp15 “What is Websta.” February 2017. https://websta.zendesk.com/hc/en-us/articles/200818536-What-is-Websta-
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comment on posts of theirs and other Instagram accounts, and content is constantly updated to
real-time Instagram content using the Instagram API.
Data Collection
In order to find artist accounts, we needed to come up with a method that would generate
a set of artists that is larger than a list of “top 20 Instagram artists to follow on Instagram” and
include accounts that are also involved in the Instagram art world. Therefore, the theory behind
our artist account generation is that artists involved on Instagram would be followed by art
collectors and influencers, because presumably they would follow the artists whose work they
respect and had a presence on Instagram. Along this line of thought, we decided that we would
generate Instagram accounts followed by at least five out of a list of 50 art influencers. This list
of art influencers was found on Larry’s List.16
In order to execute this, we needed to scrape the list of accounts that each influencer
follows from Instagram. For each influencer, we went to their account, chose “following”, and
created a command for the browser console to extract the profile names. From there we
programmed an algorithm in Python to generate the accounts that were followed by five or more
of the influencers. A prime component to the algorithm was the use of maps to associate a key
with a value. Each influencer (key) was mapped to the set of accounts followed by that
influencer (value). We iterated over the values in the map to create a new map containing each
account followed to the number of times they are followed by an influencer. If this count was
greater than or equal to five, it was added to a results list. This results list is the output containing
the generated profiles. The program outputted 1121 profiles, but these needed to be manually
16 Bouchara, Claire. "Top 50 Art Collector Instagrams Part I." Top 50 Art Collector Instagrams Part I | Larry's List -Art Collector Interviews and Art Collector Email Addresses. Accessed February 16, 2017. http://www.larryslist.com/artmarket/features/top-50-art-collector-instagrams-part-i/.
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sorted to find the artist profiles by checking them on Instagram. If the Instagram account holder
had an Artsy profile or came up as featured on gallery websites in a Google search of their name,
we classified them as an artist. After examining the profiles, the majority of the profiles followed
by five or more influencers were other influencers or gallery profiles. We ended up with 186
artist profiles, or 16.6% of the generated accounts.
Although private, primary sales data are not available, secondary auction sales data are
accessible to us through ArtNet. The fact that private sales data is not available was a huge
limitation to the data that we were able to collect, so art sales through auctions were a crucial
piece of data to our project. Therefore, if an Instagram artist did not have data on ArtNet, they
were excluded from our data set. 28 artists had no ArtNet data, leaving us with 158 artists.
However, due to unfortunate circumstances, only 147 of these artists’ sales data were scraped
before we lost access to the Duke University ArtNet account. We scraped the auction sales data
by copying the results page for the artists and using regular expressions to format the entries. We
then used Google Refine to manipulate the data into columns organized by the artist, the name of
the work, the description of the piece, the medium, the year of work, the size of work, the
auction house, the auction date, the price estimate range, and the final price the work sold for. In
total, we obtained 10,362 auction sales data points.
Next, we needed to collect the data from each artist’s Instagram profile. This includes the
total number of posts, the total number of followers, the total number of accounts that they
follow, and the total number of hashtags of the artist’s name. The number of posts, number of
followers, and number of following are accessible figures at the top of an Instagram profile. The
number of hashtags was acquired by using the Instagram search bar for the #ArtistName, and the
search results gives the number of times anyone on Instagram has used that hashtag in their post.
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For example, if we were getting the number of hashtags for the artist Alex Isreal, we would
search #AlexIsreal. If an artist goes by a name that is not their own, that name was used instead.
One anomaly was the artist Saber, whose number of hashtags we could not determine because
saber is also a word that people could hashtag in a different context and no distinction can be
drawn. This is an indicator for how much people in general are talking about a specific artist or
posting about their work.
Another piece of data that we wanted from Instagram was the number of likes and
comments on each post of an artist. However, this information is not immediately obtainable
from Instagram without clicking on every single post to find the number of likes and comments it
has. For this reason, we turned to Websta. Websta’s interface was appealing for this process
because it formats the Instagram posts in a grid with the number of likes and comments
underneath each post on one page without having to access each post individually. Websta’s grid
loads 20 posts at a time, so we had to load 20 at a time until we reached the end of a profile’s
posts. At this point, we ran a command in the browser console to parse the HTML and extract the
numbers of likes and comments. We cleaned and formatted these numbers using a Python script
and aggregated them to find the total number of likes and comments for all the posts per artist. In
total, 175,489 posts were scraped. In addition, we wanted the date the artists joined Instagram.
The conception date for an account is also not accessible through Instagram, so we used the date
of the first post an artist made as a substitute for the date they joined. With this date, we could
determine whether an auction sale was made before or after the date of their first Instagram post
using the date the work was sold for at auction.
Variables
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The raw variables recorded for our final dataset are: artist name, artist Instagram handle,
date of first Instagram post, artist age, number of posts, number of followers, number following,
total likes, total comments, and number of hashtags. The calculated variables for our final dataset
are: year of first post, median sales after Instagram, median sales before Instagram, count of sales
after Instagram, count of sales before Instagram, difference in median sales price before and after
Instagram, difference in count of sales price before and after Instagram, likes per post, comments
per post, likes per follower, and comments per follower.
Of the auction sales variables, we found the auction date, the number of total sales of an
artist, and the individual sales prices to be the most important pieces of information. From these
we developed our dependent variables – the median sales and count of sales. However, to
discover any potential correlations, we divided these sales at the date when the artist joined
Instagram. The 10,362 auction sales data points were paired down to the median sales price of
the works sold by the artist before the date of the first Instagram post and after the date of the
first Instagram post using the date the work sold at auction for comparison. The same method
was applied to the count of sales before and after the first Instagram post for an artist. The
differences in the median sales and count of sales variables “before” and “after” were obtained
by subtracting the “before” number from the “after” number.
We created the variables likes per post, comments per post, likes per follower, and
comments per follower by dividing the total likes and total comments by number of posts and
number of followers. These calculated variables were useful metrics to represent “engagement.”
To reiterate, we hypothesized that higher levels of engagement of followers was positively
correlated with art sales. Therefore, these variables based off Instagram date were the basis for
our independent variables.
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Therefore, we ended up with nine independent variables: total likes, total comments, total
posts, number of followers, number of hashtags, likes per post, comments per post, likes per
follower, and comments per follower. We had four dependent variables: median sales after,
count of sales after, difference in median sales, difference in count of sales.
