Handy

Automated Tax Refund Service for US Gig Workers

Role	Co-Founder, Product Manager (3-person team)
Context	Early-stage US Fintech Venture (Handy)
Stage	Validation & Prototype
Time	2024.05 – 2025.05

Summary	Automated tax refund and deduction diagnostic workflow for US gig workers.
Scope	Compressed a 12-hour manual tax preparation process into a 1-minute automated flow.
Outcome	CTR 11.48% / CPC $0.39 (Top 3% Techstars shortlist)
Tags	Product, Fintech, TaxTech, Validation

Problem

Capability thesis	Structured an ambiguous US tax-tech opportunity into three quantitative GTM and product validation loops, reaching the top 3% shortlist for Techstars Economic Mobility.
TL;DR	Built an automated tax deduction and refund diagnostic service for US gig workers that reduced a 12-hour manual filing preparation process to 1 minute, lifting ad CTR from 4.27% to 13.38% and cutting CPC from $0.76 to $0.27.
What	A mobile-first tax refund diagnostic and pre-settlement workflow that aggregates scattered driving, mileage, and expense records for US platform workers (Uber, Lyft, DoorDash).
Why now	Over $1B in legitimate tax refunds goes unclaimed each year in the US because 1099 independent contractors face complex Schedule C deduction rules and fragmented income records.
Problem	US gig workers must manually reconcile 1099-K/1099-NEC forms, mileage logs, gas, insurance, and maintenance expenses across multiple apps. Traditional tax software asks accounting questions users cannot answer without hours of manual bookkeeping.

Role & ownership

Role	Co-Founder, Product Manager
Team	3-person founding team (1 PM / GTM Lead, 2 Engineers)
Ownership	End-to-end ownership of market hypothesis definition, meta-ad funnel experiments, onboarding UX, Uber/Argyle data aggregation flow design, and investor/accelerator readiness.
Constraints	Operating remotely from Korea without an initial US entity, strict IRS compliance boundaries, and high drop-off risk whenever users were asked to upload tax PDFs manually.

Validation loops

Pivot

Loop 1 — Broad Market Demand Validation

If we offer a simplified mobile tax refund check for general US workers, users will click through and complete a basic refund eligibility questionnaire.

Discover

Why	Identify whether unclaimed tax refunds represent an urgent pain point for everyday US filers.
Actions	Researched IRS unclaimed refund statistics and analyzed competitor flows (TurboTax, Cash App Taxes).
Outputs	Confirmed over $1B/year in unclaimed refunds, but general W-2 filers already had free filing options.

Define

Why	Test baseline acquisition cost and message resonance in the US market.
Actions	Designed broad-appeal ad creatives and a lightweight landing funnel.
Outputs	Defined baseline benchmarks (target CTR > 5%, CPC < $0.50).

Run

Why	Measure real user acquisition metrics via paid social campaigns.
Actions	Ran Meta ad campaigns targeting general US working-age adults.
Outputs	Recorded 4.27% CTR and $0.76 CPC—interest existed, but acquisition cost was too high for broad consumers.

Review

Why	Determine which segment showed the highest urgency and largest refund delta.
Actions	Segmented ad engagement and interviewed respondents about filing friction.
Outputs	Pivoted ICP exclusively to 1099 gig workers (rideshare and delivery drivers) whose deductions were chronically under-claimed.

Supported

Loop 2 — Niche ICP Pivot to US Gig Workers

Targeting rideshare and delivery drivers with a specialized 1099 deduction calculator will more than double CTR and halve CPC.

Discover

Why	Understand why gig workers leave thousands of dollars in mileage and vehicle deductions on the table.
Actions	Analyzed driver forums (Reddit r/uberdrivers, Facebook driver groups) and 1099-K tax rules.
Outputs	Found that drivers struggle to separate personal vs. business miles and fear IRS audit penalties.

Define

Why	Align ad messaging and onboarding questions around gig-driver pain points.
Actions	Rebuilt landing copy around 'Find your hidden Uber/DoorDash tax deductions in 1 minute'.
Outputs	Created a gig-specific diagnostic funnel.

