Mike Brown
The job was never ranking. It was being the answer, across organic growth and AI driven search, proven in revenue.
- In AI visibility for online higher education, 5x the next source
- #1
- In AI visibility for online higher education, 5x the next source
- Incremental leads from a demand channel built in one year
- 4,000+
- Incremental leads from a demand channel built in one year
- Paid search equivalent value of organic coverage added
- $1M/mo
- Paid search equivalent value of organic coverage added
- Brands run under a single owner at peak portfolio scale
- 80+
- Brands run under a single owner at peak portfolio scale
01The frame
One discipline,
two surfaces.
Most organizations are rebuilding measurement from scratch for AI search, and paying twice for it. I did not have to. The model I built for classic search ported to answer engines without a redesign, same stages, same terminal metric, which is why the AI program was reporting results to executives in months instead of years.
Answer Engine Optimization
Being the answer, wherever the answer is served.
SEO surface
+69%
Position one rankings
On a 32 partner university portfolio, while keyword footprint grew 41%: better positioned, not simply bigger. Fifteen years of the same discipline across six employers and portfolios reaching 80+ brands under a single owner.
- Technical architecture, content systems, structured data and off site authority
- Migration defense across 1.78M mapped links and redirects
- Schema programs earning FAQ, HowTo and rich results
- Local search turnarounds across multi campus networks
GEO surface
#1
Most cited domain
28.2% citation share across every major answer engine, roughly 5x the next source. Measured from 437 prompts tracked daily and 188,800 AI answers analyzed, benchmarked continuously against a live competitive set.
- Seven engines instrumented daily: ChatGPT, Perplexity, Copilot, Gemini, AI Mode, AI Overviews and Grok
- Citation share treated as the durable authority metric, not visibility spikes
- A GEO measurement framework mirroring the SEO one, stage for stage
- Results reported monthly to a university president
One team, one framework, two surfaces. No separate AI budget line to defend, and no second reporting model to maintain.
02Signals
What the work
actually returned.
Four live programs and the number each one came back with, plotted from the platform export or executive report it came from, so the shape and the figure can be checked against each other.
AI visibility, non branded queries
Share of AI generated answers featuring each brand on non branded queries, across seven answer engines. Competitor names withheld.
Non branded is the real contest. Only a 1.5 point lead when students don't search by name, which is why the strategy targets non branded topics rather than defending the easy branded number.
Ranking quality outgrew ranking quantity
19 months of keyword coverage against position one rankings, both indexed to month one so the two can be compared directly.
The portfolio didn't just get bigger. It got better positioned. Footprint grew 34% while position one rankings grew 70%. Coverage is easy to buy; position has to be earned.
Revenue grew 25× faster than traffic
One ecommerce account, true year over year. Traffic barely moved. Everything else did.
+3.9%
Sessions
+96.3%
Revenue
+109.7%
Transactions
+118.1%
Conversion rate
Pure conversion, not acquisition. Average order value actually fell 6%, so the entire gain came from converting more of the same visitors.
Five verticals, one operator
Nine months of tracked revenue across five accounts, run through the group's agency alongside an 80 brand in house portfolio.
Average order values spanned $40 to $2,069 , a 50× range. Five accounts needing five different conversion models, running alongside 80+ in house brands.
03Record
Portfolios, not sites.
Every role has been multi brand, and every one has a before and an after. Open any role to see what moved, by how much, and off what starting number.
Eighteen years, six employers, one continuous thread. The outlined bar is a consulting practice running continuously since 2008, alongside all of them.
Owns enterprise answer engine strategy across a four university system and corporate, directing an in house team and partnering with marketing, product, academic, compliance and executive stakeholders. Results go to the board and to a university president.
Three programs that did not exist before now run continuously. An AI visibility program instrumenting seven answer engines daily took the portfolio to the most cited domain in its category, roughly 5x the next source. A 30/60/90 experimentation model across five brands ended the internal argument about what organic was worth. A faculty content engine turned 48 subject matter experts into published authors at volume. Defensive work ran alongside all of it: platform migrations and a four catalog consolidation, with every link, redirect and ranking signal mapped before the switch.
