'The Biggest Challenge To Social Media Measurement Is The Declining Popularity Of Twitter'

Interview with Matt Kautz, Head of Business Intelligence, Analytics & Research at Machinima


Matt Kautz is VP of Business Intelligence, Analytics and Research at Machinima, where his team uses social media data to guide content development, optimize marketing, discover emerging talent and find high affinity audiences for advertisers. Prior to his role at Machinima, Matt was responsible for forecasting opening weekend box office using social data at Walt Disney Studios, was a member of the Facebook Preferred Marketing Developer at an early stage startup, and established social media standards for the live events industry at Ticketmaster.

We sat down with him ahead of his presentation at the Social Media and Web Analytics Innovation Summit, taking place in Chicago this November 29-30.

What do you see as the key trends impacting the world of social media measurements and web analytics in 2016? 

I think the biggest trend impacting our world is mainstream organizational acceptance of the insights generated from social and web analytics. Digital-first companies (ex 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.

Are there any particular developments that will make it more challenging for marketers and analysts to derive insight from social/web data? 

I think the biggest challenge to social media measurement is the declining popularity of Twitter. Because all Twitter data is publicly available, it has been the most prominent, useful data set for social analytics. As Twitter becomes less mainstream, more niche, that data set becomes less valuable, and there aren't any obvious sources to take its place.

What are the developments that will make it easier to derive insight and enhance business value? 

All of the data engineering solutions which allow for real-time data processing – Redshift, Spark w/ Hadoop, etc – are transforming the way that we evaluate quantitative data sets to allow for immediate optimization. I think advances in cognitive analytics and natural language processing offer the same opportunities for the qualitative data sets businesses rely on for creative/strategic decision making.

Are there any obvious winners or losers as the social media & web analytics and optimization markets continue to mature and evolve? 

DMPs like Krux are winning big by marrying insights with action in real time. Facebook's announcement that topic data would no longer be made available makes Datasift an obvious loser.

What do you intend to talk about in your presentation? 

In my presentation we’ll look at the ways social media data is being used today by media companies to generate audience insights for product development, financial forecasting, marketing optimization and talent discovery. This includes the analytical framework, BI toolsets, and pros/cons of the various data sources. 

You can hear from Marc, along with other experts in data analytics, at the Social Media and Web Innovation Summit. View the full agenda here.


Image courtesy of Twin Design/Shutterstock.com

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