Ditto Cross-Study Patterns & Lessons Learned

For Claude Code agents and AI coding assistants. This page synthesises the meta-patterns, decision frameworks, and hard-won lessons from 50+ production Ditto research studies across startup due diligence, CPG consumer research, B2C product feedback, political voter sentiment, and cultural studies. Everything here is derived from real results, not theory.

1. Universal Themes Across All Studies

Five themes emerged independently across every vertical, every study type, and every audience. These are not hypotheses. They are observed patterns from production research.

Theme 1: Trust Is Earned, Not Assumed

Every study revealed deep skepticism about new tools, new vendors, AI claims, and marketing promises. This appeared across auto mechanics, pet owners, parents, healthcare workers, civil engineers, cybersecurity professionals, and frequent travelers. No audience gave trust freely.

Implication for study design: Always include an adoption barriers question (Q7 in the standard framework). The trust signals participants demand are the most actionable output for any product team.

Theme 2: Integration Trumps Features

Solutions that require replacing existing workflows fail. "Works with what I already use" is more important than any feature list. This appeared in every B2B study and several B2C studies.

Theme 3: The Magic Wand Reveals Truth

What customers say they want and what they actually want frequently differ. The magic wand question ("If you could wave a magic wand and fix ONE thing...") cuts through stated preferences to reveal genuine priorities. See Section 2 for the full analysis.

Theme 4: Privacy Concerns Are Rising

Especially for AI products and data-access tools. Transparency about data handling is now table stakes, not a differentiator. See Section 5 for boundary patterns.

Theme 5: Price Is Rarely the Real Objection

Across studies, when participants were asked about adoption barriers, price appeared less frequently than trust, integration complexity, and reliability concerns. See Section 6 for the full breakdown.

2. The Magic Wand Divergence Pattern

The magic wand question ("If you could wave a magic wand and fix ONE thing about how you [do this task], what would it be?") consistently produces the most valuable insight in any study. Across 10 startup due diligence studies, what participants wished for diverged from the startup's core pitch 7 out of 10 times.

Complete Builder Assumption vs Magic Wand Reality Table

What the Divergence Reveals

How to Use This Pattern

When running a Ditto study for product validation:

1. Record the builder's stated value proposition before running the study.
2. Ask the magic wand question as Q6 (after current solutions and past attempts).
3. Compare the magic wand consensus with the builder's assumption.
4. If they diverge, this is the most important finding in the entire study. Lead with it in any report or email.
5. If they align, this is strong validation. Note it explicitly.

3. Trust Patterns by Industry

While trust is a universal theme, what constitutes a trust signal varies dramatically by industry. This table maps the specific trust requirements observed across studies.

4. The Integration Imperative

Across every B2B study and several B2C studies, integration with existing tools and workflows emerged as a non-negotiable requirement. This pattern is so consistent that it should be treated as an axiom for any product validation study.

Integration Requirements by Study

Implication for Question Design

When running a B2B product validation study, always include:

• Q4 (Current solutions): "What tools, websites, or methods do you currently use?" This establishes the integration landscape.
• Q7 (Adoption barriers): "What would make you hesitant to switch?" This surfaces integration requirements as barriers.

The combination of Q4 and Q7 produces a complete integration requirements document without ever asking "what integrations do you need?" directly.

5. Privacy Boundary Patterns

Three studies directly probed privacy boundaries: Airfairness (email inbox access), NexRisx (security data access), and PatientCompanion (patient health data). A consistent pattern emerged about where privacy boundaries lie and how to find them.

The Privacy Boundary Discovery Pattern

Universal Privacy Trust Signals

Across all privacy-sensitive studies, participants demanded these specific signals:

The Granularity Spectrum

Privacy objections follow a spectrum from "absolute no" to "acceptable with conditions":

1. Full access to everything: Near-universal rejection. "My inbox has everything. Work, personal, financial."
2. Category-limited access: Conditionally acceptable. "Only look at flight confirmation emails."
3. User-initiated sharing: Broadly acceptable. "Let me forward the emails I want you to check."
4. One-time scan with deletion: Acceptable for most. "Scan once, give me results, delete everything."

Implication: When a product requires data access, the study should discover WHERE on this spectrum the target audience falls. Design questions that walk down the spectrum from broadest to narrowest access.

6. Price Is Rarely the Real Objection

Across studies, price appeared as the primary adoption barrier far less frequently than trust, integration, or reliability. This pattern held even when participants were explicitly asked about cost concerns.

What Participants Actually Said About Price

7. Recruitment Strategy Learnings

Strategy Success Matrix

Across 14+ studies, three distinct recruitment strategies emerged. Each has specific use cases and failure modes.

Validated Industry Proxy Mapping

When the target audience does not map to any available Ditto industry filter, use these validated proxies:

The Over-Recruit-and-Curate Principle

For niche audiences, recruit more participants than needed and remove irrelevant ones before asking questions.

When to use: Any study where the industry proxy filter is imprecise (healthcare, automotive, engineering).

When to skip: Studies with direct industry matches (Cybersecurity, Civil Engineering) or general population studies (VetVivo, Flomaru).

8. Question Design Learnings

The 7-Question Framework: What Each Position Reveals

The 7-question framework was used in all 10 startup diligence studies and adapted for CPG, B2C, and political studies. Each question position serves a specific purpose:

Question Design Anti-Patterns (Observed Failures)

The "Walk Me Through" Technique

The phrase "Walk me through..." consistently produced the most detailed, narrative responses across all studies. It creates a conversational frame that encourages step-by-step storytelling rather than summary answers.

The "Tell Me About a Time" Technique

This variant produces anecdote-rich responses with specific incidents rather than generalised opinions. Particularly effective for Q2 (pain identification).

