Understanding TV audience using advanced analytics

The changing world of the TV viewer

Digital innovation has triggered volatility in where and how the viewers are watching TV, or should I say consuming TV content. The viewer no longer needs to own a TV set. TV content can now be accessed across multiple platforms.  This has led to proliferation of “segments” each depicting different viewing habits and content preferences.

With the onset of DTH, social TV, digital TV (OTT apps, smart TVs), the viewer is leaving breadcrumbs of information along the trail of viewing patterns.  Added to that, unstructured data is available from viewer reactions and feedback available from non-traditional sources like social media platforms and portals. Emergence of social TV, digital TV (OTT apps), smart TVs is fueling this momentum.

The TV industry is making strides in leveraging this information to gain insights about:

  • their customer – the viewer
  • their product – the content
  • their ecosystem – brands, distributors
  • their strategies – programming, promotions

“Switch-on” data analytics – the use cases

  • Analytics for understanding the audience better: Understanding viewer preferences in terms of genre, time-of-viewing, loyalty to channel v/s loyalty to content, flipping behavior, paths to content discovery.
  • Creating analytics assets from the content itself: Analytics of scripts, fan blogs to mine sentiments and emotions, model topics, character popularity.
  • Analytics for sharpening the promotion and advertising strategies: Analytics around same-channel, inter-channel promos, marketing efficiency, and effectiveness, market mix modeling.
  • Leveraging the social network world: Analysis of social network activity – likes, shares, tweets, posts – can lead to better understanding of opinion formation and influence.

Driving better “picture clarity”

Understanding the audience behavior and patterns has always been a challenge, more so in wake of the digital evolution.  The opportunity that the industry has, however, is to leverage non-conventional data science based approaches beside the traditional analytics. Integrating data from multiple sources and applying cutting edge data science techniques can deliver rare insights.

This requires a sound combination of big data ecosystem, machine learning techniques, with conventional business knowledge.  That’s the mantra for high-definition audience understanding!

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