go-to-market

Go To Market

Turn a product, feature, or company narrative into a concrete market-facing plan that can actually be sold, validated, measured, renewed, and expanded.

Use this skill as the umbrella operating system for:

• early selling before PMF
• launch planning after initial proof
• technical pre-sales for enterprise or complex deals
• growth experimentation, pacing, and weekly scorecards
• revenue intelligence for call insights, attribution, and reporting
• revenue-operations reviews once pipeline and spend matter
• retention, renewal, and expansion planning after customers land

Keep SKILL.md focused on the operating model. Load only the reference file that matches the task:

• references/technical-sales.md for technical discovery, RFP/RFI work, demos, POCs, and technical proposal support
• references/growth-engine.md for playbook-first experimentation workflows, experiment design, scoring states, weekly scorecards, pacing alerts, and next-test queues
• references/revenue-intelligence.md for call-transcript analysis, objection and buying-signal extraction, content ROI, attribution models, and unified client reporting
• references/revenue-ops.md for pipeline coverage, forecast quality, GTM efficiency, and operating metrics
• references/customer-growth.md for health scoring, churn diagnosis, renewal risk, and expansion planning
• references/commercial-docs.md for proposals, SOWs, NDAs, MSAs, and close-support artifacts

For runnable growth experimentation, use the local scripts in skills/go-to-market/scripts/ before inventing ad hoc spreadsheets or one-off analysis code.

Use this skill for

• positioning and differentiation
• ICP or segment definition
• first-customer or first-100-customer strategy
• launch plans and commercialization strategy
• messaging hierarchy and value proposition work
• pricing strategy for early traction
• channel and campaign planning
• sales or CS enablement for launches
• growth experimentation across content, lifecycle, paid, outbound, or landing-page motions
• weekly scorecards, pacing alerts, and living playbooks of proven winners
• technical discovery, RFP/RFI support, proof-of-concept design, and competitive deal support
• sales-call transcript analysis, objection mining, buying-signal extraction, and competitor mention analysis
• content-to-pipeline or content-to-revenue attribution
• client-ready or executive-ready reporting across analytics, CRM, SEO, and call data
• content-gap analysis tied to funnel stages, revenue outcomes, or repeated objections
• pipeline reviews, forecast confidence checks, and GTM efficiency analysis
• retention, renewal, expansion, and net revenue retention planning
• proposal, SOW, NDA, MSA, or implementation-plan support tied to closing revenue

Start with the stage

Pick the operating model before writing the plan.

Stage 1: First customers

Use this mode when the product is early, adoption is unproven, or the user needs initial paying customers.

Core principle:

Skip the launch. Focus on selling.

Do not default to broad awareness campaigns, polished launches, or channel sprawl. The job is to find people with a live problem, talk to them directly, learn quickly, and get paid.

Stage 2: Broader launch

Use this mode when the product already has evidence of demand, a repeatable buyer story, or enough readiness to justify a coordinated market launch.

Do not treat Stage 1 and Stage 2 as the same problem. Early traction is mostly direct selling and learning. Broader launch is about scaling a message and motion that already has some proof.

Stage 3: Revenue scaling and retention

Use this mode when the user already has active pipeline or paying customers and needs a tighter commercial system.

Core principle:

Treat GTM as a revenue system, not just a launch system.

At this stage, the work is no longer only about acquisition. It includes deal qualification, technical proof, forecast discipline, retention risk, expansion potential, and whether the motion is economically healthy.

Core model

Every GTM output should answer these eight questions:

1. Who is the buyer or user segment?
2. What problem is urgent for them now?
3. Why is this offer credible and differentiated?
4. What message should each audience hear first?
5. What motion or channel should carry that message?
6. What proof, validation, or technical evidence is needed to close?
7. What has to be true operationally for the plan to work?
8. How will experimentation, revenue intelligence, retention, expansion, and unit economics confirm that the motion is healthy?

