Startup Funding

November 9, 2025

How to Diligence a Marketplace Startup

10 min read How to Diligence a Marketplace Startup The Hidden Complexity of Marketplace Investing Marketplace startups look deceptively simple—connect buyers and sellers, take a transaction fee, and scale. Yet beneath the surface lies one of the most intricate business models in venture capital. Each marketplace comprises three intertwined systems: supply acquisition, demand generation, and transaction trust. Diligencing such a company requires investors to look beyond vanity metrics and into the structural mechanics that sustain the network. Unlike SaaS or product companies, a marketplace’s moat emerges not from technology alone but from network density, unit economics, and behavioral liquidity. Accurate diligence measures how these forces interact over time—not just whether the platform is growing, but whether it is compounding. 1. Market Definition and Network Dynamics Total Addressable Market (TAM) and Fragmentation Start with clarity on the target market’s size and fragmentation. The best marketplaces often enter highly fragmented, inefficient markets where incumbents lack digital coordination, such as real estate agents, local services, or niche B2B verticals. A large TAM alone isn’t enough; investors should assess whether participants are ready for platformization. Markets with strong offline incumbents or regulatory friction may resist the shift. Ideal markets have: Many small, independent providers have poor discovery tools. High-frequency transactions that encourage repeat use. A clear “pain point” in finding, vetting, or paying counterparties. Two-Sided Liquidity: Solving the Cold Start Problem The cold-start problem — how to attract both sides of the market simultaneously — is the defining risk for early marketplace ventures. During diligence, look for tactical liquidity strategies: Single-vertical focus: Does the company start narrow to seed density before expanding? Demand priming: Are they subsidizing one side (often the supply side) until cross-traffic builds? Community seeding: Is there an existing user base or offline network that can be digitized quickly? Early liquidity in even a small segment signals the flywheel potential that investors prize. 2. Business Model and Unit Economics Revenue Model Fit The most common monetization models include take rates (transaction fees), subscription tiers, and lead-generation fees. Each implies different risk structures. Transaction fees require trust, and an integrated payment is high-value but high-friction. Subscription models indicate recurring revenue but can mask low transaction activity. Lead models work early but limit scalability once competition rises. Investors should ask: Is the monetization aligned with the core user value? A mismatch between value creation and value capture, like charging suppliers before buyers exist, can choke early growth. Economics per Transaction Healthy marketplaces exhibit a positive contribution margin once acquisition costs normalize. During diligence, evaluate: Take rate vs. CAC: Does the average customer transaction justify the acquisition cost? Repeat usage: Is retention improving as users deepen engagement? Cohort profitability: Do earlier cohorts improve over time (a sign of compounding trust)? An investor-grade model includes LTV/CAC ratios above 3x, declining CAC, and evidence that organic or referral traffic is growing faster than paid channels. 