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How IRN-BRU made its first million (YouTube views)

Abstract

IRN-BRU commissioned a new commercial that was distributed via a single tweet linking to YouTube before being broadcast. This article examines the way the link was distributed across Twitter and the impact that different networks of followers had on views of the ad, as well as the contribution made by TV in helping the commercial to gain over 1 million views online.

Background

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From a single view to a million-plus

IRN-BRU had commissioned a new commercial. A YouTube link to it was given exclusively to a single fan of the brand. Using the Topsy API, the brand's digital agency, Blonde, then watched what happened next: the film racked up over 1 million views in less than a month; the link was shared across Twitter; and three very different network dynamics drove that sharing.

  1. 1

    A few high-influence accounts — this Twitter dynamic coincided with 100,000 YouTube views within 24 hours of launch.

  2. 2

    Lots of small, connected groups — this was the dominant Twitter dynamic as YouTube views increased from 100,000 to over 650,000 in the next 3 weeks.

  3. 3

    Lots of isolated mentions — 3 weeks after the initial tweet, the ad was aired in a few high-profile television spots during the European Football Championship. This generated an additional 300,000 YouTube views in 48 hours. Moreover, it triggered hundreds of additional, isolated mentions of the link on Twitter.

At the time of writing, this IRN-BRU film had received over 1.5 million YouTube views.

Awesome content

The new commercial was the brainchild of IRN-BRU's agency, Leith. It is hard to predict the word-of-mouth (WOM) performance of branded content in advance, but the digital agency ‘knew’ from the moment it saw the script that this was going to be special. IRN-BRU and ‘Fanny’ were as close to a sure social media thing as it gets.

That belief and confidence led the agency to be more adventurous with its digital seeding and amplification strategy than usual. Belief in the awesomeness of the content gave strategic options. Rather than going in with all social media and PR guns blazing, it was decided to give the new ad to a single, randomly selected fan and see what happened.

Meet Rachel …

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One lucky winner to launch the ad

One IRN-BRU fan would be given exclusive access to the YouTube link for the new ad. And they would enjoy the 15 min of internet fame that came with sharing that link with the world. To be selected for this honour, they simply had to enter a lottery competition. The lottery itself was launched with a one-word tweet — ‘IRNBRUFanny.com’ — via the Twitter channel @NewFromIRNBRU. (At this point, we understood the Fanny reference but no one else did).

IRNBRUFanny.com was a landing page on the IRN-BRU website. It announced that one lucky fan would launch the new IRN-BRU ad and invited people to enter their details — 545 did so. Rachel Orr (@larachie as she is known to her Twitter friends) was the chosen one. She is a 23-year-old student from Motherwell, who had 153 Twitter followers.

Before she launched the ad, Blonde did everything it could to boost her profile, tweeting ‘Follow @larachie like she was a yellow brick road with a shiny new BRU advert at the end’ and ‘Following @larachie makes you richer, faster, cooler and something else-er. She's going to release our new ad. What are you waiting for?’ This helped to double her following to 329 before she tweeted the link into an unsuspecting Twittersphere.

The most influential tweeters in Scotland

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Incentive to prove their influence

As soon as Rachel was chosen as the ‘vector’, Blonde did all it could, as publicly as it could, to draw attention to her through IRN-BRU's various social profiles. It was also working behind the scenes to pique the interest of some of Scotland's most influential tweeters. They were asked to follow Rachel and retweet her when she first shared the link. However, no matter how warm the relationships, asking for social media favours can be a bit crass.

Therefore, we introduced an added incentive, which took the form of a friendly challenge. Because Twitter data was being collected from the outset, Blonde could assess the ‘influence’ of each person mentioning the link as measured by the number of reactions generated by their tweets. It is a crude measure of influence, but it allowed a blog post to be written announcing Blonde's collaboration with digital agency Face to collect and visualize the campaign data (http://blonde.net/blog/2012/05/19/the-most-influential-tweeter-in-scotland/).

