Readers, Families, Users and Mobs
Readers, Families, Users and Mobs
Beyond data, how do we imagine our audiences?
Theo Wangemann (center), phonograph recording in Edison Laboratory Music Room, circa May 1905. NPS image 29-430-003. (Credit: National Park Service)
Who do I think you are?
When I sit down and start typing into a keyboard, I am writing for at least two groups. The first is for myself - I’ve had a bunch of ideas gradually taking shape in my head, and now it’s the frustrating process of getting the words in the right order.
The second is - well, who exactly? I’m writing this as a Substack newsletter, so I have some stats about how many of you will get it (nearly 200 at the time of writing) and how many will read it (about 70% - which is good, but that other 30% kills me). I recognise some of the names on the emails, but by no means all of them. I know some of you signed up from other newsletters, so can guess at your interests or jobs (digital, arts, media - that sort of thing).
But who are you? Or more precisely, who is the *you* in my head as I’m writing this? Do I have a clear idea of who you are, and what will make you interested enough to keep reading? As I edit and rewrite, who is the ideal reader that I am working for? And why have I imagined you in this way?
The word ‘audience’ has the same latin root as ‘audio’ - audentia - and literally means ‘people within hearing distance’. I spoke at a media event in Munich last week, and even though I was speaking in English, I could see the immediate response from the audience - a few nods here and there, a few checking their phones, a good smattering of applause at the end, and (always the most gratifying thing for a speaker) a few people coming up afterwards to ask questions.
But if you can’t see the audience, you can’t get this immediate feedback. In last week’s post I talked about how Arthur C. Nielsen and George Gallup pioneered ways to measure invisible audiences and turn them into data. This week we’re going to look at the other side of the coin - how we turn that data back into (imaginary) people.
When I worked at the BBC in the 2000s, I went to an away-day organised by the Audience Insight team for senior management. This was part of a move to get BBC leaders closer to their audiences, so they had researched a diverse sample of the British public and filmed them answering questions about their hobbies and media habits. We then had to work in groups to think about what BBC content they would like, and took it in turns to feed this back to the group.
Then the Audience Insight team announced a surprise - the people they interviewed were here, in the same room, and we were going to get one of them assigned to each group so we could find out more about their media consumption.
You could almost feel the air being sucked out of the room as a couple of hundred BBC top brass gasped. The audience was actually here? The people who listened, viewed and clicked on our stories? In the same room as us?
We were given a ‘gamer’ for our group - a nice enough guy in his early 20s who very politely answered the questions of the dozen or so media execs on my table. Yes, he did play games for a couple of hours a day. No, they weren’t all about killing things. No, he didn’t play alone - his friends would come round for the night. Yes, he did find time to watch a bit of television as well. Yes, he did prefer gaming to television. No, he didn’t think that was weird.
The reaction in my group was almost as if an alien had landed. I realised that behind the ratings, media execs had all sorts of assumptions about who their audience was, but none of them were anything like these real people, in the room, with us.
Last week, the Columbia Journalism Review published an excellent research project from James G. Robinson on how journalists imagine their readers. Robinson suggests these imaginary audiences fall into four different groups - their peers; family and friends; their sources; and an ‘institutional audience’ - a kind of archetype created and passed on through the history of the publication they were writing for.
The last one is probably the most recognisable. If you think of a publication you know - like Wired, the New York Times, or Vogue - you can quickly summon a picture of who their typical reader might be. Sometimes these imaginary archetypes are based on actual research, and sometimes they accrue over generations. Robinson shares the story of the New York Daily News, who in the 1920s sent a young researcher to the Lower East Side to find out more about their working class audience. The marketing department synthesised his research into an archetype named ‘Sweeney’ who quickly became a shorthand for their editorial direction, and the focus of a trade advertising campaign with the slogan ‘Tell it to Sweeney!’
These institutional audiences become self-reinforcing. They are passed down to new staffers, as they read the journalism of their peers and subtly adjust their writing style for the conventions and imagined audiences of their predecessors. Sometimes these audiences are based on demographics, and sometimes they’re based on the newspaper’s mission or values. But they’re not the only imaginary audience in play - at any one time there are multiple imaginary audiences in the same organisation, including editors, sources, peers, competitors, and the writer’s own friends and family. This last one is particularly influential - Robinson quotes an editor at a Southwestern US paper saying:
“we’re all kind of the same age … [and] an awful lot of those folks are just now having kids. You can test this. Pick up a paper; there are plenty of stories about how to raise 3-year-olds.”
