Narrative Financial Model

Narrative is a data operating system that automates the full lifecycle of data transactions - discovery, compliance, contracting, integration, pricing, reporting. It eliminates data brokers (opaque margins, lock-in) and in-house builds (expensive, slow, non-scalable) by providing:

• A Federated Data Lake for atomic-level data storage and processing.
• A marketplace for one-to-many seller-to-buyer data transactions.
• Private deal infrastructure for one-to-one agreements.
• Onboarding workflows, pricing tools, and reporting for both sellers and buyers.
• A third-party application ecosystem built on top of Narrative APIs/infrastructure.
• A productized AI feature ("Discover Assistant") that uses Pointwise Mutual Information to identify correlated attributes in a buyer's seed data, surfacing relevant data sources automatically.

• TAM: Global data monetization market estimated at $700 billion by 2025.
• Growth rate: Not quantified in deck; narrative context references expanding ML/AI use cases as demand driver.

• Primary model: Marketplace take-rate / transaction fee on data bought and sold through the platform. The deck references pricing tools on the sell side and a marketplace clearing mechanism.
• Secondary: Channel partners on both buy and sell sides suggest a revenue-share or referral layer.
• Tertiary: Third-party applications built on Narrative's platform suggest a potential platform/API licensing revenue stream.

• Marketplace participants: 31 sellers, 28 buyers on the marketplace at time of deck.
• Revenue: Chart shows net revenue growth from Q3-2018 (first quarter of revenue) through Q2-2019 (actual), with consistent QoQ growth across 4 quarters.
• Note: Y-axis has no dollar values - bars show relative growth only; no absolute revenue figures disclosed.
• Product in-market: Discover Assistant feature confirmed "Live in the platform."
• Example customer use case: Uber (Uber Drivers / Uber Eats seed data, 2,340,830 observations, PMI score 0.638).

• Competitive alternatives framed as: (1) data brokers and (2) in-house builds - both positioned as inferior.
• Named competitors placed on a 4-axis landscape (Transparency / Liquidity / Fidelity / Autonomy): Enigma, Crux Informatics, LiveRamp, Dawex, Twitter, Segment, Bidswitch, Oracle, Data.world.
• Narrative claims to be the only player covering all four pillars simultaneously.
• Moat: Federated Data Lake (network effects as more sellers/buyers join), atomic-level data fidelity, automation of a previously 6-month manual cycle.

• Comparable exit data shown as market context: Datalogix/Oracle $1.2B; bluekai/Oracle $400M; LiveRamp/acxiom $310M; Epsilon/Publicis $4.4B; Krux/Salesforce $700M; DataPlusMath/LiveRamp $150M; Treasure Data/ARM $600M; arbor/LiveRamp $150M; acxiom/IPG $2.3B.

• Archetype + why: Marketplace GMV / take-rate model with SaaS layer overlay. Narrative earns on data transaction volume (GMV) at a take-rate, potentially supplemented by SaaS subscription fees for platform tooling. This is the correct archetype because revenue is driven by number of sellers × number of buyers × data transaction volume × take-rate, and the deck's traction metric (31 sellers, 28 buyers) directly maps to marketplace supply/demand dynamics.

• Forecast horizon & granularity: Quarterly for Years 1–2 (matching deck's own Q3-2018 to Q2-2020 cadence), annual for Years 3–5. Total 5-year model.

• Key drivers & assumptions:
• Marketplace sellers (starting 31); growth rate: ~15–20% QoQ in early growth phase, decelerating to ~5–8% QoQ by Year 3, based on typical two-sided marketplace ramp curves
• Marketplace buyers (starting 28); growth rate: slightly faster than sellers as demand side is easier to add once supply is established; ~20–25% QoQ early phase
• Take-rate / platform fee: 10–20% of GMV is a typical data marketplace range; requires management input
• Private deals: separate revenue stream; model as % of total GMV at higher margin (no marketplace fee sharing)
• Channel partner revenue share: negative adjustment to net revenue; typical 15–30% of gross fees shared
• COGS: data infrastructure (Federated Data Lake hosting, compute); high fixed-cost base, scaling sub-linearly - gross margins likely 60–75% at scale for SaaS/marketplace hybrids
• S&M: significant for enterprise sales (long cycle per slide 3's 6-month manual process analogy); model as % of revenue declining over time
• R&D: material given platform + data lake + AI feature development; model as % of revenue
• G&A: standard 10–15% of revenue

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: Sellers +15% QoQ, Buyers +20% QoQ, take-rate 12%, average transaction value held flat
• Bull: Seller/Buyer growth +5pp vs. base; take-rate expands to 15% as network deepens; channel partner ecosystem multiplies GMV
• Bear: Seller/Buyer growth stalls (+5% QoQ); a major competitor (LiveRamp, Oracle) replicates features; take-rate compressed to 8%

• Required sheets / outputs:

1. Assumptions dashboard (all drivers in one place)
2. Marketplace model: sellers, buyers, active pairs, GMV build-up
3. Revenue bridge: marketplace gross revenue → take-rate → channel share → net revenue
4. P&L (Income Statement): Net revenue → Gross Profit → EBITDA → Net Income
5. Simplified Cash Flow / Burn: operating cash flow, capex for data infra, runway
6. Scenario toggle (Base / Bull / Bear)
7. Comparable exits summary (from slide 11 data - valuation context for exits)