SafeGraph Financial Model

Three data products:

• Core Places - business listings for 6.8M points of interest (POI).
• Geometry - building footprints with spatial hierarchy for POI.
• Places Patterns - foot-traffic insights derived from anonymised mobile devices.

Underlying differentiator: "maniacal focus on truth / veracity" - pure data, no analytics or visualisation. Placekey is a free, open universal identifier for physical places that allows customers to join SafeGraph data to third-party datasets; 200M+ places globally.

• Annual recurring subscription (ARR) per customer.
• Customer ARR ranges illustrated: $100K–$500K (mid-market) and $500K–$1M (enterprise).
• Sold into: Retail & Real Estate, Geospatial, Supply Chain & Logistics, Healthcare.
• Self-serve data store and APIs flagged as future channel.
• No per-seat or usage pricing disclosed; model implies fixed annual data licence.

• 4 customer ARR snapshots:
• Customer A (Retail & Real Estate): $500K–$1M ARR
• Customer B (Geospatial): $100K–$500K ARR
• Customer C (Supply Chain & Logistics): $100K–$500K ARR
• Customer D (Healthcare): $500K–$1M ARR
• Placekey community: 1,000+ organisations; 7,000 Placekey community members using SafeGraph data.
• Slack community: 6,600 members including CDC, Federal Reserve.
• 300+ peer-reviewed academic papers written by consortium members in 2020.
• White House, Dallas Fed, CDC cited as data users.
• Coverage: 6.8M POI, 5,800 major brands, U.S. & Canada; 200M+ places globally via Placekey.
• Total ARR, YoY growth rate, customer count, and churn not disclosed.

• LTV to CAC: >4x.
• SaaS Magic Number: ~1.0.
• Efficiency Score: >1.5.
• Gross margin, absolute CAC, LTV, or payback period not disclosed.

Not explicitly addressed in deck. Implied moats:

• Winner-takes-most data network effect (more users → more data → better product → lower price).
• Placekey as open standard creates ecosystem lock-in and data-joining infrastructure.
• 5,800 brand relationships and 6.8M POI scale creates high replication cost.
• Named competitors: not named.

Team:

• Auren Hoffman - CEO (previously LiveRamp)
• Felix Cheung - VP Engineering (previously Uber, Microsoft)
• Lauren Spiegel - VP Product (previously Scality)
• Jason Cook - VP Sales & Customer Success (previously Radius)
• Nicole Berger - VP Operations (previously Slyce, SIG)
• Evan Barry - VP Marketing (previously Raken, Branch)
• Karissa Paddie - VP Corporate Strategy (previously Bolt)
• Ross Epstein - VP New Projects (previously Sendbloom, LinkedIn)
• Jonathan Wolf - VP Partnerships (previously Envestnet, Bazaarvoice)

• Archetype + why: DaaS / B2B subscription ARR model. Revenue is pure annual data licence subscription; unit economics language (LTV/CAC, Magic Number, Efficiency Score) confirms SaaS-adjacent metrics. No marketplace or transactional GMV. Close to enterprise SaaS but with DaaS nuances (data refresh costs vs. hosting, no seat-based pricing).

• Forecast horizon & granularity: 3 years (2021–2023), monthly for Year 1 then quarterly for Years 2–3. Given COVID-era tailwinds and international expansion plan, monthly granularity in Year 1 captures ramp dynamics.

• Key drivers & assumptions:
• Starting customer count: ~20–50 paying enterprise customers; deck shows 4 illustrative logos but references broad industry coverage - estimate conservative; no disclosed number.
• Blended ACV (annual contract value): ~$300K based on midpoints of disclosed ARR bands ($100K–$1M range, two buckets); weight toward mid-market given product stage.
• New logo adds per quarter: 3–6 net new logos in Year 1, scaling to 8–12/quarter by Year 3 as self-serve channel opens.
• Net revenue retention (NRR): 110–120%; DaaS businesses with diverse use cases typically expand as customers add more products/geographies; Placekey ecosystem increases stickiness.
• Gross margin: 70–80%; data businesses have high gross margins once data is collected; mobile data ingestion + cleaning costs are the primary COGS.
• LTV/CAC >4x; implies payback <3 years at assumed ACV and gross margin.
• SaaS Magic Number ~1.0; implies S&M spend ~= incremental ARR - use as constraint/check on S&M budget.
• Efficiency Score >1.5; use as forecast sanity check (ARR added / (S&M + R&D spend)).
• International expansion: begins Year 2, contributing 10–15% of new logos by Year 3.
• Placekey community conversion rate to paid: 2–5% of 7,000 community members convert annually; drives a freemium-to-paid funnel in model.

• Scenarios (Base / Bull / Bear - which variables flex):
• Bear: ACV stays flat, NRR = 105%, new logo adds 2–3/quarter, no international contribution.
• Base: ACV grows modestly, NRR = 115%, new logos per assumptions above, self-serve channel adds ~10% of new ARR by Year 3.
• Bull: ACV expands via upsell to $400K+, NRR = 125%, acquisition of complementary data companies adds inorganic ARR, international = 20% of new ARR by Year 3.

• Required sheets / outputs:

1. Assumptions - all drivers with scenario toggle (Base/Bull/Bear via CHOOSE).
2. ARR Bridge - opening ARR, new ARR, expansion ARR, churn ARR, closing ARR per period.
3. Revenue & Gross Profit - ARR → recognised revenue (assume annual prepay = ARR), COGS, gross margin.
4. Unit Economics - CAC by cohort, LTV, payback, Magic Number, Efficiency Score (to tie back to deck).
5. P&L - Revenue, GM, S&M, R&D, G&A, EBITDA.
6. Headcount - by function, drives S&M and R&D opex.
7. Cash & Runway - if raise not disclosed, show cash burn and implied runway at various funding levels.
8. Dashboard - ARR growth, NRR, Magic Number, Efficiency Score, LTV/CAC.