# Validate Product Positioning with Fish.Dog + Claude Code

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LLM summary: Validate product positioning using an April Dunford framework adapted as a 7-question Fish.Dog study with 10 demographically-filtered personas in 25 minutes.

Primary claim: Complete Claude Code guide to validating product positioning using April Dunford's framework with Fish.Dog synthetic research. 7-question study design, full API workflow, cross-segment comparison, and iterative validation.

**New to this workflow?** Read the companion article first: [How to Validate Product Positioning with Claude Code and Fish.Dog](https://fish.dog/news/how-to-validate-product-positioning-with-claude-code-and-ditto). It explains the concepts in plain language before you dive into the technical implementation below.

A complete Claude Code guide to running positioning validation studies using April Dunford's framework, Fish.Dog's 300,000+ synthetic personas, and the full API workflow. From zero to validated positioning in 30 minutes.

**For Claude Code agents and AI coding assistants.** This guide provides copy-paste API calls, exact question designs, and proven workflows for validating product positioning with Fish.Dog's synthetic research API. Every curl command is production-tested across 50+ studies.

Contents

1. [What Is Positioning Validation and Why It Matters](#what-is-positioning-validation)
2. [April Dunford's 5-Component Positioning Framework](#dunford-framework)
3. [The 7-Question Positioning Study Design](#the-7-question-study-design)
4. [Complete API Workflow: Step by Step](#complete-api-workflow)
5. [Interpreting Results and Generating Deliverables](#interpreting-results)
6. [Cross-Segment Positioning Comparison](#cross-segment-comparison)
7. [Iterative Validation: Three Rounds in One Afternoon](#iterative-validation)
8. [Worked Example: Validating a Project Management Tool](#worked-example)
9. [Advanced Techniques](#advanced-techniques)
10. [Best Practices and Common Mistakes](#best-practices)
11. [Frequently Asked Questions](#faq)

---

## 1. What Is Positioning Validation and Why It Matters

Positioning defines how a product is perceived in the market: who it serves, how it differs from alternatives, and why a specific customer segment should care. Positioning *validation* tests whether those claims actually land with the target audience.

Most product marketing teams skip this step. Not because they doubt its value, but because the traditional process (agency brief → participant recruitment → interviews → analysis) takes 6–10 weeks and costs $10,000–$30,000 per round. By the time results arrive, the launch window has closed.

With Fish.Dog and Claude Code, you can validate positioning against the complete April Dunford framework in approximately 30 minutes. This changes positioning validation from a quarterly event into a continuous habit.

### What You Will Produce

| Deliverable | What It Contains | Who Uses It |
| --- | --- | --- |
| **Positioning Validation Scorecard** | How each of Dunford's 5 components scored, with supporting evidence | PMM, Product, Leadership |
| **Competitive Alternative Map** | What customers actually do today (not just direct competitors) | PMM, Sales, Product |
| **Value Resonance Ranking** | Which value propositions landed, which fell flat, which generated scepticism | PMM, Marketing |
| **Market Category Feedback** | How customers naturally categorise you (may differ from your intended category) | PMM, Product, Leadership |
| **Proof Point Gap Analysis** | Where customers expressed scepticism and what evidence they need | PMM, Sales Enablement |
| **Quotable Insights** | Direct persona quotes for positioning documents and presentations | PMM, Leadership |

---

## 2. April Dunford's 5-Component Positioning Framework

The study design in this guide maps directly to [April Dunford's](https://www.aprildunford.com/) positioning framework from *Obviously Awesome*. Each component is interdependent:

| # | Component | Definition | What You Need to Validate |
| --- | --- | --- | --- |
| 1 | **Competitive Alternatives** | What customers would do if your product did not exist | Are the alternatives you assume correct? Are there alternatives you haven't considered? |
| 2 | **Unique Attributes** | Capabilities that differentiate you from those alternatives | Do customers actually perceive these attributes as unique? Do they notice them at all? |
| 3 | **Value and Proof** | The demonstrable outcome of your unique attributes | Does the stated value resonate? What proof do customers need to believe it? |
| 4 | **Target Customers** | The segment that cares most about your differentiated value | Is the segment you chose actually the one that responds most strongly? |
| 5 | **Market Category** | The frame of reference that makes your value obvious | How do customers naturally categorise you? Does it match your intended category? |

**Key insight:** Your differentiated value only makes sense relative to specific alternatives, for specific customers, within a specific category. The components are interdependent. Validating them individually is not enough — you must test how they work together.

