Role: Product Manager · Tenure: 2021 – 2023 · Domain: Multilingual Consumer Social · Live Audio/Video · Creator Economy
Scale: ShareChat (~180M MAU) + Moj (~120M MAU) = 400M+ combined MAU, 15 Indic languages, 8B notifications/day, 1.2B streamed minutes/month, 32M+ creators — India's first profitable social media platform (2024)
ShareChat serves Bharat's non-English internet. Every product decision had to hold across 15 simultaneous language contexts, first-time smartphone users, and low-bandwidth environments — problems structurally different from English-first platforms.
I owned seven product areas end-to-end, each run on the same discipline: Problem → Diagnosis (research method) → Approach → Outcome.
The scope: Full subscription lifecycle PM across both platforms — purchase flows, billing integration, renewal orchestration, and failed-payment recovery across 150M+ users in 10+ Indic-language markets with materially different payment preferences (UPI, card, carrier billing, wallet).
Problem: Revenue attribution across the combined user base was fragmented. Marketing tracked channel spend against acquisition volume; Product tracked in-app conversion rates; Finance saw end-state billing numbers. Nobody owned the full journey from channel click to active paid subscriber by cohort, creating blind spots that prevented informed market-level investment decisions.
Approach: Built a joint revenue and acquisition analytics framework with the Marketing team: mapped channel spend to user acquisition by market, then tracked acquired cohorts through subscription conversion rate, renewal, and churn. This gave Finance, Marketing, and Product a single shared measurement model for the first time, enabling cohort-level decisions on where to invest and where to reduce spend per market. On the product side, redesigned failed-payment recovery orchestration: tiered retry logic with progressive wait intervals and fallback payment-method prompts reduced renewal-failure-driven churn and revenue leakage.
Outcome: Joint analytics framework became the canonical revenue attribution model across markets. Subscription lifecycle visibility enabled market-level investment decisions previously made on intuition. Reduced revenue leakage from failed-payment churn across a 10+ market footprint.
Problem: One English-first UI architecture created friction across 15 Indic scripts — font-rendering failures, RTL/LTR conflicts, culturally misaligned iconography.
Diagnosis: Field research in Tier-2/3 cities (Patna, Bhubaneswar, Coimbatore) conducted in the user's native language; cohort analysis showed Bengali & Tamil D7 retention materially below Hindi despite similar acquisition volume; journey mapping found Hindi CTA copy reused verbatim in Bengali UI.
Approach: Ran Design Thinking sprints (Empathize→Define→Ideate→Prototype→Test) with native speakers embedded as co-designers, not testers. Rebuilt the UI component library with language-aware string containers and dynamic font scaling; defined a new north-star metric — Language Comprehension Score (LCS), measured via task-completion rate per language — as the shared number across design, engineering and localization.
Outcome: Bengali D7 retention +18pp; onboarding completion +31%; language drop-off 38% → 11%; localization turnaround cut from 14 days to 3 days per language (5x).
Problem: 8B notifications/day across 15 types, each team optimizing independently — a tragedy-of-the-commons dynamic. Users receiving >12/day had 3x higher opt-out.
Approach — 3 phases: (1) Rate Limiting — user-personalized daily caps via ScyllaDB at 400K reads+writes/sec, <5ms latency; (2) Right Routing — language/content/time-of-day preference signals (Bengali users get Bengali-language hooks, not Hindi-translated copy); (3) Intelligent Timing (ML) — send-time optimization per cohort (Bengali peak engagement 7–9PM vs. Hindi 10–11AM).
Outcome: ~5% CTR uplift across 10 languages; opt-out rate −28%; notification volume −22% while DAU contribution held at 18% — precision targeting outperforming volume inflation.