How It Works

A three-step workflow from diagnosis to assets to measurement—built to stay maintainable and honest about what metrics can prove.

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How It Works

The operating hypothesis behind BioticaBio is that startups improve visibility among partners and investors when they publish a digitally discoverable, objective, and data-grounded narrative that makes asynchronous evaluation easier—so interest can form before and between live conversations. The third-party sources below do not speak to biotech specifically; they describe general B2B buying and trust dynamics that are useful analogs for how technical companies are screened online.

Step 1 — Diagnose hidden value

Identify where technical evidence lives (and where it conflicts)
Identify missing company-to-concept linkage
Define high-value terms and entities for your next diligence window

Step 2 — Build public-facing scientific assets

Create pages and summaries with a single navigable spine
Clarify mechanism and indication without flattening uncertainty
Connect evidence to brand identity without overclaiming

Step 3 — Measure discoverability

Track indexed pages and query impressions (directional)
Review concept-to-page visibility changes over time
Use analytics engagement as a secondary sanity check

Visibility metrics are directional and operational. They help teams see indexing and query trends; they do not prove fundraising or deal outcomes.

Sources (tiered)

These links are grouped by authority type. Together they support pieces of the thesis—that public, structured, credible information supports outsider learning and that screening and diligence are central in how capital providers allocate attention—without proving that a specific website causes financing or inbound term sheets.

Peer-reviewed finance (public information & investor-side learning)

Top-tier empirical finance on how disclosure and information access shape outsider learning—not startup “content marketing” outcomes.

Gao, Huang & Goldstein (2020), Review of Financial Studies — “Informing the Market: The Effect of Modern Information Technologies on Information Production” (DOI)Public dissemination of corporate disclosures and information production.
Kerr, Lerner & Schoar (2014), Review of Financial Studies — “The Consequences of Entrepreneurial Finance: Evidence from Angel Financings” (DOI)Rigorous evidence on angel financing and selection; context for early-stage financing institutions.
Gompers, Gornall, Kaplan & Strebulaev (2020), Journal of Financial Economics — “How Do Venture Capitalists Make Decisions?” (DOI)Large VC survey: screening/selection/monitoring practices.
Same study — Stanford GSB publication overview (non-PDF entry point)Publisher-adjacent summary page for the JFE article.

Theory & reviews (signals, asymmetric information)

Foundational and review work on why credible, verifiable information reduces evaluation friction.

Spence (1973), Quarterly Journal of Economics — “Job Market Signaling” (RePEc record)Canonical signaling under asymmetric information.
Connelly, Certo, Ireland & Reutzel (2011), Journal of Management — “Signaling Theory: A Review and Assessment” (DOI)Highly cited management review; maps to partner-facing diligence narratives.
Lambert, Leuz & Verrecchia, NBER Working Paper 14881 — “Information Asymmetry, Information Precision, and the Cost of Capital”Theory on disclosure, information precision, and capital-market pricing logic.

Venture capital process (foundations & diligence intensity)

Kaplan & Strömberg (2001), American Economic Review — “Venture Capitalists As Principals: Contracting, Screening, and Monitoring” (RePEc record)Classic framing of screening, contracting, monitoring.
NBER Working Paper 33987 — Fu & Taylor (2025), “Due Diligence and the Allocation of Venture Capital”Working paper; diligence intensity, uncertainty, and outcomes—cite version/date if quoting.

Industry survey (VC data & inbound in the sourcing stack)

Vendor-run survey (2018 fieldwork). Strong for directional “inbound + databases matter alongside networks,” not for causal inbound marketing claims.

PitchBook — PR Newswire summary of VC Data Usage Survey findings (Feb 14, 2019)n=391 global VC investors (winter 2018); includes inbound leads vs networks statistics.
PitchBook — accompanying blog / methodology entry (“VCs have traditionally relied on their personal networks…”)Linked from the release; use for methodology context.

B2B evaluation (partners & enterprise buying-group analogues)

Useful when the audience is BD / pharma / strategic partners evaluating asynchronously—not a substitute for VC-specific evidence.

Institutional (SME digitalization & finance — macro context)

OECD-level evidence on digitalization and financing conditions; not startup-level inbound dealflow.

Operational measurement (site discoverability)

Google — Search Console documentation (start here)Indexing and query trends for public pages; directional, not financing proof.

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