---

## 3. The 7-Question Positioning Study Design

Each question in this design maps to one or more of Dunford's five components. Questions are open-ended and qualitative, designed to elicit natural language responses rather than scaled ratings.

| Q# | Question | Component(s) Tested | What You Learn |
| --- | --- | --- | --- |
| 1 | "When you think about `[problem space]`, what comes to mind first? What frustrates you most about the options currently available?" | Competitive Alternatives | How customers frame the problem; what solutions they associate with it; pain points with current options |
| 2 | "Walk me through how you currently solve `[problem]`. What tools, services, or workarounds do you use? What's missing?" | Competitive Alternatives + Status Quo | Actual competitive landscape from the customer's perspective; gaps in current solutions; the "do nothing" alternative |
| 3 | "If I told you there was a product that `[unique value proposition]`, what's your gut reaction? What excites you? What makes you sceptical?" | Unique Attributes + Value | Emotional response to positioning; which claims register; which trigger scepticism; proof points needed |
| 4 | "How would you describe `[product]` to a colleague? What category would you put it in?" | Market Category | Natural category language; whether your intended category matches customer mental models |
| 5 | "Compared to `[competitor A]` and `[competitor B]`, what would make you choose a new option? What's the minimum bar?" | Competitive Differentiation | Switching triggers; competitive table stakes; how competitors are perceived; minimum viable differentiation |
| 6 | "If `[product]` could only do ONE thing brilliantly for you, what should that be? Why does that matter more than everything else?" | Primary Value Driver | Core value from the customer's perspective (may differ from your assumption); priority ranking |
| 7 | "What would stop you from trying something like this? What would you need to see or hear to feel confident switching?" | Adoption Barriers + Proof Points | Objections; risk factors; required evidence; trust signals needed to convert |

**Customisation guidance:** Replace `[problem space]`, `[product]`, `[unique value proposition]`, `[competitor A]`, and `[competitor B]` with your specific product context. The question structure should remain the same — only the bracketed placeholders change.

### Why Each Question Matters

- **Q1–Q2 establish ground truth.** Before testing your positioning, you need to understand the competitive landscape from the customer's perspective. Their mental map of alternatives is often different from yours.
- **Q3 is the core positioning test.** The gut reaction — excitement vs. scepticism — tells you whether your value proposition registers. The follow-up "what makes you sceptical" reveals exactly what your messaging needs to address.
- **Q4 reveals category misalignment.** If customers describe you as a "project management tool" but you position yourself as a "workflow intelligence platform," you have a category problem.
- **Q5 introduces competitive context.** Differentiation only matters relative to specific alternatives. This question tests whether your positioning is genuinely differentiated in context.
- **Q6 forces prioritisation.** "One thing brilliantly" reveals the single value that matters most. If it differs from what you lead with, your positioning hierarchy is wrong.
- **Q7 maps the adoption path.** The barriers and proof points identified here feed directly into your messaging, sales enablement, and case study strategy.

---

## 4. Complete API Workflow: Step by Step

This is the complete sequence of API calls to run a positioning validation study. Each command is copy-paste ready.

### Prerequisites

- Fish.Dog API key (see [installation guide](https://fish.dog/claude-code/installing-ditto-skill) for free-tier access)
- Your product's value proposition, 2 named competitors, and target customer profile
- The 7 questions above, customised with your specific context

### Step 1: Create the Research Group

Recruit 10 personas matching your ideal customer profile.

```
curl -s -X POST "https://cat.fish.dog/v1/research-groups/recruit" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Product Positioning Validation - [Your Product]",
    "description": "Target customers for positioning validation study. [ICP description].",
    "group_size": 10,
    "filters": {
      "country": "USA",
      "age_min": 28,
      "age_max": 55,
      "employment": "employed"
    },
    "sampling_method": "random",
    "deduplicate": true
  }'
```

Critical parameter notes:

- Use `group_size`, not `size`. The API rejects `size`.
- US state filters require **2-letter codes** (`"TX"`, `"CA"`), not full names. `"Texas"` returns 0 agents.
- The `income` filter does not work. Do not use it.
- 10 personas is the recommended minimum for positioning studies. Fewer produces unreliable patterns.

