3 Reasons Why Business Innovation Matters in OTT

The habits of consumers worldwide have already changed significantly since the advent of online streaming, but the COVID-19 pandemic sped up this transformation even more. For a typical consumer, social distancing regulations not just meant washing hands and wearing a mask, but it also meant seeking alternative entertainment sources to escape reality. The flurry of online streaming platforms like Netflix, Amazon Prime, and Disney+ filled this gap quite perfectly. 

While OTT platforms were already witnessing a viewer incline, the recent world events helped them surpass even their most optimistic growth forecasts. For example, in the last few months, Netflix more than doubled their new subscriber projections, and Disney+ achieved something similar by reaching the 50 million subscribers mark. 

But with the foray of giants like HBO, Apple, Disney, and AT&T in the market, the battle for grabbing viewer attention is heating up. The plethora of offerings in front of the viewer makes it challenging to compete and grab a piece of this money-making pie. Simultaneously, technological shifts in media management and delivery empower organizations to build backends that are effective and efficient. Technological personalizations help OTT providers stand out from the crowd with applications and interfaces that focus on stakeholders and end-users alike.

Three most crucial reasons organizations should rethink their OTT strategy and create innovative business models for monetization are as follows:

Subscription fatigue is real in the OTT industry. As the number of services increases, so does the difficulty of choosing the viewers. A global survey revealed that almost 60.1% of the viewers are tired of signing up for new services, and about 22.8% can't even keep up with the new services that are entering the market. More and more direct to consumer and niche offerings entering the market leads to a growth in this fatigue and makes it challenging for existing providers to find methods to monetize their services. 

Multiple subscription or viewing options, i.e., having subscription tiers or alternate monetization models, should be an industry norm. Subscription-based models supplemented with alternate advertising or pay-per-view models are some increasingly popular methods for monetization. Another popular method gaining traction is bundling different providers or creating telco-OTT packages. These bundles provide more value to the customers and better bulk deals for the providers. 

Similar to traditional broadcasters, OTT providers also need to see advertising as a source of revenue. SVOD (Subscription video-on-demand) services have set consumers' expectations of user experience much higher. As AVOD (Advertising video-on-demand) services become common grounds, serving relevant and targeted advertisements is crucial.

Dynamic advertising technology is utilized by many big providers today to serve relevant, personalized, and targeted ads to their viewers. DAI (Dynamic ad insertion) coupled with SSAI (Server-side ad insertion) method can seamlessly stitch relevant advertisements to the viewer's video stream, without being intrusive for the viewer and securing the ads from blockers. These ads can be explicitly targeted to viewers based on their viewing habits, tastes, and demographics to increase conversion rates.

The concept of big data has been there for ages. Even before the term existed, broadcasters were using TRPs and localized surveys to tune show timing, product placement, advertising, and slot pricing. But now AI (Artificial intelligence) and ML (Machine learning) empower providers in all industry verticals to leverage this big data more efficiently to find hidden correlations and valuable insights.

Today, OTT providers can leverage AI and ML not only to create personalized recommendations, catalogs, and enrich the metadata; they can also explore monetization opportunities. Some of the most innovative ways AI helps in OTT are: 

Scene Analysis - Analysing the video, audio, and subtitle to understand the video's content and the objects in the frame. 

Indexed words and faces - Indexing words through voice to speech and faces through image recognition can enable search and seek to find a particular moment in the video and also helps analyze keywords and famous personalities for context.

Contextual advertising - The in-depth analysis, image recognition, indexed words, and faces provide enough keywords and information to prepare an accurate model for highly contextual advertising. Ads related to a particular actor/actress, object on the screen, or related to the sentiment can automatically be stitched in the video or pushed on other screens in the form of notifications.  

Multi-screen experience - The enhanced experience can be carried over to a second screen, for example bringing the metadata access, advertisement, or e-commerce notifications to supplementary devices.

For streaming services, the end goal is to provide exceptional user experience paired with valuable content. Enhancing the viewer experience will not just yield better revenues but also help extend global audiences.

But the reasons, as mentioned earlier for innovation, can only be realized when providers have the right infrastructure. The growing ecosystem of best of breed solutions for AI, advertising, and content management needs an infrastructure that allows for integrations while not ending up in huge monoliths with zero extensibility and stakeholder concern. The infrastructure should leverage modern SOA (service-oriented architecture) to create efficient and effective media supply-chain.