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Data Analytics Example in Media: Zee Entertainment

 July 9  | 0 Comments

In a recent interview, Zee Entertainment’s Head for Strategy and Consumer Insights – Venkat Nettimi – revealed how his company is setting a data analytics example in the entertainment industry. Zee is using data analytics to personalize user experience and gain actionable insights that translate to effective investments.

Nettimi, who has worked for the Citigroup and CIBIL among other top organizations, has much experience in the field of data analytics. He has seen the field change in leaps and bounds from manual data tabulation to complex analytics solutions and even analytics for data from mobile devices.


Zee’s leaders are now able to make decisions much faster due to the close to real-time data that they receive from these improvements. Although, this was not always the case. Not all of Zee’s employees were comfortable using analytics before. Especially, the creative folks. Now, they are better at it.

Credit also goes to more intuitive and visual dashboards in programs like Tableau. In Nettimi’s view, these advancements and better skills of his colleagues will help his company build quality predictive models using artificial intelligence and machine learning algorithms.

Data Analytics in Entertainment vs Other Domains

The basic function of analytics is the same across industries, according to Nettimi. Analytics tries to solve problems using data. What is different in each domain, however, is the kind of problems that they focus on and the type of data they use. So, a good data analytics example in industries that produce consumer goods, is using feedback from customers to improve the products. In finance, a good data analytics example would be monitoring transactions.

For Zee and in television entertainment, measure of audience (data from the (Broadcast Audience Research Council) is very important. Again, the specific domain challenge is gaining a competitive advantage with data that is available to all. What separates one tv channel from another is simply their analysis and how they use it.

Zee’s Data Analytics Mission

Zee is trying to use data analytics for two purposes – increase the number of their viewers and the duration these users spend watching their shows.  Nettimi believes this is possible by learning more deeply from data. Artificial intelligence and machine learning are helping them achieve this. A data analytics example is Zee using image recognition for meta tagging. Discrete choice models, market basket analysis and more qualitative methods of customer-centric research are all improving their ability to make key business decisions.

Machine Learning for Scheduling

Zee has over 10 movie channels and 3,500 movies. They schedule over 5,500 hours of movies for their audiences. Given the large number of hours, accurate or better scheduling – scheduling that matches customers tastes and preferences – is guaranteed to give Zee a competitive advantage and set a data analytics example.

Since most of this content is old, another good data analytics example is Zee using data from the past to predict future interest. Overall, machine learning has made scheduling more effective both – in terms of content management, and, time management. It now takes less hours for them to create their schedules. Not to mention, this has also resulted in improved ratings.

Using AI to Learn Customer Preference

Zee is also a major producer of films and television shows. They produce a lot of content over the course of the year. Hence, one of Nettimi’s objectives is to discover what kind of shows work best with the audience.

To this end, Zee uses artificial intelligence to meta tag videos. These tags describe everything from nature of characters to the emotions that they display and even things in the background. This data is correlated with the data from BARC to better understand what types of content best work with the masses.  Nettimi is confident machine learning algorithms will become more prominent very soon.

Over-The-Top Media Analytics

Zee was one of the first companies to dip their feet in video streaming services. There was Ozee and Ditto even before Zee5. These services help Zee make TV “anytime, anywhere” to their audiences, who can also easily catch-up on the shows they miss.

On the other hand, they give Zee rich data on customers. Then there is social media, which gives Zee more feedback on the narratives their viewers use to discuss their shows. So, a good data analytics example is Zee paying attention to what the customer is saying on social media to make better decisions pertaining to content.

Data Visualization

Data visualization is also getting a lot of importance from the Zee bosses. All departments have their own dedicated team of analytics SMEs, who communicate insights to a variety of stakeholders. Hence, Tableau was always popular in the organization. Now, Zee is training its employees on R, which is known for statistical computing and its features for graphics.

Data Analytics at Zee

Data analytics are affecting all decision-making processes at Zee. Apart from helping Zee improve their products, reduce cost and increase gains, serve their customers better, analytics is helping Zee make fast progress in business.

Another good data analytics example is Zee being better able to help advertisers. They can make better purchase decisions and confidently launch new products – Flix and Prive (the two channels were launched after understanding viewer preferences). They are also launching regional channels using insights from data.

Zee is using data to brand themselves (in case of new products) and even re-invent themselves (in case of old products) to appeal to a larger audience. Finally, they are relying on external analytics firms to gain fresh perspectives.

Setting a Good Data Analytics Example

Times are exciting, according to Nettimi. Better tools and techniques widen the scope of data analytics in entertainment. But, the relevance of new methods or tools need to be tested before implementation. There can be a bias among leaders like him to start using the “next best thing” before it is the “best thing” – when tried-and-tested tools and techniques may be the better option.

The challenge obviously is to analyze the growing volume of data at greater speeds and to still use it to make business decisions that are profitable. To this end, Zee is part of Essel Group’s larger vision. Essel wants to leverage the data from their various sources across sectors. They are creating a ‘data lake’ for this purpose. They are making sure to set a good data analytics example by aiming to improve their analytical capabilities in all their divisions.