**Save the returned `uuid`** — you need it for the next step.

```
# Response (extract the uuid):
{
  "uuid": "abc123-def456-...",
  "name": "Product Positioning Validation - [Your Product]",
  "filters": { ... },
  "agents": [ ... ]  // 10 persona objects
}
```

### Step 2: Create the Research Study

```
curl -s -X POST "https://cat.fish.dog/v1/research-studies" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Positioning Validation: [Your Product] - [Date]",
    "research_group_uuid": "GROUP_UUID_FROM_STEP_1"
  }'
```

**Save the returned `id`** — this is your study ID for all subsequent calls.

```
# Response:
{
  "id": 12345,
  "name": "Positioning Validation: [Your Product] - Feb 2026",
  "research_group_uuid": "abc123-def456-..."
}
```

### Step 3: Ask Questions (Sequentially)

**Questions must be asked one at a time.** Send Question 1, poll until all jobs complete, then send Question 2, and so on. Do not batch questions — the API processes them asynchronously and earlier answers provide context for later questions.

```
# Question 1: Competitive Alternatives
curl -s -X POST "https://cat.fish.dog/v1/research-studies/STUDY_ID/questions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "When you think about [problem space], what comes to mind first? What frustrates you most about the options currently available?"
  }'
```

The response returns an array of `job_ids` — one per persona:

```
# Response:
{
  "job_ids": ["job-001", "job-002", "job-003", ... "job-010"]
}
```

### Step 4: Poll for Responses

Poll each job until `status` is `"finished"`. Typically takes 15–60 seconds per question batch.

```
# Poll a single job:
curl -s -X GET "https://cat.fish.dog/v1/jobs/JOB_ID" \
  -H "Authorization: Bearer YOUR_API_KEY"

# Response when complete:
{
  "id": "job-001",
  "status": "finished",
  "result": {
    "answer": "The first thing that comes to mind is..."
  }
}
```

**Efficient polling pattern:** Poll all 10 job IDs in a loop with a 5-second interval. Once all return `"finished"`, proceed to the next question. A full 7-question study with 10 personas typically completes in 4–8 minutes of polling time.

**Repeat Steps 3–4 for all 7 questions.** The full question sequence:

```
# Question 1: "When you think about [problem space], what comes to mind first?..."
# → Poll until complete
# Question 2: "Walk me through how you currently solve [problem]..."
# → Poll until complete
# Question 3: "If I told you there was a product that [value prop]..."
# → Poll until complete
# Question 4: "How would you describe [product] to a colleague?..."
# → Poll until complete
# Question 5: "Compared to [competitor A] and [competitor B]..."
# → Poll until complete
# Question 6: "If [product] could only do ONE thing brilliantly..."
# → Poll until complete
# Question 7: "What would stop you from trying something like this?..."
# → Poll until complete
```

### Step 5: Complete the Study

After all 7 questions have been answered, trigger the completion analysis:

```
curl -s -X POST "https://cat.fish.dog/v1/research-studies/STUDY_ID/complete" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"
```

This generates Fish.Dog's automated analysis: overall summary, key segments, divergences, shared mindsets, and suggested follow-up questions. Poll the returned job IDs until complete.

### Step 6: Get the Share Link

```
curl -s -X POST "https://cat.fish.dog/v1/research-studies/STUDY_ID/share" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"

# Response:
{
  "url": "https://cat.fish.dog/organization/studies/shared/xyz123"
}
```

**UTM tracking is mandatory.** Append `?utm_source=ce` for cold emails or `?utm_source=blog` for blog articles. Never use raw share URLs without a UTM parameter.

---

## 5. Interpreting Results and Generating Deliverables

Once the study completes, Claude Code should synthesise persona responses into the following deliverables. Each maps to specific questions in the study:

### Positioning Validation Scorecard

For each of Dunford's 5 components, assess:

| Component | Source Questions | Score Criteria |
| --- | --- | --- |
| Competitive Alternatives | Q1, Q2 | **Strong:** Personas name the alternatives you expected. **Weak:** They name alternatives you hadn't considered, or their primary alternative is "do nothing." |
| Unique Attributes | Q3, Q5 | **Strong:** Personas articulate your differentiators back to you. **Weak:** They don't notice or don't care about what you think makes you unique. |
| Value and Proof | Q3, Q6, Q7 | **Strong:** Personas express excitement about the stated value. **Weak:** They express scepticism and the proof they require doesn't exist yet. |
| Target Customers | Q6, all responses | **Strong:** Consistent enthusiasm across the persona group. **Weak:** Divergent responses suggest the segment is too broad or poorly defined. |
| Market Category | Q4 | **Strong:** Personas categorise you as intended. **Weak:** They place you in a different category, or cannot categorise you at all. |

