Simulating genuine viewer engagement on Glance TV with emojis
Case study
Role: UX/UI design (individual contributor)
Company: Glance TV, Inmobi group
Year: 2023
Introduction: Enhancing viewer reactions on Glance TV
Glance TV is a dynamic application for Mi TVs, Samsung TVs, and Android/Google TV devices that delivers engaging video content across various genres. A standout feature is its interactive emoji system, which allows viewers to react to content in real-time, fostering a sense of community and engagement.
The challenge: Overcoming artificial emoji interactions
Initially, the emojis were system-generated and mechanical, failing to provide the authentic social interaction desired by viewers. The primary goal was to revamp the emoji interaction system to make it appear as if reactions were coming from other viewers, fostering a stronger sense of community and viewer connection.
The objective: Creating authentic and engaging viewer connections
The goal was to transform the emoji interaction system so that it felt as though the reactions were coming from real viewers, thus enhancing the sense of community and engagement.
Previous implementation: The struggles with mechanical emoji responses
The earlier implementation featured system-generated emojis in the background that appeared mechanical and artificial which were rising always taking away the sense of timing and naturality. This setup failed to deliver a genuine interactive experience, detracting from viewer engagement rather than enhancing it.
Identifying the problem: Understanding why viewers disliked the emojis
Despite achieving an average of 800 daily visits, Glance TV faced significant challenges with viewer retention, as many users exited the platform prematurely. User testing and feedback highlighted that the constant, system-generated emojis were perceived as fake and distracting, undermining the intended social interaction.
User feedback
The feedback was overwhelmingly negative regarding the emoji interaction. Users expressed that the emojis, which were intended to enhance engagement, were instead detracting from the experience. The constant, system-generated flow of emojis was perceived as artificial and distracting. Test participants could easily discern that the reactions were not from real users, which undermined the authenticity of the interactive experience and, ironically, made the platform feel less engaging.
The call for change: Recognizing the need for a major overhaul
It became clear that a significant overhaul of the emoji system was necessary to create a genuinely interactive and enjoyable user experience. The challenge was both technical and rooted in user perception and experience design.
Research and insights: Learning from viewer feedback to drive innovation
Secondary research findings:
Interactive TV engagement: TV platforms with interactive elements see up to a 30% increase in viewer engagement
Competitor benchmarking: Competitors integrating seamless interactive experiences report up to 25% higher retention ratesSocial presence impact: Simulated social presence enhances satisfaction levels by over 40%
Technical advancements: Advances in AI and algorithmic modeling show promising results in simulating human responsesViewer preferences: Over 60% of viewers prefer interactive features that are subtly integrated
Technical advancements: Advances in AI and algorithmic modeling show promising results in simulating human responses
Viewer preferences: Over 60% of viewers prefer interactive features that are subtly integrated
User insights
Conducted user interviews to gather deeper insights into how viewers wanted to interact with content.
Findings
75% felt the current emoji interactions were too rigid and predictable
Common feedback highlighted a preference for more organic and less intrusive emoji displays
Data analysis: Identifying engagement patterns and areas for improvement
Utilized data analytics to refine the realism of emoji interactions.
Key findings:
Drop-off rates: Data showed a 35% increase in early drop-offs when emojis were displayed continuously from the start of videos
Engagement with emojis: Only 10% of viewers interacted with emojis, indicating low engagement and perceived relevance
Applying insights to design decisions: Designing realistic and relevant emoji interactions
Context-sensitive interactions: Implement a technical algorithm to sync emoji reactions with viewer inputs and content narrative peaks for enhanced relevance
Enhanced emoji set and usability: Broaden the emoji selection and refine the user interface to better match the full range of viewer emotions and improve interaction ease
Dynamic interaction model: Adapt emoji display frequency and visibility based on real-time user engagement and content intensity to prevent viewer overwhelm
Design iterations: Developing more authentic emoji engagement
Emoji set refinement:
Reduced the number of emojis to those most relevant and emotionally resonant with the content, based on viewer feedback.
