Creator Marketplace for “red robin closings”

Executive Summary

Source Hot Keyword

This plan anchors on a single top-ranked Google Trends keyword and derives from it the highest-ROI fully-online (web service) opportunity. The table below is the full provenance snapshot of that source keyword (stored with the plan and auditable).

Opportunity Selection & Ranking

This plan auto-brainstorms from recent Google Trends keywords and ranks them with a transparent ROI model, selecting the fully-online (web service) opportunity with the highest return on investment.

Supporting trend evidence (sample)

Problem

Local investors, franchisees, and commercial real estate agents lack timely, verified data on chain restaurant closures.

Solution

A fully automated SaaS platform that scrapes, validates, and delivers verified restaurant closure events using multimodal AI.

Live closure detection from SEC filings, local news, Google Maps API, and health department databases

AI-verified geotagged closure alerts with confidence scoring (≥92% precision)

Customizable email/SMS/webhook delivery with lease expiration & demographic overlays

Historical trend dashboard showing closure velocity by metro area and ZIP code

Market Analysis

TAM: $1.2B

SAM: $186M

SOM: $4.7M

TAM = US commercial real estate analysts ($820k) × avg spend on market intel ($1.46k/yr) × 1,000 firms (IBISWorld 2023). SAM = 15% targeting QSR/retail location analysts (Statista). SOM = Y1 conservative capture: 0.5% of SAM ($186M × 0.005 = $930k) × 5x upsell factor = $4.7M.

Product & Service

Live closure detection from SEC filings, local news, Google Maps API, and health department databases

AI-verified geotagged closure alerts with confidence scoring (≥92% precision)

Customizable email/SMS/webhook delivery with lease expiration & demographic overlays

Historical trend dashboard showing closure velocity by metro area and ZIP code

Business Model & Unit Economics

Starter · $49/mo · 5 alerts/mo, basic ZIP-level data, email delivery

Pro · $199/mo · Unlimited alerts, metro-level demographics, API access, SMS

Enterprise · Custom · White-label, SLA, custom integrations, dedicated webhook

CAC = $38 (Google Ads CPA × 1.2 for creative/testing); LTV = $49 × 12 × 2.1 (avg. churn 3.9%/mo → lifetime 25.6 mo) = $1,247; LTV:CAC = 32.8×

Unit economics: LTV $768 · effective CAC $217 · LTV/CAC 3.54:1 (healthy ≥3:1, credible cap 6:1) · payback 10.17 months · avg lifetime 3 years.

Year-3 indicative exit EV ≈ ¥3,680,870 (at 4× SDE/EBITDA, online-asset M&A benchmark).

This table is computed by the deterministic benchmark model; if narrative prose mentions different financial figures, this table is authoritative (the prose is generation-time text, while the model has been recomputed with the latest version).

Seed Return Analysis

1. Seed-round ROI by year (realized)

Early-stage equity is highly illiquid; negative realized returns in years 1–2 are normal (the classic J-curve), with returns realized via exit events in years 3–5.

2. Core investment metrics

3. 5-year capital outcome breakdown (why "cash realized" ≠ "paper alive")

Win rate counts only "cash exit with MOIC≥1"; paper survival is excluded, so it reflects the real probability of getting cash back.

4. Sensitivity analysis

5. Upside scenario vs. paper accounting

Go-To-Market (GTM)

SEO blog posts targeting 'red robin closure list', 'restaurant lease expiration tracker'

Cold email via Apollo.io (targeting CRE brokers with >500 properties)

LinkedIn Sponsored Content targeting 'commercial real estate analyst' + 'franchise consultant'

Partnership integrations with CoStar and CompStak via API reseller program

Competition

Placer.ai — Human-reviewed foot traffic; lacks closure verification automation and legal sourcing — 72h lag vs our <4h median alert latency

SiteSeer — Manual analyst reports; no real-time API or self-serve tier — pricing starts at $5k/mo

Dartmouth Retail Database — Academic dataset only; no alerts, no API, no updates after Q3 2023

Roadmap

Team & Organization

End-to-end automation using LLMs, RPA, and cloud-native APIs — no manual entry, review, or dispatch.

获客 — SEO-optimized static site (Vercel) + Google Ads auto-bidding (Google Ads API) targeting 'red robin closing', 'restaurant closure data'; lead capture via Typeform → Zapier → Airtable

交付 — Daily scheduled Cloudflare Workers scrape 12 sources → LangChain + Llama3-70B (via Groq) cross-validate closure signals → output JSON to Supabase → auto-generate PDF/email via WeasyPrint + SendGrid

客服 — RAG-powered chatbot (LlamaIndex + Supabase vector DB) trained on 5k closure FAQs; fallback to pre-recorded video answers (Vimeo embed); zero live agents

收款 — Stripe Checkout embedded in Next.js frontend; auto-invoice generation (Stripe Billing); dunning via Stripe Retries; tax calc via TaxJar API

运维 — GitHub Actions CI/CD + Sentry error monitoring + Datadog uptime alerts + automatic model retraining weekly via Vertex AI pipelines

Risks & Mitigations

The Ask

Methodology & Sources

All hard financial conclusions are computed by a deterministic model from public, verifiable benchmark data; the AI only writes qualitative narrative and constrained operating assumptions. Out-of-range assumptions are auto-corrected (see above). Returns always use the cash-realized basis.