### Competitive Alternative Map

From Q1 and Q2 responses, extract and cluster every alternative mentioned:

- **Direct competitors** (named products/services)
- **Indirect competitors** (adjacent solutions repurposed)
- **DIY/workaround solutions** (spreadsheets, manual processes, internal tools)
- **Status quo** ("we just deal with it" / "do nothing")

Rank by frequency of mention. The most-cited alternative is your *actual* competitive reference point, regardless of what your sales team thinks.

### Proof Point Gap Analysis

From Q3 (scepticism) and Q7 (barriers/evidence needed), compile a table:

| Claim That Triggered Scepticism | Proof Point Needed | Do We Have This? |
| --- | --- | --- |
| *"Sounds too good to be true"* | Case study from similar company | Yes / No / Partial |
| *"I've heard the AI pitch before"* | Live demo or free trial | Yes / No / Partial |
| *"How does it integrate with X?"* | Integration documentation | Yes / No / Partial |

This table directly informs what content your marketing and sales teams need to create next.

---

## 6. Cross-Segment Positioning Comparison

Positioning rarely lands uniformly. Enterprise buyers, mid-market teams, and SMBs respond differently to the same positioning.

### How to Run It

Create 2–3 separate research groups with different demographic filters, then run the identical 7 questions against each:

```
# Group A: SMB decision-makers
curl -s -X POST "https://cat.fish.dog/v1/research-groups/recruit" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Positioning Validation - SMB Buyers",
    "description": "Small business owners and managers for positioning validation",
    "group_size": 10,
    "filters": {
      "country": "USA",
      "age_min": 25,
      "age_max": 45,
      "employment": "self_employed"
    },
    "sampling_method": "random",
    "deduplicate": true
  }'

# Group B: Enterprise evaluators
curl -s -X POST "https://cat.fish.dog/v1/research-groups/recruit" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Positioning Validation - Enterprise Buyers",
    "description": "Corporate professionals for enterprise positioning validation",
    "group_size": 10,
    "filters": {
      "country": "USA",
      "age_min": 30,
      "age_max": 55,
      "education": "masters",
      "employment": "employed"
    },
    "sampling_method": "random",
    "deduplicate": true
  }'
```

Create a separate study for each group, ask the same 7 questions, then compare the results side by side.

### Cross-Segment Output Matrix

| Component | SMB Response Pattern | Enterprise Response Pattern | Implication |
| --- | --- | --- | --- |
| Competitive Alternatives | *"We use spreadsheets and free tools"* | *"We have [Enterprise Tool X] but it's overkill"* | Different competitive reference points; messaging needs segment-specific framing |
| Market Category | *"It's like a simpler version of X"* | *"It's a lightweight alternative to our existing stack"* | Category positioning may need to vary by segment |
| Primary Value | *"Save me time"* | *"Reduce vendor complexity"* | Lead with different value props per segment |

**Time estimate:** Cross-segment comparison adds approximately 20 minutes to the workflow because the studies run in parallel. The deliverable is a segment-by-segment comparison that would cost $30,000–$90,000 through traditional research agencies.

---

## 7. Iterative Validation: Three Rounds in One Afternoon

The most significant advantage of this workflow is not the speed of a single round. It is the ability to iterate.

