Inspiration
This project was inspired by a simple but frustrating habit we all share: opening a streaming platform, scrolling endlessly, and still not knowing what to watch. Even when we finally choose something, we often abandon it halfway through. The paradox became clear to us — despite powerful algorithms, content discovery feels harder than ever. We realised this wasn’t a content problem, but an attention and trust problem. Algorithms optimise for engagement, but people trust friends. At the same time, modern users suffer from decision fatigue and reduced attention spans, a consequence of constant dopamine stimulation. This combination leads to low completion rates and passive consumption.
What it does
Our platform adds a social and incentive-driven layer on top of existing streaming services. Users can recommend movies and series directly to friends, bypassing algorithmic noise. When a recommendation is accepted and the content is fully watched, both users earn points that can be redeemed for rewards such as digital vouchers or brand discounts. This helps users decide faster, stay engaged longer, and finish what they start watching.
How we built it
We designed a system that connects to existing streaming services and adds a social recommendation layer. Users can recommend movies or series directly to friends. When a recommendation is accepted and fully watched, both users earn points. These points can be exchanged for rewards provided by partner brands, creating a win-win ecosystem. At its core, the platform transforms content discovery from a solitary, algorithm-driven process into a shared, goal-oriented experience. Conceptually, the idea can be summarised as: Better Trust + Social Incentives=Higher Engagement
Challenges we ran into
One of the main challenges was balancing simplicity with meaningful incentives. We had to ensure that rewards encouraged genuine engagement rather than superficial interactions. Another challenge was designing a system that complements existing streaming platforms instead of competing with them. Finally, translating an abstract problem like attention fatigue into a concrete, scalable solution required constant iteration and user-centric thinking.
Accomplishments that we're proud of
We’re proud of identifying a real and relatable problem and translating it into a scalable, user-centric solution. Creating a concept that aligns the incentives of users, platforms, and brands was a major achievement. We also successfully designed a system that improves engagement without introducing more algorithmic complexity.
What we learned
Throughout the project, we learned that social trust outperforms algorithmic relevance when it comes to decision-making. We also explored how incentives and gamification can positively influence user behaviour, increasing motivation and commitment. From a technical and product perspective, we gained insights into integrating social layers on top of existing platforms without replacing them.
What's next for NextWatch
The next step for Next Watch is turning the concept into a fully functional product. Our priority is to build the actual software: a scalable backend to handle recommendations, social interactions, and reward logic, and a user-facing mobile app that integrates seamlessly into existing streaming habits. We plan to validate the idea through a closed beta with real users, focusing on key metrics such as recommendation acceptance rate, content completion rate, and retention. Based on this data, we will iterate on incentive mechanisms to ensure they drive genuine engagement. On the business side, the next phase includes forming partnerships with brands for reward offerings and exploring collaboration models with streaming platforms. Long-term, Next Watch aims to become the social recommendation layer for streaming — independent of platforms, but deeply embedded in how people decide what to watch.
Log in or sign up for Devpost to join the conversation.