Start with the market, not keywords
Turn customer evidence into a prioritized map of the problems, comparisons, and questions that create pipeline.
You will leave with one demand map that tells SEO, content, product marketing, and AEO what to build first.
Buyer demand control room
Combine CRM outcomes, sales language, search demand, and AI prompts before deciding what to publish.
Demand by decision stage
Weight themes by commercial evidence, not search volume alone.
Turn demand data into a strategy.
Traffic is an input. The decision is where evidence, commercial intent, product fit, proof, and team capacity justify action.
Start with modifiers, then inspect the live SERP, ranking page types, sales calls, and the next question. A human assigns the final intent.
CRM opportunities and closed revenue outrank modeled value. Use modeled pipeline only where attribution is missing, and label it as directional.
Rank every demand family, then limit the active list to actual quarterly capacity. Everything else is validated, supported, or parked.
Modifiers suggest intent. The ranking page types and buyer evidence confirm it.
| Intent | Query clues | SERP proof | Buyer decision | Likely asset |
|---|---|---|---|---|
| Diagnose | why, problem, meaning | Guides and definitions | Understand the problem | Expert guide |
| Solution | how to, automate, improve | Processes and use cases | Choose an approach | Use-case page |
| Category | software, platform, tools | Product and category pages | Choose a solution type | Product / category |
| Compare | best, top, vs, alternatives | Lists and comparisons | Build a shortlist | Comparison / best-of |
| Vendor | brand, reviews, demo | Vendor and review pages | Evaluate a named vendor | Vendor / proof |
| Pricing | price, cost, plans, ROI | Pricing and calculators | Validate commercial fit | Pricing / ROI |
| Validate | integration, security, migration | Docs, proof, implementation | Remove purchase risk | Integration / proof |
Use the strongest evidence available and keep weaker estimates visibly separate.
| Method | Evidence | How to use it | Confidence |
|---|---|---|---|
| Sourced revenue | Landing page or content is the recorded acquisition source | Use directly | Highest |
| Influenced revenue | Asset was touched before opportunity or close | Report separately with window | High |
| Opportunity evidence | Demand family appears in calls, forms, notes, or campaigns | Opps x win rate x ACV | Medium-high |
| Paid-search evidence | CPC, conversion, and opportunity quality exist | Use as commercial validation | Medium |
| Modeled potential | No direct CRM history exists | Volume x CTR x conversion x ACV | Directional |
Use your own GSC, analytics, and CRM rates. These assumptions affect modeled potential, not observed revenue.
| Now | Demand family | Intent | SV | CRM revenue | Opps | Win % | ACV | Fit | Evidence | Gap | Sales | Effort | Page state | Opp expected revenue | Modeled pipeline | Score | Next action |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| YES | $39,440 | $13,260 | 90.5 | Refresh + prove | |||||||||||||
| YES | $45,360 | $102,600 | 89.0 | Refresh + prove | |||||||||||||
| YES | $43,560 | $31,680 | 88.0 | Refresh + prove | |||||||||||||
| YES | $61,380 | $38,880 | 87.0 | Build revenue page | |||||||||||||
| YES | $43,320 | $17,670 | 85.0 | Refresh + prove | |||||||||||||
| LATER | $49,980 | $24,570 | 84.5 | Build revenue page | |||||||||||||
| LATER | $20,800 | $18,720 | 74.0 | Fix blocker first | |||||||||||||
| LATER | $17,600 | $60,000 | 69.5 | Consolidate URLs | |||||||||||||
| LATER | $4,320 | $86,400 | 44.0 | Park | |||||||||||||
| LATER | $0 | $104,400 | 31.5 | Park |
80-100: build or refresh now. 65-79: validate and queue. 50-64: supporting backlog. Below 50: park. Overlap and technical blockers override the score.
Best: sourced or influenced CRM revenue. Next: opportunity and win evidence. Last: modeled pipeline using explicit assumptions. Never present modeled value as revenue.
Keyword tools show what people type. They do not tell you which pains create urgency, which objections stall deals, or how buyers ask AI for a shortlist. Start with evidence from the market, then translate it into search queries and prompts.
Choose one revenue segment
A broad ICP produces a broad content plan. Pick one segment where the problem, buying process, and product fit are similar enough to write a precise answer.
- 01
Export the last 20 closed-won deals and group them by company type, size, use case, and buyer role.
