Context drives GTM. Start with what’s already written down.

Every go-to-market (GTM) decision is a bet placed on context.

Who to sell to. What to say in the first minute of a call. Which feature ships next quarter. What a new rep needs to know by Friday. Teams describe these as data problems, but the numbers rarely settle them. What settles them is whether the person deciding knows the specific things that make this market different from a generic one: how buyers describe the problem in their own words, what they tried before they called, what stalled the last three deals, and what the product is actually promising to do with a customer's data.

Before commissioning more research, check the context your company already holds. Look in call transcripts that have not been read as a set, in a product mockup that stayed with the design team, in support tickets, win/loss notes, and changelogs. Some of the missing context may simply be unread, unconnected, and filed under the wrong department.

This piece is about that gap, and about two examples of context hiding in plain sight: a folder of sales call transcripts, and a single product mockup with a list of API scopes on it.

Context changes how you read the data

For this discussion, think of data as the recorded observation and context as the circumstances that change how you interpret it. Counts and verbatim quotes are both data; neither explains itself.

Take an illustrative example: "42 prospects mentioned reporting last quarter." Context is the sentence a VP of operations said on one of those calls: "I rebuild the same board deck by hand every month because none of our tools agree with each other." Same topic. Different asset. The first tells you reporting matters. The second gives you a concrete workflow to investigate, language to test, and a possible demo path.

Context has three properties that make it valuable and easy to lose.

It is specific. The names, the phrasing, and the situation carry the value. Generalize it and the value drains out.

It is situated. It is attached to a person, a moment, and what happened next. A recorded pain point is evidence of what that buyer reported. A linked deal outcome adds context; it does not prove that the pain point caused the win.

It is perishable. A call from March can be easy to overlook. The rep who heard it leaves. The summary replaces it, and the summary kept the topic and threw away the phrasing.

That last one is the quiet killer. A "key takeaways" document is lossy compression. It is convenient, and without a path back to the source, it can become where the context goes to die.

Example one: your transcripts can sharpen your ICP

Take a B2B company with 100 recorded sales calls from the last two quarters. Ask how they are used. Perhaps managers spot-check a few for coaching and reps replay their own to write follow-ups. If that is where use stops, the wider team is missing a useful source.

Those transcripts can sharpen the company's ideal customer profile (ICP): which buyers describe an urgent problem, what they tried, and what holds up a purchase. They reflect the people who entered the sales process and agreed to be recorded—not the whole market.

This is not a new idea. In 1993, Abbie Griffin and John Hauser published "The Voice of the Customer" in Marketing Science, a foundational paper in the discipline. Their work examined how customer interviews uncover needs and the value of multiple analysts reading the material. It does not establish a coverage threshold for sales calls. Research interviews and sales conversations differ in recruitment, purpose, and questioning; a hundred calls can still miss buyers outside your pipeline. Read the calls as a corpus, but inspect who is absent as carefully as who appears.

Here is what a transcript can preserve that a short CRM field often leaves out, and the questions it can help sales and product investigate. These are leads for analysis, not conclusions from a single quote.

What the buyer says	What sales can investigate	What product can investigate
The words they use for the problem	The headline of the pitch, and the search query they would type	What to name the feature, and how to write the onboarding
The trigger ("we lost a customer because...")	When to reach out, and the qualifying question to ask first	The job the product has to do first, before anything else
The alternatives they weighed	The real competitive set, seen from the buyer's side	The bar to clear, and what "good enough" looks like
The objection that stalled the deal, and what answered it	Responses to compare against deal outcomes	The missing capability, or the missing proof
Who else joined the call	Potential members of the buying group, with budget authority still to confirm	Whose workflow the product is designed around
The workaround they described	The demo path	The start of a build spec: a workaround is a prototype the customer already built
What they never asked about	What to test removing from the deck	What to investigate before deprioritizing; silence may reflect the agenda

How the transcripts tell you how to sell. Read across the corpus and compare how buyers describe the problem. Some described the trigger, the workaround, and the buying group without being asked. Others were polite and asked good questions. The first group offers an ICP hypothesis, not a verdict. The second may include good-fit buyers who are early in their research or less forthcoming. Compare both groups with qualification, win/loss, retention, and segment data. Then use their phrasing to draft a talk track and test it with buyers.

The pressure to get this right is rising. Gartner's 2025 report on a survey of 632 B2B buyers, conducted in August–September 2024, said that 61% of surveyed B2B buyers preferred a rep-free buying experience, and its 2026 report said that 69% of surveyed B2B buyers preferred to validate AI-generated insights with sales reps. These are separate surveys, not a measured change in the same buyers. Together they suggest a useful role for reps: helping buyers check research against specific context—what companies like this one actually ran into, and what happened. That context is in the transcripts, if anyone has read them.

How the transcripts tell you what to build. Feature requests are the shallow layer. The deep layer is the workaround. When a buyer says "right now we export both systems to a spreadsheet and reconcile them on Friday afternoons," you have the start of a spec, a demo scenario, and a candidate measure: time spent reconciling those Friday exports. Compare what buyers said mattered in won and lost deals, including price, timing, and fit. That gives the roadmap argument cited observations, not proof of which feature would have changed the outcome. That is useful material for a product discussion, and it was sitting in the sales team's call recorder.

