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We initially looked at which individual measures correlated most strongly with views per week. The following four measures all had strong correlations with views, and they also complemented each other well. Our established Awareness Index measure (our measure of brand engagement which is a composite of enjoyment, active engagement and branding) had a strong correlation with viewings on its own. This was great news, if not unexpected. We would have been fairly disappointed if this measure which is validated against in-market tracking and both short and long-term sales did not play some role in understanding the characteristics of ads likely to go viral. However, it is clear that TV and online viral media environments operate in fundamentally different ways. As highlighted in academic papers such as Watts (2007), pass-along is a fundamental part of viral success. Therefore it was not surprising that when the AI was combined with our Buzz measure (“would you send this to someone?”), we were able to improve the regression. For ads which contained a celebrity, we noticed that the profile of the celebrity seemed to be playing a major role. This is understandable, since many people may search for celebrity videos online. As our measure of celebrity status, we used an index taken from Google Insights for search. http://www.google.com/insights/search . We call this index “Angelinas”, since the precise measure we used was an Index which compared the number of searches to the number of searches conducted for Angelina Jolie. Incorporating this into our regression improved the regression fit even further. Finally, we added the Link ‘Distinctiveness’ measure into the mix (“h ow different is this advert to other advertising that you have seen?”) , since we found ads which were different to other ads were more likely to receive greater YouTube views. We experimented with different combinations of the individual enjoyment, involvement and branding ratings, but could not improve the results achieved when using the Awareness Index composite. We also experimented with other Link measures. While some individual emotional measures such as ‘surprise’ and ‘excitement’ did have strong correlations with viewings, they did not improve the overall regression model, due mainly to intercorrelation with the Awareness Index metric.
So, to sum up, let’s place some of the major digital channels into a simple channel planning framework. Capacity to connect (your ability to reach an audience via various digital channels) will vary enormously by country and target audience. Equally, impact per contact will vary enormously based on brand objectives and quality of creative. That said, this overall qualitative summary of our learning to date may provide a useful overview of the choices facing media planners. Digital’s main issue is the lack of one simple high capacity and high impact channel – the digital equivalent of TV. As a result, digital media planners need to work either with high impact channels such as video and mobile, and figure out ways to get these messages to larger audiences, or to ‘take-off’ virally. OR, they need to find ways to optimise higher reach options such as display – through use of great creative and smart targeting. Most likely, a strong digital campaign will use a combination of channels to reach and impact large numbers of the fragmented online population. Even when all digital campaign elements have been optimised, their true value will be enhanced much further if they are integrated strongly with other offline elements such as TV, press and posters.