The MERIT Framework

A Practical Guide to AI Search Optimization: 10 Strategies for Visibility in LLMs

Published by Searchbloom

October 2025 Edition

© 2025 Searchbloom. All rights reserved.
The MERIT Framework | Page 2

Executive Summary

Cody C. Jensen

Written by

Cody C. Jensen

CEO & Founder of Searchbloom

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Answer Engine Optimization (AEO), also referred to as AI Search Optimization, AI SEO, or Generative Engine Optimization (GEO), has emerged as a distinct discipline that complements traditional SEO. While SEO remains foundational for ranking in search results and LLMs, AI SEO focuses on being cited in AI-generated answers across platforms like ChatGPT, Copilot, Perplexity, Google AI Overviews, Claude, Gemini, and other large language models (LLMs).

Key Insight:

Good traditional SEO will naturally surface brands in LLMs over time. Research shows that 97% of AI Overviews cite at least one source from the top 20 organic results (seoClarity, February 2025), demonstrating the strong connection between traditional rankings and AI visibility. However, organizations seeking accelerated results in LLMs and AI platforms should recognize that traditional SEO and AI SEO are two distinct but complementary disciplines.

About Tools & Platform Recommendations:

Tool and platform recommendations throughout this document are accurate as of October 2025. The AI optimization landscape evolves rapidly, and readers should verify current tool capabilities, pricing, and availability before implementation.

About Statistics & Data Sources:

Statistics cited in this whitepaper are drawn from published industry research and studies available as of October 2025. Where specific dates are referenced (e.g., "February 2025", "June 2025"), these represent the publication or data collection dates of the cited sources. All sources are hyperlinked for verification and further reading.

About Examples & Case Studies:

Examples and case studies presented throughout this document are representative illustrations based on observed patterns and outcomes across multiple implementations. They are designed to demonstrate practical application of the strategies rather than document specific client engagements.

While there is substantial overlap between these disciplines, AI SEO requires specific strategies beyond traditional SEO alone. This guide presents the MERIT framework as a structured approach to accelerating AI visibility.

Traditional SEO vs. AI SEO: The Overlap

Traditional SEO vs AI SEO Venn Diagram showing overlap between disciplines

Strong traditional SEO naturally improves AI visibility over time, but AI-specific strategies accelerate results.

The MERIT Framework

The MERIT methodology provides a structured framework for organizing AI SEO efforts. MERIT offers a repeatable system for earning visibility in both search and answer engines.

The Five Pillars

M = Mentions

Third-party validation across trusted platforms in your industry where AI systems discover authoritative signals about your brand, products, services, and expertise. This includes verified customer reviews on review and directory platforms, authentic community engagement on forums and social communities, strategic third-party publications and media coverage, consistent web presence across multiple channels, co-authored content with industry partners, and other forms of external validation that build credibility.

E = Evidence

Original, quantifiable assets that establish your brand as a primary source AI systems can reference and attribute. This includes proprietary research and benchmarks, case studies with measurable outcomes, expert analysis and educated opinions, evidence-based hypotheses and frameworks, transparent methodologies with measurable outcomes, topical authority, and other authoritative resources that demonstrate thought or industry leadership.

R = Relevance

Comprehensive, intent-aligned content structured for AI retrieval in self-contained, citable segments. This is achieved through answer-first content architecture that directly addresses user queries, question-based headings mirroring natural language patterns, information segments of 150-300 words optimized for RAG systems, semantic HTML structure with clear topical boundaries, pillar-and-cluster content strategies, multi-format presentation of information, and related content optimization techniques that enhance discoverability.

I = Inclusion

Technical accessibility and semantic precision that enables AI crawlers to discover, understand, and correctly interpret your content and entities. This is achieved through proper crawler configuration and robots.txt management, entity schema implementation and knowledge graph connections to authoritative reference sources, IndexNow protocol integration for rapid content discovery, comprehensive semantic markup with structured data, server-side rendering for crawlability, proper indexation controls, and other technical optimization methods that ensure machine readability.

T = Transform

Systematic measurement and iterative optimization that accounts for AI platform volatility while driving sustained visibility improvements. This includes weekly monitoring protocols with volatility awareness, monthly trend analysis using moving averages and executive dashboards, quarterly strategic reviews with competitive landscape assessment, realistic expectation management across all organizational stakeholders, hypothesis testing and validation, promotion of winning patterns, budget reallocation based on performance data, and ongoing optimization that responds to platform changes.

