Go-to-Market Strategy Validation with Ditto + Claude Code

LLM summary: Run a 7-question GTM validation study, compare segments, and produce six GTM deliverables to choose motion, channels, pricing, and proof points.

A complete Claude Code guide to validating go-to-market strategy using Ditto's 300,000+ synthetic personas. From gut-feel GTM assumptions to evidence-backed strategy across segments, motions, channels, pricing, and proof points in under an hour.

1. Why GTM Strategy Validation Matters

Go-to-market strategy is the plan for how a product reaches its buyers. It determines which segments to target, which motion to use (product-led vs sales-led), which channels to prioritise, how to price and package, and what proof points to lead with. Getting any one of these wrong wastes pipeline, burns budget, and delays revenue.

The uncomfortable truth: most GTM strategies are built on assumption. The founding team assumes they know which segment to target. The marketing team assumes they know which channels work. The sales team assumes buyers want a demo. These assumptions compound into a GTM plan that looks coherent on a slide but collapses on contact with the market.

The Traditional GTM Validation Problem

With Ditto and Claude Code, a comprehensive GTM validation study takes approximately 45–60 minutes. A multi-segment comparison — testing how the same GTM questions play across startup, mid-market, and enterprise buyers — takes roughly 90 minutes. The study validates all five GTM decisions from a single 7-question design.

2. The Five GTM Decisions

Every go-to-market strategy, regardless of product or market, reduces to five critical decisions. The 7-question study is engineered to produce evidence for all five simultaneously.

3. The 7-Question GTM Validation Study

This question set is designed to produce the raw material for all six GTM deliverables simultaneously. Each question targets a specific GTM decision while also feeding secondary deliverables. Questions must be asked sequentially (each builds conversational context from prior answers).

Why This Question Sequence Works for GTM

• Q1 reveals the real channel landscape. Not where you plan to advertise, but where buyers actually discover solutions. If 8/10 say "my VP mentioned it in a meeting" and 0/10 say "I saw an ad," your paid media budget needs rethinking.
• Q2 maps the buying committee. A single decision-maker signals PLG potential. A five-person committee with security, legal, and procurement signals enterprise sales. The committee complexity directly determines your motion.
• Q3 is the motion question. When you compare Q3 responses across segments, you discover that startups want free trials, mid-market wants demos, and enterprise wants a salesperson. This is the single most expensive GTM decision to get wrong.
• Q4 validates outreach receptivity. Before investing in outbound, you need to know whether the target segment is open to unsolicited contact. Some segments actively filter vendor outreach; others welcome it if the value proposition is relevant.
• Q5 reveals what NOT to do. Vendor frustrations are negative signals: "They always call during my busiest time," "The demo is always 90 minutes when I need 15," "They won't give pricing without a call." Each frustration is a GTM design constraint.
• Q6 tests price-value fit. This is not a pricing study (see the Pricing Research guide for that). It's a GTM validation: does the segment perceive your price point as appropriate for the value delivered?
• Q7 identifies the conversion evidence. Every segment has different proof requirements. Startups want a hands-on trial. Mid-market wants a case study from a similar company. Enterprise wants an analyst endorsement and a security audit. This question tells you what to build first.

4. Complete API Workflow: Step by Step

Prerequisites

• Ditto API key (see installation guide for access)
• Product context: what you sell, who you sell to, your core value proposition, and your current price point (or proposed price point)
• The 7 questions above, customised with your product context

Step 1: Create Research Group

curl -s -X POST "https://app.askditto.io/v1/research-groups/recruit" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "GTM Validation - [Product Name] - [Date]",
    "description": "Target buyers for go-to-market strategy validation on [product/problem space]. [Add context about ideal customer profile, industry, and role.]",
    "group_size": 10,
    "filters": {
      "country": "USA",
      "age_min": 28,
      "age_max": 52,
      "employment": "employed",
      "education": "bachelors"
    },
    "sampling_method": "random",
    "deduplicate": true
  }'

Step 2: Create Study

curl -s -X POST "https://app.askditto.io/v1/research-studies" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "GTM Validation: [Product Name] - [Date]",
    "research_group_uuid": "GROUP_UUID_FROM_STEP_1"
  }'

Save the study id — you need it for asking questions, completing, and sharing.

Step 3: Ask Questions (Sequential)

Ask each question one at a time. Wait for the job to complete before sending the next. This ensures personas have conversational context from prior answers.