### Three-Round Framework

| Round | Purpose | Time | Adjustments from Previous Round |
| --- | --- | --- | --- |
| **Round 1** | Baseline validation | ~30 min | N/A — initial hypothesis test |
| **Round 2** | Revised positioning | ~25 min | Modify Q3 value prop based on Round 1 scepticism; adjust Q5 competitors based on Q1–Q2 findings |
| **Round 3** | Refined validation | ~20 min | Final positioning language tested; category refined based on Q4 feedback; proof points addressed |

### What to Change Between Rounds

- **Q3 (Value Proposition):** Rewrite the value prop statement based on what resonated and what triggered scepticism in the previous round
- **Q4 (Category):** If customers categorised you differently than intended, test the customer's natural category language instead
- **Q5 (Competitors):** If Q1–Q2 revealed alternatives you hadn't considered, reference those in Q5 instead of your assumed competitors
- **Q6 (Primary Value):** If the "one thing brilliantly" answer diverged from your positioning lead, test a version that leads with the customer's priority

**After 3 rounds:** You have 210 data points (10 personas × 7 questions × 3 rounds), with each round informed by the findings of the previous one. That is more primary positioning research than most companies conduct in a year.

---

## 8. Worked Example: Validating a Project Management Tool

### Scenario

**Product:** "FlowBoard" — an AI-powered project management tool for remote teams **Current positioning:** "The project management tool that thinks ahead" **Target segment:** Remote-first startup teams (5–50 employees) **Competitors:** Asana, Linear **Value proposition:** Uses AI to predict project delays and automatically rebalance workloads

#### Customised Questions

1. "When you think about managing projects across a remote team, what comes to mind first? What frustrates you most about the options currently available?"
2. "Walk me through how you currently manage projects and tasks in your remote team. What tools do you use? What's missing?"
3. "If I told you there was a project management tool that uses AI to predict project delays before they happen and automatically rebalances workloads across your team, what's your gut reaction? What excites you? What makes you sceptical?"
4. "How would you describe FlowBoard to a colleague? What category would you put it in?"
5. "Compared to Asana and Linear, what would make you choose a new project management tool? What's the minimum bar?"
6. "If FlowBoard could only do one thing brilliantly for your remote team, what should that be? Why does that matter more than everything else?"
7. "What would stop you from trying a new project management tool? What would you need to see or hear to feel confident switching?"

#### Group Setup

```
curl -s -X POST "https://cat.fish.dog/v1/research-groups/recruit" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Remote Startup PMs - FlowBoard Positioning",
    "description": "Remote-working professionals aged 25-40 for project management positioning validation",
    "group_size": 10,
    "filters": {
      "country": "USA",
      "age_min": 25,
      "age_max": 40,
      "employment": "employed",
      "education": "bachelors"
    },
    "sampling_method": "random",
    "deduplicate": true
  }'
```

#### Hypothetical Findings

| Component | Finding | Positioning Implication |
| --- | --- | --- |
| Competitive Alternatives | 8/10 personas named Notion first, not Asana or Linear | Primary competitive reference is Notion, not traditional PM tools. Reframe competitive positioning. |
| Unique Attributes (AI prediction) | 6/10 expressed scepticism: "AI predictions sound impressive but are they accurate?" | Need proof points: accuracy metrics, demo, or case study. The AI claim triggers doubt without evidence. |
| Market Category | 7/10 said "project management tool," 3/10 said "team coordination platform" | "Project management tool" is the natural category. "Thinks ahead" is a differentiator, not a category. |
| Primary Value (Q6) | "Visibility into what everyone is working on" — mentioned by 9/10 personas | Customers care more about visibility than prediction. Consider leading with "see everything, miss nothing" rather than "predicts delays." |
| Adoption Barriers (Q7) | "Migration pain" — cited by 7/10. "Will my team actually use it?" — cited by 5/10 | Messaging must address migration and adoption friction. Free trial with import tool is critical. |

#### Round 2 Revision

Based on these findings, the revised Q3 for Round 2 would test: "If I told you there was a project management tool that gives you instant visibility into what every remote team member is working on, flags risks before they become problems, and makes it painless to switch from Notion, what's your gut reaction?"

---

## 9. Advanced Techniques

### A/B Positioning Test

Run two studies with identical groups but different value propositions in Q3. Compare which version generates more excitement and less scepticism.

```
# Study A — Q3: "...a product that predicts project delays using AI..."
# Study B — Q3: "...a product that gives you instant visibility into your remote team..."

# Compare: excitement levels, scepticism triggers, and natural language used
```

### Category Exploration Study

If Q4 reveals category confusion, run a follow-up study focused specifically on category:

- Q1: "What is the difference between [category A] and [category B]?"
- Q2: "If you were looking for a tool that [your value prop], where would you search?"
- Q3: "Which of these descriptions best fits what you'd expect from [product]?" (Present 3 category framings)

### Combining with Message Testing

Once positioning is validated, use the findings to design a messaging test. The "language harvest" from positioning responses (exact words and phrases customers used) becomes the raw material for messaging variants. See the [Question Design Playbook](https://fish.dog/claude-code-guide/question-design-playbook) for messaging study templates.