Moreover, it issued an invitation for people to demonstrate and test their influence — ‘Retweet our YouTube link and we’ll scientifically gauge the level of reaction you generate’, or words to that effect. The blog post link was shared with various people via Twitter direct message, after which it gained some public momentum of its own through LinkedIn, Google+ and Twitter.SidebarSidebar

Visualizing viral distribution of the link

Hence, the data visualization aspect of this story was not just an exercise in insight and learning. It was not just about telling us what had happened after the event. It also played a valuable role during the event, helping to recruit influencers to the early seeding effort. It allowed Blonde to look at the progression of the YouTube link over time to gauge the impact of the seeding strategy and the role of influencers within it.

  • Rachel tweeted the link on 20 May;

  • Within 24 hours, the ad had been viewed 100,000 times.

Figure 1 shows how the Twitter data looked a day into the campaign. The nodes are individual Twitter accounts. The black edges represent reactions (retweets) to the sharing of the YouTube link by each individual. The further from the centre, the greater the influence of the node, as measured by the number of reactions generated. It is clear that these influential early tweeters played an important, if not vital, role in generating initial momentum behind this piece of content.

Figure 1
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Early tweeters were vital

The role and identity of the individuals concerned can be seen more clearly in this alternative view of the data (Figure 2). This cut of the data includes all mentions of the YouTube link, including those generated by the television spots. In the centre is a dense mass of individuals who mentioned the link, but who did not generate any secondary reactions. This is the ‘wide reach’ effect (in social media terms) of television.

Figure 2
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Vectors and the wide reach effect

As we move away from the centre, the size of the nodes increases in proportion to the number of reactions generated. The further from the centre, the greater the level of reaction generated.

Rachel, the original vector, is the most influential tweeter associated with this campaign. However, there are several dozen other ‘influencers’, all of whom played an important role in generating WOM momentum.

You know who you are. Thank you.

The power of many small, connected groups

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Understanding connections between people tweeting

Influencers played a significant role in propelling the link to 100,000 views in the first 24 hours after launch. However, a very different dynamic can be observed, as the number of YouTube views grew from 100,000 to 650,000 over the next 21 days.

Figure 3 shows the connections of the people actually doing the tweeting. These people (grey nodes) tweeted the YouTube link. What can be seen here are the follow connections between people who have that YouTube link in common. The size of the nodes in this image is not related to the number of reactions generated. It represents the number of connections to other profiles in the ‘network of networks’.

Figure 3
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Connections in the network of networks

The data cut for this visualization was taken on 10 June, before the ad was shown on TV, and therefore it is a graphic representation of the dense network of mini networks through which the link spread under its own steam.SidebarSidebar

Achieving viral content requires relevance

We assume, but we cannot prove, that a similar dynamic was happening on Facebook. However, anyone familiar with the work of Paul Adams (http://www.thinkoutsidein.com/blog/2011/05/small-connected-groups/), based on his access to ‘big’ Facebook data, will probably think that this is a fair assumption. Everyone wants WOM. Everyone wants content to go viral. And it is intellectually comforting to believe that this can be achieved by targeting relatively few influential individuals who will put your content in front of large numbers of eager and attentive consumers.

However, social media life is not like that. As we have seen, influencers can play a role. But that role is limited. To achieve significant reach through WOM in a social environment, you need content that is relevant to many small networks of people. The biggest influencer in this case study was the content itself:

  • Humour unifies.

  • Originality and creative risk taking unifies.

  • And, in Scotland, IRN-BRU itself unifies.

This big idea relied on lots of small groups of people, with the above factors in common, to generate organic WOM at any kind of scale.

The impact of television

Watching this campaign move up through the gears by way of data analysis was an education:

  • 1st gear — the YouTube link seeded by a single, solitary fan of the brand.

  • 2nd gear — 100,000 views in 24 hours, thanks to some coordinated influencer activity.

  • 3rd gear — 650,000 views over 3 weeks as a result of relevant content working its way through a large number of small micro-networks.

