Media & entertainment

Stream smarter. Engage deeper. Monetize every viewer.

We build OTT streaming platforms, content management systems, creator tools, and subscription billing engines in 12 weeks. Software that cuts churn, grows watch time, and turns content libraries into revenue machines.

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15+

Media products

40%

Churn reduction

28%

Watch time increase

Overview

Your subscribers are leaving because discovery is broken

Media software development at RaftLabs focuses on the three areas that most directly move subscriber lifetime value - content discovery, engagement retention, and operational efficiency. We bring patterns from 100+ products across adjacent industries to build recommendation engines, churn prediction systems, content pipelines, and streaming infrastructure - each engineered for measurable watch time and retention lift.

The streaming market is saturated and attention is the scarcest resource. Viewers subscribe to 4-5 services, watch one show, and cancel. The average streaming platform loses 5-7% of subscribers monthly - not because the content is bad, but because discovery is broken, engagement drops after onboarding, and there's no personalized reason to stay.

Our recommendation engines surface the right content for each viewer - not just what's popular. Churn prediction systems trigger win-back campaigns before subscribers hit cancel. Content management platforms get new titles from ingest to streaming in hours instead of days.

Every product handles the technical complexity of video at scale - adaptive bitrate streaming, multi-CDN delivery, DRM protection, and global distribution. We integrate with your existing CMS, billing, and analytics stacks.

Experience Signal

15+ media products shipped with OTT streaming platforms, recommendation engines, content management pipelines, and subscription billing systems for streaming services, sports networks, and content studios. 12 weeks is our default, not our stretch goal.

15+

Media products

40%

Churn reduction

See case study

Industry Pain Points

What's broken in media & entertainment

01

Content discovery relies on manual editorial curation and basic genre matching - viewers see the same promoted titles regardless of their actual preferences

02

Subscriber churn runs 5-7% monthly because platforms lack personalized engagement after the initial onboarding period

03

Content ingestion and cataloging takes 2-5 days per title because metadata tagging, quality checks, and DRM encoding stay largely manual

04

Subscription billing handles one model - monthly or annual - but can't support hybrid models, bundles, trials, or usage-based pricing without custom engineering

05

Creator and rights management lives in spreadsheets, driving royalty disputes, missed license expirations, and compliance gaps

06

Long-tail content gets no exposure even when it would keep a subscriber one more month - catalog utilization sits below 15% on most platforms

Solutions

Problems we solve in media & entertainment

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Content recommendation engine

Goes beyond collaborative filtering. Analyzes viewing patterns, completion rates, mood signals, and content DNA - pacing, tone, visual style - to recommend titles each viewer will actually watch. Personalizes the homepage, search results, and next-up queue.

02

Churn prediction and engagement

Tracks viewing frequency, session depth, content diversity, and billing interactions to score churn risk weekly. Triggers personalized retention - content suggestions, exclusive previews, plan offers - based on each subscriber's pattern.

03

Automated content tagging and cataloging

AI analyzes video content to generate metadata - genre, mood, themes, scene descriptions, key moments, and content warnings. Cuts manual cataloging from days to hours. Enriches search and recommendation quality with granular content signals.

04

Streaming infrastructure and multi-CDN delivery

Adaptive bitrate streaming with multi-CDN failover for global reach. DRM protection (Widevine, FairPlay, PlayReady) for premium content. Live streaming with sub-3-second latency for events and sports. Scales from 1K to 100K+ concurrent viewers.

05

Flexible subscription and monetization engine

Supports SVOD, AVOD, TVOD, hybrid models, and bundles. Handles trials, promotions, family plans, and partner billing. AI-optimized pricing tests different price points and package configurations to grow LTV per subscriber.

06

Creator royalty and rights management

Tracks contracts, license windows, revenue splits, and royalty accruals per title and territory. Kills the spreadsheet-driven disputes that make ops spend every month-end on reconciliation.

Use Cases

Real-world use cases

Recommendation engine for a niche streaming service

Problem

A niche streaming service with 280K subscribers and 4,200 titles had 71% of viewing concentrated in the top 8% of content. Long-tail titles got almost no exposure. Monthly churn was 6.8%.

What we built

We built a recommendation engine that combines collaborative filtering with content DNA analysis - pacing, tone, visual style, and thematic similarity. Personalized homepage rows replace one-size-fits-all editorial picks. Added contextual recommendations based on time of day, device, and viewing history.

Result

Long-tail content viewing climbed 340%. Average watch time per session grew 28%. Monthly churn dropped to 4.1%. Subscriber satisfaction with content discovery improved 2.1 points on NPS.

