Analyzing the consumer behavior insights in OTT market

Over the past years, the Over-The-Top (OTT) industry has experienced tremendous expansion that has reshaped the media and entertainment landscape. The OTT market has rapidly evolved due to rising internet usage, multiple players, technological improvements, shifting consumer needs, and regional OTT preferences. A number of industry giants, such as Netflix, Amazon Prime Video, Disney+, and HBO Max, as well as smaller firms like Viu (Southeast Asia), Hotstar (India), and iQIYI (China) dominate the market.

The majority of traction in OTT is carried out by customer’s viewing preferences and “mining” customer data. Some of the behavioral insights are:

Data-Driven Personalization: OTT platforms are harnessing data to drive personalized marketing strategies. Data-driven personalization involves analyzing user behavior, preferences, and viewing patterns to deliver a more tailored user experience. From personalized “thumbnails” to “Because You Watched” suggestions, these platforms use algorithms to present content that aligns with individual tastes.

• User Behavior Tracking: OTT systems monitor a range of user activities, including device choices, viewing duration, interaction with content (likes or dislikes), search history, and watch history. Understanding user preferences and interests is aided by this data.
• Content Preferences: OTT platforms can make content recommendations that are in line with user preferences by analyzing the types of content that consumers enjoy, including action films, romantic comedies, documentaries, and kid’s programs. This ensures customer retention in each area being served.
• Demographic Data: Age, gender, region, language, and lifestyle can offer insightful data about content preferences, enabling OTT platforms to successfully segment their audience and develop ads that are specifically tailored to them.

User Data Tracked for Personalized Marketing: OTT platforms use a variety of data types to personalize content and marketing messages:

• Behavioral Data: This includes watch history, browsing behavior, clicks, and search queries. Understanding which content genres or categories users interact with helps platforms recommend similar content.
• Contextual Data: This includes data about the time of day, device type, location, and internet speed. Contextual information helps optimize the user experience, such as suggesting mobile-friendly content for users on smartphones or low-data usage options in regions with slower internet speeds.
• Feedback Data: Ratings, reviews, and content likes/dislikes provide direct user feedback. This data is essential for refining recommendation algorithms and creating a more personalized experience.
• Social Data: Some OTT platforms also integrate with social media to track social interactions around their content, such as shares, comments, and mentions. This data is often used to identify trending content and promote it accordingly.

Consumer Behavior in the OTT Market

The market diffusion of OTT services is attributed to their web-based accessibility as watching movies or television shows has become easier as thanks to streaming services and video-on-demand (VoD). Due to COVID-19, the way that consumers interact with OTT platforms has changed dramatically due to a shift in trend providing:

• Content Quality and Variety: The diverse options and high-quality content give consumers a wide range of options to choose from. The content availability is based on the preference of viewers to select from a large genre of content (documentaries, live sports, TV shows, films, live streaming).
• Regional Content Availability: There has been a rise in the regional OTT platforms also due to economic status, language, demographics, and cultural preferences. There is now a greater demand for subtitles and locally produced content with regional languages. Localized content investments typically result in increased engagement and retention rates for platforms. Platforms that provide diverse regional content in local languages cater better to consumer preferences, which leads to higher user engagement. 
• Pricing models and Subscriptions: Consumers are price-conscious. Ad-Based Video on Demand (AVOD), Subscription Video on Demand (SVOD), and freemium pricing models are examples of flexible pricing strategies that are becoming more popular. Various subscription options (basic, standard, premium) and competitive pricing give customers the flexibility to select according to their budget and screen requirements.
• User Interface and Experience: Easy navigation, personalization, and compatibility with numerous devices are essential for a seamless user experience that keeps users coming back.
• Binge-watching and On-Demand Content: The availability of entire seasons and on-demand material has greatly influenced consumer tastes by fueling the binge-watching culture.
• Experience Without Ads: A sizable percentage of users want experiences without ads, and they are prepared to pay more for continuous viewing. The demand for uninterrupted, ad-free viewing experiences is growing.

OTT behemoths are making significant investments in original series and films in an effort to set themselves apart from rivals. OTT platforms track user activities to stay competitive, decide distribution strategies, decide on content creation, and provide services accordingly. However, as these platforms develop further, they pose a threat to consumer privacy and ethical practices. Hence, they need to strike a balance between privacy and personalization to ensure that data is used ethically and judiciously.