How Netflix Acquires Customers in India
“My job here is to make more money for Netflix, that’s the shortest way to describe my brand,” Karthi Sriram Chandrashekar said in a chat with Analytics India Magazine. Director of Data Science and Engineering at Netflix, Chandrashekar and his team work on customer acquisition and retention across Netflix as well as external marketing.
Chandrashekar is also behind the introduction of low cost mobile Netflix plan in India. Analytics India Magazine caught up with him to understand his work, business strategy and other ongoing projects.
Improve return on investment
Chandrashekar graduated in Computer Engineering from BITS Pilani. Since then, he has worked for ten years in software development and machine learning. One of his first assignments was to work on ad rankings. “I also dabbled in health technology, working to provide the right data infrastructure to run electronic medical records, recommend the right drug/product dosage, and coordinate hospital operations.”
Chandrashekar said this role was not machine learning focused, so he went back to working with ad ranking. “It was like a location-based advertising company. So we were basically trying to show ads relevant to a person’s current location based on their mobile data,” he said.
His next stint was at Netflix, where he joined as a software engineer (machine learning). He is currently the director of data science and engineering for the streaming company. He manages a team of 60 people, which is a mix of data scientists, researchers, and statisticians who focus solely on causal inference.
Chandrashekar and his team are working on designing techniques to personalize and personalize the experience for non-members. “We use machine learning to make decisions about evaluating the highest ROI to get customers,” he said. The personalized feed for a paying subscriber is understood, but how difficult it is to do the same for a ‘non-member’. When we posed this question to Chandrashekar, he replied, “Customization for an unregistered member involves several steps. We get a lot of data points even from non-members, for example, which mode they use to access Netflix – mobile app, web or TV. Also, more than personalization, I think contextualization is a more appropriate term.
Building a single plan for India
One of Chandrashekar’s most significant achievements at Netflix was creating a unique low-cost mobile tier in India. This mobile plan costs INR 199 per month and is designed to attract Indian users and gain a competitive advantage over rivals like Disney, Amazon and others.
“Here I would like to talk about how data played a role as it is the most important factor that helps in making strategic decisions like pricing plans. So the first thought came when we were trying to understand how to unlock the next phase of growth in India. Not just in India, we were looking to adopt a mobile-first strategy in APAC. This coincided with the larger shifts across the industry with the launch of Jio,” Chandrashekar said.
The first step was to develop an understanding of the market using customer segmentation models. The team conducted extensive analysis to estimate the types of customers who might be better retained if they introduced a low-cost plan like this. “That said, whether a business idea is a good one is largely a strategic question. We wouldn’t know much without launching and checking out what’s going on. This led us to develop a kind of quasi-experimentation strategy where we launched the plan in several markets in the APAC region. We then used observational methods to estimate the impact it would have on other markets if we continue to expand into other regions,” Chandrashekar said.
Once the plan was launched, another challenge was to measure the return on investment in terms of revenue. And if customers using more expensive plans would switch to cheaper mobile plans, that was one of the questions. “One thing with rolling out a new service or product is that there are a few things that can happen. Randomizing costs across markets and learning the overall impact of these things through causal inference techniques was central to what we did to launch the mobile plan,” he said. .
One of the projects Chandrashekar is working on is account sharing monetization. About this project, he says it is “fundamentally an extremely data-driven growth strategy.”
“It starts as soon as the accounts that are shared are detected. Based on this information, we want to develop a product strategy that can help monetize this behavior in a very customer-friendly way. Assessing these strategies through complex experiments is central to the project I’m working on,” he said. Netflix already uses cyclical/concurrent streams, which means limiting the number of people who can watch at the same time. He added that the strategy they are currently working on will be novel in nature.
Another major project Chandrashekar and his team are working on is bringing more automated experiences to uncertain surfaces, also known as “adaptive experiences.”