The full story
The Challenge: Too Much Data, Not Enough Action
The Company Had Plenty of Data — But Execution Was Fragmented
The B2B SaaS company was already investing heavily in digital marketing.
Its teams monitored:
- Organic traffic
- Keyword rankings
- Technical SEO
- Backlinks
- Content performance
- Competitor activity
- Website health
- Marketing analytics
- AI-search visibility
- Brand mentions and citations
The problem wasn't a lack of information.
The problem was what happened after the information was collected.
SEO reports identified technical issues. Competitor analysis revealed gaps. Analytics showed performance changes. Content audits uncovered opportunities. AI-visibility monitoring showed competitors appearing in AI-generated answers.
But these insights lived across different tools, reports, teams, and workflows.
Every report created another insight — but not necessarily another action.
The marketing team was accumulating intelligence faster than it could turn that intelligence into implementation.
The Insight-to-Execution Gap
The company identified four major problems.
| Problem | What was happening |
| Disconnected data | SEO, content, competitors, analytics, and AI visibility were analyzed separately. |
| Unclear priorities | Teams could identify problems but struggled to determine which issue deserved attention first. |
| AI visibility was difficult to operationalize | The company could see AI search becoming important but couldn't easily translate those findings into a marketing plan. |
| Recommendations required manual interpretation | Reports still required marketers to determine what to fix, why it mattered, which page should change, what content to create, and what should happen first. |
This fragmentation reflects a broader marketing challenge. The source cites Salesforce research reporting that only 26% of marketing leaders were completely satisfied with how well their data sources were unified.
The Objective: From Reports to Execution
The existing marketing process looked like this:
Data → Reports → Manual Analysis → Decision → Execution
The company wanted to create a more continuous operating model:
Measure → Understand → Evaluate → Recommend → Execute → Measure
The objective was simple:
Turn marketing intelligence into prioritized, implementation-ready actions.
The Approach: From Marketing Intelligence to Action
The new workflow was organized around six connected stages.
1. Measure: Establish a Complete Visibility Baseline
The first step was to create a baseline across four areas.
Traditional Search
The team evaluated:
- Technical SEO
- Crawlability
- Indexing
- Keyword rankings
- Organic traffic
- Backlinks
- Internal linking
- Website health
Content
The analysis covered:
- Content performance
- Search intent
- Topic coverage
- Content gaps
- Content quality
- Internal-linking opportunities
Competitive Intelligence
The team evaluated:
- Competitor rankings
- Competitor content
- Backlink gaps
- Topic coverage
- Brand mentions
- Authority signals
AI Search Visibility
The company also began measuring:
- AI mentions
- AI citations
- AI recommendations
- Competitor AI visibility
- Brand representation in AI-generated answers
Together, these four layers created a broader picture of how the company was being discovered across traditional search and emerging AI-powered search experiences.
2. Understand: Connect the Signals
Previously, each metric tended to be evaluated independently.
The new approach connected the signals.
For example, if a competitor ranked above the company for an important keyword, the team didn't automatically treat the issue as a ranking problem.
Instead, it examined:
- Content depth
- Topic coverage
- Search intent
- Backlink strength
- Brand authority
- Entity clarity
- Third-party mentions
- AI visibility
This produced a more useful diagnosis.
The competitor wasn't necessarily winning because of one isolated SEO factor.
It had a stronger combination of content, authority, relevance, and visibility signals.
3. Evaluate: Prioritize What Actually Matters
Not every marketing issue deserves the same level of attention.
The company grouped opportunities into four priority levels.
Critical
Issues affecting important pages, indexing, or discoverability.
High Impact
Changes likely to improve visibility, traffic, or competitive positioning.
Strategic
Longer-term opportunities involving content, authority, and AI visibility.
Monitor
Issues that did not require immediate action.
This prevented the team from treating every audit finding as equally important.
4. Recommend: Turn Insights Into Specific Actions
This was one of the biggest changes in the workflow.
