The Datafication and Quantification of Fashion: The Case of Fashion Influencers

Abstract

The article approaches the field of fashion influencers as an instance of the pervasive power of datafication and quantification in everyday life. It discusses the role of metrics in the fashion influencer economy, and the quantification of the self it goes hand in hand with, a quantification that is also an object of struggle in the field of influencer marketing. Drawing on conceptual tools such as “like economy” and “data capitalism,” as well as on the work of Bourdieu, it points to the instrumentalisation of numbers for economic purposes, and the centrality of such numbers to the business of fashion influence. Drawing on Moore’s notion of “quantified worker” it conceptualizes fashion influencers as iterations of the “quantified self.” The article elaborates on the centrality of quantified data in influencer marketing companies’ quest for a dominant position in the field. It discusses the ways it participates in the quantification of the business of influence, further tightening the relation between capitalism, quantification and datafication in the field of fashion.

Keywords:

• fashion influencer
• datafication
• quantification
• Instagram
• influencer marketing

Introduction

In recent years a large amount of digital data has been produced, collected, stored and translated into quantifiable measures used to identify patterns and predict behavior, hereby contributing to an increased quantification of the social (Kitchin 2014; Van Dijck, Poell, and de Waal 2018). The collection of data about consumers and citizens is not a new phenomenon (Powell 2019). Calculations and numerical tabulations have long been used by nations to support bureaucratic activities, with statistics becoming in the nineteenth century a tool States deployed to categorize and govern the social (Beer 2016; Porter 2020). However, with the multiplication of online platforms and the wide reach of digital technologies, data collection and quantification have proliferated (Kitchin 2014; Van Dijck, Poell, and de Waal 2018). In 2000, 25% of the world’s information was preserved digitally, with the rest stored on analog media such as printed books (Cukier and Mayer-Schoenberger 2013). About thirteen years later under 2% only of all information was stored non-digitally (Cukier and Mayer-Schoenberger 2013).

Whilst in the nineteenth-century quantification of the social through the use of statistics was largely a State process, it has now become reliant on big corporations, which are the chief orchestrators and owners of the data collected and sold for profit (Couldry and Mejias 2019). Quantification has been fueled by neoliberalism and its logic of audits and tests and goes hand in hand with the commodification of activities that had been outside of the sphere of commerce (Van Dijck, Poell, and de Waal 2018), such as online communication and the sharing of fashion images on platforms such as Instagram. As Andrejevic (2015, 5) puts it: “we are moving into a world in which mediation becomes synonymous with marketization, and personal data emerges as a new ‘asset class’ and commercial resource.” The extent to which data pervades economic life, and everyday life more generally is captured in the term “datafication,” which refers to the process whereby practices and experiences are turned into quantitative data.

I approach the field of fashion influencer as an instance of the pervasive power of numbers and data in everyday life. I discuss the role of metrics—“those data that are used to provide some of sort of measure of the world” (Beer 2016, 3)—in the influencer economy and the quantification of the self it goes hand in hand with; a quantification that is also an object of struggle in the field of influencer marketing. Duffy (2017, 149–151) has mentioned fashion bloggers’s attention to metrics, noting “how the datafication imperative bleeds into various realms of cultural and economic life as one’s value gets transtlated into quantifiable data” (151). In this article, I zoom in and elaborate on this idea, systematically interrogating fashion influencing as taking place on Instagram through the conceptual lenses of both datafication and quantification, in dialogue with the related scholarship.

Drawing on conceptual tools such as “like economy” and “data capitalism,” as well as on the work of Bourdieu, I point to the instrumentalisation of numbers for economic purposes, and the centrality of such numbers to the business of fashion influence. Key indicators of performance and audience attention, influencer metrics examplify the “fixation with metrics” that characterizes contemporary society and its reliance on the metricization of performance at the expense of qualitative evaluation (Muller 2018). Mau (2019, 2) talks about “the metric society”: “a society of scores, rankings, likes, stars and grades.” The popularity and appeal of fashion influencers is put into numbers and stored as data the better to be monetized, by influencers, by influencer marketing companies, and by the platforms they operate on.

Drawing on Moore (2018) I then discuss fashion influencers as iterations of the “quantified self” and the “quantified worker.” I comment on the idea of “data in the workplace” and the precarity and anxiety it reinforces in the labor of fashion influencers. Finally, I elaborate on the centrality of quantified data in influencer marketing companies’ quest for a dominant position in the field, also discussing the ways it participates in the further quantification of the business of influence. The influencer marketing industry is fueled by a “trust in numbers” (Porter 1995) that contributes to the quantification of everyday life and the banalisation and legitimation of numbers and data as reliable agents of business, further tightening the relation between capitalism, quantification and datafication. Often evangelizing about numbers, influencer marketing companies are involved in a struggle for the truth on the best way to make sense of influencer data and offer brands reliable data analytics. Throughout the article, then, I underscore the significance of fashion influencers and influencer marketing in the wider process of datafication and quantification of everyday life, which the field of fashion more generally participates in.

I draw on 20 in-depth semi-structured interviews I conducted with UK-based fashion influencers in 2019 and 2020.¹ 6 of these included follow up interviews with bloggers I had first met in 2013–2014 (27 interviews conducted), and 2016–2017 (9 follow-up interviews conducted) as part of an ongoing project on fashion blogging and the field of fashion influencers started in 2009 (see e.g. Rocamora 2011).

