SV
DeepaG Media Group · Product Division
Product Requirements Document · Version 2.4
StreamVault
Unified Content Discovery & Personalization Platform
Document Owner
Deepa Gorrela
Role
Product Owner
Initiative Code
PRJ-SV-2025
Status
In Development
Created
January 15, 2025
Last Updated
May 5, 2026
Target Launch
Q3 2026
Product Pillar
Streaming · Engagement
00 · Executive Summary
Solving the Discovery Problem at Scale

DeepaG Media's 47M subscribers are churning at 18% annually, citing "can't find anything to watch" as the #1 exit reason. StreamVault introduces a real-time, AI-driven content discovery layer across all DeepaG properties — DeepaG+ Movies, DeepaG Kids, DeepaG Sports, and DeepaG NeDeepaG — replacing four fragmented search experiences with one intelligent platform.

$340M
Projected Annual Revenue Lift
-6pp
Target Churn Reduction
47M
Subscribers Impacted
18mo
Go-Live Timeline
01 · Problem Statement
Why Now — The Discovery Crisis

Our subscribers are overwhelmed by volume and underwhelmed by relevance. Four separate content verticals each run their own recommendation stack — resulting in disconnected experiences, duplicated engineering cost, and a perception of chaos.

Research Insight

Subscriber exit surveys (n=12,400, Q4 2024) show 64% of churned users cited "content I wanted felt impossible to find" as a primary or secondary reason for cancellation — above price sensitivity (52%) and content quality (41%).

Current State Pain Points

Pain Point Affected Segment Severity Evidence
Fragmented search across 4 apps All subscribers Critical Avg. 4.2 taps before content selection
No cross-vertical recommendations DeepaG+ / DeepaG Sports overlap Critical $0 cross-sell revenue captured
Stale personalization models DeepaG Movies, DeepaG Kids High Models retrained weekly; cold-start lasts 72h
No mood/context-aware surfacing All subscribers High CTR on homepage rails: 2.1% (industry avg 6.8%)
Missing watchlist sync Multi-app users (31%) Medium NPS drag of -7 points among dual-app users

Business Opportunity

Each percentage point of churn reduction retains approximately 470,000 subscribers at an average annual value of $144. A 6-point reduction — achievable based on Netflix's 2018 discovery overhaul benchmarks — represents $407M in prevented annual revenue loss. The total addressable opportunity, including upsell to Sports tier, exceeds $500M over three years.

02 · Target Users & Personas
Who We're Building For

Research distilled 8 qualitative interview clusters and 3.2M behavioral data points into three primary personas representing 84% of our subscriber base by watch-time.

SB
Sarah, "The Busy Binge-er"
34 · Marketing Director · Family Plan
Sessions/week5–7
Avg. session52 min
Primary deviceSmart TV
Pain pointDecision fatigue
GoalFast, trusted picks
SegmentPower User
MC
Marcus, "The Sports Superfan"
28 · Software Engineer · Individual Plan
Sessions/week9–12
Avg. session87 min
Primary deviceMobile + TV
Pain pointCan't find live + VOD together
GoalOne stop for all sports
SegmentSports Tier
EL
Elena, "The Casual Dipper"
52 · Teacher · Shared Family Plan
Sessions/week2–3
Avg. session28 min
Primary deviceTablet
Pain pointOverwhelmed by choice
GoalCurated, not algorithmic
SegmentAt-Risk
Design Principle

StreamVault must serve Elena as confidently as Marcus. We will not optimize solely for power-user engagement metrics — casual users represent 38% of our subscriber base and churn 2.3× more readily than power users.

03 · User Stories & Acceptance Criteria
Epic: Unified Discovery Engine

The following stories represent the MVP scope for the StreamVault discovery layer. All stories have been prioritized using MoSCoW and estimated in Sprint story points by the engineering team.

Must Have
US-001 · 8 pts · Sprint 1–2
As a subscriber, I want a universal search bar that searches across all DeepaG verticals simultaneously, so that I don't have to switch between four apps to find content.
Acceptance Criteria
Search results return within 400ms for 95th percentile of queries
Results are grouped by vertical (Movies, Sports, Kids, NeDeepaG) with clear labels
Search supports natural language: "something funny for date night" returns valid results
Recent searches persist across devices for authenticated users
Must Have
US-002 · 13 pts · Sprint 2–4
As a subscriber, I want the homepage to automatically surface content relevant to my current context (time of day, device, recently watched), so I spend less time broDeepaGing and more time watching.
Acceptance Criteria
Homepage rail updates in real-time; no stale cache older than 15 minutes
Morning (6–10am) rail prominently features NeDeepaG and short-form content
Weekend evenings surface new release movies and live sports above the fold
Model explainability: at least one rail label explains why content was surfaced (e.g., "Because you watched The Batman")
Should Have
US-003 · 8 pts · Sprint 3–4
As a family plan subscriber, I want distinct profiles with per-profile recommendations, so that my children's watch history does not contaminate my personal recommendation feed.
Acceptance Criteria
Up to 6 profiles per account; each maintains independent watch history and preferences
Kids profiles are locked to age-appropriate content by default
Profile switching takes < 2 seconds on all supported devices
Could Have
US-004 · 5 pts · Sprint 5
As a Sports subscriber, I want a "Live Now" persistent badge on sports content so I can instantly identify what's airing in real time without navigating to the Sports vertical.
Acceptance Criteria
Live badge appears on content tiles wherever they surface (search, homepage, watchlist)
Badge updates within 60 seconds of a live event status change
Clicking a live badge drops user directly into the live stream, not an interstitial
04 · Product Roadmap
18-Month Delivery Plan

