5 AI-Powered Big Data Use Cases in Video Streaming Apps

July 19, 2023

As video streaming apps continue to grow in popularity, so does the amount of data these platforms consume. Keep your video streaming apps more interesting with the help of AI-Powered Big Data! Here are 5 examples of AI-powered Big Data use cases to improve the video streaming experience.

Video streaming apps have dominated everyone's lives. From entertainment to education, we rely on mobile streaming applications to put high-quality material at our fingertips.

However, only a few mobile streaming app solutions  can make their place in this highly competitive market, like Netflix, Amazon Prime, Hulu, etc. 

How do these apps make their place in the market?  Well, the answer is implementing AI-powered Big Data. With these technologies, video streaming apps can provide users with a more personalized experience. 

The introduction of AI-powered Big Data has affected various industries as they have the potential to resolve complicated issues and offer insights that people would not be able to find on their own.  

And when it comes to video streaming app development, AI and Big Data can empower these platforms in several ways.

In this blog, we will explore AI-powered Big Data and five AI-powered Big Data use cases in video streaming apps that revolutionize how users consume content.

What is AI-Powered Big Data Analytics?

AI is a subfield of computer science that focuses on building intelligent robots, learning from data with the intention of automating, reducing decision-making time, and increasing time to value. This carries out activities that traditionally require human intellect. AI-powered mobile apps solution systems are capable of problem-solving, speech recognition, and other human-like functions, as well as learning from experience and adapting to new inputs.

On the other hand, Big Data describes the enormous volumes of organized and unstructured data that businesses produce daily. 

To gain insights and value from this data, which is too complicated for conventional data processing techniques, sophisticated analytical tools are needed. Big Data analytics entails gathering, storing, processing, and analyzing enormous datasets to get insights that can guide business choices.

However, combining some AI algorithms with Big Data has been instrumental in building top-notch mobile app development apps solutions- especially in live streaming apps. 

Big Data analytics systems with AI will effectively assess user behavior, preferences, and viewing history and suggest pertinent information to users. These recommendations are the foundation of machine learning algorithms that analyze enormous volumes of data to find patterns and trends. This strategy raises engagement and retention rates by ensuring that users are shown content that is relevant to their interests.

For example, video streaming app development companies can use Big Data analytics to identify network congestion points and optimize their content delivery networks (CDNs) to ensure a seamless viewing experience for users.

5 AI-Powered Big Data Use Cases in Video Streaming Apps

1. Personalized Content Recommendations:

71% of customers seek individualized experiences from businesses, showing that personalized Content Recommendations are a game-changing feature in any Video streaming app development. With the help of this feature, you can keep your users engaged by suggesting relevant content based on their preferences. 

However, integrating personalized content recommendation features is one of the key challenges for video streaming apps, as analyzing vast amounts of user data such as viewing history, preferences, and demographics is not easy.  But with AI-powered Big Data, it is a matter of a few clicks. 

AI algorithms analyze vast user data to generate personalized recommendations, including viewing history, ratings, and demographic information. Moreover, leveraging Big Data in mobile streaming apps can provide users with a tailored content discovery experience, increasing user satisfaction and longer viewing sessions.

Like Netflix's recommendation engine uses AI algorithms and Big Data analysis to suggest movies and TV shows based on users' viewing habits, ratings, and similar profiles. 

Video streaming apps can increase user engagement and retention by providing personalized content recommendations.

2. Content Tagging and Metadata Enrichment:

Manually arranging and classifying every video on a streaming app can be challenging, especially considering the enormous content libraries these apps typically maintain. Using AI-Powered Big Data, video streaming app development companies can categorize movies automatically based on their visual and aural components, facilitating effective content search and discovery.

Moreover, Big Data analytics enable video streaming apps to customize their content libraries according to customer patterns and preferences.  By using a combination of machine learning algorithms and human editors to tag its content and add metadata to it, allowing users to search for content based on their interests, such as comedy, action, horror, or drama

For instance, if a user wants to find a comedy film that from 2020, the app can use metadata enrichment to filter films and display only the relevant films. The perfect example of this feature is YouTube. 

Its automatic video tagging system uses AI and Big Data to analyze video content and generate relevant tags, making it easier for users to find specific videos.

3. Real-Time Video Quality Optimization:

Low-quality video and buffering are major turn-offs for users while watching videos. 

If you integrate AI-powered Big Data, you solve these problems. 

For example, if a user has a slow internet connection, the video streaming apps can automatically reduce the video quality to prevent buffering. AI algorithms can continuously analyze user gadgets, network conditions, and video content to enhance quality.