Migration defense meant mapping 448,998 backlinks, 1,335,903 internal links and 20,344 redirects before anything moved. Rankings are easy to lose in a replatform and expensive to win back.
04Evidence
Results you can
ask me to prove.
Twelve outcomes from live programs, each one a number a business acted on. Every figure traces to the board deck, executive review, analytics export or written attestation it came from, so any of them can be walked through line by line in an interview.
28.2%
Citation share, the #1 cited domain across all AI platforms, roughly 5× the next source
816,300 citations tracked
9 / 10
Branded topics ranked first; second overall on the harder non branded battleground
188,800 answers analyzed
7
Answer engines instrumented daily, with the results a university president reads every month
437 prompts · 383 tags
60,000
Net new marketable keywords added, worth roughly $1M a month if the same coverage were bought in paid search
Executive review
+48%
Page one rankings year to date across a four institution portfolio
Board presentation
$17.5M
Organic value delivered on one education account, priced at a $44.73 average CPC
Business case, 2021
+96%
Ecommerce revenue YoY on +3.9% traffic, conversion rate up 118%
Analytics export, true YoY
+95%
Year over year lead growth from embedded lead capture across a partner portfolio
Weekly program reporting
24 mo
Consecutive months of gains in a local search turnaround, with not one negative reading
Published case study
32
University partners under one organic strategy, against a 12,600 lead annual target
Annual strategy plan
1.78M
Links and redirects mapped to protect rankings through platform migration
449K backlinks · 1.3M internal
~30
In market tests run to a written hypothesis, each read three times before it counted as a win
Live experimentation log
05Method
Fix the measurement
first.
Organic programs get cut first when nobody can prove what they returned. Fixing that is the first thing I do in a role. Everything after it runs as a test: a hypothesis written before the work starts, a declared way to judge it, a named data source, and readings at 30, 60 and 90 days.
SEO surface
SEO Testing Measurement Framework
Opportunity
Footprint · Rankings · Features
Acquisition
Clicks / Views · Impressions · Sessions
Engagement
Leads: RFIs, inquiries, MQLs
Conversions
Sales, applications, signups
Revenue
Pipeline, bookings, order value
GEO surface
GEO Testing Measurement Framework
Visibility
Visibility Score · Rank · Share of Voice
Citation & sentiment
Sentiment · Citation Share · Citation Rank
Engagement
Leads: RFIs, inquiries, MQLs
Conversions
Sales, applications, signups
Revenue
Pipeline, bookings, order value
Same structure, two surfaces, one destination. Whatever a business actually counts, whether leads, conversions or revenue, sits at the bottom of both frameworks, and nothing gets reported as a win until it reaches there. Generative search did not need a new measurement model. It needed the existing one pointed at a new surface.
Read at 30 days
Engagement and conversion
Time on page, bounce, exit rate, and conversion rate. These move first, so they are read first. The opening goal is protective: ensure no drop in conversion.
Read at 45 to 60 days
Rankings and sessions
Page one rankings, top five positions, organic sessions, and branded versus non branded split. Rankings lag, so reading them early produces noise rather than signal.
Read at 90 days
Direction, not a verdict
The third reading is what separates a real gain from a spike. Several tests look strong at 30 days and decay by 90. That is the point of reading three times.
06In their words
Fourteen people,
on the record.
Managers, direct reports, agency colleagues, clients and a partner CEO, spanning four employers and a decade. Ordered by what tends to decide an executive hire: judgment under pressure, ownership without supervision, and whether people asked to work with me again.
Six of the fourteen speak specifically to leading people, and they come from managers, peers and direct reports independently rather than from one source. One former direct report now works for him again, at a different company, seven years later.
07Tooling
Depth, not a
checklist of logos.
67 tools, ranked by what the work shows was actually built with each one. The top tier is reserved for tools that were extended or had a methodology built on top of them: Search Console turned into a test validation system across roughly 30 experiments, Profound into a 437 prompt AI measurement program, Apps Script into a rank monitor that logged 18,192 rows against the Custom Search API.