Analysis
n=x (# of artists) Average Median Max Min
Age 143 44 42 85 27
Total Posts 154 1147 754 13929 8
Total Followers 153 51889 15620 952307 467
Total Likes 150 759677 132231 15773453 1230
Total Comments 150 12313 3990 162462 28
Median Sales Price ($) 125 27305 11543 365000 59
Sales Count 148 18 5 392 0
The sample group of artists presented in our data set varies widely in terms of basic
demographics and measures of engagement. To present a general description of the artists in the
data set, Figure 1 presents, for artists for whom data was obtained, the average, median,
maximum, and minimum age, total number of posts, total number of followers, total number of
likes, total number of comments, median sales price at auction after artists’ first post on
Instagram, and total number of sales at auction after artists’ first post on Instagram. As shown in
the figure above, the artists within the data set are, on average, middle-aged with a total follower
Figure 1: Data Summary Table, n=x(# of artists) (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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count above 10,000, placing them within the “influencers” category of Instagram, have sold 18
works on average at auction after their first post on Instagram, and have a median sales price of
$27,305 since their first post on Instagram. However, as illustrated above, artists within the data
set vary widely in terms of age and overall measures of engagement.
Demographic Analysis By Follower Count
Having gathered the data related to artists’ measures of engagement, including total
number of followers, total number of likes, total number of comments, total number of posts, and
total number of hashtags containing or related to artists’ names, it seemed reasonable to gauge
the level of correlation that exist in the relationship between artists’ popularity on Instagram and
their sales performance at auction.
Figure 2 presents the top ten artists within our data set with the highest follower count. As
assumed, other engagement levels for artists’ Instagram profiles, including total number of likes,
Figure 2: Most Followed Artists, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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total number of comments, and total number of posts correlated relatively closely with the
number of followers an artist had on Instagram.
As shown in Figure 3, which presents the artists with the most hashtags containing or
related to their name, no evidence of any significant correlation exists between the number of
Instagram followers artists have and the number of Instagram hashtags for artists. Thus, it is the
case that as much as the number of Instagram followers an artist’s account might be correlated to
other measures of engagement, Instagram follower count is not a significant indicator of an
artist’s being actively searched for or trending on Instagram.
It also seemed reasonable in the case of this study to gauge the level of correlation that
existed between artists’ popularity on Instagram and their sales performance at auction.
Figure 3: Artists with the Most Hashtags, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Figure 4 presents the artists with the highest median sales price at auction. As shown in the
graph, there was no evidence of any apparent correlation existing between the number of
Instagram followers artists have and the median sales price of artists’ work. Thus, in this case,
the number of followers an artist had or the overall level of their Instagram engagement was not
a good indicator of artists’ sales performance.
Figure 4: Artists with Highest Median Sales Price, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Figure 5, which presents the artists with the highest number of sales at auction, reveals no
evidence of any apparent correlation existing between the number of Instagram followers artists
have and artists’ quantity of auction sales. Thus, as suggested by each of the above figures, the
total number of Instagram followers artists had on Instagram did not seem to be correlated with
artists overall performance at auction.
Demographic Analysis By Age
Having access to information about artists’ age within the data set, it seemed useful to
gauge the relationship and assess the correlations between artists’ age, artists’ Instagram
engagement levels, and artists’ overall sales performance at auction.
Figure 5: Artists with Highest Number of Sales, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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For the purpose of establishing where artists of different ages fall within the context of the ages
of all the artists in our data set, all artists were organized into quartiles based on age. The artists
belonging to the first and lowest quartile, shown in the figure above, were between the ages 27
Figure 6: Youngest Age Quartile, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
Figure 7: Oldest Age Quartile, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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and 37 years old. The artists belonging to the second quartile were between the ages 37 and 43
years old. Artists belonging to the third quartile were between the ages 43 and 50 years old.
Lastly, the artists belonging to the fourth and oldest quartile, also shown in the figure above,
were between the ages 50 and 85 years old.
Figure 8 presents the artists within the data set with the highest number of Instagram followers.
While there is no significant evidence to suggest that artists’ age is directly correlated to the
number of followers artists had, the graph reveals that of the artists with the highest number of
Instagram followers, the majority fell into the second or third quartiles of age ranges (37-43
years old & 43-50 years old).
Figure 8: Most Followed Artists, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Controlling for other measures of Instagram engagement, the Figure 9 presents the artists within
the data set with the highest number of total Instagram likes. Likewise, no apparent evidence of
any direct correlation between artists’ ages and the total number of Instagram likes that artists
received on Instagram was found. However, the graph reveals that of the artists with the highest
number of Instagram likes, the majority again fell into the second or third quartiles of age ranges
(37-43 years old & 43-50 years old).
Figure 9: Most Liked Artists, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Controlling for the number of Instagram comments, Figure 10 presents the artists within the data
set with the highest number of total Instagram comments. No apparent evidence of any direct
correlation between artists’ ages and the total number of Instagram comments that artists
received on Instagram was found. Furthermore, the above graph reveals, similarly to those before
it, that of the artists with the highest number of Instagram comments, the majority again fell into
the second or third quartiles of age ranges (37-43 years old & 43-50 years old).
Figure 10: Artists with the Most Comments, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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When controlling for the number of Instagram posts, as presented in Figure 11, showing artists
within the data set with the highest number of total Instagram posts, no apparent evidence of any
direct correlation between artists’ ages and the total number of Instagram posts that artists
received on Instagram was found. In this case, the majority of artists with the highest number of
Instagram posts fell into the fourth and highest quartile of age ranges (50-85 years old) while all
other artists presented in the graph belonged to the second and third quartile of age ranges (37-43
years old & 43-50 years old).
Figure 11: Artists with the Most Posts, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Finally, Figure 12 presents the artists within the data set with the most hashtags containing or
related to their names. As with the other figures before it, controlling for this measure of
engagement shows no apparent evidence of a correlation existing between artists’ ages and
artists’ total number of associated hashtags. In this case, just over half of the artists with the
highest number of associated hashtags belong to the fourth and highest quartile of age ranges
(50-85 years old) while the rest belong to the second and third quartile of age ranges (37-43
years old & 43-50 years old).