- 02
Score each group from 1-5 for win rate, ACV, sales-cycle speed, retention, and access to customer evidence.
- 03
Select one segment for this 90-day cycle. Write it as: [role] at [company type] who need [job] before [trigger].
- 04
Collect: Export 12 months of closed-won, closed-lost, and stalled opportunities with segment, role, ACV, and loss reason.
- 05
Score: Score revenue evidence, frequency, strategic fit, current visibility, and production effort separately.
Choose the business priority, interpret exceptions, protect confidential data, challenge weak evidence, and approve the final decision.
Clean, classify, compare, calculate, and draft rows from supplied evidence. AI may surface patterns; it does not own strategy.
HubSpot / Salesforce, plus tagged call evidence from Gong / call notes.
| Segment | Wins | Win rate | Median ACV | Sales cycle | Retention | Evidence | Weighted score |
|---|---|---|---|---|---|---|---|
| Mid-market agencies | 14 | 34% | $18k | 41 days | 93% | 8 calls | 23.4 / 30 |
CLAUDE / CHATGPT PROMPTAnalyze the evidence without outsourcing the decision+
Remove personal or confidential data. Attach the named exports, explain every column, and tell the model when the dataset was collected.
You are assisting a human B2B SaaS SEO and AEO operator with Module 01: Start with the market, not keywords. Current lesson: Choose one revenue segment Objective: A broad ICP produces a broad content plan. Pick one segment where the problem, buying process, and product fit are similar enough to write a precise answer. Required artifact: Buyer demand map BUSINESS CONTEXT I WILL PROVIDE - Product, category, target market, pricing model, sales motion, and geography - The priority customer segment and the commercial outcome for this 90-day cycle - Opportunity export exported from HubSpot / Salesforce - Tagged call evidence exported from Gong / call notes - Definitions for any internal fields, stages, scores, and abbreviations TASK 1. Export the last 20 closed-won deals and group them by company type, size, use case, and buyer role. 2. Score each group from 1-5 for win rate, ACV, sales-cycle speed, retention, and access to customer evidence. 3. Select one segment for this 90-day cycle. Write it as: [role] at [company type] who need [job] before [trigger]. 4. Collect: Export 12 months of closed-won, closed-lost, and stalled opportunities with segment, role, ACV, and loss reason. 5. Score: Score revenue evidence, frequency, strategic fit, current visibility, and production effort separately. REQUIRED OUTPUT Return a table using these exact columns: Segment | Wins | Win rate | Median ACV | Sales cycle | Retention | Evidence | Weighted score. For every recommendation, cite the source row, URL, call note, or data point that supports it. Add a confidence column in your analysis: High, Medium, or Low. List missing evidence separately instead of guessing. Finish with a section named HUMAN DECISIONS REQUIRED. RULES - Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts. - Do not treat correlation as causation. - Preserve contradictory evidence and explain why it conflicts. - Do not make the final priority or publishing decision. Prepare the evidence for a human owner. - Use this example only as a format reference, not as evidence: Head of Marketing at a 20-100 person B2B SaaS company who needs reliable attribution before the next board meeting.
One segment is named precisely. A named human owner must verify this before the lesson is complete.
Head of Marketing at a 20-100 person B2B SaaS company who needs reliable attribution before the next board meeting.
Mine buyer language
Your best query research is already hiding in sales calls, onboarding notes, support tickets, and reviews.
- 01
Collect 10 sales transcripts, 10 support conversations, five reviews, and the notes from five lost deals.
- 02
Highlight exact phrases for pain, desired outcome, failed approach, evaluation criteria, objection, and trigger.
- 03
Keep the buyer wording. Add a tally each time the same idea appears; frequency reveals the category vocabulary.
- 04
Listen: Review at least 10 calls and tag exact problem, trigger, objection, comparison, and proof language.
Choose the business priority, interpret exceptions, protect confidential data, challenge weak evidence, and approve the final decision.
Clean, classify, compare, calculate, and draft rows from supplied evidence. AI may surface patterns; it does not own strategy.
Gong / call notes, plus query export from GSC + Semrush / Ahrefs.
| Exact phrase | Role | Stage | Source | Frequency | Meaning | Human approved |
|---|---|---|---|---|---|---|
| Reports take all Monday | Ops lead | Problem | Call 184 | 7 | Manual consolidation | Yes |
CLAUDE / CHATGPT PROMPTAnalyze the evidence without outsourcing the decision+
Remove personal or confidential data. Attach the named exports, explain every column, and tell the model when the dataset was collected.