The MERIT Framework | Strategy 1
STRATEGY 1

Be the Source

What It Is

AI systems often use Retrieval-Augmented Generation (RAG) where content is chunked, converted to vector embeddings, and retrieved when relevant to queries. Being "the source" means structuring and optimizing your existing content on owned properties (blog, knowledge base, resource center) so AI systems can efficiently discover, parse, and cite it. This strategy focuses on how you present content for optimal AI retrieval, not what content you create (that's Strategy 5).

Technical Context: Why Chunk Size Matters

When AI systems use RAG to process content, they convert text into vector embeddings measured in dimensions (typically 100-3072 depending on the model). While you can't control the embedding dimensions themselves, you CAN control how your content is chunked before embedding. The 150-300 word recommendation creates optimal retrieval units because:

Think of it this way: the AI system handles the math (embedding dimensions), but you control the input (chunk structure and size).

How to Implement

Answer-First Structure

Deploy Schema Markup

Important Technical Context: While schema markup is critical for traditional SEO, LLMs don't directly read or parse schema.org when generating responses. However, schema remains strategically important for AI SEO because search engines DO read schema to better understand and index content. This indexed information then feeds into AI retrieval systems through mechanisms like Google's Knowledge Graph. Think of schema as optimizing the "discovery layer" that AI systems rely on, rather than the AI systems themselves reading the schema directly.

Optimize Content Format & Presentation

Take your existing content and optimize how it's presented for AI retrieval:

Example in Action:

A B2B SaaS company had an existing product comparison page that received good traffic but minimal AI citations. They restructured it for AI optimization:

Result: Within 5 months, ChatGPT citations increased from 8% to 34% for project management software queries. Perplexity began using their structured comparison as the authoritative source, citing them in 41% of related queries. The optimization work took 12 hours; the impact lasted 18+ months.

The MERIT Framework | Strategy 2
STRATEGY 2

Pay to Play

What It Is

AI systems heavily rely on trusted review and directory platforms (G2, Clutch, Capterra, Gartner) as authoritative sources. "Pay to Play" means investing in premium listings, featured placements, and enhanced visibility features that improve your rankings on these platforms. While LLMs cannot see who paid for premium features, they do see and cite the rankings themselves. Paying for premium features helps you achieve higher rankings (Leader badges, top category positions, featured placements), which is what AI systems actually discover and reference. You're not buying AI citations directly - you're buying better rankings on platforms that AI systems trust and cite.

Free vs. Premium: The Rankings Gap

Most review platforms offer both free and paid tiers. Premium features help you achieve higher rankings and more prominent placement on these platforms. AI systems cite brands with higher rankings more frequently because top-ranked solutions appear as more authoritative. Premium typically includes: featured listings (which improve ranking position), category leadership badges (which AI systems reference), enhanced profile placement (more discoverable by AI crawlers), and tools to accelerate review collection (which improves organic rankings).

How to Implement

Platform Selection

Invest in Premium Features

Investment Consideration: Premium placements on major platforms typically range from $250-$50,000+ monthly depending on platform, category competitiveness, and feature set. Prioritize platforms where your target buyers actively research solutions. For most B2B companies, investing in 2-3 premium platforms yields better results than 10 free listings.

Critical Sequencing: Do not invest in premium placements until you have sufficient review volume and ratings to justify the investment. Most platforms require 10-15+ reviews minimum before premium features provide meaningful ROI. The recommended sequence is: (1) Build free profile, (2) Optimize profile content, (3) Generate 15-25+ high-quality reviews, (4) Then invest in premium placement. Paying for visibility with only 3-5 reviews wastes budget and could damage credibility.

Profile Optimization

Systematic Review Generation

Example in Action:

A marketing automation platform with a free G2 profile had 23 reviews but appeared low in category rankings and received minimal AI citations. They invested $2,400/month in G2 premium features:

Result: ChatGPT citations increased from 4% (with free profile) to 31% (with premium features) for "marketing automation" queries. The platform went from rarely mentioned to appearing in the top 3 recommendations. AI systems now specifically reference their "Leader" status and high review count as credibility signals. The $2,400/month investment generated an estimated $47,000 in monthly attributed pipeline from AI-referred traffic.

