Performance metrics are a specific type of data that are used to evaluate how an individual or organization \u201cperforms\u201d regarding an established objective. Our work and our personal lives are saturated and shaped by such metrics and their resulting rankings and ratings. Few domains, activities, or sectors escape the desire and ability to metrify. As academics we are all too familiar with our citation counts. As social media users we privately wonder why a friend\u2019s post received so many likes, or why a colleague has more followers. These metrics increasingly shape how we see ourselves and others \u2013 and how we present ourselves to others.<\/p>\n\n\n\n
Musicians have not escaped this pervasive phenomenon. Indeed, as Richard Osborne points out in Music by Numbers<\/em>, the music industry\u2019s engagement with statistics and numerical rankings \u2013 through popularity charts and sales awards (i.e., gold, platinum, and diamond discs) \u2013 has arguably been \u201cmore widespread, more public, and more profound\u201d1<\/a><\/sup>Richard Osborne and David Laing, eds. Music by Numbers: The Use and Abuse of Statistics in the Music Industries<\/em> (Intellect Books, 2020), 2<\/span> than other creative industries. The shift to online streaming has resulted in a widening and deepening of data on music listening consumption. Today, performance metrics that range from streams, to views, skips, likes, and shares are increasingly important determinants of career viability in a data-driven music industry. Indeed, revenues often directly depend on such metrics.<\/p>\n\n\n\n Data is never merely descriptive however \u2013 it is always also performative. In other words, it doesn\u2019t simply represent the measured subject but actively shapes their understanding of the world and their place within it. Feedback in the form of performance metrics challenge how artists see themselves, their relations to others, and how others see them. In turn, such metrics can profoundly influence how artists approach, produce, and release music.<\/p>\n\n\n\n However, this is where the difficulty lays: How can we conce<\/em>ptualize the influence that performance metrics have on artists? <\/em>Should we think of performance metrics as a \u201cmagic bullet\u201d that exerts a direct, immediate, and powerful impact on music artists?<\/p>\n\n\n\n Metrics can certainly prompt artists to consider how to best \u201coptimize\u201d their content, performances, and selves for discovery and circulation through online platforms. There are no doubt musicians who alter their music according to what their YouTube or Soundcloud metrics are telling them. However, after conducting over 40 in-depth interviews with musicians at various stages in their careers and representing a diverse array of genres and music cultures, I am convinced that very few artists relate to their metrics in such an instrumental or strategic manner.<\/p>\n\n\n\n I propose that we can better formulate the relationship between musicians, metrics and music in the following way: Musicians are<\/em> influenced by their metrics; but this influence is 1) refracted<\/em> and 2) reflected<\/em>. The analogy of light shining through a prism is potentially helpful here. A prism is a triangular-shaped glass. Prisms work by bending \u2013 or refracting \u2013 the light that hits them. Light bends again when it leaves the prism. In addition to refracting light, prisms also reflect light. Following this analogy, I argue that metrics are both refracted and reflected by the prisms through which recording artists view them.<\/p>\n\n\n\n Before explaining this perspective in more detail, I will first discuss some key related literature upon which my research builds. <\/p>\n\n\n\n In the past decade diverse societal sectors and fields <\/a>\u2013 from computer science to politics and marketing \u2013 have celebrated the availability of massive quantities of information produced by and about people, things, and their interactions. These vast amounts of digital data have the potential to be analyzed to reveal patterns, trends, and associations. The academic literature on so-called Big Data is extensive, covering a wide range of topics and perspectives.2<\/a><\/sup>Viktor Mayer-Sch\u00f6nberger and Kevin Cukier, Big data: A Revolution That Will Transform How We Live, Work, and Think<\/em> (Boston: Houghton Mifflin Harcourt, 2013); Amir Gandomi and Murtaza Haider, \u201cBeyond the Hype: Big Data Concepts, Methods, and Analytics,\u201d in International Journal of Information Management <\/em>35, no. 2 (April 2015): 137-44; Kirsten E. Martin \u201cEthical Issues in the Big Data Industry,\u201d in Strategic Information Management<\/em>, eds. Robert D. Galliers et al.,(Routledge, 2020), 450-71.