Together, the above figures suggest no direct correlation between artists’ age and any of
artists’ respective measures of engagement, including total number of followers, total number of
likes, total number of comments, total number of posts, and total number of hashtags containing
or related to artists’ names. The figures presented above reveal that the majority of the artists
within the data set with the highest measures of engagement belong to the second and third
quartiles of age ranges (37-43 years old & 43-50 years old).
Figure 12: Most Hashtagged Artists by Age, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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To gauge the relationship between age and artists’ overall sales performance at auction,
the sales performances of the artists with the highest median sales price and highest number of
sales were assessed.
Figure 13, presenting the artists within the data set with the highest median sales prices, reveals
no apparent evidence of any direct relationship existing between age and the median sales price
of artists’ works at auction. The figure shows that of the ten artists presented with the highest
median sales price, three belonged to the first and lowest quartile of age ranges (27-37 years old),
two belonged to the second quartile of age ranges (37-43 years old), two belonged to the third
quartile of age ranges (43-50 years old), and three belonged to the fourth and highest quartile of
age ranges (50-85 years old).
Figure 13: Artists with Highest Median Sales Price, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Similarly to that of the artists with the highest median sales price, Figure 14, presenting the
artists within the data set with the highest number of sales at auction, reveals no apparent
evidence of any direct relationship existing between age and the number of sales artists make at
auction. The figure shows that of the ten artists presented with the highest median sales price,
one belonged to the first and lowest quartile of age ranges (27-37 years old), two belonged to the
third quartile of age ranges (43-50 years old), and seven belonged to the fourth and highest
quartile of age ranges (50-85 years old). Thus, in the relationship between age and sales
performance, the data, as shown in the figures above, reveals no direct or significant correlation.
Figure 14: Artists with the Highest Number of Sales, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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Google Trends Analysis
With no statistically significant relationships between our main independent variables
(relating to Instagram) and our dependent variables relating to ArtNet sales records, the figure
above considers Google Trends as an additional dependent variable. As presented in the figure
above, for n=71 artists, we found that a majority (n=49) of artists had a lower average search
volume after starting their first post on Instagram. 21 artists had higher average search volumes
Figure 15: Average Change in Google Trends Interest, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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recorded after their first post, and one artists had no change in average Google Trends index.
This finding also complements the other results found and overall, shows no proof that Instagram
shows any signs of directly impacting artists in both auction markets and search interest.
Regression Analysis
The Final Regression Model (FRM) was derived from transforming the data in two ways:
1) taking the differences in the dependent variables of sales performance before and after an
artist’s first Instagram post, and 2) taking the natural log of all the independent and dependent
variables in the dataset. To learn about the process behind the FRM’s derivation, please see the
Appendix, Exhibit A.
Given the transformation of the data, the FRM results in nine independent variables and
two dependent variables with the following generic regression, in which β is the coefficient for
the independent variable and ε is unsystematic error (or disturbance term) in the model:
Dependent Variable = β(Independent Variable) + ε
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INDEPENDENT VARIABLES DEPENDENT VARIABLESLn (Total Comments) Ln (Difference in Median Sales Price Before and
After First Instagram Post)Ln (Total Likes)
Ln (Total Followers) Ln (Diff in Average Sales Per Year Before & After First Instagram Post)
Ln (Total Posts)Ln (Comments Per Follower)
Ln (Likes Per Follower)
Ln (Comments Per Post)
Ln (Likes Per Post)
Ln (Hashtags)
After deriving the FRM, all the transformed dependent and independent variables were
regressed against each other, resulting in 16 final regressions:
Although the FRM is sound with respect to its eight regressions for the Ln(Difference in
Median Sales Price Before and After First Instagram Post), there is one major flaw in the eight
regressions pertaining to the Ln(Difference in Number of Sales Before and After First Instagram
Post). In these eight latter regressions, the model simply counts the number of sales an artist
made before his or her first Instagram post; however, the variation in the number of years an
artist was selling works at auctions before his or her first Instagram post can heavily skew the
data. For example, if two artists both started Instagram accounts in 2012, but one artist had been
selling works at auctions since 2005 and the other artist since 1980, then the latter artist would
logically have a higher pre-Instagram sales count due to selling works for a longer time.
Therefore, to control for this large variation in the number of years an artist could be
Figure 17: Natural Log of Difference Before and After Instagram Count Sales, n=71 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
Figure 16: Natural Log of Difference Before and After Instagram Median Sales, n=71 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
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selling works at auctions pre-Instagram, the dependent variable was transformed. Rather than
simply counting all the works an artist sold before and after his or her first Instagram post, an
artist’s pre-Instagram number of works sold was divided by the number of years pre-Instagram
that the artist was selling at auctions. Analogously, an artist’s post-Instagram number of works
sold was divided by the number of years post-Instagram that the artist was selling at auctions.
These calculations then yielded the average number of works an artist sold per year pre-
Instagram and post-Instagram. Finally, these two numbers were subtracted by each other to
capture the impact of Instagram presence on auction performance in terms of average number of
works sold per year. The final output from the FRM for this transformation is as follows:
As shown in FRM outputs for the Ln(Difference in Median Sales Price Before and After
First Instagram Post) (Figure 16), the coefficients describe an elastic relationship between the
independent and dependent variables due to the natural log transformation. In effect, the
coefficient outputs are interpreted as percent change in the dependent variable for a one percent
increase in the independent variable. For example, 1% increase in the number of total comments
would yield approximately a 0.04% increase in the difference between before and after
Instagram median sales. Applying this interpretation across the FRM outputs, the coefficients
range from approximately 0.06% decrease to 0.13% increase in the difference between before
Figure 18: Natural Log of Difference Before and After Instagram Average Sales Per Year, n=71 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
26
and after Instagram median sales, depending on the independent variable. Moreover, the
“Comments Per Follower” and “Comments Per Post” are the only independent variables that
display a negative relationship against the Difference in Median Sales Price Before and After
First Instagram Post (dependent variable).
Although the coefficients reveal that there are mostly positive relationships between the
independent variables and the Difference in Median Sales Price Before and After First Instagram
Post, the p-values tell a different story. The p-values in Figure 16 range from 0.11 to 0.98 with
most them being closer to 1. Since the p-value states the probability that the null hypothesis
(there is no statistical significance between the independent variable and the dependent variable)
is mistakenly rejected, any p-value greater than 0.05 prevents us from rejecting the null
hypothesis. Therefore, since all p-values in Figure 16 are greater than 0.05, we are unable to
reject any of our null hypotheses, meaning that there may actually be no statistical relationship
between any of our independent variables and the Difference in Median Sales Price Before and
After First Instagram Post.