AI and answer engines
- Profound
- Surfer SEO
- ChatGPT
- Perplexity
- Copilot
- Gemini
- Anthropic API
- OpenAI API
- Peec
- Waikay
Analytics and measurement
- Google Analytics
- Search Console
- Domo
- Data Studio
- YouTube Analytics
- UTM frameworks
- Adobe Analytics
- Tag Manager
- Salesforce
- Power BI
- Microsoft Clarity
SEO platforms
- SEMrush
- BrightEdge
- Screaming Frog
- Ahrefs
- Majestic
- Moz
- DeepCrawl
- Searchmetrics
Technical and performance
- Schema.org
- Level Access
- GTmetrix
- Yoast
- Regex
- PageSpeed Insights
- New Relic
- Bing Webmaster
Local search
- LocalViking
- Google Business Profile
- Yext
- BrightLocal
Paid media and attribution
- Google Ads
- CallRail
- Infinity
- Bing Ads
- Meta Ads
- Keyword Planner
Platforms and CMS
- WordPress
- Sitefinity
- WooCommerce
- BigCommerce
- Shopify
- HTML
- PHP
- MySQL
Automation, cloud and delivery
- Apps Script
- Google Cloud
- Windsor.ai
- DataForSEO
- Obsidian
- RoboHead
- AWS
- Jira
- Azure DevOps
- Asana
- Figma
- PandaDoc
Architect means a system or methodology was built on top of the tool, not that the buttons are familiar. Advanced means it produced work that clients and boards acted on.
08Practice
Systems, not campaigns.
The same four stages have now run at three employers, more formal each time. Each version made the work measurable, repeatable and terminal in revenue rather than traffic, which is why these programs compound instead of resetting every time the team or the agency changes.
Opportunity
- Keyword footprint
- Rankings
- SERP features
Acquisition
- Impressions
- Clicks
- Sessions
Engagement
- Conversion rate
- Transactions
- Reviews
Commercial
- Leads & applications
- Revenue
- Organic value
Measurement frameworks
A four stage model running keyword footprint, rankings, sessions, then leads and applications. Built at agency in 2020, formalized enterprise wide, ported intact to AI search in 2026 with no redesign. It has never terminated in a traffic number, which is why organic keeps its budget in the rooms where budgets get cut.
Experimentation programs
Every test carries a written hypothesis, a declared validation method, a named data source and fixed 30/60/90 day readings. Failed tests stay on the board with their numbers attached. That is what makes the wins believable to a CFO, and why roughly 30 tests produced a body of knowledge rather than 30 separate opinions.
Content engines
Trained 48 faculty subject matter experts to pair their expertise with optimization tooling, producing 157 authoritative articles through editorial, SEO and compliance review. It solved the expensive problem in expertise driven content: getting genuine authority published at volume.
Governance
Authored the AI operating framework for a marketing organization spanning four brands: principles, a human in the loop model, a risk register, and the governance questions leadership had to answer. It is the document that lets an organization adopt AI at speed without the legal and brand exposure that usually follows.
09Credentials
Qualified, certified,
and used to explaining it.
A computing degree underneath fifteen years of marketing. It is why the technical work goes as deep as it does, why the tooling gets built rather than bought, and why engineering teams treat the specs as engineering specs.
Education
B.S., Computer Information Systems
Saint Leo University, 2009 to 2011
A.S., Computer Information Technology
College of Central Florida, 2005 to 2008
Certifications
BrightEdge Professional Certification
Enterprise SEO platform
Data Integration Expert Certification
Analytics and data pipelines
Teaching and speaking
When Search Meets Data
Deck and produced video
Market Reporting and Automation
Practitioner session
The Nature of SEO
Plus a foundational primer for non specialists
Let's talk.
Open to VP or C level roles owning organic growth and AI search across multi brand portfolios. Strongest fit where the portfolio is complex, nobody can currently prove what organic returns, and the answer engines have started to matter.
- Based in
- Maryland
- Preference
- Remote preferred
- Also open to
- Maryland onsite