Instead of producing generic recommendations, the team moved toward recommendations that could be implemented directly.
Before
Improve internal linking.
After
Add contextual links from high-authority related pages to the priority commercial page using descriptive, relevant anchor text.
Before
Create more content.
After
Create a comparison page covering buyer questions currently addressed by competitors but missing from the existing content ecosystem.
Before
Improve AI visibility.
After
Strengthen entity clarity, topical coverage, and supporting authority signals around the category where competitors are receiving AI citations.
The workflow changed from:
Insight
to:
Insight → Cause → Recommendation → Action
The source also references the GEO research showing that techniques such as citations, statistics, and quotations can improve visibility in AI-generated responses.
5. Execute: Connect Recommendations to Owners
A recommendation only creates value when someone can act on it.
Each priority was therefore connected to:
What needs to change → Why it matters → Who should do it → What to measure
For example:
| Opportunity | Recommended Action | Team |
| Technical SEO issue | Fix crawl/indexing issue | Development / SEO |
| Content gap | Create missing topic or comparison page | Content |
| AI visibility gap | Improve entity and topical coverage | SEO / Content |
| Authority gap | Build relevant external references | SEO / PR |
| Internal linking gap | Connect relevant pages | SEO |
| Schema gap | Implement structured data | Development |
This transformed recommendations from items in a report into implementation tasks.
6. Measure Again: Close the Loop
Execution became part of the measurement process rather than the end of it.
After implementation, the company tracked changes across four areas.
SEO Performance
- Keyword rankings
- Organic traffic
- Search visibility
- Technical health
Content Discoverability
- Question coverage
- Direct-answer opportunities
- Structured content
AI Search Visibility
- Entity visibility
- Generative-search presence
- Citation opportunities
- Content authority
Marketing Performance
- Engagement
- Leads
- Conversions
- Campaign performance
This created a continuous feedback loop:
Measure → Understand → Evaluate → Recommend → Execute → Measure Again.
The Marketing Intelligence Workflow
The complete operating model can be summarized as:
Measure
Understand the current state.
↓
Understand
Identify the causes behind performance.
↓
Evaluate
Compare opportunities and competitors.
↓
Recommend
Determine what should change.
↓
Execute
Turn recommendations into actions.
↓
Measure
Track the outcome and repeat.
This is the fundamental shift from marketing reporting to marketing operations.
SEO + DEO: Expanding the Definition of Visibility
Traditional search remained important.
But the company also needed to understand how its brand appeared when buyers searched through AI-powered experiences.
For that reason, the company evaluated digital discoverability across two connected layers:
SEO + DEO
DEO, or Discovery Engine Optimization, was used in the case study to describe optimizing content for discovery, extraction, citation, and representation across AI-powered search experiences.
SEO: The Traditional Search Foundation
The SEO layer evaluated:
- Technical SEO
- Crawlability
- Indexing
- Core Web Vitals
- Page experience
- Internal linking
- Canonical tags
- Schema markup
- Backlinks
- Keyword rankings
The goal was to understand the company's visibility across traditional search engines.
SEO score: [Verify final baseline and post-implementation score]
DEO: AI Discovery Readiness
The DEO layer evaluated whether content was structured and supported in ways that could help AI systems discover, understand, extract, and cite it.
This included:
- Question-based content
- Direct answers
- FAQs
- Structured data
- Entity clarity
- Topical authority
- External trust signals
The goal was not to replace SEO.
It was to extend the company's definition of discoverability to include the environments where buyers increasingly ask questions and receive synthesized answers.
SEO vs. DEO
| SEO | DEO | |
| Primary goal | Traditional search visibility | AI discovery |
| Primary surface | Search engines | AI-powered search experiences |
| Core focus | Rankings, traffic, technical health | Mentions, citations, recommendations and representation |
| Content approach | Search-intent optimization | Direct-answer and retrieval-friendly content |
| Authority | Backlinks and traditional authority signals | Mentions, citations, entity and external trust signals |
| Measurement | Rankings, traffic, CTR and visibility | AI mentions, citations and competitive AI visibility |
The two disciplines are complementary rather than interchangeable.
