case study -- Media and entertainment
Entertainment Web App
A streaming platform moved from solo viewing to shared, data-informed viewing, and gained the analytics to keep improving it.
- industry
- Media and entertainment
- region
- United States
- timeline
- June 2023 - ongoing
project snapshot
Syndell rebuilt a US streaming platform on the MERN stack with Google BigQuery analytics and D3.js visualization, adding watch parties, cross-device sync, and AI-driven recommendations. Live since June 2023, turning solo viewing into a shared, data-informed experience built on Google Cloud.
overview
A streaming and entertainment platform in the USA
Streaming platforms live or die on two things: whether viewers can find something to watch, and whether watching feels social instead of solitary. A United States entertainment client brought both problems to Syndell, alongside a harder one underneath them -- a lack of the data needed to know what was actually working. The rebuild has been running since June 2023, spanning frontend development, backend development, UI/UX design, and QA testing on one MERN-stack codebase.
the challenge
What A streaming and entertainment platform in the USA needed to solve
The client's platform had real content but was missing the connective tissue that keeps modern streaming products competitive: shared viewing, cross-device continuity, and the analytics to guide what to build next.
- 01
Lack of data analytics for competitor analysis and platform improvement
- 02
Shows confined to specific devices, limiting sharing opportunities
- 03
Viewers stuck watching solo with limited interactive options
- 04
Limited engagement and lack of interaction across the platform
- 05
Absence of user behavior insights, making content optimization guesswork
the solution
What did Syndell build for A streaming and entertainment platform in the USA?
Syndell built the platform on the MERN stack (MongoDB, Express, React, Node.js), layering in Google BigQuery for data analytics and D3.js for data visualization, all running on Google Cloud.
Google BigQuery implemented for comprehensive data gathering and competitor analysis
Cross-platform sync and sharing so a show is never locked to one device
Watch parties, co-viewing, and real-time chat for a genuinely shared viewing experience
Enhanced data representation via D3.js for clearer competitor and usage analysis
Transparent, explained recommendations that build user trust and encourage exploration
technology stack
- MongoDB
- Express
- React
- Node.js
- Google BigQuery
- D3.js
- Google Cloud
the outcome
What did this project achieve?
The rebuild shipped a broad feature set aimed squarely at the engagement and data gaps identified at the start, and the platform has continued evolving since its June 2023 launch.
results below are self-reported by the client, as published in the original case study on syndelltech.com
AI-driven personalized recommendations built on real usage data rather than static rules
Multi-device streaming with cross-device resumption and offline viewing
Watch parties and real-time chat turning solo viewing into shared viewing
A BigQuery-backed analytics foundation for ongoing competitor and content decisions
what this means for you
If your streaming or media product has content but not community, adding social features and real usage analytics together, the way this build did, is what turns passive viewers into engaged ones. Your platform needs to know what viewers actually do, not just what they say they want.
why it worked
Streaming products compete on retention as much as content, and retention is won in the small frictions: can I keep watching on another device, can I watch with someone else, does the platform know what I actually like. Syndell's build targeted exactly those frictions, with the BigQuery analytics layer giving the client a way to keep making that case with data instead of guesswork. Running Google Cloud underneath the MERN stack also meant the BigQuery pipeline and the application layer could scale together as usage grew, rather than analytics becoming a bottleneck the product had to work around.
This project drew on Syndell's full-stack development practice. Source case study reviewed on the live Syndell site at syndelltech.com.
more work
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frequently asked
Questions about this project
01How do streaming platforms add social viewing features?
Syndell built watch parties, co-viewing, and real-time chat directly into this platform, alongside cross-platform sync so a show is never locked to one device.
02What analytics does a streaming platform need for content decisions?
This build layered Google BigQuery for data gathering and competitor analysis with D3.js for visualization, giving the team the user behavior insight it lacked before.
03Can recommendations be personalized without existing user data?
The platform now generates AI-driven personalized recommendations built on real usage data gathered through BigQuery, rather than static, rule-based suggestions.
04What tech stack supports a scalable streaming app?
This platform runs on the MERN stack (MongoDB, Express, React, Node.js) on Google Cloud, with BigQuery and D3.js handling analytics and visualization at scale.
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proof -- AI-driven personalized recommendations built on real usage data rather than static rules