3. Supply and Demand Validation Supply-Side Diligence Strong supply is the backbone of marketplace liquidity. Look for evidence of supply stickiness: Contracts, integrations, or switching costs that prevent churn. Platform tools that embed suppliers’ inventory management, CRM, and analytics. Precise segmentation of high-value vs. low-value suppliers. Investors should scrutinize how supply quality is maintained at scale. The best marketplaces curate, not just aggregate through reputation systems, ratings, or algorithmic filtering. Demand-Side Diligence Demand validation is equally critical. Look for signals of habitual usage: Percentage of users completing transactions vs. browsing. Repeat rate within 30–90 days. Conversion from search to transaction. If acquisition is primarily through paid channels, ask whether organic channels (SEO, referrals, word of mouth) are growing. A healthy marketplace eventually “earns” its traffic through brand trust and liquidity, not just ad spend. 4. Trust, Safety, and Transaction Infrastructure Trust is the invisible currency of marketplaces. Investors often underestimate the importance of dispute resolution, escrow systems, and user verification. Diligence questions to ask: How does the platform mitigate fraud or low-quality interactions? Is there buyer and seller verification? How quickly are disputes resolved? What share of GMV occurs on-platform versus off-platform? A marketplace with a strong safety reputation accelerates network effects. Each satisfied user adds both volume and confidence to future participants. 5. Technology, Data, and Defensibility Technology as an Enabler, Not the Moat While marketplaces are technology-enabled, their defensibility lies more in data loops than in code. Assess: Proprietary matching algorithms or dynamic pricing systems. Unique datasets built from repeated transactions. Predictive analytics is improving the balance between supply and demand. Technology amplifies the moat once scale is achieved, but rarely substitutes for it. Diligence should confirm that technology shortens the distance between intent and transaction. Network Effects and Switching Costs Ask: Do more users make the platform better for all participants? Are switching costs increasing (data lock-in, reputation scores, embedded tools)? Is there evidence of local network effects, city-by-city or niche-by-niche density? True network effects are measurable: declining CAC, improving retention, and rising transaction frequency as density grows. 6. Regulatory and Operational Risks Marketplaces often enter semi-regulated sectors (transportation, healthcare, finance). Investigate compliance exposure early: Licensing or labor classification risks (e.g., gig economy). Data privacy or payment compliance (PCI, GDPR). Local versus national jurisdiction variance. Operational diligence should review internal controls—how the company handles disputes, refunds, and payment reconciliation. Hidden liabilities here can erode margins later. 7. Team, Culture, and Execution Capability In marketplaces, execution discipline matters as much as vision. Founders must balance product growth with operational rigor. Red flags include: Overemphasis on growth without tracking unit economics. Weak analytical culture or reliance on anecdotal success stories. Lack of expertise in supply-chain or logistics for physical marketplaces. Green flags include: Founders who deeply understand both sides of the market. Clear frameworks for scaling liquidity (e.g., city rollout models). Experienced data and operations leadership. Diligence should include references and operator interviews with those who have scaled networks before, as they are rare and invaluable. 8. Investor Fit and Exit Pathways Investors must map the marketplace’s growth to their own fund model. Marketplaces can deliver large exits but often require long gestation periods before compounding effects take hold. Key investor diligence checkpoints: Time to liquidity: Are transaction volumes doubling