You are assisting a human B2B SaaS SEO and AEO operator with Module 01: Start with the market, not keywords. Current lesson: Mine buyer language Objective: Your best query research is already hiding in sales calls, onboarding notes, support tickets, and reviews. Required artifact: Buyer demand map BUSINESS CONTEXT I WILL PROVIDE - Product, category, target market, pricing model, sales motion, and geography - The priority customer segment and the commercial outcome for this 90-day cycle - Tagged call evidence exported from Gong / call notes - Query export exported from GSC + Semrush / Ahrefs - Definitions for any internal fields, stages, scores, and abbreviations TASK 1. Collect 10 sales transcripts, 10 support conversations, five reviews, and the notes from five lost deals. 2. Highlight exact phrases for pain, desired outcome, failed approach, evaluation criteria, objection, and trigger. 3. Keep the buyer wording. Add a tally each time the same idea appears; frequency reveals the category vocabulary. 4. Listen: Review at least 10 calls and tag exact problem, trigger, objection, comparison, and proof language. REQUIRED OUTPUT Return a table using these exact columns: Exact phrase | Role | Stage | Source | Frequency | Meaning | Human approved. For every recommendation, cite the source row, URL, call note, or data point that supports it. Add a confidence column in your analysis: High, Medium, or Low. List missing evidence separately instead of guessing. Finish with a section named HUMAN DECISIONS REQUIRED. RULES - Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts. - Do not treat correlation as causation. - Preserve contradictory evidence and explain why it conflicts. - Do not make the final priority or publishing decision. Prepare the evidence for a human owner. - Use this example only as a format reference, not as evidence: “We cannot explain which campaigns influence pipeline” is stronger source material than “marketing analytics software.”
At least 25 buyer questions are mapped. A named human owner must verify this before the lesson is complete.
“We cannot explain which campaigns influence pipeline” is stronger source material than “marketing analytics software.”
Build the demand ladder
Map the questions a buyer asks from first pain to final vendor validation. This prevents a content plan made entirely of awareness posts.
- 01
Create five stages: problem, approach, category, comparison, and validation.
- 02
Write five real buyer questions at every stage, using evidence gathered in the previous lesson.
- 03
For each question, add the best page type, proof needed, business value, and whether buyers ask it in Google, AI, or both.
- 04
Expand: Add GSC queries, paid-search terms, competitor pages, review sites, communities, and AI prompt variants.
- 05
Map: Assign one primary asset and one owner to every priority demand family.
- 06
Validate: Have sales and customer success challenge the map before content production begins.
Choose the business priority, interpret exceptions, protect confidential data, challenge weak evidence, and approve the final decision.
Clean, classify, compare, calculate, and draft rows from supplied evidence. AI may surface patterns; it does not own strategy.
GSC + Semrush / Ahrefs, plus prompt families from ChatGPT / Perplexity.
| Problem | Approach | Category | Vendor | Validation | Target asset |
|---|---|---|---|---|---|
| Manual client reporting | Automate reports | Agency reporting software | Swydo alternatives | White-label proof | /agency-reporting/ |
CLAUDE / CHATGPT PROMPTAnalyze the evidence without outsourcing the decision+
Remove personal or confidential data. Attach the named exports, explain every column, and tell the model when the dataset was collected.