Furthermore, the R-squared outputs in the FRM model is interpreted as the variation in
the independent variable explaining x% of the variation in dependent variable. In Figure 16, the
output reveals that R-squared terms range from explaining approximately 0% to 4% of the
variation in the dependent variable, given the variation in a certain independent variable. The low
R-squared values ultimately indicate that the FRM model has a very weak fit against the data.
Following the same line of interpretation as Figure 16 for the Difference Between Before
and After Instagram Average Sales Per Year dependent variable, we see similar findings.
Depending on the independent variable, coefficients range from approximately 0.02% decrease
to 0.19% increase in the difference between before and after Instagram average sales per year.
27
“Comments Per Follower” is the only independent variable that displays a negative relationship.
In addition, the p-values in Figure 18 range from 0.02 to 0.89 with only the “Hashtags”
independent variable having a p-value less than 0.05. Thus, we are only able to reject the null
hypothesis for the “Hashtags” independent variable, indicating that there is a significantly
positive relationship between “Hashtags” and the Difference Between Before and After
Instagram Average Sales Per Year. For all other independent variables, we are unable to reject
the null hypothesis because their p-values are greater than 0.05, meaning that there may be no
statistical relationship between those independent variables and the Difference Between Before
and After Instagram Average Sales Per Year.
Finally, the R-squared outputs in the Figure 18 range from explaining 0% to 7% of the
variation in the Difference Between Before and After Instagram Average Sales Per Year
dependent variable, given the variation in a certain independent variable. Although the R-squared
values are, on average, higher in Figure 18 as compared to Figure 16, the relatively low R-
squared values in general indicate that the FRM model still has a very weak fit against the data.
Discussion
For our three iterations of our regressions, we were overall unable to reject the null
hypothesis; we could not disprove that there exists no statistically significant relationship
between our dependent variables (median sales price and median sales count) and our
independent variables relating to Instagram. Given the dearth of quantitative research analyzing
the art market implications of Instagram usage, we cannot say whether our results overall are
unexpected or particularly profound. However, across iterations, variance among the statistical
28
significance did prove interesting when we found stronger correlations between certain pairs of
independent and dependent variables.
Overall Finding 1: Poor R^2 across all iterations of regressions
Our first major finding was that we had low R^2 values across all iterations of our
regressions. We used linear regressions to compare various combinations of our independent and
dependent variables. None of these linear regressions fit the data very well, which is why our
R^2 values were closer to 0 than to 1. Since R^2 is interpreted (in our final model) as the
variation in the independent variable explaining x% of the variation in dependent variable, our
Instagram variables do not explain much of the variation in our sales data.
Overall Finding 2: Poor p-values except for natural log of hashtags and natural log of
difference of count of sales
P-values represent the probability that the null hypothesis is mistakenly rejected (i.e. the
possibility that we mistakenly claim a correlation between our variables as stated by an
alternative hypothesis). As noted in the analysis, we had poor p-values across almost all
iterations of our regressions. This means that there may be no statistically significant relationship
between our Instagram-related independent variables and our sales-related dependent variables,
and that we can’t disprove our null hypothesis.
There was one improved p-values for difference and log difference models with respect
to count of sales as the dependent variable. This was for the “hashtags” independent variable,
which indicates a positive relationship between “hashtags” and the “difference between before
29
and after Instagram averages count of sales per year.” In the next section, we will specifically
explore potential explanations for this case.
Overall Finding 3: Positive coefficients for count of sales and hashtag regression
Despite being statistically insignificant given the R2 and p-values, higher numbers of
hashtags are associated with higher counts of sales. There is also a positive association between
hashtag usage and median sales price.
To explain the meaning of this finding, we must first consider what a hashtag is and why
hashtags are used in social media. When someone hashtags a word, that word goes into the world
of hashtags, which means it becomes available for people outside your network to see your
Instagram post. The same function applies to tweets, Google+ posts, among other social media
sites. SocialMediaToday says, “People are only one hashtagged word away from possibly being
seen by thousands, if not millions of people through social media.”17 In that sense, hashtags can
be viewed as a valuable marketing tool. An artist who uses more hashtags on their Instagram
account will have users not following their account being able to see their content, which means
more eyes of potential buyers.
For our study, we measured the number of times the artist’s name as a hashtag (ex.
#cecilybrown) was used by anyone on the Instagram platform. Hashtags with the artist names
create an opportunity for potentially disparate users to view and participate in a thread of
conversations linked together by the hashtag, a symbol of the artist’s personal brand. If many
people were using the name, it could appear as a “trending topic.” Increased numbers of hashtags
17 “The Importance of #Hashtags.” February 28, 2013. http://www.socialmediatoday.com/content/importance-hashtags
30
therefore demonstrate increased visibility of the artist on the platform, which we expected to
have a positive impact on sales.
We couldn’t prove that this positive impact fit linearly, however. Furthermore, our data
did not account for the number of different people using the hashtag. This means that the artist
themselves could be the majority user of this hashtag and the main participant in the
conversation about themselves. This is increasingly likely if the artist is a frequent poster on
Instagram in general. An artist’s use of their own hashtagged name may not have any
explanatory power to their sales performance—though it does represent a conscientious effort to
self-brand, which likely couldn’t hurt sales.
Overall Finding 4: More negative coefficients for engagement vs. sales, filtered by followers
One of the more striking findings that we found more negatively-oriented coefficients for
our regression model of natural log of sales performance and natural Instagram engagement,
measured by likes and comments per follower. In the analysis, we noted that there was a
correlation between number of followers and total number of likes and comments. However, we
learn from Figure 16 and Figure 18 that there is no evidence of an apparent correlation between
either number of followers and median sales price or count of sales. In fact, when we transform
our independent variables into likes per follower and comments per follower, we actually see
that our coefficients become generally more negative.
Additionally, we filtered data by follower count to compare lesser-known versus famous
artists of Instagram. Our three-pronged filtering approach created buckets for “amateurs,”
“influencers,” and “famous” artists as determined by the number of followers each had.
