Planning for Patterns

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One of the interesting aspects emerging around multiplatform approaches to content is the dissemblance in device consumption patterns. Differences occur in not just how people use different devices to access content but when. The Media Briefing had a good round up featuring the graph above showing subscriber access to the FT during the day. Desktop and laptop access (in blue) peaks when subscribers get to their desks before declining steadily during the day, and this type of access is really low at weekends.


Mobile access (in orange) is almost the reverse, with a spike as people check the FT on a mobile device when the get up, and into their commute, lower usage during work hours, and then a smaller spike on the evning commute and during the evening at home. At weekends, mobile is by far the most used form of access, with a peak on Saturday and Sunday mornings, and a small one on Sunday evening. It’s interesting that the FT have pointed out that much of this consumption is additive and not substitutional – in other words people are using different devices to access the FT at times when they wouldn’t have before, and multi-device consumption means that they are reading more and for longer.

This pattern is replicated at the Guardian which shows a variable pattern of platform access across the day:

…and at weekends:

The implications stretch beyond distribution and platform strategy, and into a requirement for media owners to think carefully about changing workflows, and daily working patterns and commissioning processes for journalists and editorial staff.

Twitter recently released the Twitter TV Book, a useful guide to the relationship between Twitter and TV, produced using data from social analytics firm SecondSync. According to SecondSync upto 40% of all UK Twitter traffic at peak TV viewing time is about TV content. One of the interesting aspects to SecondSync data is the ability to construct ‘social profiles’ for programming showing the patterns of tweets that occur as the content is broadcast. These profiles differ by genre, demographic and time slot, and are often driven by storylines, content and on-air integration. It’s all rather fascinating. Tweets for entertainment programmes are typically content led and also happen during ad breaks, those for films follow storylines and often incorporate quotes, whilst dramas often see peaks in conversation bookending episodes, and patterns for factual programming peak at key events or revelations, but may have a long-tail of discussion that stretches out beyond the programme itself. 

The granularity of this kind of data leads to some interesting possibilities. In the last series of X-Factor for example, the mentions for the eventual winner James Arthur consistently outstripped those of every other contestant through the series. In the final, the pattern of conversation (below) showed over four times the volume of tweets (and consistently higher tweet-peaks) for James compared to the runner up, Jahmene Douglas, showing how Twitter can act as a barometer for public opinion.

Unsurprisingly perhaps, the research also shows the benefit of integration (hashtags prompt and organise conversation, trends drive discovery, links help spreadability, integrating with content helps talkability) which you get a sense of seeing the profile for the on-screen launch of the #DancePonyDance campaign for Three, across five hours of a Friday night.

The interesting angle to this though, as SecondSync point out, is how the second biggest peak of conversation (in Alan Carr’s Chatty Man, with under 2 million viewers) was not that far behind the biggest peak which happened when the ad premiered in Coronation Street (with 8.5 million viewers). Despite the latter having a much larger TV audience, the former had a larger social audience, showing that awareness of a programme’s ‘social profile’ enables advertisers to optimise the positioning of creative designed to stimulate online buzz. 

Conversation is not the same as engagement of-course, but it’s easy to see how this kind of data might be used not only to maximise the opportunity to create conversation, but to schedule creative with programme social profiles in mind, and optimise the scheduling of brand tweets and online content sharing to capitalise on dual-screening. A more granular understanding of the specific content within programming that is generating the most conversation also enables the referencing of popular moments or key story-line events to inform content creation. With Twitter’s acquisition of Bluefin Labs, SecondSync’s integration with BARB data, and with the potential to create a syndicated-standard social TV ratings metric in the form of Nielsen’s Twitter TV Rating, this space is starting to get really interesting.

The point is (again) how both publishers and marketers/agencies will need to adapt working practices and processes in order to deal with a world where content is optimised for delivery using a more three dimensional view of how and when people consume it, and where realtime feedback can inform more adaptive planning, and content adapted on the fly in response to that feedback. That may not feel like a fully formed possibility right now but its intriguing to think of the potential once (instead of traditional linear scheduling) more TV creative is ad served, which we’re already starting to see the beginnings of. In that world, conversation patterns and social programming profiles could become another useful data point by which to schedule creative in far more agile and targeted ways. Automation would perhaps play a significant part in dealing with the compexity of optimisation using multi-dimensional data points, but so might smart human intuition and application of that data.

Most publishers are a way off figuring out the kind of working and commissioning processes that enable true platform-neutral creation but which are platform-specific enough to optimise based on more three dimensional consumption and conversation patterns. Brand are too. But for how long?


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