In 2016 and 2017 blogs were still very active, but bloggers were embracing Instagram more systematically. 2016/17 is also the time when the social media platform started to really emerge as a key fashion platform. By 2019 many of the bloggers I had first met in 2013 had stopped blogging (their blog was left dormant or was deleted) to move on to Instagram only, a move that also marks the shift from the term blogger to that of influencer. Although the former is still in use, with many fashion blogs still active, it has tended to be taken over by the latter. Finally, I also draw on the large body of texts I have archived and analyzed since I started researching blogging, and which includes on and off-line media and business articles on fashion blogging and influencing, and influencer marketing textbooks and websites.²

Datafication

The vast amount of quantified data produced through digital means is known as “big data” (Holmes 2017), a topic that has become the object of numerous academic and journalistic articles, as well as a business attention. Big data consists in the computerized gathering and rapid processing of large sets of mostly quantitative data that can be used to develop predictive algorithms (Mosco 2017). Big data is not about understanding why something is happening or not, but about establishing patterns and correlations to predict whether something might happen. Cukier and Mayer-Schoenberger (2013) put it thus: “Big data helps answer what, not why, and often that’s good enough.” It is, they add, “only the latest step in humanity’s quest to understand and quantify the world.” Seen as too much of a hype, the term “big data” has somewhat lost some of its traction in current academic and business literature (Kennedy 2016), but the process it refers too has not waned, and is still at the heart of much scholarly research and business practice, not least the business of fashion influence as I show throughout this article.

Cukier and Mayer-Schoenberger (2013) call the “ability to render into data many aspects of the world that have never been quantified before,” datafication, thereby coining a term that has in turn become a focus of attention in the recent work of many scholars and, in particular, the growing field of critical data studies. Datafication is the conversion of everyday practices and processes into digital information and computerized data sets (Couldry and Yu 2018), with data meaning “a numerically quantified format” (Cukier and Mayer-Schoenberger 2013). Quantification, then, is central to datafication, and although it goes back many millennia, like datafication it has intensified recently with the development of digital technologies for the collection and processing of data (Mau 2019). Mau (2019, 2) talks of a “quantification cult,” which is linked to the digitization of vast areas of everyday life.

Datafication goes hand in hand with commodification (Van Dijck, Poell, and de Waal 2018), and to capture the extent to which data has become central to capitalism, Morozov (2015) talks about “data capitalism,” a type of capitalism that seeks “to capture our behavior (in the forms of clicks or location) in real-time and to store it for personalized use.” It creates value out of digital traces (Myers West 2019), such as the ones we leave behind us whilst browsing online for fashion. Contemporary capitalism is focused on the production of value through the extraction of data (Couldry 2018), which is now “the core business” of internet companies (Berry 2019, 73), whilst mobile devices such as smartphones have become an opportunity for market researchers to collect data (Lupton 2016).

Key to the process of datafication is platforms, a programmable and automated architecture that, orchestrated by algorithms, shape users’ interactions for the production of data that can be used for commercial purposes (Van Dijck, Poell, and de Waal 2018). On social media platforms, data has become “an agent of capital interests” (Kitchin 2014, 46/285). Although corporations have used the term “platform” to fashion themselves as neutral intermediaries (Gerlitz 2016, 23), platforms are “driven by business models” (Van Dijck, Poell, and de Waal 2018, 9; see also Nieborg and Powell 2018). Bringing users in contact with service providers and brands, platforms are a key feature of one’s everyday life, from booking a cab (e.g. Uber) to ordering food (e.g. Deliveroo), networking (e.g. Linkedin), socializing (e.g. Instagram; Twitter), listening to music (e.g. Spotify), or indeed selling and buying fashion (e.g. Shopify).

Current developments suggest that datafication and quantification are rampant throughout the fashion industry (See also Rocamora 2023, Forthcoming). With headlines and statements such as 'How Centralised Customer Data Platforms Can Drive Retail Growth' (Business of Fashion, 12 January 2022, https://www.businessoffashion.com/articles/retail/how-centralised-customer-data-platforms-can-drive-retail-growth/); 'Beauty's Big Data Opportunity' (Business of Fashion, 18 August 2021, https://www.businessoffashion.com/articles/beauty/beautys-big-data-opportunity/); or 'Fashion Brands have always loved style, but now they've come to love data' (Women's Wear Daily, 21 November 2022, WWD, 21 Nov 22, https://wwd.com/voices/retails-responsible-reset/wwd-voices-vf-corp-1234999401/), business titles regularly feature articles on the role of data in the business of fashion."Data analyst" has become a key occupation in the field of fashion, witness the job offers for the position sites such as Fashionunited.uk or businessoffashion.com regularly post on their pages. In November 2021 the latter advertised that they themselves were looking for a Head of Data and Analytics ‘to unlock business growth and customer insight’. The datafication of fashion is particularly visible in the fashion influencer economy, and especially as articulated on Instagram.

Metrics of (valuable) influence

When using the expression “fashion influencer economy,” I am referring to the economy that emerged out of the professionalization of fashion bloggers at the beginning of the twenty-first century (Findlay 2017, Pedroni 2015, Rocamora 2018, and, which, with the rise of Instagram (owned by Facebook, renamed Meta in October 2021), has largely become, in the field of fashion, dependent on it. In this article, I focus on this platform. Although at the time of writing TikTok is increasingly emerging as a significant fashion media player, Instagram remains the main social media space for fashion. According to digital marketing executive Aaron Edwards, this is due to Facebook and Instagram’s ability to provide data: they “are the go-to […] and that’s simply because they have the highest share of data and metrics available than most other platforms” (cited in Mondalek 2021). With the professionalization of bloggers and influencers, new business pratices have emerged, such as influencer marketing, which I return to later, that have participated in the consolidation of what could be called, following Bourdieu, the field of fashion influencers (Rocamora 2016, Rocamora 2023, Forthcoming).

Drawing on Rose (1991) I approach the fashion influencer economy as an instance of an “economy of numbers,” a term the sociologist uses to refer to the monetization of numbers that has characterized economic life since the nineteenth century. This economy of numbers is in turn tightly linked to capitalism’s, and, particularly, neoliberalism’s reliance on measurements and quantification for its functionning (Beer 2016). Metrics are instrumental to this; they allow for the deployments and realization of competition, which is key to neoliberalism (Beer 2016).

Fashion as articulated on Instagram in the work of influencers is one of the spaces where neoliberalism’s economy of numbers is rampant. Indeed numbers pervade the architecture of Instagram, and have become integral to the activities and definition of fashion influencers, as well as the many stakeholders involved in the business of influence, such as influencer mareketing companies.

