#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

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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

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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/

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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”

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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

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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/

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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

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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”

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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.

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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

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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.

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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

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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.

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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.

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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

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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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