Top trends of data engineering in OTT

The rise in OTT in the number of platforms leads to innovation of Data engineering in OTT in terms of user personalization, personalized recommendation.

Subscription-based on-demand content and media platforms have risen in demand over the years as they offer more flexibility and better entertainment to users. As OTT platforms replace traditional TV sets, they are becoming the preferred medium for consuming content, be it around entertainment, news, sports streaming, or education.

Resulting in overwhelming competition Data engineering for optimizing user experience has been a requisite for any over the top platform.

In India, the growth of OTT players is astounding. According to a report by the Boston Consulting Group, the Indian OTT market will grow ten times to reach $5 Bn by 2023 from $500 Mn in 2018.

As television programming undergoes a renaissance, the promise of more and better data allures advertisers. Data and analytics stay as the key to television’s profitability. Advertisers now have more access to data due to OTT platforms, while content providers and networks leverage data to guide programming decisions.

The Need for Data Engineering in OTT Content.

The growing abundance of data available to marketers, advertisers, and content providers makes it necessary to have systems in place to analyze, process, and transform this data into something meaningful.

Data engineering in OTT

To offer better personalization, content discovery, and push the continuous development and transformation of underlying systems, it is critical for OTT platforms to use data and Data engineering in OTT to its full potential.

How OTT Players are Leveraging Data and Engineering in OTT?

Data to drive content

For homes that have a subscription service, OTT players are using data to guide viewers to the right content. The most famous example of data-driven viewership is Netflix’s House of Cards. The content is designed to be a hit considering platform data such as ratings, preferences, user viewing habits, and so on.

Netflix’s data pipeline is fed with data from millions of set-top boxes and online accounts, which the company processes and stores using the Hadoop ecosystem and leverages Amazon’s AWS platform for cloud computing resources. Data from A/B testing has led Netflix to insights that have given tangible results such as an up to 30 percent increase in a particular content’s viewership. 

Personalization for loyal viewing and revenue

Alluring and retaining the generation of cord-cutters and cord-never takes more than great content. These viewers expect tailored services in the form of personalization, served based on their preferences and choices.

Personalization is not just about the content. Everything from subscription plans to metadata, frontends, and recommendations can be personalized by breaking through data engineering for greater impact in OTT in terms of viewership, which ultimately drives revenue.

Enhancing user experience

All OTT players sit on a huge pile of relevant data about their viewers and their behaviors. By putting this data through a cleaning process and leveraging insights for improving the recommendation engine or personalization feeds, OTT players are enhancing the user experience.

By knowing what will keep users coming back for more, companies are making personalized recommendations and accurate predictions for cross-selling or upselling opportunities.

Having a more nuanced and sophisticated view of the customer can help companies keep track of this trend in data analytics and OTT media suggestions.

Personalized Emails and Push Notifications

Customer-specific data collected by means of AI and ML, and churned with the help of analytics, helps OTT players recommend shows, series, and movies to viewers through personalized push notifications and emails.

Since both of these marketing media are proven to have exceptional RoI, OTT companies can now use them to make relevant recommendations on the basis of viewer interest and viewing behavior.

Furthermore, the push notifications and emails can be timed for when the user is more likely to access the OTT media site or mobile app- for greater impact and conversion. 

Personalized digital advertising

Programmatic media promises data harnessing for advertisers, using set-top box data to inform OTT players about viewers and their behavior in consuming content. Building on the viewer’s inclination for customized viewing experiences, programmatic television can deliver unique advertisements, even though viewers are watching the same programming.

According to eMarketer, only 5-10 percent of the TV inventory is available and addressable for programmatic. Even then, the market looks massive and promising. Programmatic advertising is all about the efficient spending of dollars by careful targeting and retargeting.

This is why data and analytics look vital for personalized digital advertising.

Omnichannel advertising

Companies are now working with entertainment players to measure audience metrics across platforms and channels. By aggregating viewers and their behavior, companies get a more holistic view of their audience, offering advertisers opportunities to execute an omnichannel advertising plan.

By augmenting data such as viewing history, demographics, political inclination, and so on, companies can now offer in-depth insights to advertisers for improved ad delivery and execution.

Converging OTT with E-commerce

OTT ad spending has skyrocketed, and providers are maximizing their investments in correlating viewership with consumer purchasing behavior. The convergence of OTT with e-commerce, driven by analytics and personalized advertising is opening up better and more avenues for profit for providers.

AI and ML-based content creation and recommendation

The cutting-edge technologies of artificial intelligence and machine learning are also playing a strong role in facilitating content discovery, creation, and recommendation in the OTT arena.

A personalization approach backed by these sophisticated technologies can help companies make leaps of progress in regions such as India where people in the regional market have their own sensibilities and entertainment preferences, besides language inclinations.

Data Engineering in OTT for Better Viewership and Viewer Retention

Data available to OTT players needs to be churned and utilized for insights so that effective decision-making can be practiced. When that happens, data utilization will be the driving fuel behind the success of OTT players and their expansion.

Speaking what user wants is vital for optimized customer experience and engagement. Leading-edge advancement in data engineering in ott has helped itself in developing a user-centric approach which has been a game-changer.

At RecoSense, we use artificial intelligence to build deeper insights across touchpoints for OTT players and digital media providers. Learn more about how we do it here.

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About RecoSense

RecoSense offers AI-based Customer Data Platform with data intelligent solutions for unified customer view, product discovery, advance search, and user engagement solutions. RecoSense has built expertise in technology areas of Data Mining, Text Analytics, NLP, and Knowledge Graph to build deep context definition, User Personalization & Recommendation, Actionable Insights, Analytics, Marketing Automation Integration, and Data Visualization.

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