This anecdote will feel familiar to anyone working in the creative industries. We might get incredibly sophisticated data about our audiences and their desires, but a lot of the time we tell the stories that we think people like us will find interesting. This might affect the career choices we make as creators - would you join a publication or broadcaster if your own interests were wildly out of sync with the ‘institutional audience’ of that organisation?
Sometimes, these imaginary archetypes work across whole sectors, not just individual organisations. In the US TV sector, the ‘Nielsen Family’ is shorthand for the entire ratings system. The Nielsen family is an almost mythological beast, with the power to make or break TV shows with their attention. The phrase comes from the fact that TV ratings are measured by ‘households’, with multiple viewers in each household called a ‘family’ - Nielsen’s own website talks about the process of joining their sampled TV panels using the term ‘Nielsen Families’.
This is a very loaded term. It feels like a very traditional, analogue idea of how people watch TV - mom, dad, 2.4 kids, on the sofa watching linear TV at the same time. The phrase ‘Nielsen Family’ has a cosy ring to it, but the process of who gets chosen to be a Nielsen Family is wrapped in mystery, although in reality there’s dozens of accounts of what it feels like to be a Nielsen Family around the web, and Nielsen’s site has instructional videos with interviews from past Nielsen Families.
Criticism of Nielsen ratings has often been about how representative these imaginary families are of actual US audience behaviour. Is the multi-billion dollar TV industry really driven by a few thousand families (each Nielsen family represents around 50,000 US households), some measured by electronic meters, and some filling in diaries to record what they watch? Do they actually measure how people watch TV now, and are diverse or more transient communities (eg student dorms, prisons, etc) under-represented?
It’s hard to overstate how much the archetype of the Nielsen Family has influenced TV commissioning in the last fifty or so years, but this is now starting to change. Netflix famously doesn’t reveal ratings for its shows (with a few exceptions), and their commissioning decisions are based on analysis of its huge amount of viewer data. With every click and view tracked digitally, Netflix has no need for the sampling techniques of Nielsen’s Families.
But I’d imagine there are new archetypes developing within Netflix to describe their audience. Perhaps they are closer to fandom archetypes built around talent and story IP, or audience behaviours rather than demographics. Maybe Nielsen talks about ‘first day series-bingers’, or ‘weekend movie-marathoners’ rather than ‘Nielsen Families’?
Archetypes exist for imaginary audiences in pretty much every creative industry. Even in sectors that are built around digital delivery and data, we still need to think and communicate using archetypes. Perhaps the most influential archetype in digital design for the last few decades - ‘the user’ - is one that seems to run against the idea of archetypes altogether. Despite that, it has become as powerful an archetype in digital design as ‘Nielsen Families’ were for the TV industry.
When I was at BBC New Media in the early 2000s, we used to talk about the ‘former audience’, a term coined around 2003 by US tech writer Dan Gillmor to describe the rise in citizen journalism. It was a provocative phrase, meant to shake traditional BBC execs out of their imaginary archetypes of passive audiences slumped in front of the TV.
At the same time, software designers were using the term ‘user-centred design’ to describe practises that foreground the needs of users in software design, rather than the brand or client commissioning the work. This came out of Don Norman’s hugely influential book ‘The Design of Everyday Things’, that urged designers to think about the experience of a product as well as its aesthetics.
User centred design processes emphasise the role of research into user needs at the beginning, and throughout, the design process. Unlike the commissioning decisions of newsrooms and broadcasters, user-centred design processes aim to route around our hunches about audiences’ needs, insisting instead on data and direct audience research.
Some of my colleagues at the BBC in the early 2000s later set up the ground-breaking Government Digital Service in the UK, that shifted the culture of digital services in government away from the politics and needs of civil service departments towards the needs of the end users. Ben Terret, the Head of Design at GDS, argued strongly against using any archetypes for imaginary users, even the design personas that are often used in the design process:
“Someone had put some persona posters up and I got angry, saying that the whole country were our users, not some neat persona. I ripped a hole in that piece of paper and stuck it in the window.”