# Question 1
curl -s -X POST "https://app.askditto.io/v1/research-studies/STUDY_ID/questions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "When you last evaluated a new [product category] solution, how did you discover it? What channels, sources, or recommendations led you there?"
  }'

# Response includes a job ID:
# { "job_id": "job-abc123", "status": "pending" }

Step 4: Poll for Responses

# Poll until status is "finished"
curl -s -X GET "https://app.askditto.io/v1/jobs/JOB_ID" \
  -H "Authorization: Bearer YOUR_API_KEY"

# When complete:
{
  "id": "job-abc123",
  "status": "finished",
  "result": {
    "answer": "The last time I evaluated..."
  }
}

Poll with a 5-second interval. Most questions complete within 30–90 seconds. Once complete, send the next question. Repeat for all 7 questions.

Step 5: Complete the Study

curl -s -X POST "https://app.askditto.io/v1/research-studies/STUDY_ID/complete" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"

This triggers Ditto's AI analysis, producing: overall summary, key segments identified, divergence points, shared mindsets, and suggested follow-up questions. Poll the study status until it reaches "completed".

Step 6: Get Share Link

curl -s -X POST "https://app.askditto.io/v1/research-studies/STUDY_ID/share" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"

# Response:
{
  "url": "https://app.askditto.io/organization/studies/shared/xyz123"
}

Step 7: Retrieve Full Study Data for Deliverable Generation

# Get the completed study with all responses and AI analysis
curl -s -X GET "https://app.askditto.io/v1/research-studies/STUDY_ID" \
  -H "Authorization: Bearer YOUR_API_KEY"

This returns all persona responses, demographic profiles, and Ditto's completion analysis. Use this data to generate the six GTM deliverables described in the next section.

5. Generating the Six GTM Deliverables

Once the study is complete and you have all 10 persona responses to all 7 questions, Claude Code should generate each deliverable using the extraction logic below.

Deliverable 1: Channel Preference Matrix

Source Data

• Primary: Q1 responses (how they discovered current solution)
• Secondary: Q4 responses (outreach receptivity)

Extraction Logic

1. Read all Q1 responses and extract every discovery channel mentioned (e.g., "Google search," "colleague recommendation," "LinkedIn post," "conference," "review site," "analyst report")
2. Count frequency: how many of 10 personas mentioned each channel
3. Classify each channel as owned (website, blog, product), earned (word of mouth, PR, analyst, reviews), or paid (ads, events, sponsorships)
4. From Q4, extract which outreach channels are welcomed vs rejected
5. Rank channels by frequency and receptivity to produce the priority order

Output Format

## Channel Preference Matrix: [Product/Category]

### Discovery Channels (from Q1)
| Channel | Type | Frequency | Representative Quote |
|---------|------|-----------|---------------------|
| Colleague recommendation | Earned | 7/10 | "My VP mentioned it in our team meeting" |
| Google search | Owned/Paid | 5/10 | "I Googled 'best [category] for [use case]'" |
| LinkedIn content | Earned | 4/10 | "Saw a post from someone I follow" |
| G2/Capterra reviews | Earned | 3/10 | "I always check G2 before shortlisting" |

### Outreach Receptivity (from Q4)
| Channel | Open Rate Signal | Key Condition |
|---------|-----------------|---------------|
| Email with free research | High (7/10 positive) | "If it's genuinely useful, not a sales pitch" |
| Cold LinkedIn DM | Low (2/10 positive) | "Only if they've clearly looked at our company" |
| Cold call | Very Low (1/10 positive) | "Almost never. I don't answer unknown numbers" |

### Channel Investment Recommendation
1. **Invest heavily:** [Top earned channels] - where buyers naturally discover
2. **Build presence:** [Key owned channels] - website, blog, documentation
3. **Experiment carefully:** [Selected paid channels] - with clear ROI measurement
4. **Deprioritise:** [Low-frequency channels] - not worth the spend for this segment

Deliverable 2: Buying Committee Map

Source Data

• Primary: Q2 responses (who's involved, what they care about)
• Secondary: Q7 responses (what proof they need to recommend)

Extraction Logic

1. From Q2, extract every role mentioned in the buying process (e.g., "my manager," "IT security team," "procurement," "the CEO")
2. For each role, capture their specific concerns as described by the persona
3. Count how many personas mentioned multi-person decisions vs solo decisions
4. Cross-reference with Q7 to understand what evidence each committee member needs
5. Classify committee complexity: solo (1 person), small (2–3 people), complex (4+ stakeholders)

