Marketing AI Audit Frameworks
Channel-specific diagnostic frameworks for AI-assisted marketing analysis—designed for teams using ChatGPT, Claude, or similar tools without backend integration.
AI has made marketing analysis easier to access.
It has not made marketing systems easier to understand.
Most marketing teams now use AI conversationally—opening ChatGPT or Claude and asking questions about their campaigns, metrics, and performance data.
But here’s the problem: conversational AI without diagnostic structure produces plausible explanations that miss systemic issues.
When you ask AI “Why is our paid media performance declining?” without a structured framework, you get answers that:
- Mirror your existing assumptions
- Optimize for visible metrics while ignoring underlying constraints
- Provide tactical suggestions without diagnosing root causes
- Sound confident even when data quality doesn’t support conclusions
This product exists to solve that problem. It provides the diagnostic infrastructure your team needs to use AI responsibly—finding real constraints, not just symptoms.
What You’re Getting
Marketing AI Audit Frameworks is a collection of five channel-specific diagnostic frameworks designed to work inside the AI tools you already use—no integration, no automation, no technical setup required.
These frameworks work with:
- ChatGPT (Free, Plus, Team, or Enterprise)
- Claude (Free or Pro)
- Any internal LLM interface your company provides
- Any conversational AI tool with copy/paste capability
They work even when:
- Your marketing tech stack is NOT connected to AI
- Your data lives in dashboards, spreadsheets, and disparate tools
- You have limited technical resources
- You need analysis today, not after months of integration work
Important: This is NOT a prompt library. These are structured diagnostic frameworks that force AI to ask the right questions, acknowledge data limitations, and identify constraints—not just generate confident-sounding recommendations.
The 5 Frameworks (What’s Actually Inside)
Each framework follows a forced-start diagnostic process that prevents AI from jumping to conclusions. Here’s exactly what you’re getting:
Core System-Level Marketing Audit
“Where are the systemic constraints preventing marketing performance?”
This is your starting point. It diagnoses system-level issues before diving into channel specifics.
Framework Components:
- Context Questions: Business model, revenue motion, sales cycle, data trust issues
- System Diagnosis: Demand creation vs capture, lead qualification logic, attribution confidence
- Constraint Identification: Identify the 2-3 constraints explaining multiple symptoms
- Diagnostic Routing: Determines which channel framework(s) to use next
Use this when: You need to understand where problems originate before analyzing specific channels
Paid & Performance Media Audit
“Are we scaling performance—or just activity?”
Diagnoses whether paid spend is driving outcomes or merely inflating vanity metrics.
Framework Components:
- Context Questions: Which channels, downstream outcomes, attribution confidence, distrust areas
- Diagnostic Analysis: Spend vs impact, optimization vs lift, attribution inflation
- Signal vs Noise: Separates real performance from platform-reported metrics
- Primary Constraint: The single dominant issue limiting paid media effectiveness
Use this when: Paid spend is increasing but pipeline/revenue impact is unclear or declining
Lifecycle & Email Audit
“Are we nurturing buyers—or managing engagement?”
Exposes the gap between engagement activity and actual buying intent progression.
Framework Components:
- Context Questions: Lifecycle definitions, progression triggers, engagement proxies, stall points
- Diagnosis: Engagement inflation, progression assumptions, signal decay
- Engagement vs Intent: Separates activity metrics from buying signals
- Lifecycle Constraint: Why leads stall, progress incorrectly, or leak from nurture
Use this when: High email engagement but low conversion, or lifecycle stages don’t match sales reality
Content & Organic Audit
“Is content influencing revenue—or just consumption?”
Determines whether content is creating pipeline influence or just generating traffic.
Framework Components:
- Context Questions: Channels in scope, success definitions, assumed influence, misleading metrics
- Diagnosis: Consumption vs contribution, visibility vs influence
- Signal Integrity: Which content signals actually predict outcomes
- Organic Constraint: Why content isn’t converting traffic into pipeline
Use this when: Content metrics look strong but attribution to revenue is weak or anecdotal
ABM & Field Marketing Audit
“Are accounts moving because of us—or coincidentally?”
Distinguishes between marketing influence and natural account progression.
Framework Components:
- Context Questions: Account segments, field motions, influence crediting, anecdote override
- Diagnosis: Influence vs coincidence, attribution confidence, follow-up decay
- Influence Assessment: What evidence supports claimed field marketing impact
- Field Constraint: Why events/ABM aren’t driving measurable account progression
Use this when: Event attendance is high, stories are great, but account movement to sales isn’t clear
How This Actually Works
These frameworks use a “forced-start” methodology that prevents AI from generating premature conclusions.