There’s a really interesting duality in Terrett’s quote - he rails against the way that personas create archetypes that obscure a more detailed understanding of user needs, but in saying ‘the whole country’ he suggests an even larger archetype. How on earth can you think about the user when your audience is so huge? The hole in his poster shows how difficult this is - in any 10 minute period, the ‘users’ passing through by the window will have included an incredibly diverse range of ages, genders, backgrounds and needs.
The trouble with the ‘user’ as an archetype is that it is singular. User-centred design usually focuses on an individual person’s interactions with a closed system - the archetypical user story structure uses the first person to describe their needs:
“As a <type of user> I want to <achieve a goal> in order to <reason for that goal>”
But user needs exist in a much more complex social system, and user stories can struggle to capture this. We’re starting to see how digital products designed around singular users can meet those individual needs, whilst creating huge issues for broader societal groups. The algorithmic newsfeeds of Twitter, Facebook and Youtube might help us see more content that we like, but they also enable bad actors to quickly spread misinformation. The UK charity DotEveryone has produced resources to help software designers address these consequences, but the reality is that a lot of software designers focus on the needs of an archetypal set of users or personas, not the more complex interactions of a community, society or country.
Perhaps the archetype we should be using for our current era is not the user, but the mob. The word ‘mob’ is a highly charged one, but it comes closer to describing the unpredictable swirling mass of audience activity and algorithmic amplification that underpins our digital culture.
Bill Wasik’s book ‘And Then There’s This’, written in 2010, is a fantastically rich and immersive account of what it feels like to create online mobs. Wasik was an early participant in the contagious media challenges set up by Jonah Peretti when he was at the NYC digital art organisation Eyebeam. Researching how content went ‘viral’ led to him starting his own ‘Mob Project’, emailing invites anonymously to friends inviting them to participate in a happening in a random downtown NYC location. The events quickly spread, and Wasik’s accounts of the spiralling flash mobs perfectly capture the almost visceral experience of seeing something you’ve created taken over by a fast-paced, hyper-connected audience, and what it feels like when you’re no longer in control:
“As the media frenzy over the mobs grew, so did the mobs themselves. For MOB #4, I sent the mob to a shoe store in SoHo […] I was astonished to see the mob assemble: as I marched with one strand streaming down Lafayette, we saw another mounting a pincers movement around Prince Street from the east, pouring in through the glass doors past the agape mounts of the attendants, perhaps three hundred bodies, packing the space and then, once no one else could enter, crowding around the sidewalk, everyone gawking, taking pictures with cameras, calling friends on cell phones (as the instructions for this mob had ordered), each pretending to be a tourist, all feigning awe - an awe I myself truly felt - to be not merely in New York, but so close to the center of something so big.”
Wasik’s book is only slightly coloured by the negatively possibilities of the mob. The experiences he described are from an earlier, pre-lapsarian era of the social web, when hyper-connected mobs could still be imagined as progressive forces, rather than as vectors for trolls and electoral hacks by nation states.
But it is a hugely valuable book for understanding what it feels like to try and design for the mob, to have the mob as your ‘imaginary audience.’ The mob is not a passive reader or isolated user. The mob is a moment in time, an energy that captures and accelerates the actions of a crowd in a way that is powerful, but ultimately outside of its creators’ control. To design for the mob, you need to think about consequences as much as needs.
The archetypes we use to describe our audiences define what kinds of stories are possible, whether you’re writing for the institutional audience of a newspaper, a Nielsen Family, or a user. These archetypes are how we turn data back into people, how the ‘ghosts’ of our invisible audiences become real to us again.
Ideally, they should be able to capture as much diversity as possible - like the hole in the poster on Ben Terrett’s window. But in reality, archetypes end up focusing on what is politically, economically or culturally most important to the people creating them.
In the end, like all metrics, they are mirrors of the organisation as much as they are the audience. It is only rarely, as in the BBC workshop I attended, that we manage to break through the archetypes and see the mob as real, living people instead.