Run

Why	Validate whether niche targeting improves unit economics.
Actions	Launched targeted campaigns for US gig workers.
Outputs	Achieved 11.48% CTR (2.7x increase) and $0.39 CPC (49% reduction).

Review

Why	Inspect post-click behavior inside the diagnostic funnel.
Actions	Analyzed step-by-step funnel drop-off.
Outputs	Discovered that users wanted the refund estimate but dropped off when asked to manually type annual mileage and 1099 numbers.

Supported

Loop 3 — Automated Uber API Import & Instant Pre-Settlement Model

Eliminating manual input via direct Uber Driver account linking and offering instant refund pre-settlement will maximize conversion and validate monetization.

Discover

Why	Remove the manual data-entry bottleneck and test willingness to pay.
Actions	Evaluated gig-economy data APIs and Uber Driver portal scraping/API flows.
Outputs	Confirmed that linking the driver account could automatically pull annual trip mileage, gross earnings, and platform fees.

Design & Build

Why	Compress 12 hours of receipt/mileage reconciliation into a 1-minute automated flow.
Actions	Built the automated driver-data import pipeline and added a 'Get refund advance now (fee-based pre-settlement)' option.
Outputs	Working prototype that calculated net Schedule C deductions automatically.

Run

Why	Test combined demand for zero-input diagnostics plus instant payout.
Actions	Ran Loop 3 campaigns highlighting automatic Uber import and fast payout.
Outputs	Reached 13.38% CTR and $0.27 CPC; selected into the Top 3% shortlist for Techstars Economic Mobility.

Review

Why	Evaluate requirements for full commercial launch.
Actions	Assessed US lending/ERO compliance capital requirements vs. team runway.
Outputs	Documented validated GTM metrics and architectural playbook before concluding the venture.

Design decisions

Problem zoom	Reframed the bottleneck from 'tax form filing' to 'data aggregation before filing'—users did not abandon filing because of IRS forms, but because they could not gather 12 months of scattered work and expense records.
Options	(1) Manual receipt and PDF upload wizard, (2) Bank-only transaction scraping, (3) Direct gig-platform account linking (Uber Driver API) combined with rule-based deduction estimation.
Trade-off	Full tax-return e-filing required heavy legal/ERO licensing upfront, whereas a pre-filing deduction diagnostic + instant refund advance let us validate demand and willingness-to-convert with minimal regulatory friction.
Final rules	Replace manual data entry with one-click Uber account linking, auto-calculate standard mileage vs. actual expense deductions, and surface expected refund amounts within 60 seconds.
Tools	Figma, Python, Meta Ads Manager, Google Analytics, Notion
Frameworks	Hypothesis-Driven Validation Loops, Funnel Drop-off Telemetry, Pretotyping

Outcomes

Metrics	Ad CTR improved from 4.27% (Loop 1) → 11.48% (Loop 2) → 13.38% (Loop 3); CPC dropped from $0.76 → $0.39 → $0.27; onboarding time reduced from ~12 hours of manual prep to ~1 minute.
Qualitative outcome	Proved strong product-market pull among US rideshare drivers and validated both the data-aggregation UX and the pre-settlement revenue model.
Social proof	Selected into the Top 3% final interview shortlist for the Techstars Economic Mobility Accelerator (backed by Sam Altman).

Learnings

Learnings	In consumer fintech, narrowing the ICP from 'all taxpayers' to 'Uber/DoorDash drivers losing money on mileage deductions' multiplied conversion efficiency and clarified the exact API integration needed.
If again	Establish US ERO (Electronic Return Originator) partnership and banking rails in parallel with Loop 2 so validated demand can immediately convert into live tax settlements.

Evidence

Before	Broad tax-refund value proposition with manual document upload: 4.27% CTR, $0.76 CPC, and severe user drop-off at the document collection step.
After	Gig-worker specific positioning with automated Uber Driver API import and instant pre-settlement offer: 13.38% CTR, $0.27 CPC, and a 1-minute diagnostic flow.