Exploring diverse ideas:
As part of enhancing the user experience on Glance TV, a range of innovative ideas were explored to optimize emoji interactions. Concepts included:
Final selection: Sine wave graph theory
This method captures the natural flow and spontaneity of human responses, aligning closely with our goal to make digital interactions feel lifelike. It allows emojis to appear at moments of high emotional impact, enhancing the viewing experience without overwhelming the audience.
Inspiration behind the concept: Using rhythm and storytelling to enhance interaction
The sine wave theory was inspired by Freytag’s Pyramid in storytelling, which outlines the natural rhythm of exposition, rising action, climax, falling action, and resolution. This mirrors a sine wave, suggesting a rhythm that enhances emotional engagement. Additionally, rhythms in music and animation informed this approach, using timing and pacing to create lifelike, engaging interactions in video content.
Application to Glance TV video content: Synchronizing emojis with emotional beats in video content
Emotional rhythm in storytelling:
By synchronizing emoji displays with key points in the narrative and emotional rhythm of the video, the interactions become more intuitive and contextually integrated, enhancing the overall viewer experience without disrupting the narrative flow.
Technical implementation
The sine wave algorithm customizes emoji appearances by analyzing natural beats in the video, such as moments of high action, gradual developments, and quieter periods. Developers can manipulate the algorithm to align emoji interactions with these beats, integrating audio and visual cues to detect and respond to key moments.
Algorithm development: Creating natural interactions with sine wave theory
The sine wave algorithm customizes emoji appearances by analyzing video content, audio cues, and viewer engagement data. It generates a dynamic sine wave pattern, allowing developers to fine-tune emoji timing and frequency to align with key video moments. This ensures emojis appear naturally, enhancing the viewer experience without overwhelming it.
Design solution iterations: Testing and refining emoji interaction strategies
Watch the design demo: Observe how emojis rise and fall in sync with the sine wave graph, simulating a natural flow of viewer interactions.
Refining emoji interaction strategies
Rolled out different versions of the emoji interaction system to segments of the user base to test and refine the approach based on actual usage. In these demos, you can observe:
Incoming emojis based on sine wave theory, which evoke the feeling of being sent by other viewers
Emojis being actively sent by the viewer currently watching Glance TV.
Final solution implementation: Meeting platform needs with enhanced features
The final solution, incorporating changes requested by Samsung for their platform, includes removing the comments section on the right and enabling full-screen video playback.
Results: Achieving positive reception and enhanced viewer experience
Internal feedback:
Project management and technical teams: Significant improvements in integration and functionality were noted
Executive approval: The VP and the co-founder of Glance TV expressed satisfaction with the innovative approach and execution quality
Viewer reception:
Although specific post-release metrics are not available, anecdotal feedback from viewers was positive, reporting improved engagement and a more genuine viewing experience
The design is effective even without a real social scenario, simulating real-time interaction authentically
Reflections: Insights gained and plans for future improvements
Insights gained:
User-centric design: Reinforced the importance of user feedback in the design process
Iterative design: The iterative approach proved crucial, allowing quick adaptation to user needs.
Viewer reception:
Although specific post-release metrics are not available, anecdotal feedback from viewers was positive, reporting improved engagement and a more genuine viewing experience
The design is effective even without a real social scenario, simulating real-time interaction authentically
Future considerations:
Continual monitoring and refinement: Continuous monitoring of user interactions is essential to ensure ongoing effectiveness and engagement
Expansion of interactivity features: Further exploration of additional interactive elements could enhance the social experience of Glance TV
Algorithm improvement: The current algorithm is a work in progress. We aim to refine it continuously, leveraging user behavior data to improve accuracy and ensure natural, engaging emoji interactions
Thank you!
Company: Glance TV
Project: UX/UI design, Motion design
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