proprietary-data-generator

name	proprietary-data-generator
description	Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection for content moats. Triggers on: "create original data", "proprietary data", "survey design", "benchmark study", "original research", "data-driven content", "create a survey", "industry benchmark", "aggregated data", "unique data", "first-party data", "data moat", "generate research data", "create a study", "original statistics", "data nobody else has", "competitive data advantage".
license	MIT
version	1.0.0
tags	["affiliate-marketing","automation","scaling","workflow","data","original-research"]
compatibility	Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent
metadata	{"author":"affitor","version":"1.0","stage":"S7-Automation"}

Proprietary Data Generator

Create original surveys, benchmarks, and aggregated data that nobody else has. Proprietary data is the ultimate content moat — competitors can copy your writing style but they can't copy YOUR data. Automates the design and execution framework for data collection that feeds unique content angles.

Stage

S7: Automation & Scale — Generating data at scale requires automation. This skill designs the collection system, not just one data point. Creates repeatable data assets that compound over time.

When to Use

• User wants to create content that can't be replicated by competitors
• User asks about "original research", "surveys", "benchmarks", "proprietary data"
• User says "data moat", "unique data", "first-party data", "original statistics"
• After content-moat-calculator identifies the need for differentiated content
• User wants to build authority through data-driven content
• User wants to create linkable assets that earn backlinks naturally

Input Schema

niche: string                 # REQUIRED — topic area for data collection
                              # e.g., "AI video tools", "affiliate marketing"

data_type: string             # OPTIONAL — "survey" | "benchmark" | "aggregation" | "case_study"
                              # Default: recommend based on niche and resources

audience_access: string       # OPTIONAL — how you can reach respondents
                              # e.g., "email list of 500", "Reddit community", "Twitter followers"
                              # Default: suggest options

budget: string                # OPTIONAL — "zero" | "low" ($0-100) | "medium" ($100-500) | "high" ($500+)
                              # Default: "zero"

goal: string                  # OPTIONAL — "content_moat" | "backlink_magnet" | "authority" | "lead_gen"
                              # Default: "content_moat"

Chaining from S3 content-moat-calculator: Use competitive_advantages to identify data moat opportunities.

Workflow

Step 1: Identify Data Opportunity

Analyze the niche for data gaps:

1. web_search: "[niche] statistics 2025" OR "[niche] survey" OR "[niche] benchmark" — what data already exists?
2. Identify gaps: what questions does the industry ask that nobody has answered with data?
3. web_search: "[niche] reddit" "I wish I knew" OR "does anyone know" — find unmet data needs

Step 2: Design Data Collection

Based on data_type (or recommend the best fit):

Survey Design:

• 8-12 questions (shorter = higher completion)
• Mix: 70% multiple choice, 20% scale (1-5), 10% open-ended
• One "surprising" question that will generate headline-worthy data
• Target sample size: 100+ for credibility
• Distribution plan: where and how to reach respondents

Benchmark Study:

• Define metrics to measure (3-5)
• Data sources: public data, API calls, manual collection
• Collection methodology: how often, what tools
• Comparison framework: how to present findings

Data Aggregation:

• Sources to aggregate from (public databases, APIs, web scraping targets)
• Aggregation logic: how to combine and normalize
• Update frequency: one-time or recurring
• Visualization plan

Case Study Collection:

• Template for collecting stories (5-7 structured questions)
• Outreach template for requesting case studies
• Anonymization rules
• Minimum viable sample: 10+ cases

Step 3: Create Collection Assets

Produce ready-to-use assets:

1. Survey questions (if survey) — complete question list with answer options
2. Collection template — spreadsheet structure or form layout
3. Outreach template — email/message to recruit respondents
4. Data analysis plan — how to turn raw data into insights
5. Content plan — how to present findings (blog post, infographic, report)

Step 4: Design Automation

Create a repeatable system:

• Schedule: when to collect data (monthly, quarterly, annually)
• Tools: recommended platforms (Google Forms, Typeform, Airtable)
• Automation: how to automate collection and reporting
• Update process: how to refresh and republish with new data

Step 5: Self-Validation

• Data gap is real (verified by search — nobody else has this data)
• Sample size is realistic given audience access
• Questions are unbiased and well-structured
• Collection method is feasible with stated budget
• Output content plan is specific (not just "write a blog post")
• Data is ethically collected (no scraping private data, survey has consent)

