BARC gears up for launch of TV audience measurement system

The technology handshakes are in place and ratings are being generated from the system now. However, the final date of launch has not been announced yet

BestMediaInfo Bureau | Delhi | January 13, 2015

Broadcast Audience Research Council (BARC) India, which is mandated to bring a robust audience measurement system by broadcasters, advertisers and ad agencies, has released an update on its progress. However, the update did not mention a launch date of the much awaited new audience measurement system.

According to BARC, with upwards of 275 channels having ordered for Watermarking Embedders, all major networks in each region and across genres are on-board and are participating to create history with the world's largest television audience measurement system. Playout monitoring facilities are in action too and meta-tagging of content across watermarked channels is in full throttle in Mumbai and Bangalore.

BARC has tested the end-to-end integration of the system and claimed that it was working “perfectly fine”. The technology handshakes are in place and ratings are being generated from the BARC system now.

In a run-up to the launch, BARC has shared a few learnings and insights on the importance of Relative Errors and Confidence Levels in audience measurement for new beginnings as mentioned below:

Importance of Relative Error

Over the past few months, BARC India has highlighted its commitment to data robustness and has spoken about lower Relative Errors at high Confidence Levels. It has repeatedly highlighted that Relative Errors are an important factor to be considered “whenever we evaluate the ratings data, or read any research report, for that matter”.

It is not possible to sample every individual (except perhaps, in a Census). Hence, sample surveys are undertaken. Statistics offers scientific methods to estimate phenomena across entire populations by studying samples. Any sample survey suffers inherently from various errors. Owing to these, statistics never talk about an average (or mean), without talking simultaneously about a measure of dispersion, usually the standard deviation.

A researcher has to balance between demands of greater accuracy and constraints of finite resources. Statisticians, therefore, work with defined 'Confidence Intervals' and 'Sampling Errors'. One of these sampling errors is the 'Relative Error', or the deviation (in percentage) of the observed value from the actual (expected) value.

Confidence Level (or Confidence Interval)

Confidence Level is generally defined as a percentage or a decimal figure less than 1. So, if a researcher says that the Confidence Interval is 90%, what he means is that 90% of the samples of the same size taken from the same population will produce results within a defined range.

Relative Error

A TV ratings measurement system estimates that the programme has 1 TRP with a standard deviation of 0.25. This means that the actual rating is expected to lie between 1-0.25 and 1+0.25 or 0.75 and 1.25. The relative error is simply 0.25/1.0 or 25%.

A simplistic explanation, that may antagonise a purist, can be provided in the following diagram:

In other words, it is important for a research to ensure least possible Relative Error at the highest possible Confidence Level; else it risks generating data with such wide variance that it becomes meaningless. Just imagine saying that a programme has 1 TRP at the above Relative Errors. What you're really saying is that there is a two-thirds possibility that the TRP is between 0.5 and 1.5 and a one-third possibility that the TRP could be either lower than 0.5 or higher than 1.5! And even for this, you are only 67% sure. If someone asks you what is the range of possibilities for which you are 90% sure?

Factors affecting Relative Error

The most important factor that affects Relative Error is Sample Size. Relative Error increases in geometric magnitude as sample size decreases, while it becomes independent of sample size beyond a certain threshold.

Sampling is also relatively simpler when estimating a homogenous population and more complex for heterogeneous population. It is, hence, extremely important to have a significantly large sample size, especially when calculating estimates for large heterogeneous universe.

How BARC India intends to handle issues related to sample size to ensure robustness of data

Consider a hypothetical scenario – a planner wishes to evaluate programme viewership for the following TG for a premium brand - Males, NCCS AB, 40+ in Delhi

Total Sample Size: 130

Approx. Sample Size for a programme with a rating of 1% viewers: 13

A sample size of 13 is way too low to do any meaningful evaluation. Hence, BARC India would not encourage such evaluations.

To circumvent this issue, BARC India intends to aggregate the data through one of the following means-

• Aggregate viewership data across two or more weeks

• Add more cities to the sample, aggregating geographically

• Instead of considering a particular individual programme or a limited time, evaluate a day-part, thus aggregating by time bands

Each of the above methods would increase the sample size and would allow the planner to make his decision based on robust relevant data. The BARC India Technical Committee is evaluating options of either hard-coding the aggregations in the pre-publishing stage itself, or allowing the planner to decide the aggregation based on his / her requirements. This decision would be taken only after seeing the data for all panel homes and assessing the pros and cons of each method.

BARC is planning to conduct another round of road shows in early February on the GUI (User Interface).

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