<\/span> Performance metrics comprise a subset of these debates. <\/p>\n\n\n\n Metrics, as defined by David Beer, are \u201ca form of data through which value can be measured, captured, or even generated.\u201d3<\/a><\/sup>David Beer, Metric power<\/em> (London: Palgrave Macmillan, 2016), 10.<\/span><\/p>\n\n\n\n Like data in general, the range and availability of performance metrics is growing rapidly. Social media is held partly responsible for this flood: Benjamin Grosser describes how Facebook activates the desire for \u201cmore friends, more \u2018likes\u2019, more comments, more photos, more connections, and more points of analysis.\u201d4<\/a><\/sup>Benjamin Grosser, “What Do Metrics Want? How Quantification Prescribes Social Interaction on Facebook,” Computational Culture<\/em> 4 (2014).<\/span> In turn, as Helen Kennedy has argued, \u201cvalue becomes attached to quantification, worth is synonymous with quantity.\u201d5<\/a><\/sup>Helen Kennedy, Post, Mine, Repeat: Social Media Data Mining Becomes Ordinary<\/em> (London: Palgrave Macmillan, 2016), 150.<\/span> While quantification has long engendered faith or trust by acting as \u201ca technology of distance,\u201d 6<\/a><\/sup>Theodore M. Porter, Trust in Numbers<\/em> (Princeton University Press, 1996).<\/span> social media has seemingly brought about a new intimacy to our relationship with numbers and metrics. As a result, some commentators have argued that in recent years we are witnessing a renewed faith in numbers. 7<\/a><\/sup>Beer, Metric power<\/em>; Helen Kennedy and Rosemary Lucy Hill, “The Feeling of Numbers: Emotions in Everyday Engagements With Data and Their Visualisation,” Sociology<\/em> 52, no. 4 (2018): 830-48.<\/span><\/p>\n\n\n\n Until relatively recently, the social impacts of quantification were largely ignored by social scientists. The focus, as Wendy Espeland and Mitchell Stevens have remarked, tended to be on \u201cthe accuracy of the measures\u201d rather than their \u201csocial implications.\u201d 8<\/a><\/sup>Ibid.<\/span> This has changed somewhat in recent years as sociologically-minded scholars have turned their attention to the costs of living in a so-called metric society. For example, in The Tyranny of Metrics<\/em>, Jerry Z. Muller argues that the widespread use of metrics in education, healthcare, and government, has led to unintended consequences that undermine the very goals that these metrics were designed to achieve. 9<\/a><\/sup>Jerry Z. Muller, The Tyranny of Metrics<\/em> (Princeton University Press, 2018).<\/span> Other scholars have blamed our metric society for heightening status competition, 10<\/a><\/sup>Steffen Mau, The Metric Society: On the Quantification of the Social<\/em> (John Wiley & Sons, 2019).<\/span> affecting well-being and our sense of self, 11<\/a><\/sup>Robert Prey, \u201cPerforming Numbers\u201d in The Performance Complex: Competition and Competitions in Social Life<\/em>, ed. David Stark (Oxford: Oxford University Press, 2020), 242-59.<\/span> and impinging upon our capacity for self-governance. 12<\/a><\/sup>Beer, Metric power.<\/em><\/span><\/p>\n\n\n\n However, there is still a lack of in-depth work on how individuals in different fields, domains and cultures are adapting to metrics and life in the so-called metric society. There is some empirical research that focuses on individuals and how they engage with metrics on a daily basis. 13<\/a><\/sup>Nick Couldry et al., “Real Social Analytics: A Contribution Towards a Phenomenology of a Digital World,” The British Journal of Sociology<\/em> 67, no. 1 (2016): 119, doi:10.1111\/1468-4446.12183.<\/span> Such an approach aims to capture how individuals reflexively ignore or incorporate data into their daily practices, and as a recently developed research paradigm, social analytics has been mainly used to study data practices in civic, 14<\/a><\/sup>Stefan Baack, “Datafication and Empowerment: How the Open Data Movement Re-Articulates Notions of Democracy, Participation, and Journalism,” Big Data & Society<\/em> 2, no. 2 (2015): 1-11, doi:10.1177\/2053951715594634.<\/span> community,15<\/a><\/sup>Couldry et al., “Real Social Analytics.\u201d<\/span> and public sector 16<\/a><\/sup>Kennedy, Post, Mine, Repeat.