If one of these is missing, the plan is incomplete.

First-customer model

When the user is pre-launch or pre-PMF, bias toward manual selling.

Treat sales as helping the right people solve a problem, not as persuasion theater. If the user acts embarrassed to sell, correct that framing and move back to direct customer contact.

Concentric circles of sales

Sell outward from the people most likely to care to the people least likely to care:

1. Friends and family
2. Existing community
3. Strangers through cold outreach

The point is not vanity, virality, or a launch event. The point is conversations, learning, and paid conversions.

Circle 1: Friends and family

• Ask for purchases or real introductions, not generic encouragement.
• Pitch them as first customers when plausible, not default investors or cheerleaders.
• Ask for candid feedback after the pitch.
• If close contacts will not buy or refer, treat that as signal to inspect the problem, buyer, or offer.

Circle 2: Community

• Identify the community already tied to the problem space.
• List people who write, post, teach, advise, or build around the problem.
• Reach out personally and show the product in context.
• Ask for honest feedback, not social posts or surface-level praise.
• Favor repeated direct conversations over broad community announcements.

Circle 3: Cold outreach

• Cold email, calls, DMs, and direct outreach are valid early GTM channels.
• Personalize each message around the prospect's current workflow, pain, or workaround.
• Treat each rejection as product or message data.
• Do not rely on one perfect template; improve from real responses.

Standard output structure

Unless the user asks for a different artifact, produce the sections that match the stage.

For first customers

1. Market focus

• target segment
• urgent buying trigger
• why this segment should be first
• exclusions or low-priority segments

2. Offer and positioning

• category framing
• problem statement
• differentiated value
• proof points or credibility gaps

3. First-customer path

• 10 friends, family, or warm contacts to pitch this week
• 10 community members or operators to contact next
• cold outreach targets or profile criteria
• why each circle is ordered this way

4. Outreach and sales motion

• warm outreach angle
• community outreach angle
• cold outreach template that is personalized, not mass-blast copy
• call/demo goal
• follow-up cadence

5. Pricing

• initial price
• pricing model: cost-based, value-based, or hybrid
• competitor or alternative price anchors
• what makes the offer a "no-brainer" for the first buyer
• what price makes the business viable from customer #1
• why free is worse than charging something in this case
• whether a free trial is warranted and how it converts to paid
• conditions for raising price later

6. Weekly execution and metrics

• weekly outreach goal
• weekly conversation goal
• weekly close goal
• how learnings will be captured
• leading indicators of traction
• customers needed to reach sustainability, if relevant

7. Proof and close support

• technical questions that could block the sale
• evidence, demo, or implementation proof needed to answer them
• proposal, pilot, or SOW assets needed to turn interest into revenue

For broader launch

1. Market focus

• target segment
• buying trigger
• timing or market reason
• exclusions or low-priority segments

2. Positioning

• category framing
• problem statement
• differentiated value
• proof points

3. Messaging hierarchy

• one-line headline
• 3-5 supporting messages
• objections and rebuttals
• message variations by audience when needed

4. Motion and channels

• sales-led, product-led, partner-led, content-led, or hybrid motion
• primary channels and why they fit
• assets required for each channel

5. Pricing and packaging

• price point or price range
• pricing model: cost-based, value-based, or hybrid
• package or tier structure
• trial, pilot, or intro offer if needed
• upgrade path and conditions for price increases

6. Launch plan

• audience
• milestone
• owner
• date or sequencing note
• dependency

7. Risks and validation

• biggest adoption risks
• assumptions needing proof
• leading indicators to watch after launch

8. Revenue operating scorecard

• pipeline coverage target and current gap, if known
• leading conversion metrics by stage
• forecast confidence and major unknowns
• retention or expansion metrics that would validate the launch over time

9. Measurement and attribution

• systems or data sources used to measure performance
• attribution model: first-touch, linear, time-decay, or user-defined
• key content, campaign, or call signals expected to influence pipeline
• content gaps, missing instrumentation, or join-quality issues
• anomalies or shifts that would trigger a launch adjustment

