# SuperMetrics Financial Model #1 marketing data integration solution - connects 70+ marketing data sources to spreadsheets, BI tools, and data warehouses. - Canonical: https://finamodel.com/startups/supermetrics - Excel download: https://finamodel.com/startup-models/supermetrics.xlsx - Category: Enterprise/Security - Model type: SaaS ARR / Valuation - Funding round: Series B - Funding: $50M - Founded: 2020 - Geography: Global (HQ Finland; offices Atlanta & Vilnius) [DECK slide 9]. - Customer: B2B ## About the company Supermetrics connects more than 70 marketing-data sources to spreadsheets, BI tools, and data warehouses. It gives marketers and analysts a managed integration layer for bringing fragmented campaign data into the reporting and analytics tools they already use. The company serves a large self-serve customer base as well as enterprise buyers, with data-warehouse integrations creating a clear upsell path. Its deck cites 14,000-plus customers, 2.2 times year-on-year growth, and a 35% profit margin, so segment mix matters to the plan. The model separates SMB and enterprise cohorts, tracking customers, pricing, warehouse upsell, and churn. Connector costs, product-led acquisition, sales hiring, gross margin, and operating expenses test how the established subscription base supports continued profitable growth. ## 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 - Automated data pipeline pulling from 70+ marketing sources (Facebook Ads, Google Ads, etc.) into destinations: Google Sheets, Excel, Data Studio, Tableau, Qlik, Power BI, Google BigQuery, Supermetrics API. - No-code; targets non-technical marketers and "citizen data scientists". - Key differentiators: high-quality connectors (clean/unsampled data), zero data storage (data moves, never sits in Supermetrics systems), no new platform to learn. - Next strategic bet: Marketing Data Warehouse - investing heavily to make data warehousing accessible to marketers. ## Market - 7,000+ data sources exist in the marketing tech landscape - used to frame problem scale. - €28bn+ in ad spend processed through the platform - signals the scale of customer budgets flowing through. - No explicit TAM/SAM/SOM figures in deck. ## Revenue model - Subscription SaaS - users pay per product/connector (inferred from product-suite structure: Supermetrics for Google Sheets, for Excel, for Data Studio, API, for BigQuery). - Two product tiers implied: (1) Spreadsheets & Dashboards (self-serve, lower ASP), (2) Data Warehouses & BI tools (API + BigQuery, higher ASP / enterprise). - Specific pricing tiers, ARPU, or contract lengths not shown in deck. - Distribution channel: primarily product-led / marketplace-led (Google Workspace Marketplace: 359 ratings, 371,610 users on Google Sheets add-on alone). ## Traction & metrics - €21M ARR - 2.2x YoY growth - 14,000+ paying customers - 500,000+ total users - 35% profit margin - €28bn+ in ad spend processed - Rule of 40 score: 150+ (growth rate ~120% YoY implied + 35% profit margin) - MRR chart (slide 9): starts near zero in 2011, near-flat through ~2014, then compound acceleration through 2020; rightmost bar (early 2020) reading ~€1.6–1.7M MRR, consistent with ~€21M ARR run-rate - Key milestones on MRR chart: Excel (2011–12), Incorporation (2013–14), 1st employee + Google Sheets (2015), OpenOcean investment (2017), Data Studio launch (2017–18), Expansion to Atlanta & Vilnius (2018–19), Google BigQuery (2019–20) - #1 app in Google's marketplaces by ratings, reviews, and users; rated #1 PPC reporting tool by influencers - Enterprise customers include Nestlé, L'Oréal, Dyson, WB, Canon, Pfizer, HubSpot, dentsu, Havas Media ## Unit economics - Profit margin: 35% - implies highly capital-efficient, likely EBITDA or operating margin. - CAC, LTV, payback period, gross margin (vs. operating margin) not disclosed. - Implied ARPU: €21M ARR / 14,000 customers = ~€1,500/customer/year. ## Competition / moat - Category leader: #1 by ratings, reviews, and user count in Google's marketplaces. - Rated #1 PPC reporting tool by influencers and marketers. - Presence on G2, TrustRadius, Capterra. - Moat sources (inferred from deck): connector quality and breadth (70+ sources), network effects via marketplace distribution, high switching cost (embedded in customer reporting workflows), brand trust with enterprise logos. - Named competitors not shown in deck. ## Team & funding ask / use of funds - Leadership: - Mikael Thuneberg - CEO; built automated data fetching to solve his own problem (founder). - Henna Mäkinen - CFO; ex-CFO at Wolt, CFO at Ilmatar, Legal Counsel at Nokia. - Duleepa "Dups" Wijayawardhana - CTO; ex-CEO Empire Avenue, ex-Sun/MySQL. - Martin Illman - Sales; ex-Country Manager at Meltwater. - Zhao Hanbo - Marketing & Biz Dev; ex-Zokem, Co-founder at Agaidi. - Prior investor: OpenOcean. ## Recommended financial model - **Archetype + why:** SaaS ARR model with two segments (self-serve SMB / enterprise). Revenue is purely recurring subscription; 2.2x YoY growth, 14k+ customers, and a 35% profit margin make a standard SaaS ARR waterfall the natural fit. The data warehouse expansion (slide 13) adds an enterprise upsell motion worth modelling separately. - **Forecast horizon & granularity:** Monthly for Year 1–2, quarterly for Years 3–5. Five-year horizon appropriate for a growth-stage SaaS at this scale. - **Key drivers & assumptions:** - ARR starting point: €21M - YoY net new ARR growth rate: 120% in base year (back-calculated: 2.2x growth implies ~120% YoY);; assumed to decelerate to 60% / 40% / 30% in Y2–Y4 - Paying customers: 14,000+; blended ARPU ~€1,500/yr - Two customer segments: (1) Self-serve / spreadsheet products (lower ARPU ~€500–800/yr, ~80% of customer count); (2) Enterprise / data warehouse + API (higher ARPU ~€5,000–15,000/yr, ~20% of customer count) - Operating (profit) margin: 35% - New customer adds: driven by PLG / marketplace; model as % of existing base + paid acquisition - S&M as % of revenue: ~15–20% (consistent with 35% profit margin if G&A + R&D take the remainder) - R&D / headcount growth: scale with revenue; new warehouse product investment implies elevated R&D spend in near term - FX: Revenue reported in EUR; no FX complexity flagged in deck - **Scenarios (Base / Bull / Bear - which variables flex):** - Base: ARR growth decelerates from 120% → 60% → 40% → 30% → 20%; NRR 115%; profit margin holds ~30–35%. - Bull: Data warehouse product lands enterprise contracts driving ARPU expansion; NRR 125%+; growth sustains 80%+ for 2 years. - Bear: PLG growth slows as Google Marketplace matures; churn rises in SMB tier; growth decelerates faster to 30% by Y2; margin compresses to 20% as warehouse investment ramps. - **Required sheets / outputs:** 1. Assumptions - all drivers with Base/Bull/Bear toggles 2. ARR Waterfall - opening ARR, new business, expansion, churn, closing ARR; split by segment 3. P&L - Revenue, Gross Profit, S&M, R&D, G&A, EBITDA 4. Customer Cohorts - customer count by cohort-year, churn, NRR 5. Unit Economics - ARPU by segment, blended CAC, LTV, payback 6. Dashboard - KPI summary (ARR, MRR, customers, growth, Rule of 40 score, profit margin) ## Frequently asked questions ### Is the SuperMetrics financial model free? Yes. The SuperMetrics 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.