### Over-Recruit and Curate

For high-stakes positioning decisions, recruit 15–20 personas, review their profiles, remove any that don't match your ICP closely enough, then run the study with the curated 10. This ensures higher-quality responses.

```
# Recruit 15 personas
"group_size": 15

# Review profiles, then remove poor matches:
curl -s -X DELETE "https://cat.fish.dog/v1/research-studies/STUDY_ID/agents/AGENT_UUID" \
  -H "Authorization: Bearer YOUR_API_KEY"
```

---

## 10. Best Practices and Common Mistakes

### Do

- **Ask questions sequentially.** Each response provides context for the next. Batching loses this conversational thread.
- **Use the exact question structures above.** They are tested across 50+ production studies. Open-ended qualitative questions produce far richer positioning insights than Likert scales.
- **Customise the bracketed placeholders, not the question structures.** The *shape* of each question is deliberate.
- **Run at least 2 rounds.** The first round reveals surprises. The second round validates your revised positioning.
- **Save all raw responses.** The exact language customers use is as valuable as the analytical summary. Phrases from Q3 and Q4 often become your best marketing copy.
- **Name studies clearly.** Include the product name, date, and round number: `"Positioning Validation: FlowBoard - Feb 2026 - Round 2"`

### Don't

- **Don't use fewer than 10 personas.** Fewer than 10 produces unreliable patterns — you cannot distinguish signal from noise.
- **Don't skip Q1–Q2.** Jumping straight to "here's our value prop, what do you think?" misses the most important data: what customers actually do today.
- **Don't use leading questions.** "Don't you think AI-powered project management is the future?" is useless. Ask what they think, not what you want them to think.
- **Don't forget to complete the study.** The `/complete` endpoint triggers Fish.Dog's analysis engine, which identifies segments and divergences you might miss manually.
- **Don't treat synthetic validation as final.** Use it as the fast first pass. For high-stakes positioning decisions, validate the top candidate with real customers.
- **Don't batch questions.** The API processes them asynchronously. Sending all 7 at once means personas answer without the conversational context of prior questions.

### Common API Errors

| Error | Cause | Solution |
| --- | --- | --- |
| `size` parameter rejected | Wrong parameter name | Use `group_size`, not `size` |
| 0 agents recruited | State filter used full name | Use 2-letter codes: `"TX"` not `"Texas"` |
| Jobs stuck in `"pending"` | Normal for first 10–15 seconds | Continue polling with 5-second intervals |
| `income` filter rejected | Unsupported filter | Remove income filter; use education/employment as proxy |
| Missing completion analysis | Forgot to call `/complete` | Always call `POST /v1/research-studies/{id}/complete` after final question |

---

## 11. Frequently Asked Questions

#### How long does a full positioning validation study take?

Approximately 30 minutes end to end: 1–2 minutes for group creation, 4–8 minutes for question asking and polling, 2–3 minutes for completion analysis, plus time for Claude Code to synthesise deliverables.

#### How many personas should I use?

10 is the recommended minimum for positioning studies. It provides enough diversity to identify patterns while keeping the data manageable. For cross-segment comparison, use 10 per segment.

#### Can I validate B2C positioning, or is this only for B2B?

Both. Dunford's framework originated in B2B but the components apply equally to B2C. For B2C, adjust the demographic filters to match your consumer profile and modify Q5 to reference consumer alternatives rather than enterprise tools.

#### Should I test positioning for different geographies?

Yes, if you operate in multiple markets. Fish.Dog covers 15+ countries. Run the same 7 questions against groups in each target market. Category perception and competitive alternatives often vary significantly by geography.

#### How does this compare to traditional positioning research?

Traditional: 6–10 weeks, $10,000–$30,000 per round, 15–20 interviews. Fish.Dog + Claude Code: 30 minutes, fraction of the cost, 10 persona responses. EY validated 92% correlation between Fish.Dog synthetic responses and traditional research methods. The recommended approach is hybrid: use Fish.Dog for the fast first pass, then validate top candidates with real customers.