OTT platform for a regional sports network

Problem

A regional sports network wanted to launch direct-to-consumer streaming alongside linear broadcast. It needed live streaming for 400+ events per year, VOD for replays, and a subscription model with blackout management.

What we built

We built a full OTT platform with live streaming infrastructure (sub-3s latency), multi-CDN delivery, DRM protection, and adaptive bitrate encoding. Added subscription management with regional blackout rules, DVR-like rewind for live events, and multi-device support.

Result

Launched to 45K subscribers in the first 90 days. Live stream quality held 99.7% uptime across 400+ events. Average concurrent viewers for marquee events hit 18K. Subscription revenue added $2.4M in incremental annual revenue beyond linear ad sales.

Content management pipeline for a studio

Problem

A content studio distributing to 12 streaming platforms averaged 4.5 days from final cut to platform availability. Manual metadata tagging, quality checks, and format transcoding across platform specs created bottlenecks and errors.

What we built

We built an automated content pipeline that ingests final cuts, runs AI-powered scene analysis for metadata generation, transcodes to all required formats in parallel, applies DRM per platform specification, and validates quality automatically. Human review covers only AI-flagged edge cases.

Result

Ingest-to-live time dropped from 4.5 days to 6 hours. Metadata accuracy improved from 78% to 96% by platform rejection rates. The team reallocated 3 FTEs from manual cataloging to content strategy. Distribution to all 12 platforms now runs simultaneously.

Proof

Media & entertainmentOTT streaming platform
100,000+

Active users

50%

Churn reduction

“Our long-tail catalog finally earned its keep.”

Read case study

Our Approach

How we approach media & entertainment projects

1
Phase 1· Week 1-2

Content and audience audit

We analyze your content library, viewing data, subscriber behavior, churn patterns, and technical infrastructure. Where you're losing subscribers, underutilizing content, and leaving revenue on the table all get identified.

Deliverables

  • Content performance analysis covering engagement, completion rates, and catalog utilization
  • Subscriber lifecycle analysis with churn triggers and retention patterns
  • Technical infrastructure assessment for streaming quality and scalability
2
Phase 2· Week 2-4

Product design and architecture planning

We design the product with your content, product, and engineering teams. Every component - streaming infrastructure, recommendation algorithms, billing integration, DRM requirements - gets planned before build.

Deliverables

  • Product design validated by content and product leads
  • Streaming architecture with CDN strategy, DRM plan, and encoding specifications
  • Data architecture for viewer profiles, content metadata, and analytics
3
Phase 3· Week 4-10

Build, launch, and beta test

We build in sprints with a beta launch to a subset of subscribers first. Real viewing data validates recommendations, streaming quality, and engagement features before full launch.

Deliverables

  • Working platform deployed to beta audience
  • Streaming quality metrics covering buffering rate, start time, and bitrate adaptation
  • Engagement and recommendation metrics from real viewer behavior
4
Phase 4· Week 10-12

Full launch and engagement optimization

We launch to all subscribers with continuous optimization of recommendations, content positioning, and retention triggers. AI models improve as more viewing data accumulates.

Deliverables

  • Full production launch with all content and features live
  • Viewer engagement dashboard tracking watch time, churn, and content discovery metrics
  • Quarterly optimization plan tied to subscriber retention and ARPU targets

Outcomes

Measurable outcomes

25-40% increase in average watch time per session through AI-powered recommendations
30-45% reduction in monthly subscriber churn through predictive engagement and win-back campaigns
75-85% faster content ingestion through automated tagging, transcoding, and quality validation
99.5%+ streaming uptime with multi-CDN delivery and adaptive bitrate optimization
3x+ long-tail catalog utilization through personalized discovery rows

Free Tools

Free tools for media & entertainment

Calculators and assessments built from real project data. No signup required.

See all free tools

Pattern Transfer

RaftLabs built product recommendation algorithms for e-commerce clients before applying collaborative filtering to content discovery for streaming platforms. Both problems need predicting what a user wants next from behavioral signals. The items changed from products to videos. The recommendation architecture stayed the same.

Services

Services for media & entertainment

Frequently asked questions

Projects range from $80K-$300K depending on scope. A content recommendation engine that plugs into an existing platform starts around $60K. A full OTT platform with live streaming, VOD, DRM, and subscription management runs $150K-$300K. Fixed estimate after a strategy session.

Next Step

Every month at 6% churn means you replace your entire subscriber base in 18 months.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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