“Amateurs,” are users who have fewer than 10,000 followers. For our data set, this included
31
n=51 artists. “Influencers” are users who have 10,000-99,999 followers, numbering n=57 artist
accounts. Instagram “famous” artists were those with over 100,000 followers, and numbering
n=11 for our data set.
These buckets were not chosen arbitrarily, but rather according to ability to make money
for sponsored posts on Instagram. Popular accounts often partner with brands, and can make
money using their “influence” on social media. There are different scales of influencers, but the
most common accounts to participate are “micro-influencers” with fewer than 100,000
followers.18 The prices brands are willing to pay vary, but Instagrammers with more than 1,000
followers could earn $25 or more a post, according to the app Takumi, while bigger users could
make up to $1,000 per post. Those with 10,000 followers could earn $10,000 a year, while the
biggest influencers - those with 100,000 followers, could earn $100,000.19 Furthermore,
companies like Grapevine, which matches brands with like-minded social media influencers, set
minimum follower requirements to have this label.20 For our report, we had very few artists in
this top bracket of fame level. Many artists who are Instagram famous are also real-life famous.
We had quite a few very famous artists, including Anish Kapoor and Damien Hirst, who we were
not able to get sales data from due to Duke’s loss of access to the ArtNet database. Thus, our
samples size is quite a bit smaller here.
Nevertheless, we found that regardless of how many followers an artist has on Instagram,
lower engagement of the followers (# likes and comments per follower) doesn’t seem to hurt
median sales prices of the artists; in fact, lower engagement per follower may be associated with
having higher sales prices.
18 “I’m a Micro-Influencer, Now What?” March 12, 2017. http://blog.influence.co/im-a-micro-influencer-now-what/19 “These Instagram users earn thousands.” March 12, 2017. http://www.mirror.co.uk/money/instagram-users-earn-thousands-single-681049720 “#Getsponsored.” https://www.grapevinelogic.com/
32
Though this finding was not statistically significant, it does bring up another notable topic
– the importance (or lack of importance) of engagement on social media. “Engagement” is a term
used by digital marketers to describe the activity level of participants in your brand’s social
media network. Theoretically, you want people who follow your account to engage with the
brand by liking, commenting, or sharing your content, so that it improves the visibility of your
brand. We hypothesized that having higher engagement would be especially important to artists
trying to sell work, since those choosing to buy would potentially like and engage with a post
about a piece they are interested in.
However, there is an inverse relationship between engagement and popularity. A 2017
AdWeek article about sponsorships reports, “Influencers with fewer followers were usually more
engaged with their audiences, while more popular influencers were less so. Likewise, more
influencer and audience engagement did not result in more money per post.”21 This is fitting with
our findings with respect to auction sales as well (thus the negative coefficients), though we
expect that different confounding variables are having more causal effects.
One consideration is that the most popular artists on Instagram were likely successful
before the advent on Instagram, and thus do not rely on trying to cultivate and engaged network
of users and potential customers. For example, Ai Weiwei is an artist with a popular Instagram
account (297,000 followers as of March 2017) who also previously had great success prior to his
adoption of the platform. His median sales prices before joining Instagram were higher than his
prices after joining.22 Ai Weiwei is also the most active user of the platform in our sample,
having posted over 13,000 images since 2011 (by comparison, most artists in the sample posted
21 “Sponsored Instagram Posts Average $300 Each.” February 25, 2017. http://www.adweek.com/digital/what-is-the-real-cost-of-instagram-influence-infographics/22 Source: Instagram Art & Markets Data Codebook, sheet “Artist Data”
33
1,000 times since starting their accounts).23 The content that Weiwei posts is also mostly not
related to his artwork. He doesn’t use the platform to sell, because he doesn’t need to, but instead
uses the platform to allow followers access into to his day-to-day personal life.
A second consideration is that engagement on Instagram doesn’t necessarily translate to
genuine interest in a piece of art, and certainly, as we’ve shown, doesn’t translate to intent to
buy. To examine further the intent behind an Instagram “like,” LendEDU performed a qualitative
survey of 3,000 college students. LendEDU found that “64 percent of millennials believe
Instagram, a mobile photo-sharing application, is the most narcissistic social media platform.”24
This statement is backed by other sentiments of Instagram likes not equating to “real-life”
success:
The formula is quite simple. If you post enough artsy, chic pictures of yourself that rack
up plenty of “likes,” then real life accomplishments will not matter because the popularity
of your social media accounts will determine your status on the social hierarchy.25
Another interesting finding is that the study participants enunciate feeling like the process
of engaging with material can be quite “scheme-like”:
The large majority of Instagram users have formed unspoken alliances with each other to
ensure they each tally enough “likes” to make their posts stand out. It does not matter if
Instagram users genuinely enjoy other Instagrammers posts; the only thing that matters is 23 ibid24 https://lendedu.com/blog/millennials-instagram-narcissistic-social-media-platform/25 ibid
34
that each insincere expression of emotion from you will lead to your own Instagram page
gaining more status.26
Among our top bracket of famous Instagrammers, there is probably less of this
“scheming” going on. Still, this may be occurring in are lower bracket, where the followers that
those artists do have are at least not “liking” posts with art because they intend to buy them.
Overall Finding 5: Disconnect between Instagram users and auction buyers
Our p-values emphasize that our two populations – followers of artist accounts and the
buyers in art auctions – do not appear to overlap in any statistically significant manner. An
important consideration is that the demographics of Instagram users skew young. According to
Pew Research, 59% of 18-29 year-olds use Instagram, while only 33% of 30-49 year-olds and
18% of 50-64 year-olds use the platform.27 Formal auctions, like those at Christie’s and
Sotheby’s are probably not well attended by this demographic – both because of buying patterns
of millenials and because of buying power. Millennials’ expenditure allocation, due to their lack
of home ownership and increasing tendency to rent instead of buying,28 likely does not make
them active participants at art auctions. Instagram users also tend to live in urban areas, as in
places with less space to put art.29 “Buying art at auction not for amateurs!” declares fine art
blogger Joseph Levene, exaggerating the likelihood of a potential disconnect between the core
26 ibid27 “Social Media Update 2016.” November 11, 2016. http://www.pewinternet.org/2016/11/11/social-media-update-2016/28 “Why Millenials Love Renting.” October 7, 2014. https://www.forbes.com/sites/trulia/2014/10/07/why-millennials-love-renting/#13fca7b174d129 “Social Media Update 2016.” November 11, 2016. http://www.pewinternet.org/2016/11/11/social-media-update-2016/
35
demographic of Instagram users compared to the group attending art auctions, which likely skew
older and more experienced.30
On this note, it is a potentially faulty comparison to look at auction house sales data,
when people purchasing after discovering an item on Instagram will not necessarily go to an
auction for the piece. A provocative article about collector Brett Gorvy’s $24 million art sale
within 3 days of posting a piece on Instagram, 31 seems to indicate that this platform has great
effects on sales for artists, an idea that led to our initial research questions. However, we realized
that such a sale does not replicate the “auction” environment represented by the sales data that
we had access to on ArtNet. Auctions are planned, staged events that are advertised in advance to
potential buyers. By contrast, flash sales of pieces, like Gorvy’s are often more impulsive,
private transactions, where interested parties act without knowledge of other bids, and without a
timeline. As a platform centering on standing out and being viral, transactions are not likely to be
done involving traditional, slower-paced auction participants.
