# Citylitics Financial Model

B2B SaaS intelligence platform that aggregates and curates public-sector infrastructure procurement data for private-sector vendors, suppliers, and consultants.

- Canonical: https://finamodel.com/startups/citylitics
- Excel download: https://finamodel.com/startup-models/citylitics.xlsx
- Category: Hardware/Deep-tech
- Model type: SaaS ARR / Valuation
- Funding round: Series A
- Funding: $5M
- Founded: 2022
- Geography: North America (offices in Toronto, Milwaukee, Austin) [DECK slide 13].
- Customer: B2B

## About the company

Citylitics provides B2B intelligence on public-sector infrastructure procurement for vendors and consultants. It aggregates and curates opportunity data so companies selling into infrastructure markets can identify relevant projects and make better sales, market-entry, and account-prioritisation decisions.

The product is a data business sold to organisations with complex enterprise sales motions, not a public procurement marketplace taking a transaction fee. Its value depends on the breadth, freshness, and usefulness of the underlying opportunity dataset, while seats and product access can expand across commercial teams.

Model enterprise subscribers, seats, average contract value, data products, implementation, expansion, renewals, and churn. Include data acquisition, research, quality control, delivery infrastructure, customer success, and sales costs. New-logo growth, seat expansion, data coverage, pricing, net retention, and sales-cycle length should drive scenarios.

## What's included

- 5-year monthly revenue build with stage-appropriate growth assumptions
- Full P&L, headcount plan, and operating-expense schedule
- Cash-flow statement, runway, and burn-rate tracking
- Valuation via exit multiple with a DCF cross-check
- Returns analysis with MOIC and IRR
- Unit economics including CAC, LTV, payback, and cohort retention

## Product & value proposition

- Proprietary web crawler aggregates millions of public documents from 30,000+ city, utility, and public agency websites every 2 weeks.
- ML classifies and stores fragmented documents in a data warehouse; NLP extracts relevant content.
- Output delivered via an "intuitive UX" - customers browse, action, and rate intelligence; ratings feed back into a personalisation model (described via Netflix analogy).
- Roadmap layers: Data Foundation → Intelligence Platform → Integrated Insights → Infrastructure Marketplace (cities and solution providers connect online).
- Value prop to buyers: early-stage pre-positioning before RFP, precision targeting of sales efforts, real-time market direction vs. stale legacy data providers.

## Market

- Global infrastructure spend: $5.7T per year.
- US infrastructure industry size: $2.1T.
- Comp platform revenue benchmarks cited as aspirational ceiling:
  - Bloomberg (Finance / $20T market): $10B/yr revenue
  - S&P Capital IQ (PE / $4.6T market): $7.4B/yr revenue
  - CoStar Group (Real Estate / $2.7T market): $1.4B/yr revenue
  - ZoomInfo (B2B Sales / $1.6T market): $0.5B/yr revenue
- No explicit SAM or SOM figure given; no CAGR cited.
- Government stimulus tailwind: "Trillions of dollars in infrastructure investments planned in 2022 and beyond".

## Revenue model

- Implied B2B SaaS subscription (data/intelligence platform sold to infrastructure vendors); pricing, tiers, contract length not disclosed in deck.
- Customers are private-sector firms: manufacturers, suppliers, engineering, construction, and consulting companies.
- No transactional, marketplace, or per-seat pricing details provided.
- Future roadmap includes an Infrastructure Marketplace (connecting cities with solution providers), which could add a transaction/listing revenue layer.

## Traction & metrics

- Large named customer base shown: 40+ logos including ABB, Jacobs, McKinsey & Company, Tetra Tech, Xylem, DuPont, Suez, Atlas Copco, GHD, and others.
- "Growth trajectory is accelerating every quarter" - no specific ARR, MRR, revenue, or customer count figures disclosed.
- No revenue numbers, growth percentages, NRR, churn, or headcount metrics provided in deck.

## Competition / moat

- Legacy data providers described as delivering "stale, outdated, inaccurate data".
- Moat framed as: (1) proprietary crawler covering 30,000+ public agency sites; (2) ML/NLP categorisation pipeline; (3) user-rating feedback loop creating a personalised intelligence model that improves with use (data network effect, Netflix analogy).
- Competitive positioning: positioned as the "Bloomberg/CoStar/ZoomInfo equivalent" for infrastructure - category-defining data platform with no direct incumbent named.

## Team & funding ask / use of funds

---

## Recommended financial model

- **Archetype + why:** B2B SaaS ARR model. Revenue is subscription-based (intelligence platform sold to corporate buyers); the relevant KPIs are ARR, logo count, ACV, NRR, and gross margin. The comp set (Bloomberg, CoStar, ZoomInfo) all operate as data subscription businesses.

- **Forecast horizon & granularity:** 5 years (2022–2026), quarterly for Year 1–2, annual thereafter. Early quarters matter for burn/runway visibility.

- **Key drivers & assumptions:**

| Driver | Value | Source |
| -- | -- | -- |
| Starting ARR | Unknown | - |
| Starting logo count | ~40+ visible on deck | (exact paying customer count not given) |
| Average contract value (ACV) | $25k–$50k/yr; typical for mid-market B2B data/intelligence products at this stage | - |
| ARR growth rate | 80–120% YoY in early years, decelerating to 40% by Year 5; consistent with early-stage SaaS data co's pre-Series B | - |
| Net revenue retention (NRR) | 110–120%; data platforms with embedded feedback loops typically see expansion | - |
| Gross margin | 70–75%; data/SaaS mix - crawler infrastructure and analyst headcount create modest COGS vs. pure SaaS | - |
| S&M % of revenue | 50–60% in early years, declining as brand/word-of-mouth builds | - |
| R&D % of revenue | 25–30%; ML/NLP pipeline ongoing investment | - |
| G&A % of revenue | 15% early, declining to 10% at scale | - |
| Headcount growth | tied to ARR per FTE benchmarks (~$150–200k ARR/FTE at seed/Series A) | - |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Bear:** ARR growth 50% YoY; NRR 100%; ACV pressure from competition; slower marketplace launch.
  - **Base:** ARR growth 90% YoY Years 1–2, decelerating; NRR 115%; ACV at midpoint; marketplace revenue begins Year 4.
  - **Bull:** ARR growth 130%+ YoY early; NRR 125%; rapid ACV expansion into enterprise; government stimulus drives accelerated demand.

- **Required sheets / outputs:**
  1. Assumptions dashboard (all drivers in one place, scenario toggles)
  2. ARR bridge (new logo ARR, expansion ARR, churn ARR, net new ARR)
  3. Income statement (Revenue, COGS, Gross Profit, S&M, R&D, G&A, EBITDA, Net Income)
  4. Headcount plan by function
  5. Cash flow & runway (given pre-profitability stage)
  6. SaaS KPI summary (ARR, MRR, logo count, ACV, NRR, LTV/CAC, payback period)
  7. Comp benchmarking tab (Bloomberg, CoStar, ZoomInfo, S&P CIQ revenue multiples vs. industry size for sanity-check on long-run TAM capture)

## Frequently asked questions

### Is the Citylitics financial model free?

Yes. The Citylitics model is a free Excel download with live formulas.

### Can I change the assumptions?

Yes. The workbook is editable and its live formulas recalculate when assumptions change.