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Audience Measurement Is No Child’s Play

The media and entertainment sector remains one of the most dynamic sectors, not only in India, but globally as well. Fast-paced developments in technology are re-shaping the interplay between content and medium, which in turn is re-shaping consumption habits.

A dynamic industry demands constant innovation in technology and in that regard, the recent consultation paper from the Telecom Regulatory Authority of India (TRAI) on making the current measurement system more robust is welcome. What is interesting from the technology perspective, is that TRAI has sought opinions on exploring other add-on technologies to build upon BARC India’s robust measurement platform and expand the measurement sample.

I believe that any system, whether in the brick-and-mortar space, or in a high-tech area like viewership measurement, needs to constantly innovate to stay relevant.

Talking about technology, off late, we are seeing the use of mobile as an emerging technology to measure TV viewership habits. What intrigues me though is the usability and credibility of the data being collected using this method. While at the surface level, it may sound exciting to those who know little about viewership measurement, but it leaves me with a lot of questions. The first of which stems from the lack of transparency in the methodology being used. Using mobile as a tool to measure viewership also comes with challenges of full data capture, representativeness and to top it all privacy issues. There are also basic fallacies in this technology like it stops capturing data when an individual receives a call, and the phone always needs to be near the TV. Mobile measurement at best can give some broad directional trend at an overall level but will be devoid of accurate granular consumption habits.

It is important to understand that effective monetization of the media and entertainment sector begins with knowing who is watching or listening to what content, when, where, and for how long. This deep insight into consumer preferences in real-time fuels both content creation and advertising spend. In this scenario, having data which is not representative of actual viewing habits can lead to erroneous decisions by businesses in allocating their ad spends.

It is also important to understand that given the geography and diversity of India, it is impossible to survey each media consuming individual in real-time, and more importantly without violating the privacy of individuals.

This is where sample-based research comes into play. BARC India has brought together scientifically validated methodologies for sampling a carefully curated subset of viewers and attributing collected data to the entire population. This provides industry players with robust and confidential content consumption data.

BARC India samples a subset of the entire market of 836 million TV viewers and attributes the results across the population. Choosing the right samples and right attribution models requires deep data science and understanding of sampling across various states, town classes, social, language, and economic strata that resemble the entire population. The permutations and combinations are constantly changing, and attribution requires deep technology backbone, automation, large dispersed field force and a set of households (a panel) who willingly allow themselves to be monitored.

BARC India’s sample of 135,000 individuals compared to USA’s sample of 108,900 individuals is the largest in the world. However, there is a big mismatch between the AdEx of the two countries. While US TV viewership measurement system is supported by USD 71 billion in AdEx, the comparable figure in India is USD 3.7 billion. What this means is that for any new technology to come in, the industry will have to grow the ad pie and invest more in the measurement system.

Technologies like Sample Return Path Data (SRPD) also offer census-like data with the advantages of a panel and has the potential to be a path-breaking move in the Indian TV viewership measurement system.

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