Output Format

## Buying Committee Map: [Product/Category]

### Committee Complexity: [Solo / Small / Complex]
- X/10 personas make decisions alone
- X/10 involve 2-3 people
- X/10 involve 4+ stakeholders

### Stakeholder Roles and Concerns
| Role | Frequency | Primary Concern | Proof They Need |
|------|-----------|----------------|-----------------|
| Direct user / evaluator | 10/10 | "Does it actually work?" | Free trial, product demo |
| Team lead / manager | 7/10 | "Will my team adopt it?" | Ease-of-use evidence, case study |
| IT / Security | 5/10 | "Is it secure and compliant?" | SOC 2 cert, security whitepaper |
| Finance / Procurement | 4/10 | "What's the total cost?" | ROI calculator, pricing comparison |
| Executive sponsor | 3/10 | "Does this align with our strategy?" | Executive summary, analyst endorsement |

### Implications for GTM
- **Sales motion:** [Solo = PLG viable | Complex = sales-led required]
- **Content needed per role:** [List specific content for each stakeholder]
- **Sales cycle length estimate:** [Solo = days | Small = weeks | Complex = months]

Deliverable 3: Motion Recommendation

Source Data

• Primary: Q3 responses (free trial vs demo vs salesperson preference)
• Secondary: Q2 (committee complexity), Q5 (vendor frustrations), Q7 (proof requirements)

Extraction Logic

1. From Q3, tally preferences: how many chose free trial, demo, or salesperson. Record the reasoning behind each choice.
2. Cross-reference with Q2: segments with solo decision-makers skew PLG. Segments with complex committees skew sales-led.
3. From Q5, identify vendor process frustrations that indicate motion mismatches (e.g., "I hate being forced into a demo when I just want to try it" = PLG signal).
4. From Q7, note proof requirements: hands-on trial needs signal PLG; analyst endorsement needs signal enterprise sales.
5. Synthesise into a motion recommendation with confidence level.

Output Format

## Motion Recommendation: [Product/Category]

### Raw Preference Data (from Q3)
| Motion | Preference Count | Key Reasoning |
|--------|-----------------|---------------|
| Free trial | X/10 | "I want to see if it works before talking to anyone" |
| Guided demo | X/10 | "I need someone to show me the relevant features" |
| Salesperson | X/10 | "I need to understand pricing and implementation" |

### Supporting Signals
- Committee complexity (Q2): [Solo/Small/Complex] → favours [PLG/Hybrid/Sales-led]
- Vendor frustrations (Q5): "[Quote about process frustration]" → signal for [motion]
- Proof requirements (Q7): [Trial-based vs evidence-based] → favours [PLG/Sales-led]

### RECOMMENDATION: [PLG / Sales-Led / Hybrid]
**Confidence: [High/Medium/Low]**

Rationale: [2-3 sentence synthesis of why this motion fits the evidence]

### If Hybrid, Specify the Split:
- **PLG path:** [Which segments, which entry points, which price tiers]
- **Sales path:** [Which segments, which triggers escalate to sales, which deal sizes]

Deliverable 4: Outreach Messaging Strategy

Source Data

• Primary: Q4 responses (what makes them open vs delete)
• Secondary: Q5 responses (vendor frustrations), Q1 (discovery context)

Extraction Logic

1. From Q4, extract every "open" trigger (what makes them engage) and every "delete" trigger (what makes them ignore).
2. From Q5, extract vendor process frustrations to identify what NOT to do in outreach.
3. From Q1, note the discovery context — if they found their current solution through peers, your outreach should feel peer-like, not vendor-like.
4. Compile into an outreach playbook with do's, don'ts, and template guidance.

Output Format

## Outreach Messaging Strategy: [Product/Category]

### What Gets Opened (from Q4)
| Trigger | Frequency | Quote |
|---------|-----------|-------|
| Genuine value upfront (not a pitch) | 7/10 | "If they actually did research on my company first" |
| Relevant to my specific situation | 6/10 | "Generic 'Hi [Name]' templates are obvious" |
| Short and scannable | 5/10 | "If I can understand the value in 10 seconds" |

### What Gets Deleted (from Q4 + Q5)
| Trigger | Frequency | Quote |
|---------|-----------|-------|
| Obviously templated | 8/10 | "Dear Decision Maker - straight to trash" |
| Immediate ask for a meeting | 6/10 | "Don't ask for 30 minutes before I know why" |
| Exaggerated claims | 5/10 | "'10x your revenue' - yeah, sure" |