<\/em><\/span> organizations, but as Nick Couldry and Alison Powell point out, this approach \u201chas the potential to be expanded to many more areas.\u201d17<\/a><\/sup>Nick Couldry and Alison Powell, “Big Data from the Bottom Up,” Big Data & Society<\/em> 1, no. 2 (2014): 3, doi:10.1177\/2053951714539277.<\/span> In this regard, the music industry \u2013 and musicians in particular \u2013 would appear to present a rich field for analysis.<\/p>\n\n\n\n It was only around 2015 that music metrics became sufficiently detailed for music artists, managers, and record labels to use. The beta launch of Spotify\u2019s Fan Insight in November 2015 followed by Spotify for Artists in April 2017 was has been called a \u201cmilestone in music metrics.\u201d18<\/a><\/sup>Ibid., 23.<\/span> Around the same time competing analytics services on other platforms popular with artists were also introduced: YouTube for Artists launched in 2015 while Facebook\u2019s Audience Insights was introduced a year earlier. Today there are several prominent analytics companies operating in the music sector \u2013 such as US-based Chartmetric or France\u2019s Soundcharts.<\/p>\n\n\n\n It is easy to forget what a sea change these analytics services represented. As Nancy Baym points out, musicians \u201chave rarely had direct access to their own sales figures.\u201d19<\/a><\/sup>Nancy K. Baym, “Data Not Seen: The Uses and Shortcomings of Social Media Metrics,” First Monday<\/em> 18, no. 10 (2013).<\/span> Before the arrival of social media, musicians quantified their audiences on the basis of \u201cwhat venues they could fill, fan club memberships and the number of people who signed up for their (snail mail) mailing lists.\u201d20<\/a><\/sup>Ibid.<\/span> Today, musicians have detailed quantified insight into audiences and their consumption practices. The data trail that used to end at the record store checkout counter now includes every song skip or repeat, every thumb up or thumb down, and every social media like or share.<\/p>\n\n\n\n It is widely accepted that these online performance metrics can make or break a career in the contemporary music industry. Radio stations will look at an artist\u2019s YouTube views, while festival programmers will analyze local Spotify streams. In this way metrics provide diverse industry actors with a common \u201cdiscursive framework\u201d through which to engage each other and make decisions. 21<\/a><\/sup>Ien Ang, Desperately Seeking the Audience<\/em> (London: Routledge, 2006), 42.<\/span> However, this is a framework that is still very much in development. A recent study by Arnt Maas22<\/a><\/sup>\u00f8<\/span> and Anja Nylund Hagen explores how music professionals such as managers or label executives in Norway use metrics provided by streaming services.23<\/a><\/sup>Arnt Maas\u00f8 and Anja Nylund Hagen, \u201cMetrics and Decision-Making in Music Streaming,\u201d\u00a0Popular Communication<\/em>\u00a018, no. 1 (2020): 18-31.<\/span> Mass and Hagen acknowledge that while the volume and sophistication of data that is available is growing, most industry professionals focused on \u201csalient spikes that were noticeable \u2018at a glance\u2019\u201d and other easy-to-understand metrics.24<\/a><\/sup>Ibid., 18.<\/span><\/p>\n\n\n\n To-date there have been few studies that have examined how musicians use, understand, and are influenced by performance metrics. For musicians, datafied feedback provides unprecedented insight into the consumption patterns of their audiences. Music artists and their teams are \u2013 at least in theory \u2013 able to exploit this information to benefit their careers. Some research has argued that data provided to artists through services such as Spotify for Artists aids both signed and unsigned musicians in making important strategic decisions, such as deciding how to plan a tour and which cities to visit. 25<\/a><\/sup>Andrew S. Rae, \u201c\u2018Data Matters\u2019 \u2013 Spotify for Artists,\u201d https:\/\/www.academia.edu\/33010436\/Data_Matters_-_Spotify_for_Artists_-_Rae_A_2017_.<\/span> In recent years there has been no shortage of journalistic articles evangelizing the benefits of data for the music industry and for musicians. 26<\/a><\/sup>Allyson McCabe, \u201cWhy Big Data Has Been (Mostly) Good for Music,\u201d Wired<\/em>, 2019-12-23, https:\/\/www.wired.com\/story\/big-data-music; Bhumika Dutta, \u201cHow Is Big Data Revolutionizing the Music Industry?