10. Experiment plan

• hypothesis to test
• variable and variants
• primary metric and guardrail metrics
• sample threshold, decision rule, or scoring cadence
• what goes into the playbook if a winner emerges

For revenue scaling and retention

1. Revenue focus

• current segment mix
• target revenue motion
• biggest bottleneck: coverage, conversion, cycle length, retention, or expansion
• what part of the funnel or lifecycle deserves priority now

2. Commercial system diagnosis

• pipeline coverage and quality
• forecast confidence
• concentration risks
• recurring objections, buying signals, pricing friction, and competitor mentions from calls if available
• implementation or technical-sale friction
• renewal and expansion health

3. Intervention plan

• acquisition fixes
• experimentation fixes and next tests
• technical pre-sales fixes
• pricing or packaging fixes
• retention fixes
• expansion plays

4. Metrics and operating cadence

• weekly operating metrics
• monthly executive metrics
• experimentation, call-insight, attribution, or client-reporting cadence when relevant
• thresholds that trigger action
• owner for each metric or intervention

5. Dependencies and risks

• product or roadmap dependencies
• data quality or CRM gaps
• delivery, support, or onboarding risks
• risks to forecast, retention, or efficiency

Technical GTM layer

When the product is technical, enterprise-facing, or integration-heavy, do not stop at messaging. Load references/technical-sales.md and add a technical close path.

Use a five-part flow:

1. Discovery: capture requirements, constraints, security concerns, and buying drivers.
2. Solution design: map the product to requirements and call out gaps or customization.
3. Demo validation: ensure must-have requirements are explicitly covered in the demo script.
4. POC validation: define scope, success criteria, timeline, and go/no-go thresholds before the POC starts.
5. Proposal and close support: convert proof into a technical proposal, implementation plan, and objection-handling pack.

When the user asks for RFPs, feature matrices, demos, POCs, or technical proposals, move into this layer instead of treating the task as pure messaging.

Messaging rules

• Tie every claim to a user pain, business outcome, or proof point.
• Prefer "problem -> value -> proof" over slogan-heavy copy.
• Avoid invented differentiation. If the product context does not prove it, do not claim it.
• If multiple audiences exist, separate buyer, user, and internal team messaging.
• In early-stage work, favor direct, specific, helpful language over brand copy.

Pricing decision model

Anchor pricing in a model instead of picking a number by feel.

Cost-based pricing

Use this when unit economics are concrete and visible.

• Calculate direct costs such as hosting, time, materials, services, and payment processing.
• Add a margin and state it explicitly.
• Best fit for physical goods, services, and offers with clear delivery costs.

Value-based pricing

Use this when customer value is much more important than delivery cost.

• Price against the value or outcome for the customer, not just internal cost.
• Best fit for software, digital products, and offers where marginal cost is near zero.
• If the product saves money, time, risk, or headcount, use that value to justify the price.

Hybrid pricing

Use this when costs create a floor but customer value should determine the ceiling.

• Set a minimum viable floor from costs.
• Adjust upward based on the value delivered and market alternatives.

Pricing discovery questions

When pricing is part of the task, gather or estimate:

• variable cost per customer, seat, project, or unit
• fixed costs that affect sustainability
• competing or substitute solution prices
• what would make the offer a no-brainer for the ideal customer
• what price allows profitability from customer #1
• whether the buyer expects a trial, pilot, setup fee, or annual discount

Pricing rules

• Charge something early unless there is a strong reason not to.
• A free user and a paying customer are not equivalent signals.
• Zero is not a neutral price. Free changes behavior and obscures willingness to pay.
• Use cost-based, value-based, or hybrid pricing, and say explicitly which model is being used.
• Start with a simple price if certainty is low; complexity can come later.
• Pricing is not permanent. Start with a credible price and iterate.
• Start low enough to reduce buying friction, then raise prices as product quality, proof, and confidence improve.
• Pricing is iterative. Raise prices when proof, product quality, or buyer confidence improve.
• Recommend tiered pricing when there is real value separation across segments or usage, not just because tiers look sophisticated.
• If a trial is recommended, define the conversion path to paid. Do not treat trial users as the business model.
• Do not confuse marketing with giving the product away. Free acquisition can make later monetization harder.