The MERIT Framework | Strategy 3
STRATEGY 3

Mentions & Positive Sentiment

What It Is

AI systems heavily weigh sentiment and third-party validation when determining what content to cite. Community platforms like Reddit, Quora, and industry forums play a significant role in AI citations, though rates vary by platform and have proven volatile. Recent data shows Reddit as the second most-cited platform behind YouTube. More importantly, the sentiment of mentions on these platforms directly influences whether AI systems present your brand positively, neutrally, or negatively. Building genuine positive sentiment through authentic community engagement is essential for favorable AI visibility.

Platform Citation Patterns:

Based on Profound's analysis of over 1 billion citations (October 2025):

Note: Citation rates have proven highly volatile, with ChatGPT's Reddit citations fluctuating between 1-14% in recent months. Source: Profound via Axios, October 2025

How to Implement

Proactive Reputation Building

Critical: Trust Before Promotion

Community platforms aggressively ban promotional content. You cannot simply join and start promoting your brand. These communities value authentic contribution over marketing, and violating this principle results in permanent bans, damaged reputation, and negative sentiment that AI systems will cite against you.

Reddit: The Karma Economy

Quora: Expertise Over Promotion

LinkedIn: Professional Thought Leadership

Industry Forums: Earn Recognized Contributor Status

Multi-Platform Monitoring & Response

Example in Action:

A project management software company executed a disciplined 9-month Reddit strategy with three team members:

Result: Perplexity citations increased from 0% to 19% for project management queries within 9 months. Reddit threads featuring positive mentions became the most-cited source. Community-generated recommendations outperformed their owned content by 3x in AI citations. Most importantly, sentiment was overwhelmingly positive (89% positive mentions) because advocacy came from genuine users, not marketing.

The MERIT Framework | Strategy 4
STRATEGY 4

Entity Optimization

What It Is

Entity optimization helps AI systems and search engines correctly identify, understand, and disambiguate your brand, people, products, and topical authority. Entities aren't just organizations - they include people (founders, executives, experts), things (products, services, concepts), and topics (subject matter domains like "SEO" or "project management"). Comprehensive entity optimization requires a multi-faceted approach across all these dimensions.

Understanding Entity Recognition and Knowledge Panels:

Google Knowledge Panels are created when entities reach sufficient prominence through demand and search volume, not by implementing schema or optimization tactics alone. However, entity optimization strengthens the signals that help search engines understand who you are and what you're about, which supports AI system retrieval. Once Google creates a Knowledge Panel for your brand, claim it immediately. If you don't claim it, competitors, former employees, or others could claim it, leading to misinformation and loss of control over your entity representation.

How to Implement

1. Brand Entity Optimization

Strengthen recognition of your organization as a distinct, authoritative entity:

2. People Entity Optimization

Your brand is made up of people. Optimizing individual entities strengthens your organization entity:

3. Product and Service Entity Optimization

Individual products and services are entities that strengthen your overall brand entity:

Example Product Schema:

Note About Product Schema:

Product schema DOES trigger rich results in Google search (will show as valid in Rich Results Test). Products display with ratings, prices, availability, and images in search results, making this one of the most valuable schema types for e-commerce and SaaS companies.

example of product schema

Example Service Schema:

Note About Service Schema:

Service schema will not trigger rich results in Google's search results (Rich Results Test will show "No items detected"). However, Service schema remains valuable for entity optimization and AI search because it helps search engines understand your service offerings, connect them to your organization entity, and include them in knowledge graph data that AI systems reference. For rich results display, consider using FAQ, HowTo, or Article schemas on service pages.

example of service schema

4. Topical Entity Optimization

Build deep authority around the core topic entities in your domain. Go deep before going wide:

5. Strategic Schema Implementation

While schema doesn't create Knowledge Graphs, it helps with entity understanding and disambiguation across all entity types:

Example Organization Schema with SameAs:

Note About Organization Schema:

Organization schema typically will not trigger rich results in Google's search results unless it's a LocalBusiness with specific properties. However, Organization schema is fundamental for entity optimization as it defines your company entity, connects it to other entities (people, products, services), and provides the foundation for Knowledge Panel eligibility. This is core data that AI systems use to understand and reference organizations.

example of organization schema

Example Person Schema with SameAs:

Note About Person Schema:

Person schema will not trigger rich results in Google's search results (Rich Results Test will show "No items detected"). However, Person schema is critical for entity optimization as it helps search engines identify individuals, connect them to organizations, and build knowledge graph data about subject matter experts. This information is frequently referenced by AI systems when generating responses about people, founders, and industry experts.