\u201d Analytics Steps<\/em>, 2022-02-21, https:\/\/analyticssteps.com\/blogs\/how-big-data-revolutionizing-music-industry.<\/span><\/p>\n\n\n\n Nevertheless, the growing reliance on data has also been critiqued as further evidence of the rationalization of culture. 27<\/a><\/sup>Jarl A. Ahlkvist, “Programming Philosophies and the Rationalization of Music Radio,” Media, Culture & Society<\/em> 23, no. 3 (2001): 339-58.<\/span> Philip Napoli argues that the growing ability to collect and analyze data indicates \u201ca persistent rationalization of audience understanding,\u201d in which creative producers and the media industries they work in have become \u201cincreasingly scientific and data-driven.\u201d 28<\/a><\/sup>Philip M. Napoli, Audience Evolution: New Technologies and the Transformation of Media Audiences<\/em> (New York, NY: Columbia University Press, 2010), 11.<\/span> Indeed, there is some research that demonstrates that musicians may be tailoring or \u201coptimizing\u201d their music and activities to what these metrics tell them. 29<\/a><\/sup>Jeremy Wade Morris, \u201cMusic Platforms and the Optimization of Culture,\u201d Social Media + Society <\/em>6, no. 3 (2020): 1-10; Jeremy Wade Morris et al., “Engineering Culture: Logics of Optimization in Music, Games, and Apps,” Review of Communication<\/em> 21, no. 2 (2021): 161-175.<\/span> To the extent that this occurs, it is an example of what some scholars have called \u201cthe metricated mindset,\u201d whereby \u201cthe quantities presented by the metrics \u2013 and the anticipation of popularity expressed \u2013 privilege certain types of social action and guide behaviour in digital space.\u201d 30<\/a><\/sup>G\u00f6ran Bolin and Jonas Andersson Schwarz, “Heuristics of the Algorithm: Big Data, User Interpretation and Institutional Translation” Big Data & Society<\/em> 2, no. 2 (2015):10.<\/span><\/p>\n\n\n\n As different as \u201cdata evangelists\u201d and more critical commentators may seem to be from each other, it is interesting to note what is shared by both sides: both assume that data will be directly used to make decisions. They only differ on the question of whether this is desirable or not. Empirical research on musicians and music industry stakeholders however indicates that the reality is much more ambivalent. Baym et. al surveyed and interviewed professionals from a wide variety of music-related industries and roles, including musicians. 31<\/a><\/sup>Nancy Baym et al., “Making Sense of Metrics in the Music Industries,” International Journal of Communication<\/em> 15 (2021): 3418-41.<\/span> Their findings suggest that music professionals employ data such as performance metrics in a manner that is in fact \u201ccomplicated and highly varied.\u201d As they put it:<\/p>\n\n\n\n [M]usic workers do not take metrics on faith or reject them out of hand; rather, they make<\/p>\n\n\n\n sense of them, deploy them strategically, and narrate their meanings to give themselves<\/p>\n\n\n\n rationales to make investments and predictions and to persuade others to do so.32<\/a><\/sup>Ibid., 3419.<\/span> <\/p>\n\n\n\n Baym et al.\u2019s results do not make a distinction between music artists and other professionals within the music industry. There are no doubt important differences between how and why a concert promoter, an employee at a record label, and a musician understand and employ metrics for their work. However, their findings broadly echo what I have found in my interviews with musicians.<\/p>\n\n\n\n Our work has focused on how musicians understand and use their performance metrics. Interviews were conducted in different countries and in different time periods over the course of the past five years. I conducted seven interviews with Dutch musicians in the Netherlands and 12 interviews with musicians in South Korea. Two trained research assistants conducted a total of 22 interviews with musicians from Finland (4), the United States (2), Ireland (1), Norway (1), Switzerland (1), the United Kingdom (1) and the Netherlands (12). 33<\/a><\/sup>Interviews were conducted between November 8, 2018, and November 19, 2020 by Rosa Kremer in the Netherlands and Antti Kailio in Finland and the Netherlands.<\/span> The overrepresentation of interviewees from the Netherlands can be explained by the location of the interviewers and the qualitative sampling strategies (purposive sampling and snowball sampling) adopted in this research. In total we interviewed 27 male and 14 female musicians. The age of the interviewees ranges from 23 to 66 and included a wide range of genres \u2013 from rock, to pop, electronic dance music, country, and hip hop.