A practical method, for a team without any special tooling:

Read across, not down. Read for recurring themes across segments, not a magic call count. Check contradictory cases and repeated calls from the same account.
Keep the verbatim. Tag the sentence. Do not paraphrase it. The phrasing is the asset.
Attach the outcome. Segment, deal stage, and what happened after. Keep unknown outcomes explicit; the observation still matters.
Count. Count accounts as well as mentions, record the denominator, and compare segments. Frequency informs an ICP or roadmap hypothesis; it does not settle either.
Route it. Objections to enablement. Workarounds to product. Phrasing to marketing. The person who can act on a piece of context is rarely the person who recorded it.

Example two: a product mockup is a go-to-market document

Picture a product designer sharing a Figma mockup of an integration settings screen. Beneath the connect button sits a list of the API scopes the integration will request. Read calendar events. Read contacts and deals. Write activity notes. Read call recordings.

Engineering reads that list as a permissions spec. Security reads it as a review. Product marketing may miss it if they are waiting for the launch brief.

The mockup is raw material for the launch brief. A scope list describes proposed permissions, not shipped behavior or a complete data-use policy. It gives product marketing specific questions for engineering and security: what will use this access, who can see the result, and what has actually been implemented?

Each scope suggests use cases to validate. Read the same list as a product marketer and it becomes a draft review agenda:

Scope in the mockup	Possible job	Use case to validate	Question to resolve before claiming it
Read calendar events	Know what is coming	A pre-meeting brief	What triggers it, and which calendars can it read?
Read contacts and deals	Know who this is and where the deal stands	Account context for call preparation	Is this access read-only, and do other scopes or integrations permit writes?
Write activity notes	Support after-call admin	Draft notes for a CRM activity	What writes are implemented, and what approval and audit controls apply?
Read call recordings	Learn from the conversation	Objection analysis grounded in permitted recordings	Who can access recordings, and what consent and retention rules apply?
Scopes it does not request	A boundary to investigate	Explain verified limits of this integration	Could other scopes, uploads, integrations, or providers access that data?

That last row is easy to miss. The scopes you did not request are a positioning asset. But "We never read private messages" needs evidence across the actual data flows, not an omission in a mockup. A narrower, verified statement about one integration can be useful without promising more than security has reviewed.

Before the feature ships, product marketing can draft use cases, an enablement FAQ, and a demo outline from these questions. Engineering must verify behavior and availability; security must review access, retention, and provider boundaries. A staged rollout from read-only to write access is an option to discuss, not something the scope list promises. Early drafts buy review time without pretending the answers already exist.

The lesson generalizes past Figma. API documentation, permission models, error messages, empty states, and changelogs are all GTM context filed in engineering's inbox. Each may record a decision, proposal, or constraint in the specific terms a buyer will eventually ask about. Check its status and the implementation before turning it into a promise. Product marketing that reads those artifacts as they are produced is working from the same source of truth as the product. Product marketing that waits for the brief is working from a summary of it.

Why context stays unread

If this context is so valuable, why does it sit untouched? Four reasons, and none of them is laziness.

Wrong inbox. Context is filed by the department that produced it and valuable to the one that did not. Transcripts live with sales. Mockups live with design. Reading the other team's raw material may have no clear owner.
Wrong shape. Context is prose. A CRM may support notes and transcripts while the team still relies on deal-stage fields for reporting. Capturing a sentence is not the same as making it easy to retrieve.
Wrong lifetime. Context is read once, at the moment it is produced, and then ages out. Nothing prompts the reread when the decision that needs it finally arrives.
Wrong substitute. The summary replaces the source. Each retelling drops the phrasing, and the phrasing was the asset. Six months later the company is working from copies of copies and nobody can find the original.

This is what the tool-sprawl problem looks like from the inside. The cost is not just licenses. It is the work of connecting a sentence on a call, a scope on a mockup, and a deal that closed when those links are missing from the workflow.

What changes when context is the unit of work

None of this requires a new platform. It requires treating context as the unit of GTM work instead of a byproduct of it. Five operating principles do most of the work.

Treat raw sources as shared assets. Transcripts, mockups, tickets, and win/loss notes can serve more than the function that generated them. Share only with authorized people, respecting consent, confidentiality, and retention rules; give those readers a reason to read.
Test the ICP against the transcripts. An ICP document written in a workshop is a hypothesis. An ICP inferred from transcripts is also a hypothesis, backed by observations from a selected sample. Test it against outcomes, retention, and buyers missing from that sample; revisit it as new evidence arrives.
Read the mockup as the story. Product marketing joins the design review, not the launch review. The scope list and permission model are starting points for use cases and security questions, not proof of shipped capabilities.
Keep the verbatim next to the decision. When a positioning line, a battle card entry, or a roadmap item is written, attach the sentence it came from. Anyone who challenges the decision later can read the evidence instead of relitigating the opinion.
Attach outcomes. Link the objection to the deal outcome and the use case to adoption data. Preserve the source, sample, timing, and alternative explanations. An outcome enriches evidence; it does not turn a correlation into a cause.

Language models can help compare transcript excerpts, review a mockup's scope list, and draft an objection playbook with source references. Corpus size, retrieval coverage, permissions, and citation accuracy still need checking. The same model, given none of that, produces the fluent generic advice buyers are already bringing to the call themselves. Model capability matters, but so does supplying relevant context in a form the model can use. That is the whole argument for treating GTM intelligence as a discipline rather than a dashboard.

Bring decisions closer to the context that should shape them. Your transcripts offer ICP hypotheses. Your mockup offers use cases to validate. Read them together, check what they support, and route the useful parts to the people who can act on them.