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Amazon Music India Relaunch
Proposed Product & Growth Strategy — Case Study
A self-directed case study proposing a relaunch strategy for Amazon Music in India, built around shared listening as the differentiator: a product-led growth loop, a North Star Metric and the KPI tree beneath it. This is a proposed strategy, not an actual Amazon Music launch, and contains no company data or results.
Project overview
- Project
- Amazon Music India relaunch — proposed strategy (case study)
- My Role
- Product Manager — strategy, growth loop design, metric definition
- Timeline
- Self-directed product case study
- Tools
- Metric modelling, growth loop mapping, spreadsheets
- Product Area
- Consumer streaming · Product-led growth · Monetization
- Status
- Proposed strategy — not affiliated with or endorsed by Amazon
The problem
What are we solving, who feels it, and why does it matter?
Music streaming in India is crowded and price-sensitive. Catalogue is largely undifferentiated, installs can be bought, and the real contest is weekly engagement and premium conversion.
Who experiences it
Why it matters
Parity
Catalogue alone no longer differentiates
Leaky
Installs convert poorly into weekly listening
Social
Listening is already a shared activity off-platform
User research
Approach, target users, pain points and the insights that changed the direction.
Research approach
- Market and competitive review of streaming positioning in India
- Behavioural framing: how listening is already shared informally between friends
- Mapping the acquisition → activation → engagement → monetization funnel
- Defining the growth loop and the metric tree that would prove it
The Social Listener
Listens with friends, shares songs constantly across chat apps.
Goal: Listen together in real time instead of trading links.
The Price-Sensitive Listener
Uses the free tier and switches apps easily.
Goal: Enough value to justify a subscription, not just fewer ads.
The Regional-Language Listener
Listens primarily in a language the default catalogue under-serves.
Goal: Recommendations that reflect their actual language mix from day one.
Pain points
- Onboarding does not learn language and taste fast enough to feel personal
- Sharing music means leaving the app
- Nothing makes listening a shared, repeated occasion
- Free-to-premium conversion is pitched on ad removal alone
Shared listening is the wedge
The behaviour already exists — it just happens in chat apps. Bringing it inside the product creates engagement and distribution at once.
Personalisation must start at onboarding
In a multi-language market, the first session decides whether recommendations feel relevant at all.
Growth should come from the product, not the media budget
Invites triggered by a genuinely social feature are cheaper and better-retaining than paid installs.
Problem definition
Problem statement
Amazon Music India can win weekly engagement and premium conversion by making listening a shared occasion rather than a solo one, and by turning that shared behaviour into its primary growth loop.
User needs
- Feel understood in the first session, in my language
- Listen with friends in real time inside the app
- Build something together that keeps us coming back
- A premium upgrade that unlocks something I actually value
Listen Together
Synchronised, real-time shared listening sessions as the flagship differentiator.
Collaborative playlists
A shared artefact that persists between sessions and pulls people back.
Community discovery
Language, artist and mood-based communities that give a listener somewhere to belong.
Product strategy
Product goal
Product vision
Business objective
User objective
The solution
Proposed solution
A product-led growth loop: personalised onboarding → discover communities → Listen Together → collaborative playlists → community growth → invite friends → back to onboarding.
Onboarding learns language and taste in under a minute. The home surface offers live and scheduled shared sessions alongside personalised recommendations. Starting a session prompts an invite; the session produces a collaborative playlist that survives it.
Personalised onboarding
Fast language and taste capture that seeds the first recommendations.
Why: Relevance in session one is the precondition for everything downstream.
Discover communities
Language, artist and mood communities as a place to land.
Why: Gives new users a reason to return before they have friends on the platform.
Listen Together
Real-time synchronised listening sessions with friends.
Why: The differentiating behaviour and the engine of the growth loop.
Collaborative playlists
A shared, persistent artefact created from sessions.
Why: Converts a moment into a recurring reason to come back.
Invite friends
Invites native to the session flow rather than a settings-menu afterthought.
Why: Closes the loop: engagement produces acquisition.
Premium framed on shared listening
Upgrade positioned around shared and group listening value.
Why: Monetizes the behaviour the strategy is built on rather than competing on ads.
Prioritization
Framework: Impact vs Effort
Reasoning
Vertical axis: impact (low at the bottom, high at the top).
Now
Personalised onboarding
Cheap, and every downstream metric depends on it.
Listen Together
The differentiator and the North Star behaviour.
In-session invites
Turns engagement into acquisition at minimal cost.
Next
Collaborative playlists
Retention artefact, but only after sessions exist.
Discover communities
Solves the empty-network problem for new users.
Premium repositioning
Needs shared-listening value to be real before it can be sold.
Later
Live audio / creator features
Adjacent to the loop, not part of it.