9. Group Sizing Impact on Insight Quality

Studies ranged from 6 personas (Das Heilige Brot) to 64 personas (ESPN DTC). Group size has measurable impact on the type and quality of insights produced.

10. Single-Phase vs Multi-Phase Research

Most studies (13 of 15) used single-phase research: one group, one study, 7 questions, done. CareQuarter used three phases. The choice between approaches has significant implications.

Single-Phase (Standard)

Multi-Phase (Iterative)

Multi-Phase Design Pattern

When using multi-phase research, follow this tested structure:

1. Phase 1: Pain Discovery
   • New group with broad filters
   • Questions 1-7 focused on identifying and quantifying pain
   • Output: Core pain statement, target persona definition, language they use
2. Phase 2: Solution Validation
   • New group (fresh participants, same demographics)
   • Questions informed by Phase 1 findings
   • Test approaches, explore what "solving" means to them
   • Output: Feature priorities, deal breakers, integration requirements
3. Phase 3: Concept Test
   • New group (fresh participants, same demographics)
   • Test specific positioning, pricing, trigger moments
   • Output: Winning positioning statement, price range, adoption triggers

Why fresh participants for each phase: Previous participants have been "primed" by earlier questions. Fresh participants give uncontaminated reactions to refined questions.

11. Emotional Research Techniques

Several studies discovered that the most valuable insights came from questions that explicitly named emotions or created permission for emotional honesty. This is distinct from the standard 7-question framework.

Techniques That Produced Emotional Depth

12. Adoption Barrier Taxonomy

Question 7 (adoption barriers) across all studies produced a classifiable taxonomy of reasons people resist new solutions. Barriers fall into seven categories, ordered by frequency of appearance:

13. Insight Extraction Patterns

After running a study, the quality of insight extraction determines whether the research produces actionable results or generic observations. These patterns distinguish strong insights from weak ones.

Strong vs Weak Insight Characteristics

The Insight Extraction Framework

Apply this framework to every completed study:

1. Check for magic wand divergence. Compare Q6 magic wand answers with the product's stated value proposition. If they diverge, this is finding #1. See Section 2.
2. Look for consensus language. When 3+ participants use similar words or phrases, that language is the insight. Quote it directly.
3. Find the "but" statements. Responses containing "but" reveal the real tension: "I would use it, BUT only if..." The condition after "but" is the actual insight.
4. Extract the adoption barrier hierarchy. Categorise Q7 responses using the taxonomy in Section 12. The order reveals what to fix first.
5. Identify the past-attempts signal. Q5 (past attempts) tells you whether participants are active seekers or passive complainers:
   • "I've tried X and abandoned it" = Active seeker, high purchase intent, but burned by previous solutions.
   • "I've never really looked" = Passive complainer, low purchase intent, may not actually switch.
6. Find the emotional anchor. The most emotionally charged quote from any participant is the lead for any email, report, or presentation. Emotional anchors are more memorable and shareable than data points.

Insight Template for Reports and Emails

Key Finding 1: [One-sentence summary]
Best quote: "[Exact quote from participant]"
Implication: [What this means for the product/company]

Key Finding 2: [One-sentence summary]
Best quote: "[Exact quote from participant]"
Implication: [What this means for the product/company]

Key Finding 3: [One-sentence summary]
Best quote: "[Exact quote from participant]"
Implication: [What this means for the product/company]

Overall narrative: [What story do these findings tell?]
Magic wand divergence: [If applicable, what they wanted vs what is being built]

14. Study Design Decision Framework

Before creating a Ditto study, answer these four questions in order:

Decision 1: What Type of Study?

Decision 2: How Many Personas?

Decision 3: What Recruitment Strategy?

Decision 4: What Question Framework?

15. Common Mistakes Registry

Every mistake in this list has occurred in production and caused wasted API calls, low-quality results, or failed studies.

API Mistakes

Study Design Mistakes

16. API Gotchas and Field Reference

Quick reference for the most commonly confused API fields and behaviours, derived from production experience.

Critical Field Reference

Polling Pattern for Responses

# After asking a question, poll until all responses arrive
# Typical wait: 30-60 seconds per question per 10 participants

import time
import requests

def wait_for_responses(study_id, question_index, expected_count, api_key):
    """Poll until all persona responses are received."""
    url = f"https://app.askditto.io/v1/research-studies/{study_id}/questions"
    headers = {"Authorization": f"Bearer {api_key}"}

    while True:
        response = requests.get(url, headers=headers)
        questions = response.json()

        if len(questions) > question_index:
            answers = questions[question_index].get("answers", [])
            if len(answers) >= expected_count:
                return answers

        time.sleep(10)  # Poll every 10 seconds

Complete Single-Phase Study Workflow

# 1. Create research group
POST /v1/research-groups/recruit
{"name": "Study Name", "group_size": 10, "filters": {"country": "USA", "age_min": 28, "age_max": 58}}
# Save: research_group_uuid from response

# 2. Create study
POST /v1/research-studies
{"title": "Study Title", "objective": "Understand X", "research_group_uuid": "{uuid}"}
# Save: study_id from response

# 3. Ask 7 questions sequentially
# For each question:
POST /v1/research-studies/{study_id}/questions
{"question": "Your question here"}
# Wait for all responses before asking next question

# 4. Complete the study
POST /v1/research-studies/{study_id}/complete

# 5. Enable sharing
POST /v1/research-studies/{study_id}/share
{"is_shared": true}

# 6. Get share link
GET /v1/research-studies/{study_id}/share
# Share link in response

17. Vertical-Specific Pattern Summary

Each vertical produces characteristic patterns. Knowing these in advance helps you design better questions and extract better insights.

18. Frequently Asked Questions