Sustainability math

When the user is early-stage, connect pricing to survival:

• estimate how much revenue is needed per month to sustain the business or founder
• convert that into the number of customers required at the proposed price
• sanity-check whether that customer count is reachable through the proposed motion
• if the math only works at unrealistic volume, change the price, offer, segment, or motion

Use rough math when necessary. A directional answer is better than pretending precision.

Revenue operations layer

When the user has active pipeline, paid acquisition, quota, or board-style reporting needs, load references/revenue-ops.md.

Bias toward a compact operating scorecard instead of vanity reporting. At minimum, reason about:

• pipeline coverage ratio
• stage conversion rates
• sales velocity
• deal aging and concentration risk
• forecast confidence
• efficiency metrics such as Magic Number, LTV:CAC, CAC payback, burn multiple, Rule of 40, and net dollar retention when relevant

Do not talk about scale as if it exists unless the numbers support it.

Growth experimentation layer

When the task involves A/B testing, multivariate tests, experimentation cadence, weekly scorecards, living playbooks, or pacing alerts, load references/growth-engine.md.

Use this layer to convert GTM from opinion-led iteration to evidence-led iteration.

At minimum, reason across four elements:

1. Experiment design: clear hypothesis, variable, variants, primary metric, and guardrails.
2. Scoring discipline: sample thresholds, significance or confidence rules, and explicit keep/discard logic.
3. Playbook capture: winners should update how the team creates future content, campaigns, or offers.
4. Operating rhythm: weekly review, trending calls, and pacing alerts when targets matter.

Use the reference's operating sequence even when the user has no dedicated experiment scripts:

1. check the current playbook
2. define the experiment record
3. log shipped variants and metrics
4. score against explicit running, trending, keep, and discard states
5. promote winners or queue the next test

If the user wants to know what to test next, whether a variant won, or how to institutionalize learnings across channels, do not stop at channel strategy alone.

Revenue intelligence layer

When the task involves sales-call transcripts, objection mining, buying signals, competitor mentions, content ROI, attribution models, multi-source reporting, or anomaly detection, load references/revenue-intelligence.md.

Use this layer to close the loop between market signal and revenue proof.

At minimum, reason across three motions:

1. Call insight extraction: turn transcripts or Gong-style call data into objections, buying signals, competitor mentions, pricing discussions, and follow-up actions.
2. Attribution and ROI: map content or campaigns to pipeline and revenue using a clear attribution model and explicit data-quality caveats.
3. Client or executive reporting: combine analytics, CRM, SEO, and call evidence into a readout that explains what changed, what mattered, and what to do next.

If the user wants to know what content is working, what prospects keep saying on calls, or whether marketing influenced revenue, do not stop at messaging or top-of-funnel traffic.

Customer growth layer

When the user already has customers, GTM must include retention and expansion. Load references/customer-growth.md when churn, renewals, account health, or upsell are part of the task.

Evaluate post-sale growth across four dimensions:

• product usage and adoption depth
• engagement quality and customer sentiment
• support burden and unresolved friction
• relationship strength, executive sponsorship, and commercial posture

Look for three classes of action:

• save at-risk accounts
• stabilize neutral accounts
• expand healthy accounts through seats, modules, or additional teams

Acquisition without retention is not healthy GTM.

Commercial close layer

When the task includes proposals, SOWs, NDAs, MSAs, or implementation-backed commercial close support, load references/commercial-docs.md.