<\/p>\n\n\n\n Given the relatively small sample, in what follows I will not attempt to compare genres, genders or countries of origin in regard to the focus topic. Instead, drawing from some of these interviews I will reveal the often intimate and always complicated relationship artists have with their metrics. For the most part, I have found that music artists do not either categorically accept or reject metrics. Thus, I argue that we can best understand the influence of metrics not through a simplistic linear effects model but rather as the outcome of a complex and ambiguous process of sense-making. But how can we conceptualize this more complicated and ambivalent way that metrics pervade the lives of music artists?<\/p>\n\n\n\n In what follows, I will employ the analogy of the prism to describe the influence of metrics on musicians. While Big Data is sometimes represented as a flashlight that illuminates dark corners and facilitates the exploration of greater efficiencies, critics warn that we risk being \u201cblinded by the light.\u201d The argument I am making here stays with the analogy of light beams and rays but adds the object of the prism to describe the twin processes of refraction and reflection that take place. It is my contention that this analogy provides a framework through which to more accurately convey how the \u201clight\u201d of performance metrics impacts and influences musicians.<\/p>\n\n\n\n While the pervasiveness of performance metrics is obvious, it is not always clear what these metrics actually mean. What should the ratio of likes to views be for an emerging artist in comparison to a popular artist? Is a Facebook video view equal to a YouTube view? How many track skips on Spotify are acceptable? How do you compare metrics across genres? Within the music industry there is little agreement on how to assess success or compare artists through performance metrics. As scholars have long recognized, the challenge of Big Data is not in storing data, but in \u201cfiguring out how to make sense of it.\u201d34<\/a><\/sup>Anthony Mccosker and Rowan Wilken, “Rethinking \u2018Big Data\u2019 as Visual Knowledge: The Sublime and the Diagrammatic in Data Visualisation,” Visual Studies<\/em> 29, no.2 (2014): 155-64.<\/span><\/p>\n\n\n\n However, musicians themselves are not passively waiting for someone else to make sense of their performance metrics. My interviewees have described in detail how they figured out which metrics to pay attention to, and which to ignore; what actions gained them more followers; and how to weigh disparate metrics.<\/p>\n\n\n\n Some musicians described how they try to contextualize the metrics they receive on different platforms. A Dutch country music singer-songwriter remarked that he makes a distinction between the ratio of likes-to-views on Facebook and likes-to-views on other platforms like YouTube. As he put it: \u201cI think there’s a difference between really watching a video (on YouTube) and just scrolling over it and it starts playing (on Facebook) because that’s counted as a view too.\u201d 35<\/a><\/sup>Interview by author on April 3, 2017.<\/span> <\/p>\n\n\n\n Here, this artist is pointing out how different platforms encourage and measure engagement differently, and how any attempt at commensuration between platforms needs to recognize this distinction. Thus, even if we accept that performance metrics illuminate and provide insights into how listeners respond to an artist and their music, it is important to recognize that the \u201clight\u201d these metrics provide is always refracted through the prism of the platform\u2019s interface. Musicians must then begin the interpretative process of sensemaking,36<\/a><\/sup>Karl E. Weick, Sensemaking in Organizations<\/em> (London: Sage, 1995).<\/span> often by reassembling these refracted rays of light through the work of commensuration.37<\/a><\/sup>Wendy Nelson Espeland and Mitchell L. Stevens, “Commensuration as a Social Process,” Annual Review of Sociology<\/em> 24 (1998): 313-343.