January 3, 2024

How to become an OTT Data Analyst: A comprehensive roadmap

OTT platforms like Netflix, Amazon Prime Video, Disney+ Hot Star and Hulu constantly relies on Data Analytics. In this blog post we’ll provide you a roadmap to become a Data Analyst in these platforms.

Over-The-Top (OTT), the delivery of content over the internet, has transformed the media and entertainment industry by offering on-demand access to content without traditional subscriptions. Data analytics is integral to the success of OTT platforms, enabling them to understand user behavior, tailor content recommendations, and stay responsive in a competitive market. 

According to an estimate as of 2023 the OTT market size is around $0.45 Trillion which is expected to reach around $1.55 Trillion by 2028. As the OTT landscape grows, the demand for skilled OTT data analysts is rising. These professionals are sought after for their ability to convert raw data into actionable insights, enhancing user satisfaction and driving strategic content decisions. Lets dive into this industry.

What Does an OTT Data Analyst Do?

An OTT (Over-The-Top) Data Analyst plays a crucial role in the media and entertainment industry, specifically within the realm of streaming services and online content delivery. Here are 5 primary responsibilities for an OTT Data Analyst:

1. Data Collection and Analysis: Gather and analyze large sets of data related to user behavior, content consumption, and platform performance.

2. Content Recommendation Optimization: Develop and enhance algorithms for personalized content recommendations based on user preferences and viewing patterns.

3. User Engagement Analysis: Assess and interpret user engagement metrics to identify trends, patterns, and areas for improvement in the user experience.

4. Performance Monitoring and Reporting: Monitor the performance of the OTT platform, providing regular reports and insights to key stakeholders for strategic decision-making.

5. A/B Testing and Experimentation: Conduct A/B testing to evaluate the impact of changes to the platform, such as user interface modifications or content delivery strategies, and optimize based on the results.

Career Options and Salaries

In an OTT platform, different data roles collectively form a comprehensive data team, each playing a distinct yet interconnected role in leveraging data for optimal platform performance, as a Data Analyst you have the opportunity to explore other Data roles given that you fulfill the requirements. Let’s take quick look at these roles.

1. Data Analyst: Transforming raw data into actionable insights, data analysts optimize user experiences and guide strategic decisions in the OTT industry.

  • Avg. Salary: ₹12-13 Lakh

2. Data Engineer: Essential for managing data infrastructure, data engineers design scalable pipelines, ensuring efficient data processing and storage in OTT platforms.

  • Avg. Salary: ₹12-15 Lakh

3. Data Scientist: Applying advanced statistical and machine learning techniques, data scientists analyze complex datasets to inform critical decisions in content strategy and user engagement.

  • Avg. Salary: ₹14-18 Lakh

4. Machine Learning Engineer: Specializing in algorithm development, machine learning engineers drive intelligent systems for content discovery, enhancing user satisfaction in the OTT space.

  • Avg. Salary: ₹15-16 Lakh

Note that these salaries are an estimate for 0-2 years of experience in India from multiple platforms like Glassdoor, Ambition box and Indeed however these salaries can vary based on factors like experience, location, and the employing organization.

How to Become an OTT Data Analyst

1. Education and Academic Foundation

Building a solid academic foundation is essential for prospective OTT data analysts. Consider pursuing degrees or certifications in data analytics, statistics, or related fields to acquire a strong theoretical background.

2. Technical Skills:

Excel, SQL, Python/R, Tableau/Power BI: Acquire proficiency in essential tools for data analysis. Excel is crucial for handling tabular data, SQL for querying databases, and Python/R for data manipulation. Tableau and Power BI are valuable for creating interactive data visualizations.

Apache Kafka and Apache Spark: Familiarize yourself with real-time data streaming and processing technologies.

Super AI Free Resources:

How to learn Excel - A Comprehensive guide : Click Here

How to learn SQL -  A Comprehensive guide : Click Here

Roadmap to learn Python -  A Comprehensive guide : Click Here

Roadmap to learn R -  A Comprehensive guide : Click Here

How to learn Power BI - A Comprehensive guide : Click Here

How to learn Tableau - A Comprehensive guide : Click Here

For Apache Kafka and Apache Spark check their official documentation

Data Processing and Statistical Skills:

Manipulation of Data: Learn to manipulate and clean data for accurate analysis.

Exploratory Data Analysis (EDA): Develop skills to explore datasets, identify patterns, and extract meaningful insights.