Overall Finding 6: Google Trends as an additional dependent variable
Clearly, we had a lack of success at finding statistically significant relationships between
our main independent variables (relating to Instagram) and our dependent variables relating to
ArtNet sales records. As a result, we followed through on a suggestion from our research
assistant to consider an additional dependent variable, Google Trends records.
Google Trends is a public search analysis facility often used as a tool for search engine
optimization marketers to inform decisions about purchasing keywords. “Derived from Google’s
30 “Buying Art at Auction Not for Amateurs.” October 2010. http://blog.thefineartblog.com/2010/02/buying-art-at-auction-is-not-for.html31 “Want to Sell a $24 Million Painting Fast?” December 21, 2016. https://www.bloomberg.com/news/articles/2016-12-21/want-to-sell-a-24-million-painting-fast-instagram-for-the-win
36
search data, Trends is a numeric/historic representation of the relative volume of searches made
on Google. It creates indexes that show trending instead of actual volume,”32 so in a way, it can
explain the “popularity” of particular phrases over time. Starting in 2004, the relative search
volume is tracked periodically using an index from 0-100. Since this data is public, it can be
downloaded and compared across other phrases.
For our study, we used artist names as individual search phrases. We downloaded
monthly data recording the relative trends of each artist name available from 2004 to present, so
counts of data points were uniform across artists. Once again, we sought to discover the impact
of an Instagram account on artist success. Our null hypothesis was that creating an Instagram
account would have no influence on search volume as indicated by the difference in average
“trends” score before and after inception of an artist’s individual account. Therefore, our
alternative hypothesis is that the inception of an account would have a positive influence on
Google Trends search volume, making the difference between average search volumes positive.
For n=71 artists, we actually found that a majority (n=49) of artists had a lower average
search volume after starting their Instagram account.33 21 artists had higher average search
volumes recorded after they started their Instagram account, and one artist had no change in
average Google Trends index. This certainly disproves our alternative hypothesis – there is no
positive impact of Instagram on search volume.
This finding also complements the other results we found using sales data as measures of
artist success. Overall, we were unable to prove that Instagram helped artists in both auction
markets and search interest. These contribute to the idea that there must be a disconnect between
32 “The Google Trends Data Goldmine.” February 10, 2015. http://marketingland.com/google-trend-goldmine-11762633 Source: Instagram Art & Markets Data Codebook, sheet “Artist Data”
37
the major Instagram demographic and the overall population, and most importantly, the portion
of that population that actively buys art.
Confounding Variables
The last segment of our discussion brings up an important point that we need to officially
highlight. There is a high probability that we encountered some reverse causality in the case of
artists who were popular prior to Instagram.
As mentioned earlier in this discussion, these individuals have inflated follower counts,
and historic success at auction markets. They might be using Instagram not with the intention to
show off their art, but rather to capture daily moments in their life (as is the case with Ai
Weiwei). Alternatively, Anish Kapoor, a well-known artist with distinct success at art auctions,
uses his Instagram very much to look at art, but does not truly engage with his personal
Instagram network or the Instagram community at large. He follows 0 other accounts, so there is
no link of Instagram-based communication received by Anish Kapoor from art collectors.
A second confounding variable could arise from the varying numbers of posts that artists
have made on their Instagram accounts. Having more posts would invariably inflate that artists’
“total likes” and “total comments” counts as well. For several regressions, we did not normalize
the effects of variable post counts, meaning that artists who posted frequently and got likes from
the same cohort of followers had artificially high engagement levels.
Other limitations in our study came from difficulty in data collection. Near the end of our
data collection process, we lost access to one of our major data sources, ArtNet. Thus we didn’t
collect auction data on eleven artists. Importantly, these artists were also those who actually had
some of the highest counts of sales. This represents a significant gap in our research.
38
Alternative Explanations for Future Study
There is a lot of potential work to be done to further study the effect of social media on
the art market. Our Instagram-specific study can also be expanded to include more variables.
One direction could be to continue our work and fill the data gaps caused by our loss of access to
ArtNet data. Another direction could be to expand sales data from ArtNet to include other
galleries. This data tends to be hard to access, but could be illuminating since the demographics
of gallery-buyers could be different than those of auction buyers. Perhaps the calmer
environment of galleries (including online ones, which millenials are said to frequent)34 attract
more young, first-time buyers representative of the Instagram demographic.
In general, there could be ways to better explain which engagement measures perform at
a superior level over others. In our data, count of hashtags was superior in terms of explanatory
power, but it is unclear exactly why. Perhaps looking into a formula that compiles many metrics
(including Google Analytics as well as social media) could be used in a multivariate regression.
We are particularly interested in the results that may be found if we performed a
multivariate regression. Multivariate regressions are performed to get more explanatory power
for our variables and the coefficients that we may find. Unfortunately, given the scope of this
project, we were unable to complete the work required in data adjustments as well as
calculations. One potential issue that we foresee is multicollinearity. Given the closely tied
nature of some of our independent variables (particularly total likes and total comments), we
expect some strong correlations between variables. Further researchers should make sure that
such variables aren’t both included. Though this would this is a tedious process, it may merit
further work.