Navigating the Instagram interface, scrolling down posts and grids, means constantly coming across numbers. Quantitative metrics are as central to the visual makeup of the platform as its pictorial components. The frontend, for instance, shows numbers of followers, likes and comments. The backend gives access to “insights” such as, through the business option, available to all Instagram account holders, time spent on the platform; top posts, content interactions, accounts engaged. Charts, tables, and other graphs populate it, lending the platform an air of scientific reliability and truth, an idea I return to later.

On Instagram one’s number of followers has pride of place; the metric appears on the top left-hand side, immediately under one’s Instagram name, tying the two together as identificatory parameters. Citing fashion influencers’ number of followers has become a common way of introducing them in media articles. The InfluencerMarketing hub, for instance, devote a June 21 article to “15 Fashion Influencers to Follow.” Below a screen grab of their Instagram profile is a list of their numbers of followers by social media platforms. Zoelle Zeebo is ranked at the top with: “Followers:Instagram (@zoella) − 11.1 M, Facebook (@zoe.zoella) − 2.6 M, Twitter (@Zoella) − 65,000, Youtube (@Zoella) − 11.8M”(https://influencermarketinghub.com/fashion-influencers/).

The influencers I interviewed regularly invoked followers’ numbers to qualify themselves, their practices, their trajectories and those of others. Jenny (9.8 K) explains:

If I get new followers I will check out who they are […], if they’re an influencer with like thousands of followers and their content looks quite nice, maybe I’ll follow them. Whereas if it was someone with the same amount of content, just someone who seemed like a nobody, would I follow them? Maybe not.

Florence (6 K) does not work with an agent “because that’s a whole new level, you know, it’s like your really prominent bloggers that are being signed to agencies and things like that. So like the 100k bloggers sign to agencies.” Emma (21 K) says of her best friends: “she was on 3,000 at the beginning of the year, she’s now almost on 12 because she’s perfect, like tall model, Parisian, beige, Chanel vibe.”

Follower numbers often act as a marker of one’s social media trajectory and history: Paul (14 K) narrates his early days as an infuencer in the following terms: “I started in 2016, I had a very small following when I first started, like 2/300 followers.” Similarly, Lina (52 K) explains: “after I graduated, I got a fulltime job at [fashion brand], doing digital […] my Instagram was growing, I think I was at 13,000 at this point.”

Like Lina my respondents often refer to the idea of growing one’s number of followers. Growth is a sign of success, in keeping with capitalism’s growth imperative. Referring to a term he used during our conversation, I ask Paul what his “goals” are. He replies: “My goals is to be happy. Happy online. Find my happiness online […] But my long-term goal is to just make this grow. Numerical, followers-wise, I would love to set a goal of, okay, by the end of this year 20,000 followers would be amazing.” To grow one’s number of followers is to be “happy online,” as the influencer website growglow.com also suggests: to grow (one’s amount of followers) is to glow. A particular target is 10 K, the number at which Influencers can add a swipe-up link to their Instagram stories and generate more income. Florence explains: “10k, it’s just like a milestone. Oh my god, you hit 10k. […] with 10k there’s more scope for sales.”

Espeland and Stevens (2008) note that numbers both mark and commensurate. With the former they allow for identification and distinction, such as with a number on a footballer’s shirt. With the latter, they measure. Quantification involves both marking and commensurating. One’s number of followers is both a measurement and a mark.

Another key Instagram metric is the number of likes per posts. Indeed, having followers is one thing, another is getting “likes.” As Sarah (3 K in 2014; 25 K in 2019) already put it in 2014, comparing her 3,000 Instagram followers with accounts of “20,000, 100,000” followers: “I saw some people with lots of followers but they didn’t have as many likes on their pictures.” Bill (30 K) also explains: “if you see a post and it’s got 1400 likes, you think oh, I’ll go and have a look at that, whereas if it’s got three, then…”

The like button, represented by a thumb icon, was introduced by Facebook in 2009 for the platform’s users to express their approval of a post. When Instagram was launched in 2010, a similar affordance was built into the platform by way of a heart symbol. The like button immediately metrifies and intensifies “user affects—turning them into numbers on the Like counter” (Gerlitz and Helmond 2013, 2). A central affordance of platforms such as Facebook and Instagram, likes are stored in databases to be turned into revenue; they feed datafication and its attendent economic logic (Gerlitz and Helmond 2013; Veszelszki 2018). Many apps and platforms that collect user data are free because their commercial profitability resides in the commodification of the data collected, as is the case of the platforms known as GAFAM: google, apple, Facebook, Amazon, microsoft (Lupton 2016, 111).

Observing that the social web is “a recentralised, data-intensive infrastructure,” Gerlitz and Helmond (2013, 2) talk about a “like economy,” an expression that captures the entanglement between social media affordances, datafication and commodification. Likes allow platform providers to accumulate and commercialize insights into their users. They are also key to influencers’ chance to monetize their space by allowing them to evidence their popularity and their ability to create appealing posts. Social media’s logic of accumulation of likes and followers feeds into and is in tune with capitalism’ logic of accumulation.

The like economy partakes in the metrification of social interaction (Gerlitz and Helmond 2013, 15). It reduces individuals’ emotions to a single quantified value that brushes aside differences, nuances (Grosser 2014, 18) and the qualitative. When involving fashion posts, it does not give any information on the nature of the liking, or on the reasons why a product or image is being liked. As Espeland (2015, 65) notes, quantitative indicators are “technologies of simplification,” including of the readerly experience of fashion images, reduced, on Instagram, to a “quantifiable participation” (Hearn 2010, 422).

The fixation with metrics encourages gaming (Muller 2018), and influencers can artifically inflate their followers counts and likes by buying them or joining “a follower for a follower” and “a like for a like” WhatsApp and Facebook accounts. However, Instagram can identify fake followers and delete them from an account—as Paul puts it: “Instagram now, they’re monitoring growth and they know, they know.” Influencer marketing companies also use software they say allow them to identify fake followers, and sell the service to brand. Here fake followers are yet another opportunity for stakeholders in the business of influence to capitalize on (see Bishop 2021 on influencer marketing’s use of algorithmic tools for the “surveillance” of influencers).