On the other hand, AI may automatically modify video bitrates and encoding parameters to ensure smooth playing and high-quality streaming experiences for users by evaluating massive data on network performance.

It’s also worth mentioning that Amazon Prime, a leading Video streaming app, also uses AI-powered algorithms. With the help of these algorithms, they can optimize video streaming quality based on network conditions, device capabilities, and user preferences. 

4. Content Moderation and Copyright Infringement Detection:

Video streaming apps face the challenge of moderating user-generated content to prevent inappropriate or copyrighted material from being shared. Failure to comply with this might result in legal and reputational problems, negatively impacting the app's user base and income.

The one-stop solution to this problem is AI-powered Big Data. With AI algorithms, a Video Streaming App Development company can analyze Big Data, including Audio and Visual cues, to detect and flag potentially harmful or copyrighted content. 

For instance, if a user uploads a video that contains copyrighted content, the app may be able to detect copyright infringement and delete the offending video from the platform.

This enables platforms to maintain a safe and legal user environment while minimizing manual content moderation efforts.

YouTube's Content ID system is the best example of Content Moderation and Copyright Infringement Detection. 

It uses AI and Big Data analysis to automatically detect and manage copyrighted content, ensuring compliance with copyright laws.

5. User Behavior Analysis and Churn Prediction:

Understanding user behavior is crucial for any mobile application development -especially in video streaming apps, to improve user engagement and reduce churn rates. 

With AI algorithms, video streaming app development companies can evaluate Big Data on user interactions, such as viewing habits, engagement levels, and feedback, to find trends and anticipate user churn. Moreover, video streaming apps can use this data to customize content suggestions, provide targeted promotions, and improve retention techniques.

For example, if a user has not watched any videos in the past week, the app can send them a personalized email or notification with recommendations for new content. Similarly, if a user has given negative feedback on several videos, the app can reach out to them with an apology and offer them incentives such as free trials or discounts to encourage them to stay on the platform.

Hulu integrates AI-powered algorithms and Big Data analysis to predict user churn based on viewing habits, engagement metrics, and feedback, allowing them to take proactive measures to retain users. 

Challenges Of Implementing AI-powered Big Data In Video Streaming Applications 

  1.   Data Privacy and Security- 

Data and security are always a significant concern while implementing Big Data as it contains tons of customer data, including personal data like names, addresses, and credit card numbers. This data is susceptible and requires robust security measures to protect it from unauthorized access, theft, or misuse, which is challenging for small businesses. To combat this, some companies set up a system for tracking data activity and deployed strong security measures, including encryption, authentication, firewalls, backups, and antivirus programs.

  2. Data Quality-

Big Data analytics tools rely on high-quality data to derive meaningful insights. However, mobile app development companies – especially those with a small budget- may encounter data quality issues, such as missing or incomplete data, inaccurate data, or inconsistent data. These issues can affect the accuracy and reliability of the insights generated by Big Data analytics tools.

3. Complex and Time Consuming-

Implementing AI-Powered Big Data into video streaming apps can be complex and time-consuming as it requires the proper infrastructure, tools, and expertise to implement these technologies successfully. Smaller businesses with fewer resources may find this to be a considerable difficulty.

For a more detailed look at these challenges and potential solutions, check out our blog post - Video Streaming App Development Challenges & How To Fix Them

Key Takeaways

By integrating AI-powered Big Data analytics, video streaming apps can transform the industry by enabling personalized recommendations, optimizing video quality, and enhancing content discovery. 

And these five features demonstrate the potential of AI-powered Big Data solutions to fundamentally alter how we consume video content. Not only this, but we can anticipate even more cutting-edge AI uses in video streaming apps as technology develops, which will improve our online entertainment experiences even more.

Are you looking for a reliable partner for Video Streaming App Development that is proficient in implementing AI algorithms? Consagous is the place to look.  

We are the leading provider of video streaming app development in California, offering multiple web and mobile app development services.

Our mobile app developers have extensive experience working on machine-learning algorithms. Moreover, we also design and implement basic and advanced analytics solutions that, combined with AI technologies, can analyze vast amounts of data to identify patterns and trends. This ensures that users are presented with content relevant to their interests, increasing engagement and retention rates.

Further, we at Consagous understand that each client has unique requirements; therefore, we offer customized Video Streaming App Development solutions tailored to the client's specific needs. From research to deployment and ongoing maintenance support – we are committed to delivering excellence at every stage of the development process.

So why wait? Take advantage of our mobile app development expertise and unlock the immense potential within the digital realm. Contact us today to discuss your ideas, and let us help you realize your vision.