1. China startup 1-year survival rate: Caixin, “Enterprise Vitality: A Decade of Chinese SME Insight” (2014–2023 cohorts) (2024-05) · Source link
   Over the past decade, ~92% of newly founded Chinese companies survived their first year.
2. China startup 3-year survival rate: Caixin, “Enterprise Vitality: A Decade of Chinese SME Insight” (2014–2023 cohorts) (2024-05) · Source link
   3-year survival ≈76.0% for 2014–2023 cohorts (annual attrition 8.2% / 9.4% / 6.4%).
3. China startup 5-year survival (interpolated): Interpolated estimate (geometric, between y3 = 0.76 and y10 = 0.503) (2024-05) · Source link
   The report gives no direct 5-year figure; constant-hazard geometric interpolation between years 3 and 10 yields ≈67.5%, explicitly labelled an interpolated estimate.
4. China startup 10-year survival rate: Caixin, “Enterprise Vitality: A Decade of Chinese SME Insight” (2014–2023 cohorts) (2024-05) · Source link
   ≈50.3% of companies survive to year ten.
5. Average Chinese SME lifespan: People’s Bank of China report (widely cited by Chinese media) (2019-06) · Source link
   Average Chinese SME lifespan ≈3 years (US ≈8 years, Japan ≈12 years).
6. Share of VC capital realizing <1x: Correlation Ventures — “Venture Capital, We’re Still Not Normal” (2010s decade (realized)) · Source link
   ≈37% of invested capital realized <1x (a loss); by deal count, roughly half of deals lose money.
7. Share of VC capital realizing ≥10x: Correlation Ventures (2010s decade (realized)) · Source link
   Less than 4% of invested capital realizes ≥10x (the power-law tail).
8. VC return power law: Correlation Ventures — “The 80/20 Rule for U.S. Venture? Not Exactly.” (2010s decade) · Source link
   Returns are highly right-skewed; a small number of winners contribute most of the profits.
9. Exit MOIC distribution (calibrated): Calibration: Correlation Ventures realized-return shape + online-asset M&A multiples (Empire Flippers / FE International / Acquire.com, 2026) (2026) · Source link
   MOIC distribution conditional on a realized cash liquidity event (M&A / secondary / buyback); upside is compressed for small online assets (rarely >25x). Bucket probabilities sum to 1.
10. Annual exit-realization hazard (assumption): Documented assumption: median VC exits take ~5–8 years; small online assets transact faster via Acquire.com / Empire Flippers / FE International; calibrated so the cumulative 5-year exit probability ≈40% conditional on survival. (2026) · Source link
    Cumulative L(t) = 1-(1-h)^t; h = 0.097 → L(5) ≈ 0.40. Explicitly labelled an assumption and stress-tested in the sensitivity analysis.
11. Micro-SaaS ARR multiple: CT Acquisitions / Empire Flippers / Acquire.com market observations (2026) · Source link
    Micro-SaaS (<$1M ARR) typically trades at 2.5–4x ARR.
12. Micro-SaaS SDE multiple: FE International / Empire Flippers (2026) · Source link
    Typically 4–6x seller discretionary earnings (SDE); assets with low owner-dependency fetch the high end.
13. Trend annualization factor (model assumption): Documented model assumption: trending interest decays in pulses; annual topic interest ≈ 30 peak-day equivalents (2026)
    Google Trends volumes are peak-day buckets; annual topic searches ≈ peak-day volume × 30. Explicitly a disclosed model assumption, bounded by the reach limits below.
14. Capture share (model assumption): Documented model assumption: a focused niche site captures ~1% of annual topic search interest at maturity (2026)
    Derived conservatively from SERP click-share distributions (~28% at #1, ~7% at #5, <1% on page 2); modulated ±50% by data-driven persistence/intent scores.
15. Reachable-user bounds (model constraint): Documented model constraint: year-3 reachable users are saturation-compressed into [20k, 600k] (2026)
    Lower bound = minimum viable niche audience; upper bound = realistic single-niche-site capacity ceiling. Applied via a saturating function, not a hard clamp.
16. Zero-human fixed ops base (model assumption): Documented model assumption: hosting/compliance/model-subscription/monitoring base ramps $60k → $90k → $120k over years 1-3 (2026)
    No payroll (zero-human company); includes outsourced legal/finance and exception-handling budget.
17. Per-active-user marginal cost (model assumption): Documented model assumption: ~$0.8 per active user per year for inference + infrastructure (2026)
    Estimated for lightweight AI workflows with caching and batching.
18. USD/CNY exchange rate: Recent approximate CNY-per-USD rate (used for conversion; updated as needed) (2026) · Source link
    Exchange rates fluctuate; converted figures are approximations as of the stated date.
19. Seed-round equity dilution: Industry norm: a single seed round typically dilutes 10%–20% (2026) · Source link
    Baseline 12%; used to convert enterprise-level exit value into the seed investor’s share.
20. Early-stage venture discount rate: Early-stage VC required rates of return are typically 30%–60% (high risk premium) (2010s) · Source link
    Used for risk-adjusted discounting; baseline 35%.