None of my respondents said they bought likes or followers, but two respondents explained they take part or have taken part in “engagement groups.” Denis (5.2 K) explains:

You just follow each other and whenever you post a new photo you would share in that group chat, and then people would like and comment on that photo. So it’s really important for Instagram algorithms and comments and likes […] It helps, at least to maintain your engagement ratings. Because you always get that amount of comments and likes, you are kind of safe.

Becky (14 K), however, stopped being part of engagement groups because “Instagram can now realize and they’ll not ban you, but they will make you, like, not so visible and your engagement will drop.”

Becky and Denis’ statements draw attention to another key metric: engagement. Both my respondents and the influencer marketing literature insist on the importance of “engagement” in the evaluation of one’s success. Throughout his Influencer Marketing for Brands Levin (2020) insists on having a good “engagement rate,” which he defines as “total comments and likes divided by followers” (44). Influencerintelligence.com insists that: “an influencer could have millions of followers, but if their audience isn’t liking, commenting on or sharing the content, it is unlikely to have any real, positive effect on purchase or sentiment” (https://www.influencerintelligence.com/blog/lt/influencer-engagement-why-our-new-tools-are-a-game-changer).

According to Jay (24 K): “the significance really is engagement.[…] it’s probably more important now than the following.” This is why he wants to work on how to “make certain things a lot more engaging than not.” Caroline (11 K) sometimes tailors her content to her engagement rate: “I like an iPhone picture as much as the next person […] but I should also say that part of that is me trying to appease the fact that people like them more and it’s just really to keep my engagement rate at a certain level.” Eliza (18 K) “like[s] the engagement that I have now. […] I’m surprised when I see that accounts with maybe three, four times more followers, but they are getting very few comments or likes.”

Having a large number of followers might not be a priority for some influencers, but getting the right numbers, by way of a strong engagement rate, for instance, is something influencers monitor through various calculations. This draws attention to the calculating logic that inform fashion influencers’s presence on Instagram, and what could be called the arithmetics of influencing. The following statements by my respondents articulate the importance of numbers and calculation in one’s practice of the platform. Paul explains:

I think if it ever comes to the point where I think my engagement is dropping that bad – mine’s growing, but my followers slowly aren’t – so in terms of my percentage of likes to followers, it’s a good split but I know some people who have 50,000 followers and they struggle to get 200 likes.

As for Jay:

I’ve got 24,000 followers on Instagram […] I can’t control who’s following me, right? […] there are just bot accounts on Instagram that will just follow you, right? […] people that maybe followed me from five years ago, they might not even have Instagram any more […]. So I was actually genuinely thinking about going through my following almost every day for a week in the evening just to block and delete accounts that I didn’t think was real, because – not in a bad way – but I think you’re not doing yourself any service if you still have that number.

“Engagement” has become a significant concept not only in that it is used as a metrics for monetization, but also in that, and maybe precisely because, premised on capturing some sort of reactivity—by way of likes or comments—it taps into the ideal of interaction that has informed both Web2.0 and the rise of bloggers and influencers.

Furman (2018, 78) argues that “Engagement has become a vital element of the so-called ‘affective economy’ in public relations as well as marketing.” By “affective economy” he is referring to Jenkins's (2006) contention that a new business discourse has emerged centered on the idea that the emotional attachment consumers develop toward a brand or product is a key factor in their purchasing decision. This means that companies seek to create some sort of emotional attachment and social ties between goods or brands and consumers, who, through audience participation, become implicated in the process of brand valuation (Furman 2018). As Andrejevic (2011, 606, 612) notes, it’s not so much that the discourse is new but that it has intensified; with the proliferation of interactive media it has taken on some sort of “urgency,” with “emotional capital,” a marketing buzzword, seen as a currency, and brands more able than ever to harness consumer engagement.

But with “engagement” on social media referring to a number, the qualitative richness of one’s interaction with a media text is reduced to a quantifiable measure, with little insight into the nuanced texture and qualitative complexity of a user’s relation to images and words. Like “emotional capital,” “engagement” is a buzzword of the business literature, alongside other buzzwords such as “experience.” Companies’ imperative of extracting value and quest for profit is hidden behind the embellishing discourse of marketing and the pretense of privileging consumers’ and users’ quality of interaction with goods and commercial spaces.

Online platforms have proliferated that sell engagement tips, such as Metricool.com, for instance, who state that “By Instagram engagement rate, we’re talking about your follower’s loyalty level within this social network. It’s not about the number of fans that your profile has but about the degree of involvement, interest and interaction that your followers show toward your photos, videos, Instagram stories or any other content” (https://metricool.com/what-is-instagram-engagement-and-how-it-can-help-you/).

On platforms such as Instagram, where interactivity is an opportunity for monetization and the commercialization of the social, emotions and social ties are measured in terms of likes, comments and followers, and reduced to the quantifiable metric of engagement rate (or “degree” as Metricool put it), which influencers can capitalize on. As Gerlitz and Helmond (2013, 2) argue of the like economy, on such platforms “the social is of particular economic value, as user interactions are instantly transformed into comparable forms of data and presented to other users in a way that generates more traffic and engagement.” That is, following a Bourdieuian analytical framework, one’s social capital can be turned into economic capital (see, for instance, Bourdieu 1986). Data is capital that “is both valuable and value creating” (Sadowski 2020, 54; see also Sadowski 2019).

Not only are metrics tools influencers use to distinguish themselves and evidence their reputation, but they are also a currency they trade for financial reward when offering their services to brands (Rocamora 2023, Forthcoming). Metrics are symbols of status and authority, and have an economic value (Christin and Lewis 2021; Mau 2019). Likes, alongside one’s number of followers, are “a form of symbolic capital” (Grosser 2014, 11), which, like social capital, can be turned into economic capital (Bourdieu 1986), allowing one to secure further recognition and material gain (Mau 2019, 162).