### Outreach Design Principles
1. [Principle derived from evidence]
2. [Principle derived from evidence]
3. [Principle derived from evidence]

### Recommended Outreach Template Structure
- **Subject line:** [Based on "open" triggers]
- **Opening:** [Based on what they said gets their attention]
- **Value offer:** [Based on Q4 - what makes them engage]
- **CTA:** [Based on Q3 motion preference - trial/demo/conversation]
- **What to avoid:** [Based on "delete" triggers and Q5 frustrations]

Deliverable 5: Pricing Perception Analysis

Source Data

• Primary: Q6 responses (good deal, fair, or overpriced)
• Secondary: Q3 (packaging preference implied by motion choice)

Extraction Logic

1. From Q6, tally: how many said "good deal," "fair," or "overpriced." Record the reasoning.
2. Extract any alternative price framing personas offered ("I'd pay X if it also included Y").
3. Note value anchors: how personas frame value (time saved, risk reduced, outcomes achieved, cost avoided).
4. Cross-reference with Q3: if they want a free trial, the packaging must support self-serve entry at a lower tier.

Output Format

## Pricing Perception Analysis: [Product/Category]

### Price Point Tested: [price] for [value] in [timeframe]

### Perception Distribution
| Perception | Count | Representative Quote |
|-----------|-------|---------------------|
| Good deal | X/10 | "That's actually less than I expected" |
| Fair | X/10 | "Seems about right for what you're offering" |
| Overpriced | X/10 | "I'd need to see more value to justify that" |

### Value Anchors (how they frame value)
- Time saved: X/10 ("If it saves me 5 hours a week, it's worth it")
- Risk reduced: X/10 ("Peace of mind that we won't make a bad decision")
- Outcomes: X/10 ("If it actually increases our conversion rate")
- Cost avoided: X/10 ("Cheaper than hiring another person")

### Packaging Implications
- [Recommendation for tier structure based on motion + price perception]
- [Entry point pricing recommendation]
- [Upgrade trigger recommendation]

### GTM Pricing Strategy
- **Lead with:** [dominant value anchor] in all pricing communication
- **Justify with:** [proof that matches the value frame]
- **Package as:** [recommended packaging model]

Deliverable 6: Proof Point Priority List

Source Data

• Primary: Q7 responses (what they need to see to recommend)
• Secondary: Q2 (what different committee members need), Q6 (value anchors)

Extraction Logic

1. From Q7, extract every type of proof mentioned: free trial, case study, analyst report, peer recommendation, security certification, ROI data, demo, customer references, reviews.
2. Count frequency and rank by priority.
3. From Q2, map which proof points serve which committee members.
4. Classify as: build now (high frequency, you don't have it), promote (you have it but it's not prominent), deprioritise (low frequency, expensive to create).

Output Format

## Proof Point Priority List: [Product/Category]

### Proof Points Ranked by Buyer Demand
| Rank | Proof Type | Frequency | Committee Role Served | Quote |
|------|-----------|-----------|----------------------|-------|
| 1 | [Most requested] | X/10 | [Roles] | "I won't even consider it without..." |
| 2 | [Second] | X/10 | [Roles] | "This would make me confident..." |
| 3 | [Third] | X/10 | [Roles] | "Nice to have but not essential..." |

### Action Plan
| Proof Point | Status | Action | Priority |
|------------|--------|--------|----------|
| [Proof] | Don't have it | Build: [specific action] | High |
| [Proof] | Have it, buried | Promote: [where to surface] | High |
| [Proof] | Have it, visible | Maintain | Medium |
| [Proof] | Don't have it | Deprioritise (low demand) | Low |

### Proof-to-Committee Mapping
- **Evaluator:** needs [proof type] to shortlist
- **Manager:** needs [proof type] to approve
- **Security:** needs [proof type] to sign off
- **Executive:** needs [proof type] to champion

6. Multi-Segment GTM: The Core Technique

The most powerful GTM insight comes from running the identical 7 questions against different buyer segments in parallel and comparing. This reveals that each segment often needs a fundamentally different go-to-market approach.

