Case Study 01 · Product Strategy
The Sonic Blueprint
AI-Powered Audio Story Recommendation Engine
A product enhancement proposal exploring how a 100M+ user reading platform can bridge text and audio discovery — turning passive reading behaviour into a jump-start for personalized audio recommendations.
- Role
- Senior Product Owner (proposal)
- Client
- ThisDay (illustrative)
- Format
- Strategy deck · 14 pages
- North Star
- Avg. audio session duration
01 · Situation · Complication · Question
Scale
Situation
Massive scale & new formats — 100M+ unique users, 300% YoY growth, and a successful launch of studio-quality audio stories.
Friction
Complication
The cross-format silo. High friction in discovery — users are locked into text reading habits and audio requires active search, leaving LTV and monetization untapped.
Direction
Proposed Direction
Contextual audio discovery — an AI-powered recommendation experience that contextualizes audio based on user behaviour and content preferences.
"We have built a 100M+ user top-of-funnel, but audio discovery relies on high-friction active search rather than passive, contextual recommendation. The growth bottleneck is structural, not content-based."
02 · The Voice of the User
What users actually said
"I read a great thriller, but finding similar audio stories is impossible."
Thematic Synthesis
No Cross-Format Flow
Users want to transition text-to-audio seamlessly.
"My eyes get tired at night. I wish I could just switch to listening to what I was just reading."
Thematic Synthesis
Cold Start Discovery
Users struggle to find relevant audio without explicit search.
"Switching languages to find audio feels clunky and disjointed."
Thematic Synthesis
Bilingual Friction
Language preferences aren't bridging between text and audio.
03 · Competitive Landscape
The Discovery Standard Matrix
No regional competitor effectively bridges the text-to-audio gap. By leveraging existing text-reading data, ThisDay can out-recommend audio-only platforms.
| Platform | Text→Audio Bridging | Contextual Recs | Continue Listening | Algorithmic Curation |
|---|---|---|---|---|
| ThisDay (Current) | × | ◐ | ◐ | × |
| Pratilipi | × | ◐ | × | × |
| StoryMirror | × | × | × | × |
| Pocket FM | × | ✓ | ✓ | ◐ |
| Audible | ◐ | ✓ | ✓ | ✓ |
| Spotify (Global Standard) | ◐ | ✓ | ✓ | ✓ |
04 · Friction Heatmap
Current vs. Future State Journey
Current State — High Friction
- 01Finishes 4-minute text story
- 02Hits a dead end
- 03Returns to home feed
- 04Manually taps audio tab
- 05Manually searches
- 06Abandons app
Future State — Seamless Flow
- 01Finishes 4-minute text story
- 02AI intervention: surfaces "Listen Instead" widget
- 03One tap
- 04Audio session begins seamlessly
JTBD — When my eyes are tired after reading, help me continue experiencing stories seamlessly in audio without breaking my flow.
05 · The Cross-Format Flywheel
A self-reinforcing loop
Rich Text Primer
Users engage with text, generating language, genre, author, and dwell-time data.
Smart Audio Surfacing
AI interprets text data to bypass the audio cold-start, recommending personalized audio.
Extended Sessions
Users transition to passive listening, increasing session length and ad impressions.
Enriched Audio Profile
Extended engagement feeds back into the profile, refining the algorithm.
06 · MVP Scope
Prioritization — MoSCoW
Must Have
- — Text-reading history applied to audio recs
- — Basic 'Continue Listening' mini-player
- — 'Listen Instead' toggle on text pages
Should Have
- — Genre-based dynamic audio playlists
- — Onboarding genre-picker for cold-start mitigation
Could Have
- — Similar-user collaborative filtering
- — Social sharing of audio snippets
Won't Have
- — Generative AI audio creation
- — Complete redesign of core audio player UI
07 · Metric Tree
North Star & branches
North Star Metric
Average Audio Session Duration
Chosen because it directly reflects successful adoption of the new audio experience.
Activation
First audio play from a text-page recommendation.
Retention
D7 audio return rate (listening twice a week).
Revenue
Total ad impressions per audio session.
H1 — Users who regularly consume short text stories will double their session length if personalized audio is surfaced contextually.
08 · Rollout
Go-to-market in 3 phases
Phase 1
Weeks 1–3
Silent Training
Deploy data collection layer, metadata tagging, and background algorithm training — zero UI changes.
Phase 2
Weeks 4–6
Targeted A/B Testing
Rollout MVP features to 10% of high-engagement bilingual users. Monitor North Star metric.
Phase 3
Weeks 7+
General Availability
100% rollout. Launch creator marketing push: 'Listen to your favorite authors.'
09 · Risk Matrix
What could go wrong — and the mitigation
Risk
Poor metadata rendering recs irrelevant.
Mitigation
Phase 1 includes a manual metadata audit of the top 20% most popular stories.
Risk
New User Cold Start.
Mitigation
Implement a mandatory but frictionless onboarding genre-picker.
Risk
Cannibalization of text reading time.
Mitigation
Track overall session time — shift is acceptable if total ad impressions increase.
10 · Recommendation
The ask
Bridging the gap between 100M+ text readers and a premium audio library is not a feature update — it's the key to unlocking the next phase of monetization and retention.
Resource Request — Phase 1 MVP
- — 1 Data Engineer
- — 2 Full-Stack Developers
- — 2-week Sprint Allocation
Approval Checkpoint
Sign-off on MoSCoW scope and Phase 1 resourcing to begin the silent-training rollout.