The language of scoring.
Defined plainly.
59 terms that venture, private equity, hiring and revenue teams use when they score founders, companies, candidates and accounts. Each one has a definition, an example and sources.
Scoring fundamentals
How a score is built, kept consistent and checked: rubrics, weights, calibration and the evidence behind each point.
Benchmarking
Comparing a score to a reference point, the market or your own history, to know whether it is actually good
Calibration
Getting every reviewer to mean the same thing by a 4, and how to run a calibration session that does it
Data enrichment
Adding verified outside facts to a bare name or company record so there is enough evidence to score it
Evaluation rubric
A rubric built for one decision type, with criteria, weights, hard requirements and anchored score levels
Evidence-based scoring
Scoring on checkable facts, with every point linked to the record that supports it
Fit score
One number for how closely a candidate, company, or deal matches your bar, built from weighted criteria
Inter-rater reliability
How often your interviewers or partners agree beyond chance, and the statistics used to measure it
Longlist
The first broad set of qualified candidates or companies, built for coverage before anyone starts narrowing
Model calibration
Whether a model's 70 percent really means 70 percent, how to check it, and how to fix it
Normalization
Putting dollars, ratings and years on one common scale before they are compared or added together
Predictive scoring
Estimating the odds that a hire, founder, or deal works out, learned from past outcomes and checked against new ones
Qualitative scoring
Rating soft criteria like communication or conviction on an anchored scale, with a reason written for every rating
Ranked shortlist
A shortlist put in order by score, with the evidence attached, so you know who to meet first and why
Rubric
A written guide to what each score level means, so different reviewers give the same evidence the same number
Score drift
When this year's 4 stops meaning what last year's 4 meant, why it happens, and how to catch it early
Scorecard
The filled-in record for one candidate or company: a score and its evidence for every criterion, plus a recommendation
Scoring model
The criteria, weights and rules that turn evidence into a score you can compare across a whole pool
Shortlist
The small set of candidates or companies that earn final interviews or a partner discussion
Signal
A specific, checkable piece of evidence used as an input to a score, and how to tell a strong one from noise
Weighting
How much each criterion counts toward the total score, five ways to set it, and when equal weights are enough
Venture capital and private equity
Terms for sourcing, screening and underwriting founders, companies and deals against a fund's thesis or a firm's mandate.
Deal flow scoring
One rubric across a fund's whole pipeline, every source and every partner, so priority follows evidence
Deal screening
The first-pass review that decides which companies get partner time, before any diligence starts
Deal sourcing
How venture and private equity investors find companies, which channels produce deals, and how to measure them
Due diligence
Checking a company's claims and sizing its risks before an investment, acquisition, or senior hire
Founder scoring
Rating founders and founding teams against a fund's bar, criterion by criterion, with the evidence attached
Inbound deal scoring
Rating unsolicited pitches against the fund's thesis and bar so good cold deals reach a partner fast
Investment committee
The partners who review the memo and diligence on a deal and vote yes, no, or yes with changes
Investment memo
The written case for or against a deal that the investment committee votes on and the fund keeps as a record
Market mapping
Listing every company in a sector, ranking it against a mandate, and watching for the moment one becomes actionable
Portfolio talent
The executives and operators a fund tracks and places across its portfolio, run as one shared pool
Relationship intelligence
A team's shared record of who it knows and how well, used to decide who to meet next and who to bring together
Thesis fit
How closely a company matches a fund's mandate and the specific beliefs it has promised investors it will back
Underwriting talent
Assessing a key hire or incumbent executive with the same discipline an investor uses to underwrite a deal
VC deal sourcing
How venture funds find founders: networks, scouts, inbound, and meeting people before the round starts
Talent and hiring
Terms for scoring candidates against a role and a hiring bar, and for measuring whether the hires worked out.
AI candidate scoring
Software that rates or ranks job applicants, and the audits, notices and human review that should come with it
Applicant tracking system (ATS)
The software that stores applications and moves candidates through hiring stages, and often filters them too
Candidate scoring
Rating every applicant for one role on the same weighted criteria, with the evidence for each rating written down
Hiring bar
The written standard a candidate must clear to get an offer, set before interviews and held as the team grows
Job fit scoring
Measuring how closely a candidate matches what a role needs, starting from a job analysis rather than a wish list
Knockout criteria
Pass or fail requirements that remove candidates or companies before detailed scoring begins
Quality of hire
How well new hires turn out, measured after they join, and used to test whether the hiring process works
Structured interview
Same questions, same order, answers rated on a written scale: the interview format with the strongest research behind it
Talent scoring
Rating people against a defined bar on a common scale, using evidence of their work, so a whole pool compares
Go-to-market and RevOps
Terms for scoring leads, accounts and markets, and how investors read a company's go-to-market discipline.
Account scoring
Rating whole companies on fit and buying signals, so teams focus on the accounts most likely to buy and grow
Cold outreach scoring
Rating people you have never contacted on relevance, reachability and timing before the first email or call
Go-to-market (GTM) scoring
Rating accounts, leads and deals on fit, engagement and intent so a commercial team works the right list first
Ideal customer profile (ICP)
The written description of the companies most likely to buy, stay and expand, built from your best customers
Intent data
Evidence that a company is researching a purchase right now, used to time outreach and rank accounts
Lead scoring
Ranking individual prospects by likelihood to buy, using points for who they are and what they have done
Outbound scoring
Ranking a whole target list on fit and timing before anyone reaches out, so effort goes to the best targets first
RevOps scoring
One scoring framework that marketing, sales and customer success share, and how RevOps standardizes account scoring
Vendor scoring
Rating suppliers on weighted criteria such as cost, capability and risk, so selection and renewal rest on evidence
Governance and trust
Terms for keeping automated scoring fair, explainable and auditable, including the rules that apply to hiring.
Adverse impact
A much lower selection rate for one group than another, usually checked with the four-fifths (80 percent) rule
AI governance
The policies, owners and controls that decide how an organization approves, monitors and retires its AI systems
Auditability
Being able to trace any score back to its inputs, rules, model version, evidence and the person who decided
Bias audit
An independent check of a hiring tool's selection or scoring rates across groups, required in New York City
Bias mitigation
Finding and reducing systematic unfairness in scoring, so outcomes rest on evidence relevant to the job or deal
Explainable AI
AI whose outputs come with reasons people can understand, check against the facts and challenge
Human-in-the-loop
A design where a person reviews and can override each automated recommendation before it takes effect
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