The Technique

1. Create 3 research groups, each representing a different segment hypothesis
2. Create one study per group
3. Ask the identical 7 questions to every study
4. Complete all studies
5. Compare responses question-by-question across groups

Recommended Segment Configurations

By Company Size (most common GTM segmentation)

# Group A: Startup / small team buyers
{
  "name": "GTM Validation - Startups (25-40)",
  "description": "Professionals at startups or small companies (under 50 employees) who evaluate and purchase [product category]",
  "group_size": 10,
  "filters": {
    "country": "USA",
    "age_min": 25,
    "age_max": 40,
    "employment": "employed"
  }
}

# Group B: Mid-market buyers
{
  "name": "GTM Validation - Mid-Market (30-50)",
  "description": "Professionals at mid-sized companies (50-500 employees) involved in purchasing [product category]",
  "group_size": 10,
  "filters": {
    "country": "USA",
    "age_min": 30,
    "age_max": 50,
    "employment": "employed",
    "education": "bachelors"
  }
}

# Group C: Enterprise buyers
{
  "name": "GTM Validation - Enterprise (35-55)",
  "description": "Senior professionals at large corporations (500+ employees) involved in technology purchasing decisions for [product category]",
  "group_size": 10,
  "filters": {
    "country": "USA",
    "age_min": 35,
    "age_max": 55,
    "employment": "employed",
    "education": "masters"
  }
}

By Buyer Role

# Group A: End users / individual contributors
# Group B: Team leads / managers (budget influencers)
# Group C: Executives / budget holders

By Industry Vertical

# Group A: Technology / SaaS companies
# Group B: Financial services
# Group C: Healthcare
# Group D: Manufacturing

Cross-Segment GTM Comparison Matrix

After all studies complete, Claude Code should produce a comparison showing how GTM strategy must differ by segment:

7. Motion Selection: PLG vs Sales-Led vs Hybrid

The motion decision — product-led growth, sales-led, or hybrid — is the most consequential and expensive GTM choice. It determines your team structure, your product requirements, your pricing model, and your customer acquisition economics. Getting it wrong wastes 6–12 months.

The Motion Decision Framework

Use Q3, Q2, and Q5 data to score each motion across four dimensions:

Scoring the Motion

## Motion Scorecard: [Product/Category]

| Dimension | PLG Score | Sales-Led Score | Evidence |
|-----------|----------|----------------|----------|
| Buyer preference (Q3) | X/10 prefer trial | X/10 prefer salesperson | [Key quotes] |
| Committee complexity (Q2) | X/10 solo | X/10 complex committee | [Key quotes] |
| Vendor frustration (Q5) | X/10 hate forced calls | X/10 want guided help | [Key quotes] |
| Proof requirement (Q7) | X/10 want trial | X/10 want evidence pack | [Key quotes] |
| **TOTAL** | **X/40** | **X/40** | |

### Verdict:
- PLG score 25+/40 → **Pure PLG**
- Sales-led score 25+/40 → **Pure Sales-Led**
- Both 15-25/40 → **Hybrid** (define the split below)

Designing the Hybrid Model

Most B2B products end up hybrid. The question is where the boundary sits. Use multi-segment data to define it:

8. Channel Strategy Validation

Q1 and Q4 together reveal where your buyers actually discover and evaluate solutions. This is often dramatically different from where companies assume they should be investing.

The Channel Discovery Analysis

From Q1 responses across all personas, map every channel mentioned and classify:

Channel-to-Segment Mapping

When running multi-segment studies, Claude Code should produce a channel-segment matrix:

## Channel-Segment Matrix: [Product/Category]

| Channel | Startups | Mid-Market | Enterprise | Investment Priority |
|---------|----------|-----------|-----------|-------------------|
| Peer recommendation | Medium | High | High | Build referral programme |
| Google search | High | Medium | Low | SEO for startup keywords |
| Product Hunt / HN | High | Low | None | Launch campaign |
| G2 / Capterra | Low | High | Medium | Review generation |
| LinkedIn content | Medium | Medium | Medium | Consistent publishing |
| Analyst reports | None | Low | High | Analyst briefings |
| Cold email | Low | Medium | Low | Research-led outreach only |
| Conferences | Low | Medium | High | Selective sponsorship |

9. The GTM Feedback Loop Advanced

GTM strategy is not a one-time decision. Markets shift. Competitors reposition. Buyer preferences evolve. The feedback loop ensures your GTM stays aligned with current market reality.