The 4-Step Diagnostic Process (Built Into Every Framework)
STEP 1: Context Questions (AI Asks First)
The framework forces AI to ask 5-7 context questions before analyzing anything. This ensures:
- AI understands your specific environment (not generic best practices)
- You explicitly state what data is/isn’t trusted
- Assumptions are surfaced before diagnosis begins
AI is forced to ask: “Which paid channels are in scope? What downstream outcome matters most—pipeline, revenue, efficiency, or learning? What’s your attribution confidence level (Low/Medium/High)? Where do you distrust current paid reporting?”
STEP 2: Diagnostic Analysis
AI performs channel-specific analysis looking for:
- Signal vs noise (what metrics actually matter)
- Activity vs outcomes (engagement ≠ results)
- Attribution confidence (what can actually be claimed)
STEP 3: Constraint Identification
Instead of listing 20 “opportunities,” AI identifies the single dominant constraint causing multiple symptoms.
Instead of: “CTR is low, CPC is high, ROAS is declining”
Constraint: “Attribution delay prevents optimization within platform learning windows—you’re optimizing for signals that don’t predict closed revenue.”
STEP 4: Confidence Boundaries
AI explicitly states what it cannot determine from available data. This prevents overconfident recommendations based on incomplete information.
Why this matters: Without structure, AI optimizes for whatever data you show it. With these frameworks, AI diagnoses whether the data itself is reliable and whether your measurement system can answer the questions you’re asking.
Real Scenarios Where This Gets Used
Scenario 1: “Our paid media is driving leads but pipeline isn’t growing”
You ask AI: “Why isn’t our paid media converting to pipeline?”
AI responds with generic advice: “Try different ad copy, optimize for lower-funnel conversions, test new audiences.”
The framework forces AI to first ask about attribution confidence and time horizons. Through structured diagnosis, AI identifies: “Your paid platforms optimize for ‘conversions’ that include demo requests, but your attribution shows 60% of demo requests don’t create opps. The constraint isn’t creative—it’s lead quality definition misalignment between platforms and CRM.”
Outcome: Instead of running more A/B tests, you fix the conversion event definition in your paid platforms.
Scenario 2: “Email open rates are great but nobody is buying”
AI suggests: “Improve CTAs, segment better, add urgency, personalize more.”
Framework forces AI to ask how lifecycle stages are defined and what triggers progression. Through diagnosis, AI identifies: “Your nurture measures engagement, but your buyers are researching for 6-9 months. You’re moving engaged-but-not-ready contacts into ‘sales-ready’ lifecycle stages based on email activity, flooding sales with unqualified meetings.”
Outcome: You separate engagement tracking from buying intent signals and rebuild lifecycle stage logic.
Scenario 3: “Our event had 300 attendees but we can’t prove ROI”
AI responds: “Track more touchpoints, implement event-specific UTM codes, survey attendees post-event.”
Framework asks about account movement timing and influence crediting. Through analysis, AI finds: “Of 300 attendees, 180 were already in active sales cycles. The event created great relationships but didn’t accelerate deal timing. Your constraint isn’t measurement—it’s that events are serving existing pipeline, not creating new.”
Outcome: You redesign event strategy to target accounts NOT yet in pipeline, changing success metrics accordingly.
How This Compares to Alternatives
| Option | Cost | Setup Time | Diagnostic Depth | Best For |
|---|---|---|---|---|
| Hire Consultant | $5,000-$15,000 | 2-4 weeks | Very High | One-time comprehensive audits with implementation support |
| Marketing Analytics Tool | $500-$2,000/mo | 4-8 weeks | Medium | Ongoing monitoring if you have clean data and clear KPIs |
| Free AI Prompts | $0 | Immediate | Low | Quick generic advice without diagnostic structure |
| These Frameworks | $199 once | Immediate | High | Repeated use, structured diagnosis, no integration required |
| DIY Dashboard Building | $0 (+ your time) | 4-12 weeks | Medium | If you have strong SQL/data skills and time to build |
This product sits in the gap between free AI prompts and $5K+ consulting. You get structured diagnostic frameworks that work immediately, can be used repeatedly, and don’t require technical integration—at a fraction of consultant costs.
Professional Diagnostic Frameworks
Compare the cost of professional marketing diagnostics:
Single audit
$6,000/year
One-time • Unlimited use
Founding member pricing — increases to $249 after 50 sales
Is This Right for Your Team?
This Is Perfect For You If:
- You have access to ChatGPT, Claude, or similar AI tools
- Your martech stack is NOT integrated with AI
- You need diagnostic clarity, not more dashboards
- You want to use AI responsibly without over-trusting outputs
- Your team needs consistent analysis frameworks
- You prefer diagnosis over tactical recommendations
- You work in B2B marketing operations, analytics, or leadership
This Is NOT For You If:
- You’re building AI agents or automated workflows
- You need API integrations or backend systems
- You want AI to make decisions without human judgment
- You’re looking for execution checklists or “growth hacks”
- You need help implementing changes (this is diagnosis only)
- You’re in early-stage startup with <10 marketing activities
Common roles who use this: VP Marketing, Director of Marketing Operations, Head of Growth, Marketing Analytics Manager, Revenue Operations, Demand Gen Leaders
Frequently Asked Questions
No. If you can copy/paste text into ChatGPT or Claude, you can use these frameworks. You don’t need API access, coding skills, or data engineering knowledge.