User flow
- Step 1
Personalised onboarding
Capture language and taste in the first minute.
- Step 2
Discover communities
Land in language, artist or mood communities.
- Step 3
Listen Together
Join or start a synchronised shared listening session.
- Step 4
Collaborative playlists
The session produces a shared playlist that persists.
- Step 5
Community growth
Sessions and playlists make communities more active.
- Step 6
Invite friends
Invites are native to the session, restarting the loop.
Wireframes & prototype
Key screens from the flow. Full-fidelity frames are in the project document.
Language select
Artist taste
First recommendations
Onboarding
Language and taste capture designed for speed, not completeness.
Live sessions
Communities
For you
Home & communities
Live sessions and communities alongside personalised recommendations.
Session player
Participants
Invite friends
Listen Together
Synchronised playback, participants, queue control and invite.
Shared playlist
Contributors
Next session
Collaborative playlist
The artefact left behind after a session.
Analytics
Every metric is tied to the decision it would change.
North star metric
Weekly Shared Listening Sessions per Active User (WSLS/AU)
It captures the differentiating behaviour, correlates with retention, and cannot be inflated by installs or passive playback. If it rises, the strategy is working.
| Metric | Why it matters | Product decision |
|---|---|---|
| App installs | Top of funnel — but only meaningful when read against activation. | Installs rising with flat activation means spend is masking a product gap. |
| Activation rate (onboarding completed + first session) | Tests whether personalisation lands in session one. | Weak activation means shortening onboarding before anything else ships. |
| DAU / MAU | Habit strength of the listening behaviour. | Falling stickiness redirects effort to the return artefacts — playlists and communities. |
| D30 retention | Whether the loop compounds or spikes. | Poor D30 with healthy sessions points at content depth, not the social feature. |
| Collaborative playlist creation | The leading indicator that a session became a lasting reason to return. | Low creation means prompting the playlist inside the session, not after it. |
| Premium conversion | Whether shared-listening value is worth paying for. | Flat conversion means the upgrade is framed on the wrong benefit. |
Experimentation
Prompting an invite inside a shared session increases new-user acquisition per session.
- Experiment
- A/B an in-session invite prompt against invites only in the menu.
- Primary metric
- Invites sent per session → installs
- Expected result
- More invites and more organic installs in the in-session variant.
- Decision rule
- If flat, the loop needs a stronger incentive, not better placement.
Auto-creating a collaborative playlist after a session increases D30 retention.
- Experiment
- One variant automatically saves the session queue as a shared playlist.
- Primary metric
- D30 retention of session participants
- Expected result
- Higher retention where a persistent artefact exists.
- Decision rule
- If unchanged, retention is driven by catalogue relevance instead.
Framing premium around shared listening converts better than ad-free framing.
- Experiment
- A/B the upgrade screen messaging for users with recent shared sessions.
- Primary metric
- Free → premium conversion
- Expected result
- Higher conversion for the shared-listening framing.
- Decision rule
- If flat, the paywall is a price problem, not a positioning problem.
Technical thinking
Enough depth to scope feasibility with engineers, without pretending to design the system.
Synchronised playback
Onboarding personalisation
Instrumentation for the loop
Expected impact
This is a conceptual project. No real-world results are claimed — these are the targets the plan would be judged against.
WSLS/AU
- Baseline
- Not applicable — proposed strategy
- Target
- Growth in shared sessions per active user
- Why
- The loop is designed so every session naturally produces the next one.
D30 retention
- Baseline
- Not applicable — proposed strategy
- Target
- Directional improvement
- Why
- Collaborative playlists and communities create reasons to return between sessions.
Premium conversion
- Baseline
- Not applicable — proposed strategy
- Target
- Directional improvement
- Why
- Upgrade is positioned on a differentiated benefit rather than ad removal.
Learnings
What I learned
- In a parity market, the strategy question is which behaviour you want to own
- A North Star Metric is only useful if it is hard to fake — WSLS/AU cannot be bought
- Growth loops must be instrumented edge by edge or they cannot be managed
What I would improve
- Pressure-test the feasibility and cost of synchronised playback with engineers
- Add primary user research with Indian listeners rather than relying on market framing
Trade-offs I made
- Bet on one differentiator instead of spreading effort across catalogue, price and features
- Accepted a high-effort flagship feature because the loop does not close without it
What I would test next
- Prototype Listen Together with a small cohort and measure sessions per user
- Validate premium positioning with a pricing and messaging test
Full project document
Product Management Case Study
Amazon Music India Relaunch — Proposed Product & Growth Strategy
Proposed Product & Growth Strategy — Case Study
Full project documentation
Amazon Music India Relaunch — Proposed Product & Growth Strategy
Explore the complete product case study including research, strategy, user flows, wireframes, analytics, and product recommendations.
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