The Year Ahead For: Social Media Analytics

Social media data has had a big year, what will 2017 bring?


Last year, the true impact of social media revealed itself in ways that few could have imagined. With billions of users around the world, its importance has never exactly been underestimated, but the huge effect it had on first the EU Referendum and then the presidential race meant that it truly fulfilled its potential to influence society. Trump garnered roughly $400 million worth of free exposure using social media, compared to some $100 million for Hillary, and the results of this attention have been widely credited with putting him in the White House, for better or for worse.

In 2017, social media is only going to grow in influence. Any marketers who have to now only paid lip service to the medium and still managed to survive will likely find themselves out of the job. 71% of marketers in AdMedia Partners’ ‘23rd Annual Market Survey’ said they will buy and spend more on social media this year, and they will have to. Platforms are maturing, moving their business model towards that more commonly seen in traditional media by focusing on advertising–paid media, and they are updating their technology according. This year, they will strive to offer better targeting, flexibility, and media buying options to reach users and personalize campaigns to an unprecedented degree.

However, while this presents opportunities, it also brings with it a host of new challenges. The social media landscape is constantly evolving, and marketers must keep up with any platform trends and algorithm changes. The key to keeping up is looking at all the data produced by their campaigns. For brands, all social media activity can now be leveraged to generate detailed data and insight and has been for a number of years, and the tools being used are growing increasingly sophisticated, giving marketers a better idea than ever of whether their campaigns are working, and how to improve them.

The biggest challenge facing social media analytics now is the quest to look at deeper metrics. Many higher quality ‘second-layer’ statistics are inaccessible. Social media analytics practitioners need to look past simpler metrics like the amount of interactions, impressions and conversions to really understand how consumers are engaging with their content. A post marked as 1 impression may have been barely looked at before scrolling on, and 2017 should see companies who now collect and analyze basic metrics with ease develop their capabilities. Matt Kautz, Head of Business Intelligence, Analytics & Research at Machinima, recently told us that he believed the biggest trend in social media analytics is ‘mainstream organizational acceptance of the insights generated from social and web analytics. Digital-first companies (eg Amazon) have always put social and web analytics at the core of their business processes, but there's been more resistance from legacy organizations to take the insights from social/web analytics seriously. Now that the practice has matured, we're seeing a lot more brands incorporating these insights into everyday business practices to drive decision-making.’

A major obstacle to looking at basic metrics, though, could be the declining popularity of Twitter. Twitter is unique in that all of its data is publicly available, meaning that it has been the most prominent, useful data set for social analytics. As Twitter becomes less mainstream, that data set becomes less valuable, and an obvious alternative source has yet to reveal itself. Even well-established platforms such as Snapchat make available only limited amounts of analytics to users and paying businesses - Snaps sent and received, the number of times your sponsored filter was viewed, and completed story views - and marketers must work even harder to scrape all the data they can.

In a recent interview with us, Marc Smith, Director at Social Media Research Foundation, argued that this year companies will also look more at the 'structure' of connections: ‘Tools that go beyond the count and search model that dominates now will gain big advantages. Most tools currently look at volumes of messages, posters, and keywords. But this approach ignores entirely the 'structure' of connections that form as people link and like one another. Structure is the next big thing in social media analytics: the same number of people can form very different patterns of connection with one another. Collections of collections have patterns. Being able to recognize the type of network you are in and to understand the kind of network that you prefer is at the core of a next-generation approach to social media analytics.’

To say throwing money at social media is not the solution is, frankly, only partially true - it definitely helps. However, the pace of change and the new tools require marketers to have ever greater knowledge of data science, and they will need to be sure they are updating these skills as social media evolves to fully exploit it.

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