Income Opportunity Research

Income Opportunity Research

Systematic framework for researching and evaluating realistic online income opportunities. Designed for an AI agent with web access, Python, and browser automation. The deliverable is always the same: a brutally honest comparison table plus a phased action plan.

When to Use

• User asks "what can I do to make money online?"
• User wants evaluation of a specific opportunity ("is X worth doing?")
• User needs a realistic revenue plan with timelines
• User is comparing multiple income streams
• User has tried things that didn't work and needs a pivot

Core Principles

1. Be brutally honest — hype kills credibility. If an opportunity is bad, say so clearly and explain why.
2. Use real data, not anecdotes — market statistics, platform data, verified seller earnings.
3. Consider the user's specific context — tools they have, what failed before, their location and constraints.
4. Separate execution from strategy — what the AI agent can do vs what needs human action.
5. Prioritize time-to-first-euro — opportunities that pay in weeks beat ones that pay in months.

Research Framework

1. Understand User Context First

Before any research, establish:

• What tools/accounts already exist (Gumroad, Etsy, Fiverr, etc.)
• What has been tried and failed (and why)
• What the user CANNOT do (no bank account, limited time, etc.)
• What the AI agent CAN do (browser automation, Python, scripts, APIs)
• Monthly income target (€100-500 is realistic for a starting agent setup)
• Country/tax situation

2. Category Scan — Research Each Opportunity Type

For each category, run 3-5 targeted web searches. Batch independent searches in the same turn.

Standard categories to scan:

• Digital products (Gumroad, Etsy)
• Freelance services (Fiverr, Upwork)
• SaaS/Micro-SaaS (API subscriptions)
• Chrome/browser extensions
• Data marketplaces (Apify Store, Data Boutique)
• Web scraping/data collection services
• Automation services
• AI-powered services

Key data points per category:

• Market size and demand signals
• Top seller/creator earnings (real numbers)
• Pricing ranges (low/medium/premium tiers)
• Competition level (saturated vs underserved)
• Platform fees and restrictions
• Time to first sale/revenue

3. Deep Dive — Extract Real Data

For promising categories, dig deeper. Reference data points (verify current numbers with fresh searches before quoting them):

• Gumroad: a public dataset of 146K products exists. Median seller earns roughly $72/month; top 1% makes $10K+. Software dominates revenue (~40%).
• Etsy digital: first 6 months typically $100-500/month; 6-12 months $500-2000/month for consistent sellers.
• Fiverr scraping/dev gigs: Basic $30-50, Standard $50-150, Premium $150-500+. Top sellers have 500-2000+ reviews.
• Apify Store: "many developers earn over $3k/month"; ~$2 per active user/month program.
• Micro-SaaS: ~70% under $500/month, ~18% reach $1K-5K, ~4% break $10K MRR.
• Data marketplaces: datasets €10-500+ depending on niche; local-language datasets are often an open niche (check your region).

Distribution is the common factor in every paid case. Zero-sales problems are almost always traffic problems, not product problems. Every honest plan must include where the traffic comes from.

4. Build the Comparison Table

Opportunity	€/month Realistic	Time to 1st €	Risk	Priority
(fill from research — one row per candidate)

Include at least: realistic monthly revenue range, honest time-to-first-euro, risk level (LOW/MEDIUM/HIGH), and a priority rating.

5. For Each Opportunity, Document:

• Name/description — what it is, in plain language
• €/month realistic — not hyped, based on data
• Implementation steps — numbered, actionable
• Time to first euro — honest timeline
• Real risks — competition, platform dependency, maintenance, taxes
• Proof — links to data, reviews, case studies, platform statistics

6. Prioritization & Action Plan

Phase 1 — Fast (€ in 30 days): 1-2 fastest-to-cash plays, concretely scoped (which gigs, which datasets, which listings).

Phase 2 — Build (Month 2-3): scale what worked, raise prices after first reviews, add a second stream.

Phase 3 — Scale (Month 4-6): recurring revenue (subscriptions, retainers, SaaS) once a base income is consistent.

7. What to Flag as BAD Opportunities

Be explicit about what NOT to do. Standard exclusions (unless the user has special advantages):

• ❌ Bandwidth-sharing apps (proven not to pay meaningfully)
• ❌ PTC/faucet sites (pennies)
• ❌ Crypto speculation (not consistent income)
• ❌ "Passive income" courses that sell the dream of passive income
• ❌ Anything requiring upfront payment to "unlock" earnings

Output Format

Deliver three sections:

1. Comparison table (all researched opportunities, sorted by priority)
2. Top 3 deep dives (full documentation per §5)
3. Phased action plan (per §6) with the single highest-priority action listed first — specific enough that the user could start tomorrow.

Anti-Patterns

• Don't present best-case numbers as expected numbers
• Don't recommend an opportunity you found no data for
• Don't omit platform fees (they routinely eat 30-50%)
• Don't ignore the user's country (payout rails, taxes, language niches)