Output Schema

output_schema_version: "1.0.0"
proprietary_data:
  niche: string
  data_type: string
  data_gap: string              # What data doesn't exist yet
  headline_potential: string    # The "surprising finding" angle

  collection:
    method: string
    sample_target: number
    tools: string[]
    timeline: string
    budget_needed: string

  assets:
    survey_questions: object[]  # If survey type
    collection_template: string # Template description
    outreach_template: string   # Recruitment message
    analysis_plan: string

  content_outputs:              # Content to create from the data
    - type: string              # "blog" | "infographic" | "report" | "social"
      title: string
      skill_to_use: string     # Which skill creates this content

  data_assets: string[]        # Moat strengtheners for chaining

chain_metadata:
   
   
   
  
     
     
     

Output Format

## Proprietary Data Plan: [Niche]

### The Data Gap
**Nobody has answered:** [the question]
**Why it matters:** [why people care]
**Headline potential:** "[Surprising finding template]"

### Collection Design

**Type:** [Survey / Benchmark / Aggregation / Case Study]
**Target sample:** XX responses
**Timeline:** X weeks
**Budget:** $XX
**Tools:** [tools list]

### Survey Questions (or Collection Template)
1. [Question] — [answer type] — [why this question]
2. [Question] — [answer type] — [why this question]
...

### Outreach Template
Subject: [subject line]
[email/message body]

### Content Plan (what to publish from this data)
1. **Blog post:** "[Title]" → build with `affiliate-blog-builder`
2. **Social thread:** Key findings → atomize with `content-pillar-atomizer`
3. **Lead magnet:** Full report PDF → distribute with `squeeze-page-builder`

### Automation Schedule
- **Collection:** [frequency]
- **Analysis:** [when after collection]
- **Publication:** [when after analysis]
- **Update:** [when to re-run with fresh data]

Error Handling

• No niche provided: "Tell me your niche and I'll find data gaps nobody else is filling."
• No audience access: Suggest free distribution channels: Reddit, Twitter, niche forums, ProductHunt. "You don't need an email list — Reddit alone can drive 100+ survey responses."
• Zero budget: Design everything with free tools (Google Forms, Google Sheets, manual aggregation). "The best proprietary data costs $0 — just your time and curiosity."
• Niche already well-researched: Dig deeper. "The broad stats exist, but nobody has [specific angle]. Let's own that."

Examples

Example 1: "I want original data about AI video tools" → Design survey: "AI Video Tools Usage Survey 2025" — 10 questions about which tools, satisfaction, spend, use cases. Distribute on Reddit r/aivideo, Twitter, LinkedIn. Target 150 responses. Content plan: "State of AI Video 2025" blog post + infographic.

Example 2: "Create a benchmark for affiliate marketing earnings" → Aggregate public data from case studies, combine with original survey. Monthly recurring data collection. "Affiliate Marketing Earnings Benchmark Q1 2025."

Example 3: "Data moat for my content strategy" (after content-moat-calculator) → Identify that competitors have generic content but NO original data. Design case study collection: "How 50 Affiliate Marketers Made Their First $1,000." Instant authority.

Revenue & Action Plan

Expected Outcomes

• Revenue potential: Original data content earns 5-10x more backlinks than generic content. Backlinks → higher domain authority → higher rankings for ALL your affiliate pages. One original data post can increase total site traffic by 20-50% over 6 months
• Benchmark: Data-driven blog posts get 2x more shares and 3x more backlinks than opinion posts. "State of [Industry]" posts are the most linked-to content format in B2B niches
• Key metric to track: Backlinks earned by the data content (check via Ahrefs, Semrush, or Google Search Console). Secondary: organic traffic increase to ALL affiliate pages (rising tide lifts all boats)

Do This Right Now (15 min)

1. Launch the survey or start data collection TODAY — don't wait for the "perfect" survey. 80% good is enough to start
2. Post the survey link in 3 places immediately: your email list, one relevant subreddit, and one social platform
3. Set a 2-week deadline for data collection — urgency drives responses
4. Pre-write the blog post outline using the Content Plan section — so you're ready to publish the moment data comes in

Track Your Results

After data collection: publish the findings as a blog post with affiliate-blog-builder. After 30 days: how many backlinks did the data post earn? After 90 days: did organic traffic to your money pages increase? If yes, plan your next data collection round — proprietary data compounds.

Next step — copy-paste this prompt: "Write a blog post presenting my original research findings about [topic]" → runs affiliate-blog-builder

Flywheel Connections

Feeds Into

• affiliate-blog-builder (S3) — unique data angles for articles nobody else can write
• content-pillar-atomizer (S2) — data findings to atomize across platforms
• content-moat-calculator (S3) — proprietary data IS a moat strengthener

Fed By

• content-moat-calculator (S3) — identifies need for differentiated content
• performance-report (S6) — performance data to aggregate

Feedback Loop

• Track backlinks and citations of your data → identify which data points get referenced most → double down on those angles in next collection

References

• shared/references/case-studies.md — Real data-driven success examples
• shared/references/flywheel-connections.md — Master connection map