Treat these artifacts as close-enablers, not legal theater. The job is to remove friction, clarify scope, and help the deal convert while preserving the right caveats around jurisdiction and legal review.

Launch and sales planning rules

• Distinguish strategy from checklist.
• Call out dependencies on product readiness, pricing, packaging, legal, support, or analytics.
• Include internal enablement, not just external announcements.
• Do not assume a broad launch if the evidence supports a narrow pilot or segment-first release.
• For first-customer work, prioritize direct sales over launch theatrics.
• Treat launch as amplification, not discovery. If paying-customer proof is weak, recommend more selling before a broad launch.

Channel selection heuristics

• Choose channels that match the buyer's attention and purchase behavior.
• Explain why each chosen channel is better than the obvious alternatives.
• If resources are constrained, recommend fewer channels with tighter execution.
• For first customers, default to channels with direct feedback loops before scalable channels.

Validation heuristics

• Manual sales often dominate early growth.
• Word of mouth matters more after a base of happy customers exists.
• Product-market fit shows up as repeat usage, repeat purchase, referrals, or inbound interest that does not require constant pushing.
• Early metrics should track conversations, conversion, and learning velocity, not just traffic.
• You usually need fewer paying customers than people assume to prove demand or fund the next stage.
• Use pricing math to test realism. If the business needs an unreachable customer count at the proposed price, the GTM plan is wrong.
• If the plan needs 1,000 users to work but the user can realistically reach only 20 people, the plan is wrong.
• If the sale depends on technical proof, the plan is incomplete until the demo, POC, or implementation path is explicit.
• If experiments are being run without a hypothesis, metric, or decision rule, the learning loop is broken.
• If winners are not being carried into a repeatable playbook, the team is relearning the same lesson.
• If the forecast depends on a few large deals, call out concentration risk instead of presenting false certainty.
• If recurring objections or competitor mentions show up in calls but no message, content, or enablement response exists, the GTM loop is broken.
• If content influence cannot be tied credibly to pipeline or revenue, say so instead of overstating attribution precision.
• If growth looks efficient only because churn is ignored, the GTM plan is wrong.

Failure modes to avoid

• launch plans that are only calendars
• positioning without proof
• messaging that sounds the same as every competitor
• too many audiences in one narrative
• channel plans that ignore team capacity
• "everyone is the ICP" segmentation
• confusing awareness with traction
• asking for feedback without asking for the sale
• recommending free users when the real question is willingness to pay
• pricing that ignores either delivery costs or delivered value
• adding tiers before there is real packaging logic
• recommending a trial without a clear paid conversion path
• running experiments with too many simultaneous changes to isolate the cause
• declaring winners from noisy samples or weak metrics
• collecting experiment results without changing the playbook or next test queue
• treating a technical deal like a generic messaging problem
• running a POC without explicit success criteria
• claiming content ROI without a defensible attribution model or reliable join logic
• reporting call insights without translating them into messaging, enablement, or content actions
• presenting pipeline without coverage, aging, or concentration analysis
• calling growth healthy while ignoring churn, NDR, or expansion quality
• using contracts or proposals to hide ambiguity instead of resolving it

Quality bar

• The plan names a specific buyer and a specific problem.
• The message is differentiated enough to survive comparison.
• The chosen stage matches the user's actual maturity.
• The first-customer plan names real outreach paths, not vague growth ideas.
• The pricing recommendation states a model, a rationale, and when to revisit it.
• The customer-count math is directionally viable for the proposed motion.
• The launch sequence shows ownership and dependencies.
• Risks and leading indicators make the plan testable after launch.
• Experimentation plans name the hypothesis, metric, decision rule, and how winners propagate into future execution.
• Technical proof requirements are explicit when they matter to the sale.
• Revenue intelligence claims are backed by explicit models, source systems, and caveats.
• Revenue metrics are concrete enough to distinguish healthy growth from noisy activity.
• Retention and expansion are addressed when the user already has customers.