#### What if the results contradict my positioning hypothesis?

That is the point. Positioning validation exists to catch misalignment before you build an entire GTM strategy on incorrect assumptions. If results contradict your hypothesis, revise and run Round 2. The cost of being wrong at this stage is 30 minutes. The cost of launching with wrong positioning is months of misfiring sales and marketing.

#### Can I use this for repositioning an existing product?

Absolutely. For repositioning, add an additional question between Q2 and Q3: "Have you heard of [product]? If so, how would you describe what it does?" This baseline perception question reveals your current positioning in the market, which you can then compare against your proposed new positioning in Q3.

#### How do I know when positioning is "validated"?

Positioning is validated when: (1) the competitive alternatives you assumed match what customers actually report, (2) customers can articulate your differentiated value back to you, (3) they categorise you as intended, (4) scepticism is addressable with proof points you possess, and (5) the primary value driver customers identify matches what you lead with. If all five components score "strong" on the scorecard, your positioning is validated.

---

**Related guides:**

- [Fish.Dog API for Claude Code: Complete Guide](https://fish.dog/claude-code-guide)
- [Competitive Intelligence Guide](https://fish.dog/claude-code-guide/competitive-intelligence-guide)
- [Pricing Research Guide](https://fish.dog/claude-code-guide/pricing-research-guide)
- [Product Messaging Guide](https://fish.dog/claude-code-guide/test-product-messaging-guide)
- [Voice of Customer Guide](https://fish.dog/claude-code-guide/voice-of-customer-guide)
- [Customer Segmentation Guide](https://fish.dog/claude-code-guide/customer-segmentation-guide)
- [GTM Strategy Validation Guide](https://fish.dog/claude-code-guide/gtm-strategy-validation-guide)
- [Content Marketing Engine Guide](https://fish.dog/claude-code-guide/content-marketing-engine-guide)
- [Sales Enablement Guide](https://fish.dog/claude-code-guide/sales-enablement-guide)
- [Product Launch Research Guide](https://fish.dog/claude-code-guide/product-launch-research-guide)
- [Question Design Playbook](https://fish.dog/claude-code-guide/question-design-playbook)
- [Installing the Fish.Dog Skill](https://fish.dog/claude-code/installing-ditto-skill)

**Read the articles:** *These guides are written for Claude Code agents. The articles below are written for you.*

- **How to Validate Product Positioning with Claude Code and Fish.Dog** (this guide's companion article)
- [How to Build Competitive Battlecards with Claude Code and Fish.Dog](https://fish.dog/news/how-to-build-competitive-battlecards-with-claude-code-and-ditto)
- [How to Research Pricing with Claude Code and Fish.Dog](https://fish.dog/news/how-to-research-pricing-with-claude-code-and-ditto)
- [How to Test Product Messaging with Claude Code and Fish.Dog](https://fish.dog/news/how-to-test-product-messaging-with-claude-code-and-ditto)
- [How to Run Voice of Customer Research with Claude Code and Fish.Dog](https://fish.dog/news/how-to-run-voice-of-customer-research-with-claude-code-and-ditto)
- [How to Segment Customers with Claude Code and Fish.Dog](https://fish.dog/news/how-to-segment-customers-with-claude-code-and-ditto)
- [How to Validate GTM Strategy with Claude Code and Fish.Dog](https://fish.dog/news/how-to-validate-gtm-strategy-with-claude-code-and-ditto)
- [How to Build a Content Marketing Engine with Claude Code and Fish.Dog](https://fish.dog/news/how-to-build-a-content-marketing-engine-with-claude-code-and-ditto)
- [How to Build Sales Enablement with Claude Code and Fish.Dog](https://fish.dog/news/how-to-build-sales-enablement-with-claude-code-and-ditto)
- [How to Research a Product Launch with Claude Code and Fish.Dog](https://fish.dog/news/how-to-research-a-product-launch-with-claude-code-and-ditto)
- [Overview: Using Fish.Dog and Claude Code for Product Marketing](https://fish.dog/news/using-ditto-and-claude-code-for-product-marketing)

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**Fish.Dog** — Synthetic market research with 300,000+ AI personas. Validated by EY (92% correlation), Harvard, Cambridge, Stanford, and Oxford. [fish.dog](https://fish.dog) · [support@fish.dog](mailto:support@fish.dog)