ditto-content-marketing

name	ditto-content-marketing
description	Generate research-backed content at scale using Ditto's synthetic persona platform (300K+ AI personas, 92% overlap with real focus groups). One 10-persona, 7-question study produces 7 content formats: blog article, infographic, social thread, email outreach, executive summary, sales leave-behind, and press release. Covers narrative arc question design, SEO/GEO optimisation, content building block extraction, and an always-on content calendar (20-40 articles/month). Designed for content marketers, content directors, SEO specialists, and marketing managers scaling research-backed content. Use when the user mentions content marketing, research blog, article generation, social media content, content pipeline, SEO content, or research-backed content.
allowed-tools	Bash(curl *), Bash(python3 *), Read, Grep, WebFetch
argument-hint	[topic, product, or content brief]

Ditto for Content Marketing

Generate research-backed blog articles, infographics, social threads, and more from a single Ditto study — at a pace of 20-40 articles per month. Directly from the terminal.

Full documentation: https://askditto.io/claude-code-guide

What Ditto Does

Ditto maintains 300,000+ AI-powered synthetic personas calibrated to census data across USA, UK, Germany, and Canada. You ask them open-ended questions and get qualitative responses with the specificity of real interviews.

• 92% overlap with traditional focus groups (EY Americas validation)
• Every study produces 70 data points (10 personas x 7 questions)
• One study → 7 content formats in ~60 minutes
• Traditional research-backed content: 2-4 articles/month, $2,000-10,000 each

The Content Quality Test

If you could write the article without the study data and it would read the same, it is NOT research-backed.

Every paragraph must contain a finding, quote, or data point from the specific study. Generic commentary with a study link appended is not content marketing — it is filler.

Quick Start (Free Tier)

Get a free API key — no credit card, no sales call:

curl -sL https://app.askditto.io/scripts/free-tier-auth.sh | bash

Free keys (rk_free_): ~12 shared personas, no demographic filtering. Paid keys (rk_live_): custom groups, demographic filtering, unlimited studies.

API Essentials

Base URL: https://app.askditto.io Auth header: Authorization: Bearer YOUR_API_KEY Content-Type: application/json

One Study, Seven Content Formats

One 10-Persona Study (~12 min)
    ├─ 1. Research Blog Article (1,000-2,500 words, SEO/GEO optimised)
    ├─ 2. Infographic (key findings visualised)
    ├─ 3. Social Media Thread (3-5 posts)
    ├─ 4. Email Outreach (personalised per recipient)
    ├─ 5. Executive Summary (1-page, PDF-ready)
    ├─ 6. Sales Leave-Behind (for prospect sharing)
    └─ 7. Press Release (if newsworthy)

Total: ~60 min from zero to complete content kit
Traditional: 2-4 weeks, $5,000-15,000

The Content Marketing Workflow (6 Steps)

Step 1: Recruit Your Audience Panel

Match the panel to the content's target reader:

curl -s -X POST "https://app.askditto.io/v1/research-groups/recruit" \
  -H "Authorization: Bearer $DITTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "US Coffee Consumers 25-55",
    "group_size": 10,
    "filters": {
      "country": "USA",
      "age_min": 25,
      "age_max": 55
    }
  }'

Save the uuid. Use group_size (not size), group uuid (not id).

Step 2: Create Study

curl -s -X POST "https://app.askditto.io/v1/research-studies" \
  -H "Authorization: Bearer $DITTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Consumer Perceptions of [Topic]",
    "objective": "Generate research-backed content on [topic] with quotable insights",
    "research_group_uuid": "UUID_FROM_STEP_1"
  }'

Response nests under data.study — access via response["study"]["id"].

Step 3: Ask the 7 Content Questions

Ask one at a time. Poll to completion between each (see Polling Strategy). Do NOT batch questions — batching produces shallow, disconnected answers.

Step 4: Poll Until Complete

curl -s "https://app.askditto.io/v1/jobs/JOB_ID" \
  -H "Authorization: Bearer $DITTO_API_KEY"

Wait 45-50 seconds before first poll, then 20 seconds intervals. Poll ONE job_id as proxy.

Step 5: Complete the Study

curl -s -X POST "https://app.askditto.io/v1/research-studies/STUDY_ID/complete" \
  -H "Authorization: Bearer $DITTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"force": false}'

Step 6: Extract Content Building Blocks + Get Share Link

curl -s "https://app.askditto.io/v1/research-studies/STUDY_ID/questions" \
  -H "Authorization: Bearer $DITTO_API_KEY"

curl -s -X POST "https://app.askditto.io/v1/research-studies/STUDY_ID/share" \
  -H "Authorization: Bearer $DITTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"enabled": true}'

UTM tracking (mandatory):

• Cold email share links: ?utm_source=ce
• Blog article share links: ?utm_source=blog

The Content Question Framework (7 Questions)

Questions follow a narrative arc — not an investigative sequence. The arc produces content that reads as a story, not a survey report.