You are assisting a human B2B SaaS SEO and AEO operator with Module 01: Start with the market, not keywords. Current lesson: Build the demand ladder Objective: Map the questions a buyer asks from first pain to final vendor validation. This prevents a content plan made entirely of awareness posts. Required artifact: Buyer demand map BUSINESS CONTEXT I WILL PROVIDE - Product, category, target market, pricing model, sales motion, and geography - The priority customer segment and the commercial outcome for this 90-day cycle - Query export exported from GSC + Semrush / Ahrefs - Prompt families exported from ChatGPT / Perplexity - Definitions for any internal fields, stages, scores, and abbreviations TASK 1. Create five stages: problem, approach, category, comparison, and validation. 2. Write five real buyer questions at every stage, using evidence gathered in the previous lesson. 3. For each question, add the best page type, proof needed, business value, and whether buyers ask it in Google, AI, or both. 4. Expand: Add GSC queries, paid-search terms, competitor pages, review sites, communities, and AI prompt variants. 5. Map: Assign one primary asset and one owner to every priority demand family. 6. Validate: Have sales and customer success challenge the map before content production begins. REQUIRED OUTPUT Return a table using these exact columns: Problem | Approach | Category | Vendor | Validation | Target asset. For every recommendation, cite the source row, URL, call note, or data point that supports it. Add a confidence column in your analysis: High, Medium, or Low. List missing evidence separately instead of guessing. Finish with a section named HUMAN DECISIONS REQUIRED. RULES - Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts. - Do not treat correlation as causation. - Preserve contradictory evidence and explain why it conflicts. - Do not make the final priority or publishing decision. Prepare the evidence for a human owner. - Use this example only as a format reference, not as evidence: Problem: “Why is our attribution different in every tool?” Comparison: “Dreamdata vs HockeyStack for B2B SaaS.”
Every question has evidence and a stage. A named human owner must verify this before the lesson is complete.
Problem: “Why is our attribution different in every tool?” Comparison: “Dreamdata vs HockeyStack for B2B SaaS.”
Prioritize by revenue, not volume
A low-volume comparison can be worth more than a high-volume definition. Use a score that reflects commercial value.
- 01
Score business fit, buying intent, evidence advantage, and sales usefulness from 1-5.
- 02
Subtract execution difficulty from 1-5. Use: fit + intent + evidence + sales value - difficulty.
- 03
Move the ten highest-scoring questions into the next modules. Park everything else in a later backlog.
- 04
Cluster: Group language by buyer job and decision, not by matching words.
Choose the business priority, interpret exceptions, protect confidential data, challenge weak evidence, and approve the final decision.
Clean, classify, compare, calculate, and draft rows from supplied evidence. AI may surface patterns; it does not own strategy.
ChatGPT / Perplexity, plus opportunity export from HubSpot / Salesforce.
| Demand family | Revenue evidence | Search / prompt evidence | Current gap | Effort | Priority | Owner |
|---|---|---|---|---|---|---|
| Automate client reports | 11 wins | 1,120 queries | No use-case page | Medium | P1 | Growth |
CLAUDE / CHATGPT PROMPTAnalyze the evidence without outsourcing the decision+
Remove personal or confidential data. Attach the named exports, explain every column, and tell the model when the dataset was collected.
You are assisting a human B2B SaaS SEO and AEO operator with Module 01: Start with the market, not keywords. Current lesson: Prioritize by revenue, not volume Objective: A low-volume comparison can be worth more than a high-volume definition. Use a score that reflects commercial value. Required artifact: Buyer demand map BUSINESS CONTEXT I WILL PROVIDE - Product, category, target market, pricing model, sales motion, and geography - The priority customer segment and the commercial outcome for this 90-day cycle - Prompt families exported from ChatGPT / Perplexity - Opportunity export exported from HubSpot / Salesforce - Definitions for any internal fields, stages, scores, and abbreviations TASK 1. Score business fit, buying intent, evidence advantage, and sales usefulness from 1-5. 2. Subtract execution difficulty from 1-5. Use: fit + intent + evidence + sales value - difficulty. 3. Move the ten highest-scoring questions into the next modules. Park everything else in a later backlog. 4. Cluster: Group language by buyer job and decision, not by matching words. REQUIRED OUTPUT Return a table using these exact columns: Demand family | Revenue evidence | Search / prompt evidence | Current gap | Effort | Priority | Owner. For every recommendation, cite the source row, URL, call note, or data point that supports it. Add a confidence column in your analysis: High, Medium, or Low. List missing evidence separately instead of guessing. Finish with a section named HUMAN DECISIONS REQUIRED. RULES - Do not invent search volume, revenue, customer statements, product capabilities, or competitor facts. - Do not treat correlation as causation. - Preserve contradictory evidence and explain why it conflicts. - Do not make the final priority or publishing decision. Prepare the evidence for a human owner. - Use this example only as a format reference, not as evidence: A query with 40 monthly searches and a score of 16 outranks a 2,000-volume glossary term with a score of 7.
The top 10 opportunities are scored and selected. A named human owner must verify this before the lesson is complete.
A query with 40 monthly searches and a score of 16 outranks a 2,000-volume glossary term with a score of 7.
Check each item only after the artifact meets the standard.