The Results: From Reporting to Operational Change
The biggest change was operational.
The company didn't simply gain another reporting dashboard.
It changed the way marketing decisions were made.
Before
Collect → Report → Interpret → Discuss → Prioritize → Execute
After
Measure → Diagnose → Prioritize → Recommend → Execute → Measure
The marketing team could move more quickly from identifying an issue to determining what action should happen next.
Results at a Glance
Note: The source contains the following performance figures, but its final optimization status identifies the metrics as requiring verification/replacement before publication. Confirm these against the underlying case-study data before presenting them as customer results.
| Metric | Before | After | Change |
| SEO Score | 54/100 | 71/100 | +31% |
| DEO Score | 38/100 | 62/100 | +63% |
| AI Citations / month | 12 | 47 | +292% |
| AI Mentions / month | 29 | 88 | +203% |
| Organic Visibility | 6,400 sessions/month | 9,850 sessions/month | +54% |
| Recommendation-to-Execution Time | 18 days | 6 days | −67% |
Publication status: Verify all figures, methodology, measurement period, and attribution before publishing.
What Changed for the Marketing Team?
The transformation wasn't limited to metrics.
It changed how different teams worked.
SEO Team
The SEO team spent less time reviewing disconnected issues and more time prioritizing the fixes that could have the greatest impact.
Content Team
The content team moved beyond publishing primarily around keywords and began addressing:
- Buyer questions
- Content gaps
- Competitor opportunities
- Topic coverage
- AI discoverability
Marketing Leadership
Leadership gained a clearer view of which opportunities required immediate attention and which could be addressed later.
Management
The conversation moved beyond:
What happened?
toward:
Why did it happen, what should we change, and what should we do next?
How AI Visibility Changed the Strategy
Traditional search remained a major part of the company's marketing strategy.
But a new question became increasingly important:
What happens when a buyer asks an AI system instead of clicking through a search result?
AI assistants can synthesize information from multiple sources and recommend brands without requiring the user to visit a traditional search results page.
That means visibility can no longer be evaluated only through:
- Rankings
- Clicks
- Organic sessions
Teams also need to understand:
- AI mentions
- AI citations
- AI recommendations
- Competitor visibility
- Brand representation
- Platform-level differences
The source cites industry research suggesting that AI-search behavior can produce substantial differences in traffic attribution and citation patterns. Those external figures should be independently verified before publication.
AI Visibility Is Not One Universal Score
The company also recognized that AI visibility varies between platforms.
A brand can have strong representation in one AI system and considerably weaker visibility in another.
That makes platform-level analysis important.
The team therefore began asking:
- Does ChatGPT mention the brand?
- Does Gemini recommend it?
- Does Perplexity cite it?
- Which competitors appear?
- Which sources are being cited?
- Is the brand represented accurately?
- Which topics produce visibility?
- Where are competitors outperforming the brand?
This made AI-search discoverability part of the broader marketing strategy rather than a separate experiment.
The Role of Adviora
Adviora connects the workflows described in this case study into a unified marketing system.
The platform brings together:
- AI-driven campaign generation
- Landing page creation
- Competitor intelligence
- Keyword intelligence
- Automated media planning
- Analytics dashboards
- Traditional SEO
- DEO / AI-search discoverability
The objective is to help marketing teams move beyond disconnected reporting and toward a continuous operating workflow.
From:
Data → Insight
To:
Data → Insight → Recommendation → Execution → Growth
What a Marketing Intelligence Platform Changes
A marketing intelligence platform is most valuable when it reduces the distance between knowing and doing.
Instead of asking:
What happened?
Teams can ask:
Why did it happen?
Then:
What should we change?
Then:
Who should execute it?
And finally:
Did the change work?
That creates a marketing workflow where intelligence continuously feeds execution — and execution continuously feeds measurement.