Project Summary

North Star is a product built to convert intent into measurable career outcomes. It solves three core problems for early professionals in India: choice overload, low learning consistency, and weak proof of outcomes. Recruiters and hiring managers need verifiable signals; North Star delivers course ROI estimates, verified mini projects and a consistency score that maps to recruiter value.

Context & Problem

Many working professionals struggle to pick the right courses and sustain learning. Market signals:
• India e-learning market estimated at USD 8.2B (IMARC 2024).
• Industry completion rates for large MOOC platforms are typically below 10 percent.
These gaps create demand for a product that links learning choices to salary impact and sustained practice.

Process & Methods

Research
• Primary user testing: 5 participants (mixed Tier-1 and Tier-2 profiles) using initial wireframes and a structured Google Form.
• Competitive analysis across Coursera, LinkedIn Learning, upGrad, Udemy, Simplilearn and others.
• Secondary research: WEF Future of Jobs, IMARC market report, IBEF and local reports for India-specific signals.

Design & Product Decisions

• Prioritization: RICE and MoSCoW frameworks guided feature selection. Combined weighted score placed Roadmap + ROI + Habit system at 95/100.
• Prototype iterations: two prototypes. Prototype 2 focused on UX flow tweaks only: ROI-first ordering, earlier streak cue, global Focus entry and surfaced achievement posting.
• Validation: every key user suggestion implemented in the final wireframe (ROI badges, 3–5 shortlist, accreditation toggle, 7-day starter plan, streak visuals, budget filters, orientation stepper).

Data, Metrics & Tooling

• Measured leading and lagging KPIs aligned to a single North Star Metric: Verified Learning Weeks (VLW). VLW counts learner-weeks with ≥4 Focus sessions (≥10 minutes) and ≥1 quiz or mini project. Pilot targets: 2,000 VLW in Month 1, 5,000 VLW by Month 3.
• Tech and analytics stack specified for production: React/Next.js PWA, FastAPI microservices, Postgres, Redis, Neo4j, OpenSearch, Kafka/Kinesis, Redshift/Snowflake, dbt, Airflow, SageMaker/Faiss, AWS India infra. These are documented in SystemDesign.md.

Outcome & Quantified Impact

• Completion uplift targeted to 35–40% versus <10% industry baseline.
• Activation targets: D1 starter plan start ≥70%, D3 streak attainment ≥50%.
• Conversion and retention targets: Free→Paid conversion 8–12%, repeat purchase ≥20%.
• Product validation: combined prioritization score 95/100 (RICE + user A/B testing). Prototype testing showed clarity and adoption willingness; 100% of test participants indicated they would use the product after changes.
• Recognition: Top Fellow project in the NextLeap PM Fellowship.

Showcase

• Product skills: defining measurable North Star metric, mapping leading and lagging KPIs, end-to-end prioritization with RICE and MoSCoW.
• Execution skills: converting user feedback into concrete UX changes, producing production-ready system design and data pipeline specs.
• Outcome orientation: goals and metrics tied to hiring/revenue signals rather than vanity metrics.

Deliverables

• Final investor deck (PitchDeck.pdf).
• System design and data flow documentation (SystemDesign.md).
• Metrics and KPI definitions (Metrics.md).
• Prototype testing data and user feedback (PrototypeTesting.md and NorthStar_User_Test_Responses_5p.xlsx).
• Competitor benchmarking and market gap analysis (CompetitorBenchmarking.md, MarketGap.md).