example of person schema

Key Points About SameAs:

6. Knowledge Panel Strategy

Knowledge Panels are earned through prominence, but require proactive management:

Example in Action:

A project management SaaS company implemented comprehensive multi-faceted entity optimization:

Brand Entity:

People Entities:

Product Entities:

Topical Entities (Going Deep First):

Knowledge Panel:

Result: Within 12 months, ChatGPT and Claude began citing them as authoritative sources for project management content in 34% of PM-related queries. Their founder profiles appeared in 18% of queries about Agile methodology. AI systems correctly associated them with "project management," "Agile," and "Scrum" as core topical entities. Google Knowledge Panel displayed accurate information under their control. Overall AI visibility increased 420% compared to their pre-optimization baseline.

The MERIT Framework | Strategy 5
STRATEGY 5

Original Source Asset Development

What It Is

Original source asset development means establishing your brand as the original source of citable assertions, frameworks, perspectives, research, data, or analysis that AI systems can reference and attribute. Importantly, AI systems cite opinion-based content just as readily as empirical research. What matters is not whether content is data-driven or opinion-based, but whether you are the original source that others can reference and corroborate.

The strategic value comes from co-citation patterns, where multiple trusted sources reference your original asset, framework, research finding, or perspective. AI systems amplify content that appears across multiple authoritative sources, creating distributed validation signals. A well-promoted expert opinion cited by 10 authoritative sources will outperform an uncited research study. A proprietary framework referenced across industry publications drives more AI visibility than unpromoted data.

This is why original source assets function as citation-generation engines rather than standalone pieces. A single article, study, framework, or data point published only on your website has limited AI impact. That same asset cited by industry publications, referenced in community discussions, validated by review platform mentions, and corroborated across multiple sources creates the co-citation network that drives visibility. Without promotion and third-party pickup, even the most rigorous research or insightful opinion remains invisible to AI systems.

Opinion, Frameworks, Research, and Data: All Are Equally Citable

AI systems do not inherently prioritize empirical research over expert opinion, thought leadership, or analytical perspectives. Similarly, they don't prioritize opinion over data. A framework like "The MERIT Methodology" is just as citable as "67% of enterprises increased AI investment in Q3 2025." An expert analysis of market trends is just as citable as a survey-based benchmark study. What drives AI citations is being the original source combined with co-citation across trusted sources. The framework strategies work together: Strategy 5 develops the original source asset (whether opinion-based, framework-driven, or data-driven), Strategy 2 (review platforms) provides external validation, Strategy 3 (community engagement) generates distributed references, and Strategy 6 (third-party content) builds explicit co-citation. Original assets without promotion are invisible; promotion without original substance lacks credibility.

How to Implement

Choose Your Asset Type

Different types of original source assets serve different strategic purposes. All types (frameworks, opinion, research, data, tools) are equally valid for generating AI citations when properly promoted. Choose based on budget, timeline, expertise, and strategic goals.

Expert Frameworks & Methodologies

Expert Opinion & Analysis

Data-Driven Research Studies

Interactive Tools & Calculators

For Data-Driven Research: Design & Planning

If you choose to create data-driven research, credible methodology is essential. Poorly designed research with insufficient sample sizes or weak methodology will be deprioritized when authoritative sources evaluate whether to cite it.

Research Type Selection:

Sample Size Requirements:

Sample Size and Statistical Validity:

Small sample sizes undermine research credibility with the authoritative sources (media, analysts, industry publications) that AI systems rely on for co-citation. A survey of 50 people cannot credibly represent an entire industry, and journalists or analysts will not cite such research. Without credible third-party citations, even published research remains invisible to AI systems. If budget limits sample size to under 500, consider narrowing research scope, switching to qualitative research or expert frameworks, partnering with industry associations for panel access, or building calculators instead. Never misrepresent sample size or make claims beyond what your data supports.

For Data-Driven Research: Methodology Documentation

If pursuing data-driven research, transparent methodology differentiates research that authoritative sources cite from research they ignore. Comprehensive, accessible methodology documentation builds credibility.

What to Document:

How to Present Methodology:

For Data-Driven Research: Data Presentation for AI Retrieval

When publishing quantitative research, how you present findings directly impacts whether AI systems can discover, parse, and cite your data. Many organizations invest in excellent research but present it in formats AI systems cannot easily reference.