34 “The Online Art Market is Booming.” April 22, 2016. https://www.artsy.net/article/artsy-editorial-5-things-you-need-to-know-about-the-booming-online-art-market
39
Furthermore, there are other calculations that could be performed to measure
“engagement.” The source we used for our Instagram data, Websta, actually creates an
“engagement” score for each account’s followers, that sums likes and comments on each post
and divides it by the number of followers for that day. Unfortunately, we were unable to collect
historical follower counts, so we couldn’t apply this method over our project’s multi-year
timeline. Nevertheless, in the future, if more Instagram and sales data becomes available, these
are potential ways to further reveal potential correlations (or confirm our findings of a lack
thereof).
We also highly recommend looking into data on collectors. For our study, we looked at
artist Instagram accounts, but the Bloomberg article on the $24 million Instagram-based sale
focused on the account of an art collector, not an artist. Looking at the accounts of art influencers
and sellers could have measures of independent variables with much stronger correlations with
sales of art. If we were to create a new study, we would hypothesize that higher engagement on
these collector accounts could positively influences the sales of art associated with that collector.
Conclusion
The results of this study indicated no direct correlation between measures of
Instagram engagement for artists’ profiles (measured by the number of accumulated comments,
likes, followers, posts, and #hashtags) and artists’ overall performance at auction (measured by
number of total sales and median sales price). The overall lack of statistically significant results
fails to confirm the original hypothesis posed at the beginning of the paper - that there exists a
statistically significant relationship between the aforementioned variables.
40
In any case, experts suggest that with the exponential growth of mobile use, Instagram
continues to grow in popularity amongst the art collector community and remains at the forefront
of digital social platforms that have the potential to transform the future of the art market. As
indicated through the reports mentioned throughout this paper, Instagram has seemingly come to
act as a powerful discovery and research tool among art collectors, giving access to and
connecting a new global art buyer community. However, the study explained in this paper makes
the first stride towards answering the subsequent question that remains at the heart of this
phenomenon, to what degree are levels and patterns of engagement on Instagram indicative of or
correlated to artists’ sales performance? The results of this study, analyzing the relationship
between various measures of Instagram engagement for artists’ profiles and artists’ sales
performance at auction, suggests no significant evidence of a correlative relationship existing
between Instagram engagement levels and sales performance.
All things considered, Instagram is shown to be changing the landscape and methods by
which collectors research and discover artists and their work. Therefore, the investigation into
Instagram’s impact on art sales and potential to lead to higher conversion rates and increased
sales is a topic worthy of more in-depth study moving forward. In doing so, it is important to
consider that engagement metrics are not an end in themselves to assessing the potential for the
interactions on Instagram to impact sales and when analyzed as a whole might not be the most
insightful way of detecting buyer trends. Follow-ups to this study should move beyond it by
developing methods of locating and classifying engaged Instagram users by the value they hold
in impacting artists’ sales. When it comes to investigating the factors that drive sales, “loyalty”
and the difference between “dabblers” and “lurkers” vs. “enthusiasts” who are proven to be
active buyers is something to be considered and yet is complicated to measure via social media
41
with significant challenges in the ability to track individual customers in relationships to their
individual social accounts and activities.35 Through working to discover more insightful ways of
tracking followers’ personal interactions and relationships with artists and artworks and
analyzing them independently, it might be possible to determine with more certainty, what value
certain types of users and engagement patterns for artists and art works in their ability to lead to
increased sales performance.36
In looking deeper into the current and future potential of Instagram to act as a sales tool,
studies focused on the market segment that Instagram is currently known to cater to might
produce results more indicative of the value Instagram holds in driving sales. While it may be
true that in its current state, Instagram might not be significantly influential in driving direct
sales, it is necessary to assess Instagram and social media as a sales tool in terms of its marketing
value and its ability to drive traffic to places where users make buying decisions. More in-depth
analyses into more distinct and differently “valued” user groups might provide more insight into
Instagram’s ability as a sales tool in this regard. Thus, new methods and formulas for measuring
or “scoring” engagement types based on monitoring user actions as well as when, where, and to
whom those actions were taken towards could also prove to be more useful than overall social
engagement in offering more significant insights into what kinds of users and what types and
levels of engagement patterns hold the most value in impacting artists’ sales performance.
While reports based on active user responses indicate that Instagram’s influence on art
buying behavior is growing, this study indicates the complexity of the challenges associated with
proving this phenomenon. However, it is important to recognize Instagram in this context as one
outlet for gallery and artist content that could help drive art sales at stages that have yet to be
35 Traphagan, Mark. "Why Engagement DOES Matter As A Social Media Metric." Marketing Land. January 22, 2015. Accessed April 7, 2017. http://marketingland.com/engagement-matter-social-media-metric-114497.36 Ibid.
42
researched or studied. The challenges future studies beyond the one presented in this paper are
tasked with are developing data collection analysis methods capable of further characterizing and
defining the levels and types of users and interactions on Instagram for their indicative value in
impacting art sales performance.
43
Appendix
Exhibit A: The Derivation of the Final Regression Model
In order to test the relationship between Instagram presence and art auction performance,
the data was placed in context of three different regression models: the old regression model
(ORM), the proposed regression model (PRM), and the final regression model (FRM). Although
the output from the final regression model (FRM) yielded the most relevant results in testing the
main hypothesis, it is important to delineate the process by which the final regression model was
derived. By understanding the derivation of the final regression model (FRM), the variables’
coefficients and the regressions’ explanatory power become more intuitive and help confirm the
robustness of the conclusions.
To begin, the old regression model (ORM) stemmed from the most intuitive
understanding of statistics, economic modeling, and regression analysis. To construct the ORM,
every independent variable in the dataset was regressed against every dependent variable in the
dataset. Since the dataset was composed of two dependent variables (median sales price after
first Instagram post and number of sales after first Instagram post) and eight independent
variables (total comments, total likes, total number of followers, total number of posts,
comments per follower, comments per post, likes per follower, and likes per post), the ORM
resulted in 16 regressions. Nevertheless, since artists varied on the year of their first Instagram
post, the ORM had to be tailored to capture this variation. For example, logically speaking, an
artist that made his or her first Instagram post in 2010 was expected to have more total posts,
followers, likes, and comments than an artist that made his or her first post in 2017 simply
because the former had been active on Instagram for a longer period than the latter. If this
inconsistency persisted in the data, then an extremely popular artist with a relatively new
44
Instagram account with little presence but tremendous auction sales would skew the output to
yield a misleading regression. As a result, to control for this variation in timing, the 16 original
regressions in the ORM were filtered by the first Instagram post year among artists (ranging
from 2010 to 2017), resulting in an additional 128 regressions and 154 total regressions in the
ORM. All regression outputs for the ORM are included in the Appendix, Exhibit B.
