A go-to-market signal is the hypothesis connecting a pattern of public evidence to a specific problem you solve. It's the reasoning, not the observation. A funding round is an event. "Companies that raise a Series B after two years of founder-led selling usually break their sales process within six months" is a go-to-market signal, and the funding round is one piece of evidence supporting it.
The distinction matters because most vendors sell events and call them signals. That's why teams end up subscribed to a feed of things that happened with no idea which ones deserve a call.
The vocabulary gets used loosely enough that it's worth separating.
A trigger event is a single occurrence at a point in time. A funding round, an executive hire, an acquisition, a product launch. It's verifiable, it has a date, and it's equally visible to every vendor watching the same source.
Evidence is any public artifact indicating something is true about a company. A job posting, a filing, a press release, a conference talk, a review left on a comparison site. Evidence includes trigger events and extends past them, because a lot of what tells you a company has a problem isn't an event at all. It's a description of how they currently work.
A go-to-market signal is the interpretive layer sitting above both. It states what pattern of evidence indicates that a specific problem is present at a company, and why that problem is one you can solve.
The reason to keep these separate is that they fail differently. Trigger events fail on relevance, because the same funding announcement lands in four hundred vendors' feeds on the same Tuesday. Go-to-market signals fail on construction, because a badly built one will faithfully surface accounts that have no reason to talk to you.
Most teams start with events because events are easy to buy. Filter for Series B, filter for new VP of Sales, filter for headcount growth above thirty percent. The list populates and the work feels done.
The problem shows up in the reply rate. The event is real and its connection to your product is assumed. A company raising a Series B is now a target for every sales tool, every HR platform, every security vendor, and every agency in the market, and all of them are opening with some version of congratulations on the raise.
The buyer receiving twenty of those messages learns to ignore the whole category. What breaks through is a message about something specific they're working on, which requires knowing what they're working on, which requires more than an event.
Four properties separate a useful signal from a filter.
Six steps. This is the same work whether a person does it once or a system does it continuously.
Step 1. Write down the problems you solve. Use your customers' words rather than your category's words. "Streamlines financial operations" is marketing copy. "The controller spends the first nine days of every month reconciling three sets of books by hand and still misses the board deadline" is a problem. Pull these from closed-won notes and call recordings, since your customers already said them out loud. Aim for five to eight.
Step 2. Identify the observable symptoms of each problem. For each problem, list what a company does or says when that problem is present. Hires a specific role. Announces something that creates the problem as a side effect. Writes publicly about the workaround. Restructures a team around it. Files something describing it. Your own closed deals are the best source here, because every account you won had a reason they started looking, and that reason usually left a trace before they contacted you.
Step 3. Map symptoms to public sources. Each symptom lives somewhere findable. Job postings carry the most usable detail per record, because a job description typically names the tools in use, the team structure, and the manager who owns the problem. Regulatory and financial filings are slower and more authoritative. Press releases tell you what a company wants known. Executive commentary on podcasts, panels, and public posts tells you what they're actually working on. Review sites and public complaints tell you what their current tools can't do. Symptoms with no reliable public source get dropped.
Step 4. Write the hypothesis. State it as a sentence connecting the pattern to the problem. "A company posting for a supply chain systems analyst with a job description naming two ERP instances is consolidating post-acquisition, which usually breaks demand planning within two quarters." That sentence is the signal. Everything before it was preparation.
Step 5. Attach the value proposition and the proof. Name which of your capabilities addresses the indicated problem and which customer story supports it. If you can't, the signal isn't finished.
Step 6. Test it against accounts you already won. Run the signal backward against your last twenty closed-won deals. If the evidence would have surfaced them before they raised their hand, the signal works. If it wouldn't have surfaced any of them, the hypothesis is wrong and no amount of data volume fixes it.
Generic versions, using product categories rather than named companies.