Hearn (2010) uses the expression “reputation economy,” which draws attention to the economic value of online status symbols. The analytics and datafication logic that underpins the influencer economy must be situated within the wider context of the online “economy in reputation” that emerged in the first decade of the twenty-first century (Hearn 2010). In this economy, one’s reputation is a “digital reputation,” quantified and measured in likes, ratings and metrics, and turned into a currency (Hearn 2010).

Metrics are a constant of fashion influencers’ media packs, as both my respondents and the literature on the business of influence indicate. Talking about pitching to brands, Florence explains: “include your media kit so they have an idea of your engagement rate, they have an idea of the amount of followers you have, what’s your platform.” Online resources abound that guide influencers toward putting such kits together, insisting on stating “social stats,” as Later.com (https://later.com/blog/influencer-media-kit/), for instance, a “marketing platform for Instagram,” brands and influencers put it in their media kit template: “While there’s no hard and fast rule on what stats to include in your influencer media kit, it’s a good idea to include your followers and engagement rate on Instagram.”

Alexa Collins—“a full-time influencer with 1.2 million Instagram followers and over 400,000 fans on Tiktok”—tells businessinsider.com that she has “a pitch deck with her latest audience numbers” as it “saves time when negotiating with brands.” She puts it thus: “We don’t have to go back and forth in 20 emails to discuss all my stats […] It’s just right there in my file.” Her “about me” section “showcases her top-level audience numbers,” cue a picture of Alexa alongside said statistics (https://www.businessinsider.com/instagram-influencer-shares-media-kit-pay-rates-1-million-followers-2020-11?r=US&IR=T). In 2010, Independentfashionbloggers already insisted that “your media kit” should include “your stats,” writing “it is important to use a reliable and trusted stat tracking platform like Google Analytics for this data” (https://heartifb.com/media-kit/).

My respondents often refer to “my stats” and “my/your numbers.” Talking about her loss and gain of followers, Emma (21 K), for instance, explains, in a statement which also draws attention to the arithmetics of influence:

I lose about 50 a day and I gain about 30, whereas when I was growing I was probably gaining 300, losing 100 a day. Like so many people unfollow you. Even when I was growing massively, I would feel – my stats, I have an app and it tells me my stats for the year – so since the beginning of the year I’ve lost 12,000 followers, but I’ve actually gained.

Monica’s (49 K) media kit has “a bit about me and my background, followers, who I’ve worked with. My stats, I include the following. […] The brands will sort of think about followers, but really engagement is more important.” Similarly Jay explains, talking about his media pack: “it kind of adds a bit more weight to, like, the work that you’ve done in the past and your numbers, I guess, to kind of solidify that you mean business, basically.”

“My/your stats,” “my/your numbers” are common expressions in the discourse of and on influencers. It combines the ideas of identity, ownership, and numbers, producing and naturalizing the idea of the self as a quantifiable and quantified entity. It normalizes the notion that one’s practices and experiences can be converted into and made sense of with numbers, outside of any knowledge on qualitative context. Alongside terms such as “likes,” “followers,” “engagement,” or “traffic,” it points to the language of the business of influence as one articulated along the lines of quantities. It is a language by numbers.

Metrics are key components of the business of influence and its production of value and profit, a process captured in expressions such “like economy” and “reputation economy.” They are part of the quantification of attention that characterizes the commercialization of online interactions, and which the business of fashion influence feeds into, further contributing to the quantification of fashion and the datafication of everyday life. One’s value is generated and evaluated through “quantifiable participation” in online networks and conversations (Hearn 2010, 422), whilst users “are made legible as an asset through their monetization as ‘attention’ or ‘impressions’,” captured in metrics (Birch, Cochrane, and Ward 2021, 4).

The quantified self

The datafication logic that informs the influencer economy can be seen in light of the notion of “quantified self,” a term Wired editors Wolf and Kelly coined in 2007, initiating it also as a movement (Lupton 2016). The “quantified self” refers to the use of “numbers as a means of monitoring and measuring elements of everyday life and embodiment” through practices of self-tracking (Lupton 2016, 16).

Individuals have been tracking their practices since ancient time but in the 1990s and 2000s, and with the introduction of new technologies and digitization, this has taken on new forms, leading to an expansion of the domain of self-tracking (Lupton 2016). Large facets of one’s life and bodily functions are turned into digitized quantitative data, that is, one’s life becomes datafied. Individuals can now track their steps, their mood, fitness, personal health, amongst many things, and this includes the gathering of personal informatics and analytics through wearable digital devices.

Since the 1990s various companies have experimented with wearables, developing ways of tracking users’ emotions and bodily sensations. Apple, Hermès, Philips, Misfits, Ralph Lauren, Nike, Swarovski, Diana Von Furstenberg have all experimented with wearables, not least since self-tracking is “big business” (Wernimont 2018, 96). A recent example includes Facebook’s collaboration with Ray-Ban to create glasses that take pictures for sharing on social media, which, of course, raises alarm bells given Facebook’s track record in poorly protecting the privacy of its users (Isaac 2021). For wearables are yet another opportunity to collect user data with a view to commercializing it, a process with little transparency and accountability (Barile and Sugiyama 2020, 223; Zubow 2019). Wearables are also an instance of the many ways the fashion industry engages with data collection, and so one instance only, of the rempant datafication of fashion (Rocamora 2023, Forthcoming).

The quantified self movement is a particular iteration of the datafication of the self, and of the value attributed to quantified data for practices of the self. Influencers’ reliance on quantified data to define themselves and conduct their activities can be seen as a practice of self-tracking too, and which, like all such practices, reduces “the self to a quantity by turning personal identity into nothing more than a statistical reading, at the expense of the qualitative, subjective, and otherwise unquantifiable dimensions of life” (Mosco 2017, 101).