Statistical Concepts: Gain a deep understanding of statistical concepts to interpret and validate findings

Free Courses Courses

Data Analysis with Python by IBM on Coursera
  • Level: Beginner
  • Duration: 15 hours
  • Fee: Free to audit

Marketing Analytics Foundation by Meta on Coursera
  • Level: Beginner
  • Duration: 11 hours approximately
  • Fee: Free to audit

3. Learn About Various KPIs in the OTT Industry

KPIs serve as a compass for OTT platforms, guiding them toward optimal content delivery, user engagement, and financial success. They enable a data-driven approach to decision-making, fostering adaptability and responsiveness to the evolving preferences and expectations of the audience. Let’s take a look at the 12 KPIs in OTT industry:

Content Metrics KPIs
KPI Formula Decision it can help drive
User Engagement Rate (Total Interactions / Total Users) * 100 Optimize content for higher engagement
Churn Rate (Number of Subscribers Lost / Total Subscribers) * 100 Identify factors causing subscriber attrition
Average Session Duration (Total Time Spent on Platform / Number of Sessions) Improve content or user experience for longer sessions
Conversion Rate (Number of Conversions / Total Visitors) * 100 Optimize the conversion funnel
Retention Rate [(Number of Customers at End of Period - New Customers) / Number of Customers at Start of Period] * 100 Enhance strategies for customer retention
Content Consumption Rate (Total Content Consumed / Total Available Content) * 100 Identify popular content for production
Subscriber Acquisition Cost (SAC) Total Marketing and Sales Costs / Number of New Subscribers Acquired Optimize marketing spend for subscriber acquisition
Average Revenue Per User (ARPU) Total Revenue / Total Subscribers Increase revenue through targeted offerings
Buffering Rate (Total Buffering Time / Total Video Playback Time) * 100 Improve infrastructure for smoother streaming
Customer Satisfaction Score (CSAT) (Sum of Customer Ratings / Number of Responses) Enhance user experience based on feedback
Content Drop-off Rate (Number of Abandoned Videos / Total Videos Started) * 100 Identify and improve content retention points
Cost per Hour of Content Produced Total Content Production Costs / Total Content Hours Optimize production efficiency and costs

4. Advanced Analytics Techniques for OTT:

As you progress in the field of Data Analytics you’ll need to deepen your skills with advanced analytics techniques like:

Predictive Analytics: Use data to predict user preferences for content recommendation.

A/B Testing: Conduct experiments to optimize user engagement through data-driven decisions.

Machine Learning Applications: Apply machine learning to address specific challenges in OTT data analysis.

5. Popular Machine Learning algorithms:

Collaborative Filtering (CF): Understand the principles of collaborative filtering for personalized content recommendations.

Content-Based Filtering (CBF): Explore techniques for recommending content based on user preferences.

Hybrid Recommender Systems: Learn to integrate collaborative and content-based approaches that builds Hybrid Recommender Systems.

K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNNs): Understand machine learning algorithms like KNN and CNN relevant to OTT analytics.

Free Resource:

Machine learning specialization
  • Level: Beginner
  • Duration: 2 months at 10 Hours/week
  • Fee: Free to audit

Introduction to recommender system
  • Level: Intermediate
  • Duration: 23 Hours approximately
  • Fee: Free to audit


5. Hands-On Experience:

Gain practical experience with the help of these resources:

1. Internships and Entry-Level Positions: Utilize job boards like Indeed and LinkedIn to search for relevant internships and entry-level positions in the OTT industry.

2. Personal Projects: Explore datasets available on platforms like Kaggle to work on personal projects related to OTT data analytics. Utilize resources like GitHub for project collaboration and version control.

3. Open-Source Contributions: Contribute to open-source projects such as Apache Kafka and Apache Spark to enhance your skills and showcase your expertise. Platforms like GitHub and GitLab are valuable for version control and collaboration.

4. Networking: Engage with professionals in the industry through online forums like Stack Overflow, attend virtual meetups and conferences using platforms like Meetup and Eventbrite, and join LinkedIn groups dedicated to data analytics and OTT technology.


In summary, becoming an OTT data analyst requires a combination of education, technical skills, hands-on experience, and a deep understanding of the OTT landscape. Following the roadmap outlined here will empower aspiring professionals to navigate the complex world of OTT data analytics successfully.

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