Bijikrishna Majhee
Assocate Product manager, requirements gathering market rese
New to Contra
Bijikrishna is ready for their next project!
This is Setu, an AI-powered missing person identification platform designed for police departments and NGOs. The objective is to reduce the time taken to identify missing people while ensuring no family is contacted based solely on an AI prediction. Setu is designed as a regional pilot for Odisha, enabling police stations and verified NGOs within the state to collaborate on missing person identification.
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Informed Purchase Flow for Non-Returnable Items — Amazon
A UX case study on why customers feel blindsided after buying non-returnable items on Amazon — the root cause wasn't the return policy itself, but weak visibility and late discovery during a fast, skim-heavy mobile checkout. I segmented users into 4 buyer types, traced the issue to specific root causes (passive visibility, late discovery, mobile UX constraints), and scoped a low-effort MVP — a clear "non-returnable" badge shown across listing, cart, and checkout, plus a mandatory confirmation step — backed by a North Star Metric (drop in post-delivery dissatisfaction contacts) and a recovery path for edge cases.
Skills: Problem framing · User segmentation · Root cause analysis · MVP scoping · Metrics design
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Growing Nykaa's Tier 2/3 User Base — Budget-First Shopping Experience
A market-expansion case study addressing why Nykaa struggles to convert users in India's tier 2/3 cities — trust deficit, price sensitivity, and choice overload get in the way more than budget alone. I mapped the problem across 3 city-specific personas, designed and RICE-scored 5 solution concepts (from localized homepages to trial-size SKUs), and defined a North Star Metric — 3x growth in tier 2/3 active users within 15 months — backed by supporting metrics and honest tradeoffs on pricing, margins, and rollout sequencing.
Skills: CIRCLES framework · User personas · RICE prioritization · Metrics design · Tradeoff analysis
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Distilled verbose project details into scannable portfolio card format
Distilled verbose project details into scannable portfolio card format
Designing Memory That Holds — ChatGPT Constraint Prioritization
A product case study tackling a real UX problem: ChatGPT loses track of user-stated rules and preferences as conversations grow long, breaking trust and forcing repetition. I mapped the problem across 4 user personas, designed and prioritized 6 solution concepts, and defined a North Star Metric (Constraint-Held Session Rate) to measure success — then built it out as a live, interactive case study site.
Skills: Problem framing · User personas · Prioritization · Metrics design · CIRCLES framework
Live site: designing-memory-that-holds.vercel.app (http://designing-memory-that-holds.vercel.app)
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