Midway through our semester project, our team of 6 had hit a wall. The product we'd been building lacked direction, and team morale was at an all-time low. I saw an opportunity to pivot - but I needed to convince 5 down-beat, skeptical teammates that starting over was the right call.

The Insight

I'd noticed something: 36% of the American workforce operates in the gig economy, yet every financial tool assumes stable paychecks. Traditional budgeting apps like Mint and YNAB are built for salaried workers - they're fundamentally mismatched to the reality of someone earning $800 one week and $1,200 the next. It's a category gap.

Financial anxiety was the real problem. Gig workers described financial planning as "gambling with rent money." That emotional dimension became our North Star, and explaining the need to solve this to my teammates became 10x easier. 😄

The Approach

I led the team through a systematic business model design process using the RCAS framework, focusing on three critical layers:

1. Deep User Understanding: Rather than leading with features, I pushed us to map the complete job-to-be-done:
   • Functional: Accurate income predictions (table stakes)
   • Emotional: Eliminating 3 AM bank-account-checking anxiety
   • Social: Building financial credibility with landlords and lenders

This multi-dimensional framing shifted our value proposition from "better budgeting" to "financial confidence and control."

1. Strategic Differentiation: I identified our sustainable competitive advantage: data as a moat. The more gig worker financial data we collect, the more accurate our ML predictions become - creating a compounding advantage competitors can't replicate without years of data collection. We’re building a data flywheel.
2. Context-Driven Go-to-Market: I challenged conventional SaaS wisdom by designing our strategy around user reality:
   • Billboards as primary awareness channel - because Uber drivers see 20+ billboards per shift. Meet users where they literally are.
   • 7-day premium trial - our value isn't apparent until users experience prediction accuracy improving from 70% to 85%+ over time.
   • Freemium at $9.99/month (33% below competitors) - accessible pricing for a budget-conscious market, and a psychological “win” because it’s <$10. We calculated a 5-month payback period and 14x LTV/CAC.

Key Product Decisions

Pricing Strategy: My team had advocated for freemium over upfront payment because our competitive advantage compounds over time. Month 1 we're 70% accurate; Month 6 we're 90%+. Users need to experience that improvement curve to convert. This aligned our business model with our technical reality.

Product Roadmap: My team helped structured our expansion as "master core → expand adjacent":

1. Nail income prediction, work planning, earnings optimization