Although the ORM provided an intuitive starting point for the data analysis process, the
model had two major flaws. First, the large number of total regressions in the ORM (154
regressions) made it difficult to identify a prominent pattern or significant relationship among the
variables in the model. Second, and more importantly, when the ORM separated the data by the
artists’ first Instagram post year, regressions filtered by certain years did not have enough data
points to create appropriate regressions. To illustrate, when regressing Artist Median Sales After
First Instagram Post against Total Comments, the year 2017 regression had zero plotted data
points (See Appendix, Exhibit B). On the other hand, the year 2012 regression had 43 plotted
data points. In effect, the ORM was unable to produce any results for data of certain years;
moreover, the ORM over-weighted the final output towards the first post years that contained the
most data points.
In order to fix these flaws in the ORM, the data was transformed, resulting in the
proposed regression model (PRM). In the PRM, the data was no longer separated by an artist’s
first Instagram post year. Alternatively, any timing inconsistencies due to the variation in an
artist’s first post year were alleviated by creating a new dependent variable. This new variable
took the difference in median sales price after an artist’s first Instagram post and before an
artist’s first Instagram post. Similarly, another dependent variable was calculated by taking the
difference in the number of sales after an artist’s first Instagram post and before an artist’s first
45
Instagram post. Through these two new dependent variables, the PRM captured the effect of
Instagram presence on sales performance and accounted for both pre-Instagram and post-
Instagram sales, unlike the ORM, which only focused on post-Instagram sales. Moreover, the
PRM eliminated the need to separate the data by an artist’s first post year because the two new
dependent variables captured the difference between pre-Instagram and post-Instagram sales
performance, regardless of which year the artist made his or her first post on Instagram.
Therefore, the PRM consisted of only 16 total regressions, whereas the ORM consisted of 154
total regressions. All regression outputs for the PRM are included in the Appendix, Exhibit C.
Although the PRM improved the overall economic model, there were still some
fundamental flaws with the collected data that needed attention. Up to this point, the PRM was
implemented in order to fix any time-dependency issues resulting from the variation in the
artists’ first post year; however, the PRM was still unable to account for the fact that the
collected data was not distributed normally, and thus needed to be transformed further. To
demonstrate, most artists in the dataset had fewer than 500,000 total likes and very few artists
had more than 500,000 total likes. As a result, the regressions in the PRM resulted in an x-axis
unbalanced scatter plot and residual plot (see Figure 19 and Figure 20 below).
46
According to StatWing, an online resource for statistical testing, “an x-axis unbalanced
residual plot means that the model can be made significantly more accurate.37 Most of the time
one will find that the model was directionally correct but pretty inaccurate relative to an
improved version.” Thus, in order to fix the x-axis unbalanced residuals in the PRM, the data
was transformed by taking the natural log of all the independent and dependent variables in the
37 "Interpreting Residual Plots to Improve Your Regression." Interpreting Residual Plots to Improve Your Regression | Statwing Documentation. Accessed April 4, 2017. http://docs.statwing.com/interpreting-residual-plots-to-improve-your-regression/#x-unbalanced-header.
Figure 19: Scatter Plot: Difference Median Sales Price vs. Total Likes, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
Figure 20: Residual Plot: Difference Median Sales Price vs. Total Likes, n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
47
dataset. The log transformation of the data made all the data points better fitted to a normal
distribution, thus adding explanatory power to the linear regressions. Through this
transformation, the data mirrored an elasticity model in economics, reaching the final step of the
analysis process—the final regression model (FRM).
At this point, it is crucial to note that the dataset for the FRM is cut from 158 artist data
points to 71 artist data points. The FRM demands that the dataset exclude any artists that have no
sales data either before their first Instagram post or after their first Instagram post. If the dataset
were to include artists without sales results either pre-Instagram or post-Instagram, then the
dependent variables in the model would end up subtracting null factors when finding the
difference in median sales price or number of sales. Subtracting null variables would result in
undefined error values, thus derailing the model. Therefore, to prevent subtracting null variables
in the model, it is key that artists without auction sales either pre-Instagram or post-Instagram are
excluded, trimming the dataset from 158 data points to 71 data points.
Next, by transforming the data in the FRM to take the natural log of the independent and
dependent variables, the scatter plots and residual plots no longer remain x-axis unbalanced.
Instead, the log transformation of the data makes all the data points better fitted to a normal
distribution, thus adding explanatory power to the linear regression models in the FRM. To
illustrate, please see below for the scatter plot and residual plot of the ln(Difference in Median
Sales Price Before and After First Instagram Post) vs. Ln(Total Likes) regression (Figure 21, 22).
48
As shown in Figure 21 and 22, through the log transformation of the data, the residuals
between the Difference in Median Sales Price Before and After First Instagram Post and the
Number of Total Likes no longer carry the x-axis unbalanced trait. Instead, the residuals in the
FRM appear normally distributed, meaning that the FRM has controlled for any systematic errors
that may have occurred in the dataset.
Through the data analysis process, the FRM has now controlled for two major flaws of
the overall economic model. First, by taking the differences in the dependent variables based on
Figure 21: Scatter Plot: ln(Difference Median Price) vs. ln(Total Likes), n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
Figure 22: Residual Plot: ln(Difference Median Price) vs. ln(Total Likes), n=158 (Source: Instagram Data Codebook.xlsx, sheet “Artist Data”)
49
sales data before first Instagram post and sales data after first Instagram post, the FRM has
eliminated any inconsistencies that may have arisen from the variation among the artists’ first
Instagram post years. Moreover, this difference component in the dependent variables captured
the impact of Instagram presence on an artist’s auction performance by accounting for both pre-
Instagram and post-Instagram sales data. Second, the FRM fixed the PRM’s x-axis unbalanced
residual plots by taking the natural logs of all the independent and dependent variables. Taking
the natural log of all the variables transformed the dataset into a more normally distributed
scatter plot, thus controlling for systematic errors that existed within the model. Therefore, by
controlling for these inconsistencies, the FRM is significantly more robust than either the ORM
or the PRM, making the FRM the preferred model for regression analysis.
50
Exhibit B: ORM Regression Outputs
51
Exhibit C: PRM Regression Outputs
52
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