A value match is a company with an active, verified reason to engage, along with the evidence and context explaining why it matters to a specific seller.
The term describes a unit of targeting information built for action. A value match identifies the account, the reason it qualifies right now, the public evidence supporting that reason, the value proposition that applies, and the role inside the company that owns the problem.
Every part of the definition is load-bearing.
Active. The reason exists right now. Evidence from fourteen months ago describes a problem that has already been solved, staffed around, or abandoned. Hiring and funding events decay fastest. Migration and regulatory events hold longer. For most B2B motions, evidence older than about ninety days is background rather than a reason to reach out.
Verified. The evidence has been checked for attribution and recency. Attribution means the event belongs to the company it is filed under, which is harder than it sounds when company names collide, subsidiaries get confused with parents, and matching logic misfires. A rep opening with the wrong company's news has lost the meeting in the first sentence.
A reason to engage. The evidence connects to a problem you solve. Not to your industry, not to your persona filter, not to a topic adjacent to your category. The connection has to be specific enough that a buyer would recognize it as their situation.
For a specific seller. This is the condition that separates a value match from a signal. A company standardizing labels across three hundred suppliers matters enormously to a supplier management platform and not at all to a payroll vendor. Relevance is a property of the pair, so any system scoring an event without knowing what you sell is guessing.
A complete value match contains the following.
The account. The company, resolved to a single entity rather than a name string.
The rationale. A plain-language explanation of why this account qualifies for this seller right now.
The supporting evidence. The specific public artifacts behind the rationale, each with a source URL, a source type, and a date. Job postings, filings, press releases, executive commentary, product launches, review activity, public complaints.
The applicable value proposition. Which of the seller's value props maps to the problem the evidence indicates.
The owner of the problem. The role inside the company accountable for the initiative, which determines who to contact.
Timing metadata. When the evidence was generated and when the match was first detected, so a rep knows whether this is new or has been sitting there.
Strip any one of those and the rep has to rebuild it themselves, which is the thirty to forty-five minutes per account that outbound research actually costs.
Invalid. "Company A, inventory management, score 72." No source, no date, no named problem, no owner. This is an intent record.
Invalid. "Company A raised a Series B last month." Real, checkable, and equally true for the four hundred other vendors watching funding announcements. This is a trigger event.
Invalid. "Company A is a mid-market manufacturer with 400 employees using SAP." Accurate firmographic and technographic data describing a steady state. Nothing here indicates anything is happening.
Valid. "Company A posted a Supply Chain Systems Analyst role nine days ago, and the description names two specific ERP instances requiring consolidation. A press release from six weeks ago announced the acquisition that created the second instance. The hiring manager is the Director of Supply Chain Operations. Post-acquisition ERP duplication is the scenario in your strongest win story, and the value proposition that applies is consolidated demand planning."
The valid version is longer because it carries the reasoning rather than compressing it away.
A lead score. A lead score ranks records already in your CRM. A value match identifies companies with an active problem, including companies that have never appeared in your CRM.
An intent record. Intent data reports that a behavior occurred, usually without identifying who did it or why. A value match explains why the event matters, what to say, and who to contact.
A trigger event. A trigger event is a single occurrence at a point in time. A value match is an occurrence plus the reasoning connecting it to what one seller solves.
An ICP fit score. ICP fit describes whether a company looks like your customers. A value match describes whether something is happening at that company right now.
A contact record. Contact data tells you how to reach someone. A value match tells you why that person would answer.
The process is the same whether a rep does it by hand or a system does it at scale.
Steps four and five are where most systems stop short. Collecting evidence is straightforward. Verifying it and evaluating relevance against a particular seller's products, value propositions, and win stories is the expensive part, and it is the part that determines whether a rep trusts the output.
The distinction gets sharper when the consumer is a model rather than a person.
LLMs reason well and retrieve external data poorly, so the fix for a bad outbound agent is better context, not a better prompt. Give a model a company name and a topic score and it will produce a confident, generic email, because that is all the input supports. Give it a value match with sources, dates, and the applicable value proposition, and it can draft something a buyer would answer.
Models also cannot distinguish stale evidence from current evidence on their own. Every piece of evidence entering a context window has to be correctly attributed to the company, recent enough to matter, and relevant to what the seller solves. Those checks belong upstream of the model, which is what makes a value match a useful unit for agent workflows. It is the smallest sufficient set of validated external context for a targeting decision.
First-party context tells an agent what you sell. Third-party context tells it who needs it this week.
Hand any account your tools surfaced to a rep who has never seen it. Give them five minutes to write an opener.
If they can do it comfortably, referencing something specific and current that connects to a real problem, you have a value match. If they need to open six tabs first, you have a signal. If they cannot tell where the recommendation came from at all, you have a score.
Syft uses "value match" because the existing vocabulary describes observations rather than decisions. Intent, signals, and triggers all name something that happened. None of them carry the reasoning that makes an event worth a seller's time, and reps end up supplying that reasoning themselves, one account at a time, until the quarter gets tight and the research reverts to gut feel.
Syft is an AI sales prospecting tool that finds companies actively working on the problem a seller solves, then tells sellers and AI agents exactly who to engage and why now. It learns a company's products, value propositions, and win stories, then evaluates third-party public evidence against that profile every week. What comes back is value matches, which reps work directly in the app and agents consume through the Syft MCP or the Value Match API.
What is a value match in sales? A value match is a company with an active, verified reason to engage, along with the evidence and context explaining why it matters to a specific seller. It includes the account, the rationale, the supporting evidence with sources and dates, the applicable value proposition, and the role that owns the problem.
How is a value match different from intent data? Intent data reports that an event happened, usually a page view or content download, and usually without identifying who did it. A value match explains why the event matters, what to say, and who to contact.
How is a value match different from a buying signal? A buying signal is an observable external event indicating a company may need what you sell. A value match is that event plus verification and plus the reasoning connecting it to one specific seller's products and value propositions.
Can a rep find value matches manually? Yes, and good reps already do. It runs thirty to forty-five minutes per account across job boards, filings, press releases, review sites, and executive commentary. The constraint is throughput rather than skill.
How long does a value match stay valid? It depends on the evidence type. Hiring and funding events decay within weeks. Migration, regulatory, and expansion events can stay relevant for a quarter or more. Anything beyond roughly ninety days should be treated as background context.
Do value matches work for complex enterprise sales? They matter more there, because the research burden per account is higher and the buyer expects the seller to arrive with a point of view. Simple transactional sales are served adequately by a contact database.
Can AI agents use value matches directly? Yes. A value match is structured so a model receives validated evidence with sources and dates rather than searching the open web, which is where most stale and misattributed context enters an agent workflow.
