ShareChat · Consumer products · Data
Making discovery worth coming back to.
A diverse consumer audience needed more relevant discovery. Ranking improvements depended on both the quality of the experience and the quality of the evidence.
- Role
- Associate Product Manager → Product Manager
- Product scope
- Ranking, discovery and experimentation at ShareChat
01 / The situation
Generic ranking wasted attention across a highly diverse 100M+ MAU consumer ecosystem.
Better discovery compounds into retention, inventory and the strength of the monetization system.
02 / Ownership
Where I was involved.
I worked across feed ranking, interest graphs, instrumentation and the experimentation portfolio at ShareChat.
Product manager across feed ranking, discovery, instrumentation and the experimentation portfolio.
03 / The product decision
Improve the feed and the evidence behind it.
Helping people discover something new without losing relevance, while making sure the event data is reliable enough to guide decisions.
Balance exploration with known interests, and make room on the roadmap for instrumentation before accelerating experiments.
04 / The work
How the pieces fit together.
Ranking and discovery were rebuilt around interest graphs. The product question was how to balance familiar interests with exploration across a diverse audience.
During my APM role, exploration and exploitation changes were tested across more than 20 A/B tests. I also negotiated roadmap space for an instrumentation overhaul.
Later, the experimentation portfolio covered ranking, discovery and monetisation. The work combined product changes with a stronger basis for deciding which changes to retain.
- Behavioural signalsImprove event coverage
- Interest graphsBalance relevance and exploration
- Feed discoveryMake content easier to find
- ExperimentsUse results to inform the next change
05 / Results in context
What changed.
The published results include improved feed click-through rate and broader event coverage. Platform size describes the operating context; it is separate from the outcomes of a specific intervention.
100M+monthly active users in the platform ecosystem
Scale of the ShareChat environment, not users personally acquired or an experiment cohort.
6.1% → 8.3%feed click-through rate
Reported alongside 20+ exploration and exploitation A/B tests during the APM role. It is not presented as the result of a single test.
54% → 91%event coverage
Reported coverage change from the instrumentation overhaul.
06 / The takeaway
What I carry forward.
Experiment velocity is useful only when the measurement is strong enough to tell the team what to change.