# Audience Town Financial Model Vertical data and advertising platform targeting home movers across the entire home journey - pre-move through post-move. - Canonical: https://finamodel.com/startups/audience-town - Excel download: https://finamodel.com/startup-models/audience-town.xlsx - Category: Marketplace - Model type: SaaS ARR / Valuation - Funding round: Seed - Funding: $6M - Founded: 2022 - Geography: United States - Customer: B2B ## About the company Audience Town is a vertical advertising platform built around the US home-moving journey, from pre-move research through settling in. It combines audience data, campaign tools, media buying, attribution, and analytics in a self-serve system for advertisers serving movers. The company sells to enterprises, realtors, retailers, and home-service brands, with customers including Toll Brothers and Lennar. Its deck reported 200% revenue growth in 2020 and 212% in 2021, while a $6 million seed raise was intended to expand the self-serve product, data infrastructure, and marketplace. The financial model separates recurring platform MRR from media revenue earned on client advertising spend. Customer additions, ARPU, churn, campaign spend, and media take rate feed a blended revenue build; data acquisition, sales, engineering, and marketing then determine gross margin, burn, and runway. ## 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 Audience Town consolidates 14 traditionally separate marketing functions (DSP, identity, creative builder, media, data management, consumer data, attribution, search, analytics, social, ad agency, data science/tech, service) into a single self-serve platform. It targets the 40 million annual US home movers across the full home journey (pre-move → in-market → close → confirmed → post-move → settled home) using proprietary mover-intent data and an ML engine. Revenue claim: 5x ROI for advertisers. Customers span enterprises, realtors, SMBs, home furnishings, CPG, and retail. Named clients include Toll Brothers, Lennar, HomeLight, Invitation Homes, Serhant, Guaranteed Rate, Sunrun, 1-800-PACK-RAT, Plymouth Rock, and others. Payment model: subscription / MRR. ## Market - TAM: $1T (global online advertising) - SAM: $95B - SOM: $1B - AdTech public market caps grew from $5B (2017) to $160B (2021) - PropTech record funding: $4.5B in Q1 2021 - 40 million home movers targeted annually - 83% of searchers have looked for homes with no intention of buying - Average B2C business loses 20% of customers per year to moving AdTech comps cited: TripleLift ($1.4B PE M&A, 2021), Viant (IPO, $1.7B market cap, 2021), The Trade Desk (IPO, $27B market cap, 2021), PubMatic (IPO, $1.7B market cap, 2021). PropTech comps cited: Opcity ($210M exit to News Corp, 2018), Homesnap ($250M exit to CoStar, 2020), Porch (IPO, $1.7B market cap, 2020), Top of Mind ($250M exit to Black Knight, 2021). ## Revenue model Two-layer model: 1. **Subscription / MRR** - platform access fee for self-serve ads UI, data management, and analytics. Specific price points not disclosed. 2. **Media / ad spend** - managed or self-serve ad campaigns across all formats and channels (DSP, search, social, display). Revenue likely taken as a margin on media or as CPM/CPC fees. Specific take-rate not disclosed. Channels: direct sales (enterprise/realtor) and self-serve (SMBs). Self-service platform launched in 2021. No ARPU, contract size, or pricing tiers disclosed in the deck. ## Traction & metrics - 200% YoY revenue growth in 2020 - 212% YoY revenue growth in 2021 - First client: 2018 - Pre-seed round completed: 2019 - Self-service platform launched: 2021 - Named client logos spanning real estate, home services, insurance, energy, and moving categories No absolute revenue figures, ARR, MRR, customer count, or retention rates disclosed. ## Competition / moat Competitive framing: existing marketing vendors (Meta, Zillow, Google, RealPage, Porch) each cover only a narrow slice of the mover journey and require fragmented stacks. Audience Town's moat claims: - Proprietary home-journey intent data spanning the full pre-/post-move window - Consolidated platform (14 functions in one) reduces vendor fragmentation - ML engine for audience targeting and data management - First-mover positioning in home-mover vertical adtech No formal competitive matrix or defensibility metrics in the deck. ## Team & funding ask / use of funds - Raising: up to $6M (Seed) - Use of funds: 1. Hire engineering and marketing leadership 2. Accelerate self-serve ads UI, data infrastructure, and marketplace architecture 3. Acquire unique data to accelerate offering - Founded: 2016 (idea stage) - Team composition not disclosed in this deck (no team slide). --- ## Recommended financial model **Archetype + why:** AdTech SaaS + Media Marketplace hybrid. Revenue splits across (a) subscription MRR from platform access and (b) media revenue (take-rate or gross media margin on ad spend). Given the self-serve launch and MRR language, the primary model archetype is a **SaaS ARR / MRR build with a media revenue layer**. This is standard for vertical AdTech platforms that monetize both software access and managed/self-serve media. **Forecast horizon & granularity:** - 5-year annual model (2022–2026) with monthly detail for Year 1 (matching the fundraise close). - Monthly for the first 12 months to track MRR ramp and ad spend; annual thereafter. **Key drivers & assumptions:** *Revenue - Subscription / MRR:* - Starting MRR: $50K/month (2022 base); rationale: implied by ~200%+ growth trajectory from a small base starting ~2018–2019, no actuals given. - New logos per month: 3–5 SMB/self-serve + 1 enterprise; rationale: self-serve platform just launched, early ramp expected. - Monthly subscription ARPU - SMB: $1,000–$3,000/month; enterprise: $5,000–$15,000/month. Rationale: typical AdTech SaaS for mid-market. - Monthly churn rate: 2–3% (SMB), 1% (enterprise). Rationale: AdTech vertical benchmarks; deck claims retention benefit from mover-data stickiness. *Revenue - Media / Ad Spend:* - Media revenue as % of subscription revenue: 1.5–2.5x. Rationale: managed media revenue typically exceeds subscription revenue in early-stage AdTech platforms. - Net take-rate on media: 20–30% of gross ad spend. Rationale: programmatic AdTech industry standard. - Ad spend per client per month: $5,000–$50,000 range. Rationale: no deck data; based on named client profiles (homebuilders, insurance, CPG). *Growth:* - YoY revenue growth: 200% ('20), 212% ('21). Applied as a decelerating curve: 150% in 2022, 80% in 2023, 50% in 2024, 30% in 2025, 20% in 2026. Rationale: growth compression typical post-Series A buildout; SOM of $1B implies room but sales motion will mature. *Costs:* - Gross margin: 55–65% blended (software ~80%, media net ~30%). Rationale: typical for SaaS+media mix. - Headcount: 2022 plan focused on engineering and marketing leadership hires. Starting headcount: ~10–15 FTEs; ramp to ~30 by end-2022 with $6M raise. - S&M as % of revenue: 40–50% in early years, declining to 25% by Year 5. Rationale: customer acquisition investment during self-serve launch. - R&D as % of revenue: 25–35% Year 1, declining to 15% by Year 5. Rationale: platform and data infrastructure build-out. - G&A: 15% of revenue, declining to 8% by Year 5. *Data acquisition:* - One-time / recurring data acquisition capex: explicitly called out in use of funds.: $500K–$1M Year 1, $200K–$500K recurring. Rationale: proprietary mover-data moat is a core strategic asset. *Raise:* - Seed raise: up to $6M. Model starting cash: $6M + existing cash. - Runway target: 18–24 months to Series A. Rationale: standard seed burn horizon. **Scenarios (Base / Bull / Bear - which variables flex):** - **Base**: 150% YoY 2022, churn 2.5% SMB, take-rate 22%, 30 FTEs by year-end 2022. - **Bull**: 200%+ sustained into 2022 (self-serve flywheel kicks in early), churn 1.5%, enterprise mix higher, data monetization adds a third revenue stream. - **Bear**: Growth compresses to 80% in 2022 (macro/rate sensitivity hits real estate ad budgets), churn 4%, media take-rate compresses to 15% under competitive pressure, extends burn rate requiring bridge. **Required sheets / outputs:** 1. Assumptions dashboard (all drivers in one place) 2. MRR / ARR build (cohort-based: SMB self-serve + enterprise; new/expansion/churned) 3. Media revenue build (clients × spend/client × take-rate) 4. P&L (IS): Revenue, COGS, Gross Profit, OpEx by category, EBITDA, Net Income 5. Headcount plan (department × role × start month × salary) 6. Cash flow & runway (monthly, 24 months) 7. Scenario toggle (Base / Bull / Bear switchable) 8. Cap table (pre/post Seed; include pre-seed dilution) ## Frequently asked questions ### Is the Audience Town financial model free? Yes. The Audience Town 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.