The problem is that finance teams running multiple sets of books reconcile them by hand. Observable symptoms include an acquisition announcement, a job posting for a senior accountant with consolidation experience, an office opening in a new country, and a finance leader publicly describing close timelines. The signal: a company completing an acquisition and hiring into finance within two quarters is running duplicate ledgers and closing manually. The value proposition is consolidated close. The owner is the controller or VP of finance.
The problem is that manufacturers with hundreds of suppliers can't enforce consistent labeling and packing standards. Observable symptoms include job postings mentioning supplier compliance or vendor scorecards, a public commitment to a new traceability standard, expansion into a market with different regulatory requirements, and RFPs for logistics services. The signal: a company adding suppliers faster than it adds supply chain headcount is standardizing manually. The value proposition is supplier compliance automation. The owner is the director of supply chain operations.
The problem is that revenue teams can't make new reps productive fast enough. Observable symptoms include a first enablement hire, a sales leader posting about ramp time, hiring five or more reps in a quarter after a funding event, and a new CRO arriving from a company with a formal methodology. The signal: a company scaling headcount without an enablement function is about to see ramp time and quota attainment diverge. The value proposition is structured onboarding. The owner is the CRO or the VP of revenue operations.
Each of these is specific enough that a rep can open with it and a buyer would recognize their own situation.
Three failure modes worth watching for.
The signal is too broad. "Companies hiring salespeople" is a filter. It surfaces thousands of accounts and tells you nothing about any of them. If your signal returns more accounts than your team could work in a year, it's not a signal.
The evidence is stale. Signals decay at different rates. Hiring and funding evidence goes cold fastest, since a role posted eleven months ago has been filled, cancelled, or reorganized. Migration, regulatory, and expansion evidence holds longer. For most B2B motions, evidence older than about ninety days is background rather than a reason to reach out.
The attribution is wrong. Company names collide across industries, subsidiaries get confused with parents, and automated matching misfires more often than most teams assume. A rep who opens by referencing another company's news has lost the meeting in the first sentence, and no amount of signal quality upstream survives that.
A signal is a hypothesis. Applied to a specific company with verified evidence, it becomes something a rep can act on.
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 signal supplies the reasoning. The evidence supplies the proof. The pairing is what makes it usable, because the same event is meaningful for one vendor and noise for another. Relevance is a property of the pair, not the event.
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, builds the signals from that profile, then evaluates third-party public evidence against them every week. What comes back carries the account, the rationale, the supporting evidence with source URLs and dates, the applicable value proposition, and the role that owns the problem.
What is a go-to-market signal? A go-to-market signal is the hypothesis connecting a pattern of public evidence to a specific problem you solve. It states what a company does or says when that problem is present, and which of your capabilities addresses it.
Are go-to-market signals the same as trigger events? No. A trigger event is a single occurrence at a point in time, such as a funding round or an executive hire. A go-to-market signal is the reasoning connecting a pattern of evidence to a problem you solve, which is what makes the evidence mean something.
How many go-to-market signals should a company have? Most teams need five to eight, roughly one per problem they solve. More than that usually means the signals are describing features rather than problems, and the resulting lists overlap.
What data sources work best for go-to-market signals? Job postings carry the most usable detail per record, because a job description names the tools in use, the problem being solved, and the manager who owns it. Filings and press releases are more authoritative and slower. Executive commentary is the least structured and often the most current.
How do I know if a go-to-market signal is any good? Run it backward against your last twenty closed-won deals. If the evidence would have surfaced those accounts before they raised their hand, the signal works. If it wouldn't have surfaced any of them, the hypothesis is wrong.
How often should signals be refreshed? The hypothesis changes rarely, maybe once or twice a year as your product and market shift. The evidence needs continuous refresh, because most of it decays inside ninety days.
Can AI build go-to-market signals? AI can evaluate evidence against a signal at a throughput no person can match. Building the signal still starts with knowing what problems you solve and how customers describe them, which comes from your own deals rather than a data feed.