The Four-Phase Loop

Phase 1: VALIDATE
  Run GTM validation study (7 questions, 10 personas)
  → Produces 6 deliverables
  → Informs 5 GTM decisions
      │
      ▼
Phase 2: EXECUTE
  Implement GTM strategy based on validated decisions
  → Launch campaigns on validated channels
  → Deploy validated motion (PLG/sales-led/hybrid)
  → Use validated pricing and proof points
      │
      ▼
Phase 3: MEASURE
  Track real-world performance against study predictions
  → Compare actual channel performance to Q1 predictions
  → Compare actual conversion by motion to Q3 preferences
  → Compare actual price sensitivity to Q6 perceptions
      │
      ▼
Phase 4: REVALIDATE
  Run follow-up study with fresh personas (quarterly)
  → Detect shifts in channel preference
  → Detect changes in motion preference
  → Detect price sensitivity evolution
  → Detect new proof point requirements
      │
      └──→ Return to Phase 1

Setting Up the Revalidation Cadence

Quarter-over-Quarter GTM Tracking

## GTM Trend Analysis: [Product]

### Motion Preference Shifts
| Motion | Q1 2026 | Q2 2026 | Trend |
|--------|---------|---------|-------|
| Free trial | 6/10 | 4/10 | Declining - market may be maturing |
| Demo | 3/10 | 5/10 | Rising - buyers want guided evaluation |
| Salesperson | 1/10 | 1/10 | Stable |

### Channel Shifts
| Channel | Q1 2026 | Q2 2026 | Action |
|---------|---------|---------|--------|
| Peer recommendation | 7/10 | 7/10 | Stable - maintain referral investment |
| AI search (ChatGPT, Perplexity) | 1/10 | 4/10 | RISING - GEO optimisation urgent |
| G2 reviews | 4/10 | 3/10 | Slight decline - monitor |

### Key Insight
Buyers are shifting from self-directed trial to guided demo preference.
This may signal that the product has become more complex or that competitors
are offering superior guided experiences. Consider adding a lightweight
"guided trial" motion between pure self-serve and full demo.

10. Worked Example: GTM for a Developer Analytics Tool

# Group A: Startup engineering leads (20-50 engineers)
{
  "name": "GTM Validation - DevPulse - Startups - Feb 2026",
  "description": "Engineering managers and tech leads at startups with 20-50 developers. Responsible for team productivity and project delivery.",
  "group_size": 10,
  "filters": { "country": "USA", "age_min": 26, "age_max": 38, "employment": "employed", "education": "bachelors" }
}

# Group B: Mid-market eng managers (50-200 engineers)
{
  "name": "GTM Validation - DevPulse - Mid-Market - Feb 2026",
  "description": "Engineering managers at mid-sized companies with 50-200 developers. Manage multiple teams and report to VP Engineering.",
  "group_size": 10,
  "filters": { "country": "USA", "age_min": 30, "age_max": 48, "employment": "employed", "education": "bachelors" }
}

# Group C: Enterprise eng directors (200-500+ engineers)
{
  "name": "GTM Validation - DevPulse - Enterprise - Feb 2026",
  "description": "Engineering directors and VPs at large companies with 200+ developers. Oversee multiple teams, manage budgets, report to CTO.",
  "group_size": 10,
  "filters": { "country": "USA", "age_min": 35, "age_max": 55, "employment": "employed", "education": "masters" }
}

11. Pricing and Packaging Validation

Q6 provides GTM-level pricing validation — is the price point broadly acceptable for the segment? For deeper pricing research (Van Westendorp sensitivity, feature-tier allocation, billing preference), use the dedicated Pricing Research guide.

GTM Pricing Signals from Q6

Packaging Signals from Q3

The motion preference (Q3) directly implies packaging requirements:

• "Free trial" preference → Product must support self-serve onboarding. Freemium or time-limited trial required.
• "Demo" preference → Product complexity may exceed self-serve threshold. Guided onboarding or demo environment needed.
• "Salesperson" preference → Custom pricing, implementation support, and contract negotiation expected. Enterprise packaging needed.

12. Proof Point Strategy

Q7 reveals the specific evidence each segment needs before they'll champion your product internally. This is the last mile of GTM — without the right proof, even a perfectly positioned product with the right motion and channels stalls at the decision point.