  • 4th gear — an unfashionable, off-line medium called television.

The ‘Fanny’ film was aired in just a few high-profile spots during the European Football Championships. But those few spots took the scale of this campaign to another level, as can be seen in Figure 4. The inclusion of television in the mix was directly responsible for an additional 300,000 views in 48 hours and for propelling the YouTube version of the film to 1.5 million views over the following fortnight.

Figure 4
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YouTube uplift from TV broadcast

There is a lot of misguided ‘either/or’ talk when it comes to social media and broadcast channels. As this case study hopefully shows, an ‘and’ approach is far more effective than an ‘or’ approach. The two channels play entirely complementary roles:

  • Social — significant reach over a period of weeks with the added credibility and relevance that comes from personal endorsement (Deep Reach).

  • Television — mass scale reach in a couple of days (Wide Reach).

These complementary roles can best be seen in this visualization of the Twitter data, which combines both WOM and TV-driven mentions of the YouTube link (Figure 5). It shows the combination of deeper, networked mentions and more shallow, unconnected mentions. The visualization — and the campaign it represents — is all the richer for the integrated online/off-line approach to this campaign.

Figure 5
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Combined WOM and TV-driven mentions

Caveats

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Variables beyond Twitter

There is always a degree of post-rationalization to any case study. The posthumous telling of the story always adds a comforting linearity to the narrative that was not necessarily apparent at the time. In this instance, Twitter and TV were not the only things that happened.

We chose to launch the link on Twitter because the channel is a good control laboratory for investigating social dynamics. What happens on Twitter is a (large) niche version of what happens elsewhere. On Twitter, we can control the variables and the resulting data is entirely trackable. Thus, Twitter was our seeding channel of choice.

However, once the link was in the wild, it would have been taken from Twitter and shared via Facebook, email and other channels. We have no way of accurately allocating the credit, as measured by YouTube views, for the overall WOM effect. This project was about how a link spread across Twitter, not about how many views Twitter generated.SidebarSidebar

Gaining momentum from other events

Nonetheless, the principle of being relevant to many small networks of people definitely stands. Once the WOM effect developed its initial momentum, other nice things happened:

  • Within 3 days of Rachel posting her tweet, the ‘Fanny’ film was featured as The Poke's ‘viral of the day’.

  • The film trended on YouTube's own dashboard.

Events like these obviously boost profile and reach.

There was also a strand of paid-for activity on YouTube itself, which obviously made a significant contribution to the number of views that the film received. In fact, YouTube analytics tell us that 28 per cent of total views can be attributed to YouTube advertising.

It is worth stating again that this case study is about how the link was shared across Twitter rather than about how many views can be attributed to Twitter. The main mechanism by which the link was shared across Twitter, namely, lots of small connected groups, was unaffected by the YouTube advertising. It took the power of television to alter that dynamic.

In a nutshell

  • Awesome content is the key to interesting social media strategy.

  • Influencer theory is useful, but only up to a point.

  • Many small networks are more important than a few, highly followed individuals.

  • Think ‘and’ not ‘or’ when it comes to the role of social and broadcast channels.

Author information

Correspondence to Phil Adams.

Additional information

2For the past 10 years, he has been involved in community design, market research and immersive media as a Researcher, Web Strategist and Creative Technologist working for research agencies and web start ups. At Face Group, he focuses on developing new ways of conducting research specializing in online communities, social media analysis, open data, lifelogging and social simulations. He holds a PhD in Sociology and Digital Media and is a Regular Speaker at research, innovation and technology conferences such as WARC, MRS, ESOMAR, AURA, World Business Forum and Word of Mouth Summit.

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Adams, P., d'Orazio, F. How IRN-BRU made its first million (YouTube views). J Direct Data Digit Mark Pract 14, 229–237 (2013). https://doi.org/10.1057/dddmp.2013.15

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Keywords

  • Twitter
  • data
  • tracking
  • network
  • dynamics
  • WOM