Deep science metrics: how do you define commercial metrics without losing scientific rigor?

The first challenge in balancing scientific and commercial metrics in deep science is that they measure fundamentally different realities and generate opposing incentives. 

Let’s look at practical examples to understand how this gap emerges.

Scientific metrics such as the h-index (which assesses the impact of a researcher’s output), citation impact, or the Journal Impact Factor typically focus on productivity and influence within the research community. They are not designed to drive product or service sales.

Tools such as Web of Science o Scopus aggregate citations, publication rates of scientific articles, peer validation, and normalized citation metrics to estimate a publication’s prestige, statistical rigor, or replicability. However, they do not address return on investment (ROI) or market traction.

Business logic operates under a different framework. It focuses on key performance indicators (KPIs) such as ROI, customer acquisition cost (CAC), lifetime value (LTV), monthly recurring revenue (MRR) or conversion rates.

These indicators are essential because they determine cash flow, runway, and ultimately a startup’s market valuation.  

Metrics such as scientific time-to-market or the economic impact of patents can push teams to accelerate outcomes and intensify the monetization of intellectual property (IP).

The conflict arises when purely commercial metrics are used to evaluate deep science R&D projects. 

In this scenario, science is forced to conform to short-term timelines and objectives that are misaligned with the actual cadence of the scientific method. 

While decisions that improve immediate financial outcomes are rewarded, scientific rigor may be compromised—through oversimplified experiments, reduced controls, or premature publication of results. 

Additionally, prioritizing commercial indicators can obscure valuable scientific outcomes that have not yet translated into revenue but may be critical for future breakthroughs. 

The solution lies in hybrid KPIs. Mastering this hybrid language is essential, particularly as Spain’s deep science ecosystem positions itself as a driver of industrial innovation.

This approach is not merely theoretical. Leading deep tech funds such as the European Innovation Council (EIC) and Breakthrough Energy already incorporate hybrid metrics to evaluate projects where scientific rigor and commercial potential must advance in parallel..

Hybrid KPIs enable balance between science and business. They translate scientific outcomes into enterprise value without sacrificing methodological rigor

 Consider the following examples: 

• Science-to-business ratio: While not yet a standardized metric, this emerging concept helps assess the effectiveness of converting R&D into revenue. It links scientific outputs—such as patents, publications, or validated prototypes—to market milestones like licensing deals or pharma partnerships. 

• Validated innovation rateMeasures how many experiments or technological modules pass rigorous scientific validation criteria (internal reproducibility and external replicability) and reach a defined commercial event, such as a customer proof of concept (PoC). Proof of Concept).

• Scientific–commercial balanced scorecards or OKR frameworks (Objectives and Key Results): These management tools translate scientific objectives into measurable key results that function as hybrid KPIs, aligning research progress with concrete commercial milestones. For example, increasing statistical rigor and reproducibility may be tied to the economic impact of patents or Horizon Europe agreements.

• SMART model (Specific, Measurable, Achievable, Relevant, Time-bound)): Metrics can adopt this framework to ensure indicators remain specific, measurable, and time-bound, while respecting academic methodology and aligning with innovation best practices such as UNE-EN ISO 56002 or Spain’s Science, Technology, and Innovation Act 14/2011.

In short, hybrid indicators link scientific rigor and reproducibility to IP value creation, supporting higher company valuations and stronger performance in investment rounds.

The starting point for defining deep science KPIs is the project’s actual technological maturity—measured through Technology Readiness Levels (TRLs)—rather than market conditions, as is common in other sectors.

Each KPI must be anchored to its corresponding TRL stage.Operating at TRL 3, where experimental feasibility must be demonstrated, is fundamentally different from TRL 7, where validation occurs in real operational environments.

Just as TRLs progress through nine stages, metric definition should follow a sequential logic: 

• First: identify which scientific question has been answered. 
• Then: translate that answer into a measurable milestone with a clear commercial consequence, grounded in scientific and innovation standards.

Two recurring pitfalls deserve particular attention:

• Metrics that measure activity rather than progress, such as counting experiments performed instead of validated hypotheses.

KPIs that create perverse incentives, for example rewarding publication speed at the expense of statistical rigor or reproducibility.

To clarify these principles, consider the following examples by development vertical:  

Before validating any commercial KPI  in deep science, consider these five questions: 

1. Is the metric anchored to a specific TRL?
2. Is it scientifically falsifiable?
3. Does it have a clear commercial consequence?
4. Does it respect the true experimental cycle?
5. Could it generate perverse incentives?

Ultimately, defining hybrid KPIs is less a one-off exercise and more an organizational capability. As outlined above, the strategy for defining commercial metrics in deep science must evolve alongside the project’s maturity stage.

Expert guidance can act as an implicit KPI in itself, reflecting an organization’s readiness to operate effectively and profitably across both the laboratory and the market.

At Bolboreta Investiga, we support projects by focusing on project management excellence and scientific rigor. We design metric systems aligned with each project’s TRL stage, translating science into demonstrable business valuewhile protecting the integrity of the research roadmap. 

If you are developing a deep science project, we invite you contact us and explore how to transform scientific knowledge into real-world solutions with positive, tangible impact.