WhoEngine Research
Know why someone fits before you reach out.
Better prospect data helps you choose who to contact first and what to say. WhoEngine analyzes seven categories of public signal to find the right people and give you context for your next sales conversation, from plain English discovery to cited sources. This report explains the method behind every search.
By WhoEngine · Methodology reviewed
Seven public signal categoriesPlain English briefsCited sources per match
The problem
What traditional lead generation gets wrong.
Most B2B teams rely on one of three approaches to find the right people: a static contact database, a professional network search, or manual research. Each has a fundamental limitation that compounds over time.
Static databases decay. B2B contact data experiences 20 to 30 percent annual decay, driven by job changes, promotions, and role eliminations. A record created 18 months ago may have the wrong title, company, or contact details entirely.
Filter-based search matches demographics, not intent. A search for "VP of Sales at a Series B SaaS company" returns every person who matches those attributes regardless of whether they show any signal of needing what you sell. The list is accurate and useless.
Manual research is precise but slow. An experienced researcher can profile 5 to 10 prospects per hour with genuine depth. Most teams need hundreds of qualified leads per quarter and have hours not weeks.
WhoEngine takes a different approach. Instead of searching a static database, it reads the live public web for behavioral and professional signals that indicate genuine fit, then ranks the results and cites the specific evidence behind each match.
Signal taxonomy
Seven signal types. Every WhoEngine search.
WhoEngine weights each signal type based on the specific Ideal Customer Profile. Here is what each type reveals and why it matters.
Public writing
Newsletters, blog posts, LinkedIn articles, personal websites, and Substack publications reveal what a person actually thinks about and prioritizes. Someone who writes regularly about sales tooling is more likely to be receptive to a new sales tool than someone who simply holds a RevOps title.
Spoken content
Conference talks, podcast episodes, and panel appearances demonstrate recognized expertise and signal how publicly a person engages with their domain. Speaking at SaaStr about revenue operations is a stronger signal than having that term in a job title.
Technical contributions
Open source repositories, public project documentation, and technical writing reveal depth of expertise that a resume cannot. A senior engineer with hundreds of contributors watching their framework has demonstrated impact that a job title only implies.
Professional graph
Board memberships, advisory roles, investor portfolios, and current and past employment reveal authority, network, and career trajectory. Someone serving as an advisor to three fintech startups is a different type of decision maker than their title alone suggests.
Media presence
Press coverage, product launch announcements, and analyst report mentions establish external validation. Being featured in major tech publications or cited in industry research is a credibility signal that is difficult to manufacture.
Peer recognition
Citations, recommendations, and co-speaking patterns reveal informal authority. When multiple respected practitioners in a field independently reference the same person, that convergence is a stronger credibility signal than any single credential.
Financial capacity
Publicly available indicators including company funding stage, role seniority, and prior deal history provide context on budget authority. A Head of Engineering at a pre-seed startup and a Head of Engineering at a Series C company have meaningfully different purchasing contexts.
Methodology
How WhoEngine builds an Ideal Customer Profile.
When a user describes who they are looking for, WhoEngine does not treat the description as a keyword query. It interprets it as an Ideal Customer Profile in draft form and uses it as the foundation for a structured search brief.
WhoEngine then asks a small number of targeted follow-up questions to resolve ambiguities that affect signal weighting:
- Is budget authority more important than domain expertise for this search?
- Is this person more likely to be found through their writing or their conference presence?
- Is the target audience primarily active on professional networks or in technical communities?
These answers shape how the seven signal types are weighted for that specific search. A search for a technical advisor weights contributions and peer recognition more heavily. A search for a budget holder in enterprise sales weights role seniority and media presence more heavily.
Scoring
How WhoEngine ranks matches.
WhoEngine scores each candidate across the seven signal types relative to the ICP. The final ranking combines three dimensions:
- Signal density: how many types of signal are present for this person.
- Signal recency: how recently the person has demonstrated these signals.
- Signal relevance: how closely the signal content aligns with the ICP description.
Each result includes a cited reason: a specific piece of public evidence that explains exactly why this person ranked where they did. A match score with a citation like "Recently published a six-part series on sales stack consolidation and posted about evaluating CRM alternatives" is more useful than a score with no explanation. WhoEngine never returns a number without the reasoning behind it.
Use case insights
Signal patterns by audience.
Different use cases produce different signal patterns. Here is what WhoEngine has found to be most predictive for each core audience.
Match quality is highest from prospects with active public writing. A decision maker who blogs about the exact problem you solve is a fundamentally different lead quality than one who simply holds the right title. The writing reveals both the pain and the vocabulary to address it.
Passive candidates signal openness through subtle public shifts: winding down a current project, writing about wanting to explore a new domain, or showing a consistent pattern of engaging with posts about team culture and engineering philosophy at other companies.
Early traction signals are the most predictive: public launches, waitlist announcements, community growth, and the density of peer recognition from other respected operators. A founder cited by three respected practitioners in their market is a stronger early signal than a well-written pitch deck.
Data and ethics
What WhoEngine uses and what it does not.
WhoEngine searches the live public web using neural search technology. Every signal it analyzes is drawn from publicly available information: content the person has chosen to publish, events they have chosen to speak at, and work they have chosen to share.
WhoEngine does not access private data, proprietary network databases, or any information obtained without the subject's public disclosure. It does not scrape profiles from platforms that prohibit it, and it does not purchase data from third-party brokers.
Search criteria and saved results belong to the user. WhoEngine does not use your searches to train its models and does not sell your data or your leads to third parties.