Case study

Ecoify – from classroom project to product concept

Ecoify started as a university project to explore how consumer apps and data products could accelerate climate-friendly behavior.

Problem discovery

Researched how carbon dioxide emissions have increased over time and how an average US household emits ~7.5 tons of CO₂ per year, motivating the need for better awareness tools.

Business model canvas

Mapped key partners, activities, customer segments and revenue streams to ensure the idea was not just impactful but also financially viable.

Prototype & validation

Designed mobile mockups of trip tracking and offset screens, then evaluated competitive apps to identify gaps in user experience and monetization strategies.

My role

End-to-end ownership as a Technical Product Manager

Product discovery

  • Conducted market research on carbon tracking apps and data brokers, highlighting a $200B+ data industry opportunity.
  • Defined primary personas: eco-conscious individuals, data-driven corporations and employers targeting carbon neutrality.
  • Shaped the unique value proposition: free consumer app funded by anonymized data for companies.

Product definition & UX

  • Created the business model canvas to clarify key partners, revenue streams and cost structure.
  • Designed high-level user flows and key screens (trip impact, offset suggestions, rewards).
  • Defined success metrics such as DAU, offset conversion and trees planted.

Technical approach

  • Proposed use of mapping APIs (e.g., Google Maps) for distance and mode-aware CO₂ estimation.
  • Outlined data pipeline on cloud infrastructure for scalable collection and anonymization.
  • Considered integrations with partner APIs for planting trees, rewards and donations.

What I’d do next

  • Run small-scale pilots with a local employer and a mobility partner.
  • A/B test different offset nudge designs to maximize conversion and retention.
  • Launch a simple analytics dashboard for companies to explore anonymized trends.
  • Build AI-powered recommendations to suggest the best alternate mode of transportation based on user patterns, distance, and carbon impact.
  • Implement machine learning models to predict optimal travel routes and times that minimize both carbon footprint and travel duration.
  • Develop AI-driven personalized offset suggestions that adapt to individual user behavior and preferences.