Network effects are simultaneously the most powerful and the most commonly misrepresented concept in technology investing. Every founder pitching a marketplace, platform, or data business claims network effects. Most of these claims are genuine in the sense that the founder believes them. Very few reflect the deep, compounding structural advantage that the term was originally coined to describe.
The distinction matters enormously for long-term investment returns. Companies with genuine, compounding network effects generate returns that are structurally superior to companies that grow quickly but lack these effects. The reason is simple: a true network effect creates a moat that widens automatically as the business scales, requiring no additional capital investment to maintain. This compounds over long time horizons into a competitive position that is nearly impossible for well-capitalized competitors to overcome.
At BeMoreeDriven Capital, we apply a specific analytical framework to evaluating network effects claims. This framework has helped us avoid a significant category of investments that appear high-quality on conventional growth metrics but lack the structural characteristics that generate durable long-term value creation.
A Taxonomy of Network Effects in Enterprise Software
The venture capital literature typically distinguishes between direct network effects (where the value of a product increases as more users join the same network) and indirect network effects (where the value increases because more participants on one side of a two-sided market increase the value for participants on the other side). This taxonomy, while useful, is insufficient for the analytical demands of growth-stage enterprise software investing.
In our framework, we identify five distinct types of network effects that are relevant to enterprise software markets, each with different characteristics in terms of strength, durability, and capital intensity.
Data Network Effects
Data network effects exist when a company's product improves as more customers use it, because each customer's usage generates data that improves the product for all customers. This is the most common form of network effect claimed by enterprise AI and analytics companies, and it is also the most commonly overstated.
The key analytical questions for evaluating data network effect claims are: Does customer data actually improve product quality in a meaningful, measurable way? Is the training data architecture designed to enable cross-customer learning, or is each customer's data siloed by design or technical limitation? And critically: is the improvement in product quality from additional data sufficient to meaningfully outpace competitors who start with less data but can potentially catch up rapidly?
Our investment in Luminary Analytics is premised on a data network effect that we believe meets this standard. Luminary's data observability platform improves measurably as more customers use it, because each customer's data pipelines generate training data that improves the platform's anomaly detection and root cause analysis capabilities across all customers. The architecture is explicitly designed for cross-customer learning, and the improvement in detection accuracy compounds in ways that are increasingly difficult for new entrants to replicate.
Workflow Embedding Effects
Workflow embedding effects are not traditional network effects in the sense that the product does not become more valuable as more external parties join a network. Instead, they represent a structural stickiness that strengthens as a product becomes more deeply embedded in an organization's daily workflows and institutional memory. The switching cost increases non-linearly as the number of workflows integrated and the volume of institutional data accumulated grows.
We include workflow embedding effects in our network effects framework because they generate a similar compounding advantage: the moat widens automatically as the business scales, without requiring additional capital investment. The analytical distinction from data network effects is that workflow embedding effects are per-customer rather than cross-customer — they protect existing relationships rather than improving the product's competitiveness for new customer acquisition.
"The best businesses in enterprise software are not built on a single type of defensibility. They layer multiple compounding effects — data advantages, workflow embedding, marketplace liquidity — in a way that creates a structural position that is genuinely difficult to replicate from the outside."
— James Alderton, Managing Partner, BeMoreeDriven Capital
Marketplace Liquidity Effects
Marketplace liquidity effects are the classic two-sided network effect operating in B2B software contexts. Examples include platform businesses connecting enterprise buyers with suppliers, service providers, or talent; data marketplaces connecting data providers with data consumers; and compliance platforms connecting regulated entities with compliance infrastructure providers.
These effects are powerful but require achieving a market-specific liquidity threshold before they begin to compound meaningfully. Below the liquidity threshold, the marketplace faces a classic cold-start problem. Above it, the compounding of supply-side and demand-side growth creates a structural advantage that is difficult to overcome with capital alone.
Standards and Protocol Effects
Standards and protocol effects exist when a company's technology becomes the de facto standard for a specific type of enterprise integration, data exchange, or workflow process. These effects are the most durable of any category we evaluate because they create structural dependency at the technical infrastructure level rather than at the application layer, and because they are often reinforced by regulatory requirements or industry consortium adoption.
Community and Contribution Effects
The final category of network effects we evaluate is the community and contribution effect: a dynamic in which the quality of a product improves as the community of users and contributors grows. This is particularly relevant for developer tools, open-source infrastructure companies, and platforms where user-generated content, templates, or integrations contribute directly to product value.
How We Use This Framework in Investment Decisions
Our analytical process for evaluating network effects claims involves three specific investigative activities that go beyond what is typically conducted in standard due diligence processes.
First, we conduct technical architecture reviews specifically focused on evaluating whether the claimed network effects are architecturally embedded or aspirationally described. A company that claims data network effects but has a per-customer data architecture that prevents cross-customer learning is describing an aspiration, not a current competitive advantage.
Second, we conduct customer interviews specifically designed to surface evidence of network effect strengthening over time. We ask enterprise customers directly whether they perceive the product as measurably better than it was 12 months ago, and whether the improvement is attributable to scale effects rather than conventional product development.
Third, we model the network effect mathematically over a ten-year horizon, explicitly testing the sensitivity of our valuation assumptions to different rates of network effect compounding. This exercise consistently reveals that the difference in long-term value between a company with genuine compounding network effects and one with only conventional competitive advantages is far larger than is captured in standard five-year DCF or revenue multiple frameworks.
The Investment Implication
Companies with genuine, compounding network effects deserve a premium in the investment evaluation framework — not just in terms of the multiple applied to current financials, but in terms of the conviction level with which we will pursue and support the investment. These are the companies where the long-term return profile is most likely to be non-linear: where the outcomes at the high end of the distribution are substantially better than the central case, and where patient capital combined with operational support can materially influence the probability of achieving those outcomes.
Identifying them accurately requires analytical rigor, technical depth, and the willingness to hold a different view from the consensus when the evidence supports it. That is precisely the work we believe defines the value that a specialist growth equity investor can add to the ecosystem — and it is the work we are committed to doing on behalf of our LPs and our portfolio companies.