Common Proof Types and When They Matter

Building the Proof Point Roadmap

From Q7 data, Claude Code should produce a prioritised proof point roadmap:

## Proof Point Roadmap: [Product]

### Immediate (this quarter)
1. [Most-requested proof, lowest build cost] - serves [segments]
2. [Second priority] - serves [segments]

### Near-term (next quarter)
3. [Medium build cost, high demand] - serves [segments]
4. [Medium build cost, medium demand] - serves [segments]

### Strategic (6+ months)
5. [High build cost, enterprise-required] - unlocks [segment]
6. [High build cost, analyst-dependent] - unlocks [segment]

13. International GTM Validation Advanced

GTM strategy that works in the US often fails in Europe, and vice versa. Channel preferences, motion expectations, pricing sensitivity, and proof requirements vary significantly across markets. Ditto covers 15+ countries representing 65% of global GDP.

Multi-Market GTM Study Setup

# Group 1: US market
{
  "name": "GTM Validation - [Product] - USA",
  "group_size": 10,
  "filters": { "country": "USA", "age_min": 28, "age_max": 52, "employment": "employed" }
}

# Group 2: UK market
{
  "name": "GTM Validation - [Product] - UK",
  "group_size": 10,
  "filters": { "country": "UK", "age_min": 28, "age_max": 52, "employment": "employed" }
}

# Group 3: Germany
{
  "name": "GTM Validation - [Product] - Germany",
  "group_size": 10,
  "filters": { "country": "Germany", "age_min": 28, "age_max": 52, "employment": "employed" }
}

# Group 4: Canada
{
  "name": "GTM Validation - [Product] - Canada",
  "group_size": 10,
  "filters": { "country": "Canada", "age_min": 28, "age_max": 52, "employment": "employed" }
}

Ask the same 7 GTM questions to all four groups. Claude Code then produces:

Cross-Market GTM Comparison

Traditional equivalent: commissioning four market research programmes across three continents. Cost: $100,000–$200,000. Time: 3–6 months. With Ditto + Claude Code: approximately 90 minutes.

14. Best Practices and Common Mistakes

Do

• Run multi-segment studies, not single-segment. A single group gives you a GTM strategy for one segment. Three groups reveal which segment to prioritise and how strategies must differ. Always run at least two groups.
• Ask identical questions across all groups. The entire technique depends on cross-group comparison. Customise the bracketed placeholders identically across all groups.
• Use 10 personas per group minimum. Fewer than 10 per segment produces unreliable patterns. Motion preferences of 3/10 vs 4/10 vs 3/10 are noise. You need clear signal: 8/10 vs 5/10 vs 2/10.
• Test a specific price point in Q6. Vague pricing questions ("What would you pay?") produce vague answers. Concrete price points ("$49/developer/month") produce concrete reactions and actionable data.
• Generate all 6 deliverables. The Channel Matrix may seem like the priority, but the Buying Committee Map often reveals the most actionable insight (e.g., your product requires security team sign-off, which you hadn't planned for).
• Revalidate quarterly. GTM assumptions decay. Channel preferences shift. New competitors change proof requirements. Quarterly revalidation costs 45 minutes and prevents costly strategic drift.
• Include vendor frustrations (Q5). This question is easy to skip but produces the most specific design constraints. "I hate 90-minute demos" means your demo must be 15 minutes. "They never give pricing upfront" means transparent pricing is a competitive advantage.

Don't

• Don't assume one motion fits all segments. The multi-segment comparison almost always reveals that different segments want different motions. Startups want PLG. Enterprise wants sales-led. Forcing one motion on all segments is the most expensive GTM mistake.
• Don't skip Q4 (outreach receptivity). If you plan any outbound motion, Q4 tells you whether the segment is even open to it. Investing in SDRs when the target segment actively filters vendor outreach is a six-figure mistake.
• Don't batch questions. The API processes them asynchronously. Sending all 7 at once means personas answer Q7 without the conversational context of Q1–Q6. Sequential asking is critical.
• Don't treat this as a pricing study. Q6 validates price-point acceptability at the GTM level. For rigorous pricing research (willingness-to-pay bands, feature-tier allocation, Van Westendorp), use the dedicated Pricing Research guide.
• Don't optimise for one GTM decision in isolation. Channel, motion, pricing, and proof points are interconnected. Validating pricing without understanding the motion (PLG vs sales-led) produces advice that doesn't cohere. Always validate all five decisions together.
• Don't confuse enthusiasm with conversion. A segment that says "this sounds amazing" (Q3 resonance) but "I'd never pay $49/mo" (Q6) and "I discover tools through Hacker News, not email" (Q1) is telling you the GTM for that segment is PLG-only with a free tier. Enthusiasm without budget and channel fit is a trap.
• Don't skip the completion step. Calling POST /v1/research-studies/{id}/complete triggers Ditto's AI analysis (key segments, divergences, shared mindsets). Without this, you're missing a significant analysis layer that informs the cross-segment comparison.