<\/span><\/p>\n\n\n\n How any one particular artist makes sense of their metrics is of course highly dependent on several variables. For example, different music acts will have contrasting goals and measures of success. Not every band or artist aspires to have a charting single, and many actively resist mainstream success. How an artist justifies their response to their performance metrics is dependent on contrasting sets of principles, rules and criteria they might employ. Following the French sociologists Luc Boltanski and Laurent Th\u00e9venot, we could call these orders of worth<\/em>.38<\/a><\/sup>Luc Boltanski and Laurent Th\u00e9venot, On Justification: Economies of Worth<\/em> (Princeton: Princeton University Press, 2006).<\/span><\/p>\n\n\n\n Social life is marked by conflicting and contrasting evaluative regimes or orders of worth. In their book On Justification: Economies of Worth, <\/em>Boltanski and Th\u00e9venot identify six orders of worth that shape how people evaluate and justify their actions and decisions. Each order is characterized by its own logic and system of value. For example, the \u201cindustrial order\u201d is based on the principles of efficiency and productivity in the workplace, while the \u201ccivic order\u201d is based on the principles of rights and duties. We can think of each of these order as a \u201crepertoire of legitimate principles of justification which people can use in situations with contested values.\u201d39<\/a><\/sup>Ibid.<\/span><\/p>\n\n\n\n When I asked musicians how they relate, make use, and make sense of their performance metrics, they responded in ways that tended to correspond with several of Boltanski and Thevenot\u2019s six orders. Some music acts interpret and justify their performance metrics through the order of worth that resembles Boltanski and Th\u00e9venot\u2019s order of \u201cfame\u201d, whereby \u201cworth depends only on the opinion of others.\u201d40<\/a><\/sup>Ibid., 98.<\/span> For example, I interviewed a popular Dutch DJ who performs as part of a duo that compose both instrumental club tracks and tracks with vocals. When I asked him whether, and to what extent, the stream counts and other metrics generated by his music help him make creative decisions, he remarked, \u201cWe want to keep our output diverse, but we also want to make another hit\u2026You just see it in the amount of views or plays, that a vocal track gets more traction.\u201d41<\/a><\/sup>Interview by author on July 26, 2017.<\/span> Mentioning that he was about to release a new vocal track later that same week, this musician said that he expected the song to perform better than a previous instrumental track.<\/p>\n\n\n\n In a similar manner, \u201cMin Joon\u201d \u2013 a Korean EDM producer \u2013 reflected on the influence that stream counts have on the music that he subsequently creates:<\/p>\n\n\n\n I think it can be said that it has some influence. Because if I said that I worked on 10 different styles of songs and uploaded them, some of them might have low play counts and some might be really high. Even in my case, if I look at the songs I put on my SoundCloud, there are tracks that are around 500 (streams) on average. And the most-played song was played about 10,000 times\u2026 When I show the difference in numerical value like this, I can clearly see the data on which style people prefer among the songs I work on. (I ask myself) \u2018The song went well, but should I do it this way?\u2019 So, it seems to have an impact…42<\/a><\/sup> Interview by author and Seonok Lee on May 11, 2021.<\/span><\/p>\n\n\n\n These exchanges capture what is probably assumed to be the most common lens through which to see metrics \u2013 as straightforward measures of relative popularity. However, most musicians I interviewed rejected popularity as the dominant measure or order of worth. As \u201cRosa\u201d \u2013 a trip-hop artist \u2013 expressed: \u201cI think the danger is that you change your music into what people want to hear, and I don’t want to do that. But I think a lot of people do that.\u201d43<\/a><\/sup>Interview by author on March 28, 2017.<\/span><\/p>\n\n\n\n The drummer of a band that was signed to Sony Music at the time of our interview described how metrics can invade the creative process of making music. This musician pointed to the \u201chyper self-awareness\u201d that results from paying too much attention to metrics and other forms of online feedback. He saw this as potentially paralyzing for a creative artist. Describing some of the musical acts in his local scene he remarked:<\/p>\n\n\n\n [T]hey’re too reflective inside of the creative process\u2026[T]his is something you see a lot within the artists I know. They are very aware and very occupied with all these different questions and feedback loops that I guess were initiated by the feedback that social media gave them.44<\/a><\/sup>Interview by author on February 5, 2020.