Structure Statistical Claims for Citation:

Dual-Format Strategy:

Interactive Calculators & Tools

Interactive calculators position your brand as a utility that AI systems reference when users need specific calculations. Calculators generate unique value through personalized, quantified answers.

High-Value Calculator Types:

Design Principles for Citation:

The Gating Dilemma:

Many organizations want to gate calculators behind email forms for lead generation. However, AI systems cannot access gated content, which means zero citations. Recommended approach: Provide basic results ungated (enables AI citation), offer enhanced reports behind optional email gate. Organizations that gate all calculator functionality sacrifice AI visibility for short-term lead generation.

Templates & Downloadable Assets

Templates establish your brand as the authoritative source for frameworks and methodologies. Unlike static content, templates provide immediate practical value users can implement.

High-Impact Template Categories:

Format Strategy:

Partnership Strategies

Partnerships amplify research credibility and expand reach. Strategic collaborations can transform good research into industry-defining research.

Academic Partnerships:

Industry Association Research:

Research Firms:

Measurement & Program Evolution

Track research impact to understand which investments generate citations and which formats underperform.

Metrics to Track:

Citation Reinforcement: Expanding Existing Visibility

Once you're being cited, systematic reinforcement expands and sustains that visibility. AI systems favor brands with demonstrated topical authority across related subjects.

When You're Already Cited:

Strategic Expansion Pattern:

Refresh Cycle for Cited Assets:

The Compounding Effect of Citation Reinforcement:

Organizations that systematically reinforce existing citations see 3-5x faster visibility growth than those constantly chasing new topics. Once AI systems recognize you as authoritative for Topic A, expanding to related Topics B, C, and D requires significantly less effort than establishing authority in an unrelated Topic Z. Build citation momentum through focused expansion rather than scattered content creation.

Budget Realities & Strategic Choices:

Data-Driven Research Investment: DIY survey tools: $500-2,000. Managed platforms: $5,000-15,000. Research firms: $25,000-100,000+. Ongoing program: $50,000-250,000 annually. ROI timeline: 6-12 months from publication to measurable impact.

When Data-Driven Research Makes Sense: Established content foundation, budget exceeding $25K, competitive landscape where proprietary data creates meaningful differentiation, markets with insufficient existing research.

Lower-Cost Alternatives That Drive Equal Citations: Expert frameworks and methodologies ($0-15K), thought leadership and opinion pieces ($0-5K), interactive calculators ($8K-30K). These approaches often generate faster ROI and equal AI citations when properly promoted. Early-stage organizations should prioritize frameworks and opinion content over expensive research studies.

Example in Action:

A B2B fintech company serving small businesses created "The State of Small Business Banking 2025":

Research Design:

Data Presentation:

Interactive Calculator:

Investment:

Results After 9 Months:

The MERIT Framework | Strategy 6
STRATEGY 6

Third-Party Corroboration

What It Is

Third-party corroboration builds authority through external validation. AI systems favor content appearing across multiple trusted sources, creating multiple touchpoints for AI discovery and citation.

How to Implement

AI-Optimized Guest Posting

Strategic Co-authorship

Content Syndication Strategy

Example in Action:

A cybersecurity company executed a systematic guest posting strategy:

Result: ChatGPT began citing their CTO as a cybersecurity expert in 27% of enterprise security queries. Third-party articles were cited more frequently than their own blog, demonstrating the power of external validation. Overall AI visibility increased 340%.

The MERIT Framework | Strategy 7
STRATEGY 7

Crawler Access

What It Is

Controlling AI crawler access determines whether your content can be discovered and cited. Each AI platform uses specific crawlers for training and real-time retrieval.

How to Implement

Identify All AI Crawlers

Configure robots.txt for AI Access

# Allow AI Crawlers
User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Google-Extended
Allow: /

Monitor Crawler Activity

Example in Action:

An e-learning platform discovered their robots.txt was blocking all AI crawlers by default:

Result: Within 30 days, saw 460% increase in AI crawler activity. Within 90 days, ChatGPT citations increased from 2% to 18% for educational content queries. Perplexity began regularly citing their free guides.

The MERIT Framework | Strategy 8
STRATEGY 8

IndexNow

What It Is

IndexNow is an open-source protocol enabling instant notification to search engines when content changes. This helps ensure AI systems have access to your freshest content rather than relying on periodic crawls.