Thus, Paul insists: “You’ve got to track some analytics. I have a little tracker on my computer of where I was and where I am now and where I potentially will be in terms of followers.” Similarly, Emma explains: “when I open Instagram in the morning when I wake up, I check the stories, well, I go on, I check all the likes I’ve had in the night and all the followers and any comments that have come through.” She adds:

I have emails that are sent to me that tell me all the stats, where people live, what their age is, how they found me, all these sort of things. Like I love looking at stats and I think that’s really important. […] I’ll look at what my top nine images are, have been in the six months and go, oh, mostly are always on the outfit posts, so let’s carry on doing that. And what are my worst pictures, and I’ll go, right, I won’t do any of those pictures again.

The quantified influencer self is also that of the “quantified worker” (Moore 2018). Moore developed this notion to shift away from the existing scholarly focus on the quantified self as consuming self, such as in Lupton’s work, toward the idea of quantified self as “working self,” hereby drawing attention to the need for more research on the digital quantification of labor practices (Moore 2018; see also Christin 2020 on the metricization and quantification of the work of journalists). Looking at the field of fashion influencers through the lenses of datafication and quantification is part of this project of attending to the issue of quantified labor.

In a context in which “quantification is increasingly used to capture new avenues of labor,” metrics are a form of “data in the workplace” (Moore 2018, 36, 8). For fashion influencers, this is the workplace of the social media platform interface, with the mobile phone acting as a tracking device for the working self, including the amount of hours one spends on Instagram. As Nadia (11 K) observes: “I have a tracker [on her phone] and it tells me if I’ve gone beyond two hours, which I would say happens most days’. On Instagram, as in the ‘digitally quantified workplace” Moore (2018, 3, 121) discusses, cultural production follows the capitalist logic of rationalization through quantification, including of a self in pursuit of status, and subject to the “quantified gaze.”

An important characteristic of quantified labor is the precariousness it subjects workers to; they are “now under extreme pressure to both work with and against machines in an environment where data produced by machine captures all-of-life to serve capital” (Moore 2018, 11). In the field of fashion influencers, it is the precariousness endemic to free-lance work and creative labor (Duffy 2017; Rocamora 2018) but it is also the precariousness pertaining to depending on a private platforms whose key logic is an algorithmic logic, contingent on numbers, and behind the control of its users (see also Duffy et al. 2021).

In 2016 Instagram stopped showing posts in reverse chronological order. The platform moved to an algorithmically-led flow of content. The grid started displaying and privileging what the algorithm deemed of most relevance to the user. With little transparency from Facebook as to the way it works, the new algorithm and its subsequent iterrations are an unpredictable formation which fashion influencers have to work with, or rather around. Many of my respondents have expressed their puzzlement at the algorithm, reflecting a feeling of dismay widely shared by fashion influencers across digital platforms (see also Duffy et al. 2021). Emma, for instance, states:

My first year at uni [2013] I was on like a few thousand, it hit 10k maybe a year and a half ago. I’ve not grown much this year at all, I hit 20k in February and it’s not gone up much since. But I grew quite quickly quite soon, before the algorithm changed and ruined everyone’s lives.

Joe (271 K) talks me through his posts:

So this got 15,700 likes, which is good, I was very happy with that. It reached 66,000 accounts. So looking at that, this has reached 66,000 accounts and got 15,000 likes. I’m like, that’s amazing. […] That’s a lot of engagement for who saw it. But, I have 270,000 followers, so Instagram only shows it to 66,000 accounts. […] The algorithm is based on like interactions now. So it’ll only show it to people who it thinks wants to see it. [laughs] Right? So, and I have no control over that. […] but that’s what’s confusing to me because like the more it’s engaged with, I expect it to show it to more accounts.

As Vicky Rutwind also writes on her fashion and travel blog: “Raise your hand if you’ve felt personally victimized by the new Instagram algorithm of 2020. You probably raised your hand, right? We’ve all been there” (https://fashiontravelrepeat.com/new-instagram-algorithm/).

In the above statements, the instagram algorithm is depicted as an active agent in practices of cultural production, which points to its power as a player in its own right in the fielf of fashion (Rocamora 2023, Forthcoming). A September 2020 post by UK-based fashion influencer Pascale Banks draws attention to this “algorithmic power” and the “threat of invisibility” (Bucher 2012) influencers on Instragram are subject to. She justifies showing an image she has posted before “as Instagram decided to hide me yesterday.” To a follower who asked “how did you find out you were being hidden,” Banks responds, the use of the passive tense drawing attention to her lack of agency: “it’s sorted itself out now I think but last night lots of people, me included were getting about 5 likes in an hour… which is not normal… unless people just hated my outfit.” The post eventually garnered 650 likes.

Fashion bloggers and influencers are often ridden with an anxiety that characterizes the precarity and uncertainty of much creative labor, and especially as taking place in the platform economy (see Rocamora 2018; Duffy et al. 2021). This anxiety is compounded by the pressure numbers exercise over online workers such as fashion influencers (see also Duffy 2017, 140–151). Numbers invite comparison and are instruments of neoliberal competition (Beer 2016); through their ability to commensurate, they are used as comparative measures (Beer 2016), putting pressure on influencers to get the right number and/or bigger numbers.

Talking about his early blogging, Jack (23 K) puts it thus: “it was just so exciting at the time and I didn’t really look into the metrics of anything, it was just fun.” Joe explains:

I feel like I have mid to good engagement for my account for menswear, because I obviously look at other people in the same area as me and compare, which I shouldn’t, but I do. But I think that my likes are kind of relative to my kind of account size. It’s changed so much over the years though. I remember when I first started posting my outfits I was like, if I get 100 I’ll be happy. And then it changed to like 1,000. And I was like, forget 1,000, I’ll be happy with… and then it was like 3,000. […] at the moment it’s 10,000. If it gets to 10,000 I’m like, that’s okay. [laughs] But if it’s like eight, I’m like… ooh. But I have to take a step back and be like, 8,000 is still a hell of a lot of people to engage and that means many more people have seen it.

