Is creating original research actually worth it for AI visibility? Seems like massive effort

I’ve managed original research programs for both enterprise ($1B+) and mid-market ($30-100M) companies. Here’s the real picture:

The “10x citations” claim is accurate but misleading:

• Yes, research gets cited 10x more than blog posts
• BUT enterprise research gets cited 100x more than mid-market research
• The gap isn’t fair, but it’s real

What actually determines research citation:

Where mid-market can win:

1. Niche expertise - Don’t research “marketing trends.” Research “marketing trends for manufacturing companies under 500 employees.”

2. Proprietary data - You have data competitors don’t: customer behavior, usage patterns, support tickets.

3. Speed - You can research emerging topics before enterprises slow-roll their processes.

The honest ROI for mid-market:

• Year 1: Minimal AI citations (building foundation)
• Year 2: Starting to appear in niche queries
• Year 3+: Compounding returns if you stay consistent

It works. But it’s a 3-year bet, not a campaign.

Don’t have $50K? Here’s how we do research on a shoestring:

Low-cost research methods:

1. Customer survey research

   • Cost: ~$2K (survey tool + incentives)
   • Sample: 200-500 customers
   • Angle: What only YOUR customers can tell you

2. Proprietary data analysis

   • Cost: Staff time only
   • Source: Your product usage data
   • Angle: Anonymized trends from your platform

3. Expert interview compilations

   • Cost: Time + small honorariums
   • Method: Interview 20+ industry experts
   • Angle: “What 20 Experts Say About X”

4. Trend analysis

   • Cost: Minimal
   • Method: Analyze publicly available data in unique ways
   • Angle: Original analysis, not original data

What we’ve learned:

The key: Make it genuinely useful and unique. A well-done $5K study can outperform a lazy $50K study.

Let me share what research actually gets cited by AI:

High citation content patterns:

1. Specific statistics - “73% of X do Y” citations are common
2. Comparison data - “X vs Y” research gets pulled frequently
3. Trend data - Year-over-year changes
4. Benchmark data - “Average Z is 123”

What we’ve measured using Am I Cited:

Content with original research statistics: 4.3x citation rate Content with third-party statistics: 1.8x citation rate Content with no statistics: 1x baseline

BUT here’s what matters more than quantity:

Extractability - Can AI easily pull your statistic? Format matters:

• Good: “According to [Your Company] research, 67% of marketers…”
• Bad: Statistic buried in paragraph 12 of a PDF

Verification - Can AI cross-reference your claim?

• Good: Methodology explained, sample size stated, date clear
• Bad: “Research shows…” with no attribution

Uniqueness - Is this data available elsewhere?

• Good: Only your company has this insight
• Bad: You’re reporting what everyone else is

My advice: Before investing in research, audit what unique data you ALREADY have. Most companies sit on goldmines they don’t realize.

I worked at one of the big research firms. Let me demystify how we operated:

The enterprise research machine:

• 50+ person research team
• $5M+ annual research budget
• Multi-channel promotion
• Existing brand authority

What mid-market can learn:

1. They’re not as smart as you think - A lot of enterprise research is recycled surveys with big sample sizes. Insights are often shallow.

2. They can’t go niche - Gartner won’t write about “marketing automation for pet supply e-commerce.” You can.

3. They’re slow - Enterprise research takes 6-18 months. You can ship in 6-8 weeks.

4. They’re expensive - Their research requires massive investment to be profitable. Yours just needs to be useful.

The real competition: You’re not competing with McKinsey for “marketing trends.” You’re competing with other mid-market companies for your specific niche queries.

Most of your actual competitors probably aren’t doing original research at all. That’s your opportunity.

Strategic targeting: Find 5-10 specific questions AI gets asked about your space. Create research that answers those exact questions. You don’t need to boil the ocean.

Let me share a cautionary tale about research done wrong.

Our mistake:

Spent $80K on a “State of the Industry” report.

• 2,000 respondents
• Beautiful design
• 60 pages of charts
• Massive promotional push

Result:

• Some press coverage
• Downloaded 500 times
• AI visibility: Almost zero

What went wrong:

1. Too broad - “Industry trends” is owned by big players
2. No unique angle - Same questions everyone asks
3. PDF format - AI couldn’t parse it easily
4. No web-first version - HTML content > PDF for AI
5. One-and-done - No follow-up or updates

What we learned:

The research itself was fine. The strategy was wrong.

If we did it again:

• Narrow focus (specific segment)
• Unique angle (questions nobody else asks)
• Web-first (HTML with structured data)
• Data points in articles (not just PDF)
• Annual updates (build citation equity)

It’s not just about doing research. It’s about doing research AI can find, parse, and cite.

You don’t have to go big immediately. Here’s an incremental approach:

Quarter 1: Micro-research

• Quick customer survey (100 responses)
• One focused insight
• Single blog post with key finding
• Track if it gets any AI traction

Quarter 2: Expand if it works

• Larger sample
• More questions
• Dedicated landing page
• Monitor AI citations

Quarter 3: Full research if validated

• Comprehensive report
• Multiple content pieces
• Full promotional push
• Baseline measurement

This approach:

• Validates demand before big investment
• Builds research muscle gradually
• Shows ROI to leadership incrementally
• Reduces risk

Our results with this approach:

• Q1 micro-research: 3 AI citations
• Q2 expanded research: 12 citations
• Q3 full report: 40+ citations and growing

Each phase funded the next. Much easier to get buy-in than asking for $50K upfront.

This thread changed my thinking. Here’s my new plan:

What I was wrong about:

1. Competing with giants - We don’t have to. We can own our niche.

2. Needing massive budget - Start small, validate, then invest.

3. Research = PDFs - Web-first, HTML content, extractable statistics.

4. One-and-done - It’s a multi-year program, not a campaign.

Our new approach:

Phase 1 (Q1): Validate the concept

• Survey 200 customers on a specific pain point
• Create one insight-focused blog post
• See if AI picks it up
• Budget: $3K

Phase 2 (Q2): Expand if it works

• Bigger survey, more questions
• Dedicated landing page
• Track citations with Am I Cited
• Budget: $8K

Phase 3 (Q3-Q4): Full program if validated

• Comprehensive annual report
• Multiple derivative content pieces
• PR and distribution push
• Budget: $25K

The mental shift: We’re not creating “content.” We’re building a citation asset that compounds over time. The ROI calculation isn’t first-year. It’s years 2 and 3.

Specific niche we’re targeting: [Our specific industry segment] - a space where big players don’t focus but where our customers desperately want data.

Thanks everyone. This is actually executable now.