Markdown (.md) files that can be opened in any text editor. You simply copy the framework text and paste it into your AI tool of choice (ChatGPT, Claude, etc.). They work immediately—no software installation required.
Yes. These are frameworks you can use repeatedly—monthly audits, quarterly reviews, or whenever you need diagnostic clarity. They don’t expire and there are no usage limits.
Yes. These frameworks are tool-agnostic. They work whether you use HubSpot, Marketo, Salesforce, Google Analytics, or any other tools—because you’re analyzing your data through conversational AI, not integrating systems.
Primarily diagnosis. The frameworks are designed to identify constraints and surface root causes—not provide generic tactical recommendations. Once you understand the real constraint, the right actions become obvious.
You can start with free tools like ChatGPT (free tier) or Claude (free tier). These frameworks are designed to work with freely available AI tools—you don’t need paid subscriptions (though paid versions often provide better analysis).
Yes, for internal use within your organization. The license allows you to share frameworks with colleagues at your company. You cannot resell, redistribute publicly, or use them for consulting other companies without separate licensing.
15-45 minutes per framework, depending on complexity. The Core System-Level audit takes 30-45 minutes. Channel-specific audits typically take 15-30 minutes each. You’re trading consultant fees ($5K+) for structured self-service diagnosis.
30-day money-back guarantee. If these frameworks don’t provide diagnostic value for your team, email within 30 days for a full refund—no questions asked.
The frameworks include detailed usage instructions within each file. Email support is available for technical questions about using the frameworks. Note: We provide guidance on using the frameworks, not consulting on your specific marketing strategy.
About the Creator
Yuhanna Sherriff
Senior Marketing Analytics Leader • 12+ Years Enterprise Analytics
These frameworks were created by someone who has lived the problem they solve.
For over 15 years, I’ve led marketing analytics strategy for Fortune 500 healthcare and life sciences organizations—building attribution models, optimizing $50M+ marketing budgets, and translating complex data into executive decisions.
I’ve architected marketing analytics frameworks using Snowflake data architectures, built executive dashboards in Power BI tracking full-funnel performance, and developed predictive lead scoring models in HubSpot and Salesforce that increased conversion rates by 30%+.
But here’s what I kept seeing: teams getting access to AI tools without diagnostic structure.
Marketing leaders would ask AI questions about attribution, campaign performance, and lifecycle conversion—and get confident-sounding answers that missed the real constraints. AI would optimize for visible metrics while ignoring underlying system failures. It would recommend tactics without diagnosing whether the measurement infrastructure could even answer the questions being asked.
I built these frameworks because conversational AI needs diagnostic discipline—especially when your Martech stack isn’t integrated with LLMs and your data lives across disconnected systems.
These aren’t theoretical frameworks. They’re the diagnostic protocols I use when analyzing marketing operations for global brands—now structured so any team with ChatGPT or Claude can run the same rigorous analysis.
This product reflects the creator’s independent professional experience and does not represent the views, methodologies, or intellectual property of any employer, past or present.
Technical Expertise:
Expert SQL • Snowflake • Python • Power BI • HubSpot • Salesforce • Marketing Attribution • Predictive Analytics • Multi-Touch Attribution • Campaign Optimization • A/B Testing • Statistical Modeling • AI Engineering
Why These Frameworks Are Different
Most AI Prompts:
- Let AI analyze immediately
- Produce confident-sounding answers
- Assume data quality is fine
- Recommend tactics without constraints
- Don’t acknowledge uncertainty
- Generic across all channels
These Frameworks:
- Force AI to ask context questions first
- Identify constraints, not just symptoms
- Explicitly surface data trust issues
- Separate diagnosis from recommendation
- State confidence boundaries clearly
- Channel-specific diagnostic logic
“The value isn’t automation. The value is disciplined analysis using AI as an interpretive partner—not a replacement for strategic thinking.”
Complete Package Contents
Marketing AI Audit Frameworks
- Framework 1: Core System-Level Marketing Audit Framework
- Framework 2: Paid & Performance Media Audit
- Framework 3: Lifecycle & Email Audit
- Framework 4: Content & Organic Audit
- Framework 5: ABM & Field Marketing Audit
- Delivery: Immediate download as .md (Markdown) files
- Format: Copy/paste ready for ChatGPT, Claude, or any conversational AI
- Usage: Unlimited use, shareable within your organization
- Updates: Lifetime access to framework improvements
- Support: Email support for framework usage questions
- Guarantee: 30-day money-back guarantee
Founding member pricing — increases to $249 after 50 sales
✓ 30-day guarantee • ✓ Secure checkout • ✓ Instant download
Marketing AI Audit Frameworks 