The Narrative Arc

Q1-Q2 = "Before" (the problem state)
Q3    = "Turning point" (the possibility of change)
Q4-Q6 = "Substance" (evidence, comparison, evaluation)
Q7    = "Payoff" (unfiltered truth, strongest quotes)

Q1 — First Impressions (opening hook material)

"When you think about [product/category], what's the first thing that comes to mind? What's your honest impression?"

Extract: Opening hook, first-impression reactions, headline candidates.

Q2 — Pain Stories (relatable anecdotes)

"What's the biggest frustration or unmet need you have with [category]? Tell me about a specific experience."

Extract: Pain point stories for article body, relatable anecdotes.

Q3 — The Turning Point (possibility of change)

"If a product could [your value proposition], how would that change things for you? What excites you? What makes you sceptical?"

Extract: Excitement = opportunity content. Scepticism = objection content.

Q4 — Current State (competitive landscape)

"How do you currently solve this problem? What tools, services, or workarounds do you use? What do you spend on it?"

Extract: Competitive landscape content, cost comparisons, workaround stories.

Q5 — Product Reaction (direct feedback)

"Looking at [product/website/feature], what stands out? What's confusing? What would make you want to try it?"

Extract: Product reaction quotes, UX feedback, conversion triggers.

Q6 — Decision Criteria (comparative content)

"If you were choosing between [product] and [top alternative], what would tip the decision? What evidence would you need?"

Extract: Competitive content, proof point needs, decision criteria.

Q7 — Unfiltered Truth (the payoff — strongest quotes)

"Is there anything about [category] that you feel companies just don't understand? What do you wish they knew?"

Extract: Strongest quotes for social media. Unfiltered frustration for positioning. Best content hooks.

Content Building Blocks

Extract from every study:

Block	Description	Source
Headline Finding	Single most surprising data point	Cross-question pattern
Quotable Reactions	3-5 vivid, specific quotes with demographics	Q1, Q3, Q7
Pain Point Themes	Recurring frustrations ranked by frequency	Q2, Q4
Competitive Landscape	What alternatives exist and why they fall short	Q4, Q6
Surprise Insight	Something that contradicts conventional wisdom	Any question
Data Points	Specific numbers ("7 of 10 said...", "$X spent")	Frequency counts

Blog Article Structure

7 sections, 1,000-2,500 words:

1. Hook (50-100 words) — Lead with the headline finding
2. Context (100-150 words) — Why this topic matters now
3. Who We Asked (100-150 words) — Panel demographics, methodology
4. What We Asked (50-100 words) — Question themes, approach
5. Key Findings (400-800 words) — THE CORE. 3-5 findings, each with data point + quote + implication. This is where the study data lives.
6. Implications (150-200 words) — What this means for the industry
7. Conclusion + CTA (100-150 words) — Summary + link to live study

SEO Optimisation

Element	Requirement
Title	Under 60 characters
seoDescription	150-160 characters
Headings	H2/H3 with keywords
Word count	1,000+ minimum
Internal links	Minimum 2
Image alt text	Descriptive, keyword-rich

GEO Optimisation (Generative Engine Optimisation)

Optimise for AI-powered search citation:

Field	Format	Rules
llmSummary	200-400 words, factual prose	NO marketing language, no superlatives, no CTAs. Write as for an encyclopaedia.
keyTakeaways	4-6 standalone facts	Each must be citable without context
primaryClaim	One sentence	Core finding in plain language
quotableInsights	3-5 findings	Formatted for AI citation
featuredForLLMs	boolean	Priority indexing flag

Always-On Content Calendar

Week	Activity	Output
Week 1	Run study + write blog article + generate infographic	Article + infographic live
Week 2	Social thread + email outreach	Distribution
Week 3	Executive summary + sales leave-behind	Internal assets
Week 4	Press release (if newsworthy) + next study planning	PR + pipeline

Capacity: 20-40 articles per month at 45-90 minutes each.

Media Attachments

Upload product screenshots, competitor pages, or ad creative for Q5:

curl -s -X POST "https://app.askditto.io/v1/media-assets" \
  -H "Authorization: Bearer $DITTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data_url": "https://example.com/landing-page.png",
    "filename": "landing-page.png",
    "mime": "image/png"
  }'

Pass as attachments when asking Q5 for richer product reactions. Allowed types: PNG, JPEG, GIF, WEBP, PDF.