<\/span><\/p>\n\n\n\n As we can see from these quotes, artists make value judgements about how metrics are used, and how they should <\/em>be used. In doing so, they implicitly or explicitly compare one order of worth with another. Metrics thus can be evaluated quite differently depending on the order of worth one employs to establish meaning. Finnish pop singer \u201cElsa\u201d provides a very clear example of this:<\/p>\n\n\n\n I\u2019ve been kind of deliberately trying not to think or inspect those numbers too much because it takes my focus out of the actual doing. After all I\u2019m an artist and not a business person, even though I make a product which is branded and marketed and all\u2026. But I want to keep the art and musicianship as a priority, so I think it\u2019s a good thing to protect myself from those things. Even though it can be quite difficult because you see so much data all the time without even wanting to. So, I don\u2019t want to dig into it any deeper than I already see.45<\/a><\/sup>Interview by Antti Kailio on January 5, 2019.<\/span><\/p>\n\n\n\n Here we can see Elsa attempting to contrast what Boltanski and Th\u00e9venot call the \u201cinspired order\u201d (which is closely associated with artists) with the \u201cmarket order\u201d of business people selling branded products. Many of the artists I interviewed made this same distinction in articulating their position on performance metrics. In doing so, they are expressing the familiar sentiment that metrics are corrupting. This is because metrics are often associated with the market or commerce, and as Keith Negus and Michael Pickering put it \u201c[c]ommerce corrupts creativity and leads to compromise.\u201d46<\/a><\/sup>Keith Negus and Michael Pickering, Creativity, Communication, and Cultural Value<\/em> (London: Sage, 2004), 46.<\/span><\/p>\n\n\n\n Boltanski and Th\u00e9venot did not mean to imply that these different orders of worth were mutually exclusive: rather they interact and influence each other. When the artists I interviewed attempted to justify the actions and decisions they took in regards to performance metrics, they often drew upon multiple orders of worth at the same time. This is not surprising. Artists have always received feedback from fans, critics, their peers, and others. For career musicians this has often meant making strategic decisions about what feedback to acknowledge and what to ignore, while balancing the romantic notion of art for art\u2019s sake with the need to build a fanbase. The proliferation of performance metrics \u2013 and access to these metrics \u2013 has only amplified this struggle.<\/p>\n\n\n\n However, it is clear from the interviews I conducted that musicians are rarely \u201cblinded by the light\u201d of metrics. Instead, they judiciously apply various evaluative orders of worth through which to understand and justify how they will respond to performance metrics. To the extent that we can say that metrics influence musicians, this influence is always the result of a process of sensemaking that emerges out of the imposition of evaluative orders. The analogy of the prism helps us to conceptualize how these various orders bend or refract the \u201clight\u201d of metrics.<\/p>\n\n\n\n If the prism analogy reminds us that performance metrics are always refracted on their path to the receiver, there is also a second manner in which this analogy is productive. This comes from the recognition that light which passes through a prism is not only refracted, but also reflected.<\/p>\n\n\n\n The artists I interviewed often used their metrics to reflect upon themselves and their self-image. In other words, musicians see a reflection of themselves through their performance metrics. In this sense, metrics don\u2019t tell you what to do<\/em> so much as tell you who you are.<\/em> These numbers are thus very intimate and personal for musicians \u2013 sometimes too<\/em> personal. Irish singer \u201cOlivia\u201d remarked:<\/p>\n\n\n\n It\u2019s hard not to take it personally if some video you just put up just tanks and doesn\u2019t connect at all. I think that affects me more as a solo artist than it did with a band because it is just me, and it\u2019s under my real name. So, it\u2019s very hard not to take that personally.47<\/a><\/sup>Interview by Rosa Kremer on November 8, 2018.