Paul observes that: “as the audience grows, the pressure grows, and it’s very scary. You think, oh my days, okay, 13,000 people, 20,000 people, 50,000 people have seen my posts now, oh my days, it has to get better.” He adds:

People say the more you take time off Instagram, the harder it is to get back. My friend took a week off for moving, came back and he said his engagement halved. Yeah, which is savage. […] If you’re not on it, they will eat you. He used to get two and a half, 3,000 likes per post, he grew followers, 15,000 followers, he was getting that amount of likes, and now he has 20/21,000, half that. He gets the same amount of likes that I do. It’s crazy. It’s a race.

In 2019, presenting it as a way of alleviating social media peer pressure Instagram started experimenting with hiding likes from a feed. The amounts a post received would still be visible by Instagram account holders, but not by their followers. James (187 K) wellcomed the option, drawing attention to the pressure the competition for likes can exercise on influencers:

It is kind of like competition of how many likes you will get. And that’s where not showing the likes is coming and I’m hoping that it’s coming from a good cause from Instagram […] it’s realised the mental health that they’re leading, the likes or the engagement has become a filter of success.

Becky also supports the idea of hiding likes:

I feel like we all stress over these likes […] So for me I just think, I wouldn’t mind, because I do stress about, sometimes you’re like, especially for us, the stress for people to like something. I don’t know. All this stupid confirmation of your looks was just crazy, right?

Other respondents were more ambivalent, fearing that hiding likes migh have an effect on their like counts (which they could still be expected to show through the platform’s backend to the brands they might work with), and on their engagement rate. The anxiety is not alleviated, simply displaced from a focus on likes, to one on engagement, from one metric to another. Lina explains:

you’ll still be able to, as the publisher, see the likes. So if brands wanted to see what your engagement is like, you’d be able to show them. But, if you remove likes, I think people won’t like as much, because it’s not shown, you know? […] I think engagement will drop. […] engagement dropping might be a bit of bad news for influencers. Because engagement is how you determine most of the time if a brand wants to work with you and how much you charge.

Sarah (25 K) talks about the stress she’s been experiencing, and which involves constantly checking her phone: “say with the likes […] we’re used to expecting likes and that being a metric and now Instagram will potentially remove likes.” When I ask if she feels it’s a positive move, she says:

it will be interesting, although I’ve seen apparently in Australia likes have decreased by 20% […] for most people engagement has dropped. My most successful posts are all in the last year so […]. But then, yeah, I don’t really know how it works for me. It’s just very hard to keep the consistency up with Instagram although I’ve had some really good posts. Maybe there’s other posts that can average that amount of likes. […] you have to create things that are a bit more engaging, worthwile.

In 2021 Instagram made it possible for account holders to show or hide like counts. A random analysis, at the time of writing, of fashion influencers whose work I have been following in recent years suggests that a small portion only has opted for hiding likes. Of all the influencers I interviewed only James was hiding his like count.

Quantifying the influencers

The quantification and datafication of the practices of fashion influencers have been supported and intensified by the rise and proliferation of businesses that have capitalized on their activities. Influencer marketing in particular has become an economically significant industry. According to the LA Times writing in 2021, it “will command about $12 billion this year in the US and closer to $30 billion globally” (September 23, 2021). Quantification, categorization through numbers, and the generating of data analytics is a noticeable dimension of its business practices and discourse (see also Rocamora 2023, Forthcoming), starting with the categorization of influencers on the basis of their follower count.

“Nano,” “micro,” “macro,” and “mega” have become common ways of classifying influencers along the lines of numbers, influencer marketing companies sometimes differing as to the exact quantities those categories refer to. For Influencermatchmaker, for instance, nano influencers have under 10,000 followers; micro influencers between 10,000 and 50,000 followers; macro influencer 500,000 to 1 million followers; and mega influencers more than one million followers. They also have a “midi” category, to include influencers with between 50,000 and 500,000 followers (https://influencermatchmaker.co.uk/blog/difference-between-nano-micro-midi-macro-and-mega-social-media-influencers). Here, influencers are not defined through qualitative criteria, but solely on the basis of a quantitative metric. Irrespective of the content they post, of the qualitative differences and singularities of their grid, of its esthetic characteristic, influencers are aggregated along the lines of numbers. Influencer marketing companies also relie on algorithmic software to rank and evaluate the work of influencers (Bishop 2021), further embedding the logics of datafication and quantification in the business of influence.

This categorization by quantification allows for a standardization (Espeland and Stevens 2008) of the field of influencers, which in turns facilitates commodification and commercialization. For, through a segmentation of the field of fashion influencers, business opportunities are generated, market segments are created. “Nano,” “micro,” “macro” and “mega” have a performative quality that like all practices of naming creates the reality it purports to describe (Bourdieu 1993). As Espeland and Stevens (2008, 403) put it, drawing on Austin’s idea of the performativity of speech acts: “Numbers often help constitute the things they measure by directing attention, persuading, and creating new categories for apprehending the world.” Here, direction is directed toward apprehending the field of influencers as a market rife for business opportunities.

Numbers and quantitative indicators “create a field of action making some relations between people, institutions, and materials possible, and other relations less possible” (Nafus 2014, 208). They are the relations, for instance, that bring together brands, influencers and marketing companies on the basis of particular numbers, and make their commercial transactions possible.

The performative quality of numbers also resides in their authority and power of persuasion (Espeland and Sauder 2007). Indeed, quantification and datafication are premised on what Porter (1995) calls in his eponymous book Trust in Numbers. It is a trust, in the Western world, inherited from the “ethic of measurement” that emerged in the late eighteenth century, and consolidated, in the nineteenth century, with positivism and attendant values of objectivity, scientificism, and standardization. Measurement and quantification became seen as integral to achieving those values (Porter 1995). The “ethic of measurement” is informed by the belief that numbers, as Mau (2019, 13) puts it, “are associated with precision, one-to-one correspondence, simplification, verifiability and neutrality.” As Fashion “data and technology company” Launchmetrics put it on their website: “Data and technology bring a sharp focus to profitability, accountability, and efficiency while enabling the type of quick decision making required for agility.” “We know data” they state, hereby also asserting their authority in the field.