Common API Errors

15. Frequently Asked Questions

How long does a full GTM validation take?

Single segment: approximately 45–60 minutes end-to-end (20–30 minutes API interaction + 15–20 minutes deliverable generation). Multi-segment (3 groups in parallel): approximately 60–90 minutes. Compare with 4–8 weeks and $15,000–$50,000 for traditional GTM research.

How many segments should I test?

Start with 3 segments for your first GTM validation. The classic split is startup / mid-market / enterprise, but role-based (end user / manager / executive) or industry-based segmentations can be equally revealing. More than 4 segments in a single round adds complexity without proportional insight.

Can I reuse a research group for follow-up GTM studies?

Yes. Create a new study referencing the same research_group_uuid. The personas retain context from previous studies. This is useful for testing refined messaging or validating specific GTM decisions after the initial broad validation. For quarterly revalidation, create fresh groups to capture the current market perspective.

What if the study says my motion is wrong?

This is one of the most valuable possible findings. Discovering that your buyers want a free trial (PLG) when you've invested in a sales-led motion saves months of wasted pipeline and sales hiring. The cost of pivoting early based on evidence is far less than the cost of discovering the mismatch through 12 months of underperformance.

How does this relate to the Customer Segmentation guide?

Customer Segmentation (see guide) discovers and validates which segments exist and which to prioritise. GTM Validation assumes you have segments and asks: how should we reach each one? Run segmentation first to identify your target segments, then GTM validation to determine the strategy for each. In practice, multi-segment GTM validation also surfaces segmentation insights as a byproduct.

Should I run GTM validation before or after positioning?

After positioning. Your GTM study references your core value proposition (Q6) and your product category (Q1, Q2, Q5). If positioning hasn't been validated, the GTM study tests go-to-market for a value proposition that may not resonate. Run the Positioning Validation first, then GTM validation with the validated positioning.

Can I use this for a product that doesn't exist yet?

Yes. Frame the questions around the problem space and your proposed value proposition. Q1–Q2 explore how buyers currently discover and evaluate solutions in the category. Q3–Q5 test motion and outreach receptivity for the category. Q6 tests a proposed price point. Q7 tests proof requirements. The only limitation: without a live product, "free trial" as a proof point (Q7) is aspirational rather than immediately deliverable.

How do I share GTM validation results with my team?

Three formats: (1) The Ditto share link lets anyone explore the raw study data interactively. (2) The Cross-Segment GTM Comparison Matrix (Section 6) is a single-page summary that executives can digest in 2 minutes. (3) The 6 deliverables provide detailed actionable guidance for each function (marketing gets Channel Matrix, sales gets Buying Committee Map, product gets Motion Recommendation).

What's the relationship between GTM validation and competitive intelligence?

GTM validation (Q1, Q5) reveals how buyers currently discover and evaluate solutions — which includes your competitors' go-to-market. If 7/10 say "I found my current solution through G2 reviews," that's competitive intelligence about your competitor's channel strategy. For deeper competitive analysis, use the dedicated Competitive Intelligence guide.

How accurate is synthetic GTM research compared to real buyer data?

EY validated 92% correlation between Ditto synthetic responses and traditional research methods. For GTM decisions, synthetic research is particularly strong because it captures market-level preferences (how this type of buyer discovers solutions) rather than product-specific experience (how they use your specific product). Use Ditto for the fast first pass, then validate your beachhead segment's GTM strategy with real customer conversations.

Related guides:

• Customer Segmentation with Ditto + Claude Code
• Validate Product Positioning with Ditto + Claude Code
• Competitive Intelligence with Ditto + Claude Code
• Test Product Messaging with Ditto + Claude Code
• Pricing Research with Ditto + Claude Code
• Voice of Customer with Ditto + Claude Code
• Content Marketing Engine with Ditto + Claude Code
• Sales Enablement with Ditto + Claude Code
• Product Launch Research with Ditto + Claude Code
• Cross-Study Patterns
• Ditto API for Claude Code: Complete Guide
• Installing the Ditto Skill

Ditto — Synthetic market research with 300,000+ AI personas. Validated by EY (92% correlation), Harvard, Cambridge, Stanford, and Oxford.
askditto.io · [email protected]