Delivery is structured in three phases, each unlocking incremental subscriber value while building the infrastructure for the next. Phase gates require sign-off from Product, Engineering, and Executive sponsors.

Phase 1
Foundation: Unified Data Layer
Q1–Q2 2026 · Sprints 1–8
Universal search API
Cross-vertical data pipeline
Profile architecture
Real-time event bus
A/B testing framework
Phase 2
Intelligence: Personalization Engine
Q2–Q3 2026 · Sprints 9–14
ML recommendation model v1
Context-aware rails
Live content integration
Explainable AI labels
Watchlist sync
Phase 3
Scale: Cross-Sell & Monetization
Q3–Q4 2026 · Sprints 15–18
Tier upsell nudges
Social/group watchparty
Third-party content integrations
Conversational search (NLP v2)
Phase Gate Dependency

Phase 2 cannot begin until the cross-vertical data pipeline (Phase 1) passes data integrity validation. Engineering lead estimates a 3-week buffer to accommodate schema alignment between the Sports real-time system and the Movies/Kids Snowflake warehouse.

05 · Success Metrics & OKRs
How We Measure Victory

Metrics are organized by the HEART framework (Happiness, Engagement, Adoption, Retention, Task success). Baseline values drawn from Q4 2025 data. Targets set for 12 months post-launch.

Retention
Annual Subscriber Churn Rate
18% Baseline
12% Target
15% Minimum
Engagement
Homepage Rail Click-Through Rate
2.1% Baseline
6.5% Target
4.0% Minimum
Task Success
Time to Content Selection
4.2min Baseline
1.8min Target
2.5min Minimum
Happiness
Net Promoter Score — Streaming Experience
+31 Baseline
+52 Target
+42 Minimum
06 · Stakeholder Map & RACI
Roles & Responsibilities

The RACI matrix defines decision-making authority and communication expectations across all key activities. All escalations route to the Executive Sponsor within 48 hours.

Activity Product Owner Eng Lead Data Science UX Design Legal / Privacy Exec Sponsor
Backlog prioritization R C I C A
ML model architecture I C R A
UX / Design review A I R I
Privacy impact assessment C C C R A
Go/no-go launch decision C C I I C A / R
Post-launch metric review R C C I A
R Responsible   A Accountable   C Consulted   I Informed
07 · Risk Register
Identified Risks & Mitigations

Risks assessed using a 5×5 likelihood × impact matrix. All High-rated risks have assigned DRI (Directly Responsible Individual) and mandatory bi-weekly review cadence.

Risk
Likelihood
Impact
Level
Mitigation
ML model underperforms cold-start for new subscribers
High (4)
High (4)
High
Rule-based fallback for first 10 sessions; editorial "New to DeepaG" rail guaranteed above fold
GDPR / CCPA non-compliance on behavioral data usage
Medium (3)
Critical (5)
High
Privacy-by-design review gating Phase 2; Legal sign-off required before ML model enters prod
Legacy Sports real-time API schema breaks data pipeline
Medium (3)
Medium (3)
Medium
Schema contract tests added to CI/CD; dedicated Sports platform engineer embedded in team
Subscriber backlash to algorithm-only homepage (no editorial control)
Low (2)
Medium (3)
Low
Editorial team retains ability to pin up to 3 featured titles per rail; prominence config dashboard planned for Phase 3
08 · Document Ownership & Approvals
Prepared By
JA
Deepa Gorrela
Senior Product Owner · Streaming & Discovery · DeepaG Media Group
v2.4 Current
Department
Product — Streaming Experiences
Reporting To
SVP Product, Direct-to-Consumer
Document Cadence
Bi-weekly review; major rev at each phase gate

Approvals Log

Approver Title Version Date Status
Patricia Wren SVP Product, DTC v2.0 Feb 12, 2026 Approved
Daniel Kim VP Engineering, Platform v2.0 Feb 14, 2026 Approved
Simone Okafor Chief Privacy Officer v2.2 Mar 28, 2026 Approved
James Whitfield Executive Sponsor / CTO v2.4 Pending
Portfolio Note

This document was prepared as a portfolio artifact demonstrating Product Owner and Business Analyst competencies including: stakeholder alignment, user story writing with acceptance criteria, roadmap planning, RACI construction, risk management, and OKR/HEART metric frameworks — within a fictional major media company context.