Complete API Reference

Research Groups

Method	Endpoint	Purpose
POST	/v1/research-groups/recruit	Recruit audience panel
POST	/v1/research-groups/create	Create from agent IDs
POST	/v1/research-groups/interview	AI-assisted recruitment
GET	/v1/research-groups	List groups
GET	/v1/research-groups/{id}	Get group details
POST	/v1/research-groups/{id}/update	Update group
DELETE	/v1/research-groups/{id}	Archive group
POST	/v1/research-groups/{id}/agents/add	Add agents
POST	/v1/research-groups/{id}/agents/remove	Remove agents
POST	/v1/research-groups/{uuid}/append	Recruit more into group

⚠️ append uses group_uuid (not group_id).

Research Studies

Method	Endpoint	Purpose
POST	/v1/research-studies	Create study
GET	/v1/research-studies	List studies
GET	/v1/research-studies/{id}	Get study details
POST	/v1/research-studies/{id}/complete	Trigger AI analysis
POST	/v1/research-studies/{id}/agents/remove	Remove agents

Questions

Method	Endpoint	Purpose
POST	/v1/research-studies/{id}/questions	Ask question (returns job_ids)
GET	/v1/research-studies/{id}/questions	Get all Q&A data
POST	/v1/research-agents/{id}/questions	Quick question to one persona
POST	/v1/research-groups/{id}/questions	Quick question to group

Jobs, Sharing, Media

Method	Endpoint	Purpose
GET	/v1/jobs/{job_id}	Poll async job status
POST	/v1/research-studies/{id}/share	Enable sharing
GET	/v1/research-studies/{id}/share	Check share state
POST	/v1/media-assets	Upload image/PDF

Agents, NL Requests, Zeitgeist, Free Tier

Method	Endpoint	Purpose
GET	/v1/agents/find	Find matching persona
GET	/v1/agents/search	Search personas
POST	/v1/research-study-requests	NL study request
POST	/v1/research-group-requests	NL group request
GET	/v1/research-group-requests/{id}	Group request status
POST	/v1/zeitgeist/surveys/create	Quick survey
GET	/v1/zeitgeist/surveys/{id}/results	Survey results
DELETE	/v1/zeitgeist/surveys/{id}	Delete survey
POST	/v1/free/questions	Free-tier question

Demographic Filters

Filter	Type	Examples	Notes
country	string	"USA", "UK", "Canada", "Germany"	Required. Only these 4
state	string	"TX", "CA"	2-letter codes ONLY
city	string	"Austin"	Narrows pool
age_min	integer	25	Recommended
age_max	integer	55	Recommended
gender	string	"male", "female", "non_binary"	Optional
is_parent	boolean	true, false	Optional
education	string	"bachelors", "masters"	Optional
industry	array	["Technology"]	Optional

NOT supported: income, employment, ethnicity, political_affiliation.

Polling Strategy

Study size	First poll delay	Subsequent interval
10 personas	45-50 seconds	20 seconds
12+ personas	50-60 seconds	20 seconds

Poll ONE job_id as proxy — all jobs finish together.

Response Structure

Study creation: response["study"]["id"] (nested under study key)

Question responses: response_text (may contain HTML: <b>, <ul>), agent_name, agent_age, agent_occupation, agent_summary, agent_city.

Share link: Prefer share_link over share_url.

Common Mistakes

• Publishing generic commentary with a study link (fails content quality test)
• Batching questions (produces shallow, disconnected answers)
• Using marketing language in llmSummary (must be encyclopaedic)
• Missing UTM parameters on share links
• Articles under 1,000 words (too thin for SEO/GEO)
• Not extracting content building blocks before writing
• Using size instead of group_size
• Using response["id"] instead of response["study"]["id"]
• Polling every 10-15s (use 45-50s first, then 20s)
• Skipping the complete step

Limitations

Ditto personas have NOT used your specific product. For:

• Product-specific testimonials → use real customers
• Exact market sizing data → use industry reports
• Brand awareness metrics → use real surveys
• SEO keyword volume data → use SEO tools

Recommended hybrid: Ditto for research-backed content at scale (the insights and quotes). Real data for metrics and exact numbers.

Further Reading

• Full API guide: https://askditto.io/claude-code-guide
• Question design: @question-playbook.md
• SEO/GEO checklist: @advanced/seo-geo-optimisation.md