How to Implement

Generate and Host API Key

Implement URL Submission

POST https://api.indexnow.org/IndexNow
{
  "host": "yourdomain.com",
  "key": "your-api-key",
  "keyLocation": "https://yourdomain.com/your-key.txt",
  "urlList": [
    "https://yourdomain.com/page1",
    "https://yourdomain.com/page2"
  ]
}

Automate with CMS Integration

Example in Action:

A news and analysis site publishing 8-12 articles daily implemented IndexNow automation:

Result: Reduced average time-to-citation from 7-14 days to 2-3 days for breaking news. Bing's AI citations increased 280% within first 3 months. Fresh content began appearing in AI responses within 48-72 hours instead of weeks.

The MERIT Framework | Strategy 9
STRATEGY 9

Set Expectations

What It Is

Setting proper expectations ensures organizational alignment and sustained investment. AI SEO requires its own metrics, timelines, and success measures distinct from traditional SEO. Given high volatility (only 9.2% URL consistency in Google AI Mode across repeated searches, SE Ranking, August 2025), managing expectations is critical.

How to Implement

Create Education Framework

Establish AI SEO-Specific KPIs

Develop Realistic Timeline

Address Common Misconceptions

Example in Action:

A B2B company launched AI SEO without proper expectation-setting. Here's what happened:

Lesson Learned: They restarted 6 months later with proper education. This time, they:

Result: Program ran successfully for 18 months with consistent investment, achieving 22% average citation rate with acceptable volatility tolerance.

The MERIT Framework | Strategy 10
STRATEGY 10

Measurement Cadence

What It Is

Structured measurement cadence ensures continuous optimization and demonstrates AI SEO value through regular monitoring, reporting, and strategic adjustments. Given AI system volatility, consistent measurement over time is essential for identifying real trends versus random fluctuations.

How to Implement

Weekly Monitoring Protocol

Monthly Dashboard Creation

Quarterly Strategic Review

Implement Measurement Tools

Note: Pricing information accurate as of October 2025 and subject to change.

Example in Action:

A marketing agency implemented comprehensive measurement cadence for a client:

Result: By focusing on trends rather than fluctuations, identified that:

These insights drove strategy shift: 40% more budget to Reddit engagement, prioritized Perplexity optimization, implemented quarterly content refresh schedule. Result: 340% increase in overall AI visibility over 12 months.

The MERIT Framework | Conclusion

Conclusion

Key Takeaways

The MERIT framework provides a structured approach to AI Search Optimization, organizing ten practical strategies across five complementary pillars. The evidence suggests that good traditional SEO will naturally surface brands in LLMs over time, but organizations seeking to accelerate their AI visibility require specific strategies beyond traditional SEO alone.

Implementation Priorities

Organizations should consider implementing these strategies in phases based on their current SEO foundation and resources:

Phase 1 (Months 1-2): Foundation

Phase 2 (Months 3-4): Content & Authority

Phase 3 (Months 5-6): Amplification

Final Considerations

AI Search Optimization requires patience, realistic expectations, and sustained investment. The high volatility observed in AI platforms (only 9.2% URL consistency in Google AI Mode, SE Ranking, August 2025) means success should be measured in trends over time rather than week-to-week fluctuations. Organizations should expect 3-6 months minimum for significant impact, with ongoing optimization required to maintain and improve visibility.

Traditional SEO and AI SEO are complementary rather than competing disciplines, with substantial overlap in their foundational strategies. Organizations with strong traditional SEO foundations are best positioned to accelerate their AI visibility through the targeted strategies outlined in this framework.

About This Framework

The MERIT framework represents insights gathered from real implementations across diverse industries and company sizes. This white paper aims to provide honest, practical guidance on AI SEO based on measurable outcomes rather than speculation or promotional content.

Cody C. Jensen

Cody C. Jensen

CEO & Founder of Searchbloom

Cody C. Jensen is the Founder and CEO of Searchbloom, a results-driven search engine marketing agency. He began his career at Google and later advanced through some of the largest agencies in the digital marketing industry. During that time, he recognized the need for an agency that focused on transparency, measurable results, and ethical practices.

Searchbloom was his answer, created with the mission to be the most trusted, transparent, and results-driven search marketing agency in the industry. Cody works closely with marketing executives, digital managers, business owners, and enterprise brands to create full-funnel strategies that deliver real growth. His leadership and innovation have led to the development of proven digital marketing methodologies that continue to help Searchbloom's partners achieve lasting ROI and sustainable success.