With influencer marketing now a crowded market (Mondalek 2021), companies compete for the truth in numbers, making data not only a rhetorical tool they can draw on to sell their services but a commodity too. Influencer.com, for instance, state that they are able to measure “the impact of influencer marketing”:

By looking back over years of campaign data, we’ve been able to ascertain the relative value of different engagement metrics across different social networks - and, by applying a weighting to these metrics, we can show the value of engagements and balance out the volume. […] By applying these weightings across every engagement available, we can ascertain the true value of an audience’s engagement with a piece of content - and so define the impact that content had. (https://www.influencer.com/post/measuring-the-impact-of-influencer-marketing)

“All-in-One Media Intelligence Platform,” Meltwater.com contend that: “With millions of profiles, tens of thousands of categories, and a years worth of historical data, Meltwater’s influencer search platform is one of the most powerful and sophisticated available” (Meltwater 2020). They too claim they can measure influencer programs: “Quantify your campaign performance through beautiful reports that prove your success. Automatically track your influencers” mentions in real time, measure aggregated mentions, engagements, true reach, and return on investment.’ In a similar vain, U.K. based influencer marketing company Open Influence claim: “Data informs our every decision, from creative ideation to execution. Our platform crunches the numbers and unlocks creative and strategic insights that elevate campaigns from super to superior” (https://openinfluence.com/).

Companies such as Influencer.com and Meltwater compete for the truth in numbers in the business of influence. They are part of “the business of influence metrics” that developed in the second decade of twenty-first century with platforms such as Klout, Kred or PeerIndex, and claimed to be able to evaluate someone’s influence, captured in a score (Gandini 2016, 38). Their discourse and practices is underpinned by a “metric ideology”; the belief that what can be measured can be improved or fixed (Muller 2018). This is also what boyd and Crawford (2012, 663) refer to as the mythology of Big Data, that is “the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” Van Dijck (2014) talks about dataism; the belief that data speak for themselves and can forecast the future (Kitchin 2014, 171/285).

The quest for the truth in data analytics is, in Bourdieuian terms (see, e.g. Bourdieu 1993), a quest for authority, and therefore an object of struggle for companies to assert their position in the field of influencer marketing. As Bourdieu (2015, 36) notes “when the issue of ranking is raised, the issue of authority is at stake.” The importance of claiming to have access to data and the best way to collect and analyze it reveals data and data analytics as both object of struggle and symbolic capital companies can bank on. As Leistert observes: “Among the many phenomena that emerged within these new algorithmic regimes is the struggle over collected data, and how and by whom data may be exploited” (2016, 160). This is true of the influencer marketing and data analytics companies that compete, in the field of influencers, for a dominant position.

In that respect, the claim to mastering numbers is also a claim to mastering the reality they are said to be referring to. Influencer marketing company use data to assert their authority but they also assert themselves as an authority in data. Numbers lend the promotional discourses of such businesses an air of scientificity. Bourdieu (2015, 41) reminds us of the strength of scientific discourse: passing as neutral and universal, it pretends to “witness” only, which obscures the fact that it is performative and has “effects of imposition, effects of intimidation, of symbolic bluff.”

This symbolic bluff is often supported by the use of colorful graphs and tables that contribute to “the spectacle of Big Data” and its rhetorical and ideological work (Gregg 2015, 42; Kennedy and Hill 2017). The visualization of data through elaborate charts contributes to producing trust and truth in numbers, to the myth of big data, as well as to its performative function. They contribute to the “beautiful reports” Meltwater promotes on their website, as mentioned above. As influencer marketing company tanke.fr, for instance, also write of their marketing services: they are “Visually appealing AND validated by data” (their emphasis, https://www.tanke.fr/en/.

Conclusion

In this article, I have approached the field of fashion influencers through the conceptual lenses of datafication and quantification. I have discussed the pervasive presence of metrics in the practices and definition of fashion influencing, commenting on their role as instruments of financial and symbolic accumulation as well as of the quantified working self. The datafication of fashion as taking place in the field of fashion instantiates neoliberalism's trust in numbers and the accumulation of capital it promotes.

At a time when datafication is becoming increasingly pervasive across everyday life, it seems important to interrogate this development and identify its many iterations and impact in the field of fashion. What are the implications of datafication on creativity and cultural production in this field? In what ways do data and quantification structure the practices and experiences of, for instance, designers, marketing managers or fashion journalists? What skills do fashion players need to thrive in a field informed by data and numbers? These are some questions which scholars could turn to to advantage when investigating the datafication of fashion, and the better to understand the field’s contemporary formation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Agnès Rocamora

Agnès Rocamora is a Professor of Social and Cultural Studies at London College of Fashion, University of the Arts London, London, United Kingdom [email protected]

Notes

1 I interviewed influencers who post on/for various fashion styles and markets, and with anything between 5.2 K instagram followers, up to 271K. When I first quote them I specify in bracket their amount of Instagram followers at the time of the interview. The interviews lasted between 1 and 2 h. The participants have been anonymised and given a pseudonym.

2 Throughout my many years of research on bloggers and influencers I have navigated and been immersed in a vast internet space. I have archived and analysed a large amount of online articles and posts on blogging and influencing. Prolonged weekly if not daily visits of web and social media pages have allowed me to develop a familiarity with the field of fashion influence as present and represented online, and map its structure. I have grown familiar with the different categories of players it is made of – such as influencer marketing websites - and related discourses. This is a familiarity with one’s terrain of analysis akin to the familiarity ethnographers need to develop through immersion in their site, such as an on online space (Kozinets 2020). I have been able to identify recurring values and meanings (including on datafication), or what could also be called, following Foucault (1989), statements. Indeed, my analysis of the texts I have collected is informed by the French thinker’s notion of discourse (Foucault 1989): I approach off and online texts as sites of discursive construction, that is, as sites of production and circulation of the objects (data) and subjects (influencers) of which they speak. I have traced the statements that are repeatedly uttered across texts to form the truths and beliefs that I discuss in this article.