How to Diligence a Marketplace Startup Read More »

How to Diligence a Deeptech Startup

5 min read How to Diligence a Deeptech Startup Deeptech investing sits at the frontier of science and innovation where research, engineering, and commercialization intersect. Unlike pure software startups, deeptech ventures build defensible advantages through hard science: novel materials, AI hardware, quantum systems, robotics, medtech, or energy storage breakthroughs. These opportunities are exciting but also carry unique risks. Traditional venture diligence frameworks often fall short when technology readiness, regulatory hurdles, and manufacturing scalability dominate the risk stack. This article distills key lessons from Startup Funding Espresso episodes on diligence, technology risk, and commercialization. The goal: to help investors and founders apply a structured, evidence-based approach to evaluating deeptech startups. Why Deeptech Diligence Is Different Every deeptech company compresses three risk domains—science, engineering, and market adoption into a single domain. The challenge is not just whether the technology works, but whether it can be built repeatedly, validated independently, and sold at scale. Traditional diligence focuses on traction and market fit; deeptech diligence begins earlier, with proof-of-concept data, IP defensibility, and regulatory feasibility. Investors must verify not only what exists today but also how quickly risk can be reduced per dollar invested. Key mindset shifts include: From forecasting revenue to mapping technology readiness. From tracking KPIs to testing falsifiable hypotheses. From scaling growth to scaling evidence. 1. Technology and IP — The Foundation of Value The first diligence lens is technical validation. Investors must separate scientific promise from engineering reality. Ask: What has been independently demonstrated, and what remains unproven? Use a Technology Readiness Level (TRL) framework to map progress: TRL 3–5: Lab validation and prototype proof-of-concepts. TRL 6–7: Pilot or limited field trials with defined performance metrics. TRL 8–9: Qualified production systems ready for deployment. Request replicable data, third-party lab results, prototype test reports, and failure analyses. Look for structured experimentation and defined “next proof milestones.” Equally critical is IP defensibility. Strong patent portfolios are not measured by quantity but by claim strength, enforceability, and market coverage. Review patent claims with a freedom-to-operate (FTO) analysis and assess continuation plans. Green flags: independent validation, multiple claims granted, clear continuation strategy.Red flags: single provisional filings, overlapping prior art, or reliance on unprotected trade secrets. 2. Regulatory and Standards Pathway Many deeptech sectors—especially medtech, energy, and advanced materials face heavy regulatory oversight. Evaluate: Applicable standards and certifications (FDA, CE, UL, ISO, ITAR, etc.). Defined testing phases, costs, and lead times. How compliance and certification are budgeted within the funding round. A credible startup presents stage-gated plans, design verification, safety testing, and third-party audits with clear contingency budgets. Failing to anticipate certification hurdles can derail commercialization even when performance is strong. 3. Manufacturing and Supply Chain Deeptech ventures live or die by manufacturing scalability. A prototype in a lab is far from a product on a production line. Focus diligence on: Design for Manufacturability: Has the team engineered around tolerances, yield loss, and cost variability? Supplier validation: Are key materials single-sourced or dual-qualified? Cost curves: Does the company model BOM reduction as a function of volume? Unit economics should improve with scale—not degrade under real-world conditions. Review supplier references, pilot production data, and yield reports, along with contingency plans for critical components. 4. Market Validation and Go-to-Market Strategy Deeptech adoption is slower and more conservative. Market traction is measured by pilots, paid proofs of concept (POCs), and letters of intent (LOIs), not user downloads. Ask: Who are the anchor customers? Are there signed pilots with success metrics? Do successful pilots convert to purchase orders or rollout commitments? Look for evidence of market pull rather than push. Strong startups identify a beachhead market, a narrow, high-value initial use case to expand from once technical and regulatory risks are reduced. 5. Business Model and Unit Economics Deeptech success depends on translating innovation into a sustainable business. Assess: Pricing logic: value-based or cost-plus. Service model: warranty, maintenance, support obligations. Margin progression: gross margin by production lot and expected BOM reductions. Validate a clear path to profitability once production stabilizes. Many deeptech companies underestimate hidden costs tooling, certification renewals, and warranty exposure. Confirm how gross margins evolve with scale and how service costs are managed. 6. Team and Execution Capability Winning deeptech teams blend: Scientific depth (core technology and IP creation). Engineering discipline (system integration, QA, and reliability). Commercial skill (enterprise sales and regulatory navigation). Assess whether the team balances technical and operational expertise, or knows when to bring in external expertise. Signs of execution maturity: Detailed experiment and pilot schedules. Hiring plans tied to risk milestones. Advisors or board members with relevant domain expertise. The best teams demonstrate self-awareness and a plan to close capability gaps as they grow. 7. Financial and Capital Planning In deeptech, capital efficiency equals risk reduction per dollar spent. Each round should connect the capital directly to the risk milestones: Proof of principle → validated prototype. Prototype → regulatory clearance or pilot. Pilot → scaled production or recurring revenue. Ensure the round fully funds the next de-risking event. Partial funding during critical phases, such as certification, can dramatically increase failure risk. Common Red Flags Reliance on anecdotal or non-replicable data. Undefined or unrealistic regulatory timelines. Missing supplier validation for long-lead components. Pilots without success criteria or written commitments. IP limited to provisional filings without FTO analysis. When several appear together, they signal structural risks, not minor gaps. Clear Green Flags Independent third-party validation of test data. Detailed regulatory roadmap with time and budget estimates. Qualified manufacturing partners and early yield data. Paid pilot contracts with expansion pathways. Documented BOM and cost-reduction roadmap. These signals indicate a company transitioning from research to execution, where investors can confidently participate in scaling innovation. Read More from TEN Capital Education here.

How to Diligence a Deeptech Startup Read More »

Scroll to Top