Building an Investment Thesis With Only Public Data
There is a persistent assumption in investing that the best analysis requires proprietary data. Access to management, insider financials, private metrics. And yes, having more information is generally better than having less. But the amount of useful signal available through purely public sources has expanded dramatically, to the point where a skilled analyst can build a credible investment thesis without any privileged access at all.
This is not about making investment decisions with incomplete information. It is about being systematic with the information that is already available, and there is far more of it than most people realize.
Starting With the Company's Own Footprint
Every company leaves a digital trail that tells a story. The starting point for any public-data thesis is mapping this footprint comprehensively. The company website, social media profiles, job postings, press releases, blog content, executive interviews, podcast appearances, conference presentations, and any public filings. Just cataloging what exists gives you a foundation.
The website itself is a rich data source. Archive.org lets you see how the site has evolved over time. Changes to messaging, pricing, product positioning, and team page composition all reflect strategic decisions. A company that repositioned from SMB to enterprise six months ago tells you something about where they see their best opportunity. A company that removed pricing from their website is likely moving upmarket.
Product pages, feature lists, and documentation reveal the scope and sophistication of what the company has built. Changelog or release notes, if public, show development velocity and priorities. Customer case studies indicate target market and value proposition. Each of these elements contributes to the mosaic.
Market Sizing From Public Sources
Estimating market size without expensive industry reports is more feasible than it used to be. Government databases like the Census Bureau, Bureau of Labor Statistics, and industry-specific regulatory bodies publish data that can be used to build bottom-up market models.
Trade associations often publish market statistics for their industries. Academic research papers sometimes contain useful market data in their literature reviews. Earnings calls from public companies in adjacent or overlapping markets provide color on market trends and sizing that applies to private competitors in the same space.
The key technique is triangulation. If three independent sources suggest the addressable market is between $5 billion and $8 billion, you have a reasonable range to work with. The precision of the number matters less than the order of magnitude. You need to know whether this is a $500 million market or a $50 billion market. The difference between $5 billion and $7 billion is less important at the thesis stage.
Competitive Landscape Mapping
Building a competitive map using public data is one of the most useful exercises in thesis development. Start with obvious competitors from Google searches, industry lists, and comparison sites. Then expand to adjacent players, potential entrants, and substitute solutions.
For each competitor, you can gather a surprising amount of information publicly. Their team size from LinkedIn. Their traffic trends from SimilarWeb. Their product positioning from their website. Their customer base from case studies and review platforms. Their funding history from Crunchbase. Their technology choices from job postings and BuiltWith.
Mapping all of this onto a single view gives you a picture of the competitive dynamics. Who is growing fastest? Who has the most resources? Where are the gaps in the market that no one is serving well? Which companies are converging on the same positioning, and which are differentiating effectively?
G2 and similar review platforms are especially useful here. They provide direct comparisons between products from people who have actually used them. The feature-by-feature comparison, satisfaction scores, and momentum metrics give you a grounded view of competitive positioning that does not rely on any company's self-reported claims.
Team Assessment Through Public Signals
The quality of a company's team is one of the most important factors in any investment thesis, and a remarkable amount of information about teams is publicly available. LinkedIn profiles provide career history, education, previous companies, and the pattern of career moves that reveal ambition and capability.
For founders specifically, look at their public track record. Have they built anything before? Did it succeed or fail? What did they learn? Founders who write or speak publicly about their domain provide direct evidence of their thinking quality. A founder who has been publishing thoughtful analysis about their market for years has a different credibility profile than one who appeared out of nowhere with a pitch deck.
The team composition also tells a story. A company with deep technical talent but no go-to-market experience has a different risk profile than one with strong commercial leadership but outsourced engineering. Check the balance of skills on the leadership team and whether recent hires suggest they are addressing gaps or doubling down on existing strengths.
Financial Estimation
For private companies, precise financial data is not available. But you can often build reasonable estimates using public signals. Employee count from LinkedIn multiplied by estimated average compensation gives you a rough expense base. Web traffic trends, combined with industry-average conversion rates and pricing data from the website, give you a rough revenue range. Job posting velocity indicates whether the company is investing in growth or conserving cash.
These are not precise numbers. They are order-of-magnitude estimates that help you assess whether the narrative makes sense. A company claiming $20 million in ARR with 15 employees in a high-cost city either has extraordinary efficiency or is overstating their numbers. A company claiming to be pre-revenue but employing 100 people has significant capital behind it.
For companies that have raised publicly disclosed funding rounds, you can work backward from typical burn rates to estimate their runway. A company that raised $10 million 18 months ago and has been hiring aggressively likely has 12 to 18 months of runway remaining, depending on their burn rate assumptions.
Synthesizing the Thesis
The final step is assembling all of these data points into a coherent investment thesis. The structure typically follows a logical flow. Is the market large enough and growing? Does this company have a differentiated position in the market? Is the team capable of executing? Are the financial dynamics (even estimated) consistent with a viable business model? What are the key risks, and are they manageable?
A strong public-data thesis will have clear answers to each of these questions, with specific evidence supporting each answer. It will also honestly acknowledge the gaps, the things you cannot know without inside access, and articulate what assumptions you are making to bridge those gaps.
The analysts who do this well treat public data analysis as the first stage of a two-stage process. The public data builds enough conviction to justify deeper engagement, whether that means requesting a management meeting, accessing a data room, or committing time to a more thorough evaluation. The public-data thesis is not the final word. It is the informed starting point that separates disciplined investors from those who are flying blind.