<\/span><\/p>\n\n\n\n Just as we might be disappointed by the reflection we catch of our ourselves as we pass by a window or mirror, Olivia reminds us that metrics are not always flattering. While significant attention has been paid to the abuses and harms caused by social media comments, 48<\/a><\/sup>Maria Koutamanis et al., “Adolescents\u2019 Comments in Social Media: Why Do Adolescents Receive Negative Feedback and Who Is Most at Risk?” Computers in Human Behavior<\/em> 53 (2015): 486-94.<\/span> metrics provide feedback that is seemingly less personal, and thus less hurtful. However, the apparent objectivity of numbers is precisely what makes them so effective as seeds for self-doubt: performance metrics cannot be so easily dismissed as the errant rantings of an isolated individual. As one musician admitted to me, \u201cI\u2019m not really insecure a lot, but . . . I\u2019m a bit insecure by these numbers.\u201d 49<\/a><\/sup>Interview by author on March 28, 2017.<\/span><\/p>\n\n\n\n Internet scholar Nancy Baym describes metrics as shaping the \u201csense of self-worth and professional value\u201d of individual musicians. 50<\/a><\/sup>Baym, “Data Not Seen.\u201d<\/span> A Korean indie pop guitarist and songwriter I interviewed opened up to me about the self-doubt that can creep in after releasing an album and getting the first wave of feedback:<\/p>\n\n\n\n Working on real music is something that takes a long time. For example, if you were to prepare 10 songs for a full-length album, it was an album that you had to prepare for a whole year to release\u2026 (but) the moment I visually check the data, I start to think that the things I’ve been doing aren’t good enough.<\/strong>51<\/a><\/sup>Interview by author and Seonok Lee on July 27, 2021.<\/span><\/p>\n\n\n\n This sense of worth and value is always developed relationally. In place of any objective measure of success, musicians generally evaluate their own performance by comparing their metrics to other artists. Indeed, the very existence of metrics, as Wendy Espeland and Stacy Lom point out, \u201cmake it almost impossible not to compare.\u201d 52<\/a><\/sup>Wendy Espeland and Stacy E. Lom. \u201cNoticing Numbers: How Quantification Changes What We See and What We Don\u2019t,” in Making Things Valuable<\/em>, eds. Martin Kornberger et al. (Oxford: Oxford University Press, 2015), 19.<\/span> The authors of a recent report on British musicians\u2019 mental health and well-being write, \u201csocial media [is] often the vehicle through which [musicians] observe the achievements of others, and . . . compare their own fortunes to that of their peers and competitors.\u201d53<\/a><\/sup>Sally-Anne Gross et al., Well-Being and Mental Health in the Gig Economy<\/em> (London: University of Westminster Press, 2018), 17.<\/span> In addition, musicians \u201ccompare themselves to a version of themselves which they imagined they might be.\u201d54<\/a><\/sup>Ibid<\/span><\/p>\n\n\n\n Of course, such comparisons may also reflect favorably from time to time. \u201cHank\u201d \u2013 a singer-songwriter based in the Netherlands \u2013 revealed that he was quite happy with the spike in his Facebook metrics that followed his performance on De Wereld Draait Door \u2013 a Dutch talk show that has a live music segment:<\/p>\n\n\n\n We got around 400 likes that first day, on Facebook, from 1,400 followers. That\u2019s a good score! I saw other bands that have, like, 14,000 followers on Facebook and they played the same show and got 300 (likes), or something.55<\/a><\/sup>Interview by author on April 3, 2017.<\/span><\/p>\n\n\n\n Here Hank is determining the \u201csuccess\u201d of his performance through comparison to his peers. The fact that his ratio of likes-to-followers was higher than another band that played the same show is judged to be a \u201cgood score\u201d and sufficient evidence of a successful performance. But this was not a \u201cfair\u201d competition: pleasure is derived, and success is defined in relation to the asymmetry of the competition. As Hank continues: \u201cIt was also a pleasant surprise because you kind of feel like the underdog, with less likes than some other bands who are much bigger and have four or five times your spread, and then you\u2019re able to beat them on the same stuff.\u201dResearch on metrics and music<\/h2>\n\n\n\n
The Metric Society<\/h3>\n\n\n\n
Metrics in the Music Industry<\/h3>\n\n\n\n
Metrics and Musicians: Our Research<\/h3>\n\n\n\n
Refraction<\/h2>\n\n\n\n
Reflection<\/h2>\n\n\n\n