Funnel Architect

When to Use This Skill

Activate this module when the user's request involves any of the following:

• Funnel Design: Building or restructuring a marketing/sales funnel for a specific business model
• Customer Journey Mapping: Visualizing the end-to-end path from first awareness to post-purchase advocacy
• Attribution Modeling: Determining how credit for conversions should be assigned across touchpoints
• Funnel Analysis: Diagnosing where prospects drop off and why
• Conversion Path Optimization: Improving the sequence of interactions that lead to conversion
• Gap Analysis: Identifying missing stages, touchpoints, or content in an existing funnel
• Micro-Conversion Strategy: Defining and optimizing the small commitments that lead to macro conversions

Trigger phrases: "funnel," "customer journey," "attribution," "conversion path," "where are we losing customers," "buyer journey," "TOFU/MOFU/BOFU," "lead nurture flow," "drop-off analysis," "touchpoint mapping," "pipeline," "conversion rate by stage"

Brand Context (Auto-Applied)

Before producing any marketing output from this module:

1. Check session context — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
2. If you need the full profile, read: ~/.claude-marketing/brands/{slug}/profile.json
3. Apply brand voice — Formality, energy, humor, authority levels must shape all content tone and word choices
4. Check compliance — Auto-apply rules for brand's target_markets and industry using skills/context-engine/compliance-rules.md
5. Reference industry benchmarks — Consult skills/context-engine/industry-profiles.md for the brand's industry
6. Use platform specs — Reference skills/context-engine/platform-specs.md for character limits and format requirements
7. Check campaign history — Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns before planning new work
8. If no brand exists, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
9. Check brand guidelines — If ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json exists, load and enforce: restrictions.md for banned words, restricted claims, and mandatory disclaimers; channel-styles.md for channel-specific tone overrides (may differ from base voice); messaging.md for approved key messages, taglines, and positioning language; voice-and-tone.md for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.

Do not ask the user for information that already exists in their brand profile.

Required Context

Before executing funnel work, gather:

1. Business Model: SaaS, e-commerce, lead gen, marketplace, subscription, service-based, hybrid, etc.
2. Current Funnel State: Does a documented funnel exist? What stages are defined? What tools track it?
3. Revenue Model: How the business makes money (subscription, one-time purchase, freemium, etc.)
4. Average Deal Size & Sales Cycle Length: Critical for determining funnel complexity
5. Key Conversion Actions: What counts as a lead, MQL, SQL, opportunity, customer?
6. Current Metrics: Conversion rates between stages if available
7. Tech Stack: CRM, marketing automation, analytics platforms in use
8. Team Structure: Is there a separate sales team? Is it product-led growth? Who owns each funnel stage?

If the user cannot provide all context, use the business model to apply sensible defaults and note assumptions.

Minimum viable context: Business model and what the company sells. Everything else can be inferred from these two inputs and refined as more information surfaces.

Capabilities

• Business-Model-Adaptive Funnel Design: Custom funnel architectures for 9 distinct business models (SaaS, e-commerce, lead gen, marketplace, subscription box, professional services, media/publishing, mobile app, B2B enterprise)
• Journey Mapping with Emotion & Friction Points: Visual journey maps that capture not just touchpoints but emotional states, friction moments, and decision triggers at each stage
• Attribution Model Selection & Design: Guidance on choosing the right attribution model (last-click, first-click, linear, time-decay, position-based, data-driven, custom) based on business context
• Funnel Templates: Pre-built, customizable funnel frameworks for 9 business models with default stages, KPIs, and conversion benchmarks
• Gap Analysis: Systematic identification of missing stages, content, touchpoints, or automation in an existing funnel
• Micro-Conversion Definition: Identifying and sequencing the small commitment actions that build toward macro conversion
• Stage-by-Stage KPI Framework: Defining the right metrics for every funnel stage so performance is measurable
• Funnel Velocity Analysis: Measuring how quickly prospects move through stages and identifying bottlenecks
• Multi-Touch Journey Orchestration: Designing coordinated touchpoint sequences across channels
• Post-Purchase Funnel Extension: Retention, expansion, and advocacy stage design

Process

Primary Workflow: Funnel Design & Optimization

1. Business Model Classification

   • Identify the business model (or hybrid combination)
   • Determine the revenue model and typical sales cycle
   • Classify as product-led, sales-led, or hybrid growth motion
   • Select the appropriate funnel template as a starting framework

2. Current State Assessment

   • If an existing funnel is documented, map it stage by stage
   • Identify what metrics are currently tracked at each stage
   • Note where tracking breaks down or data goes dark
   • Document all current touchpoints (ads, content, emails, sales calls, product interactions)

3. Journey Mapping

   • Map the full customer journey from unaware to advocate
   • For each stage, document:
     • Touchpoints: What the prospect interacts with
     • Actions: What they do (micro-conversions)
     • Emotions: What they feel (excitement, confusion, hesitation, trust)
     • Friction Points: What slows them down or causes drop-off
     • Decision Triggers: What moves them to the next stage
     • Content Needs: What information they need at this moment
   • Include parallel paths (not all journeys are linear)

4. Gap Analysis

   • Compare current state against the ideal funnel for this business model
   • Identify missing stages or undefined transitions
   • Flag content gaps (stages with no supporting content)
   • Highlight automation gaps (manual handoffs that should be automated)
   • Detect measurement gaps (stages with no KPIs)
   • Spot channel gaps (stages served by only one channel)

5. Funnel Architecture Design

   • Define each stage with clear entry/exit criteria
   • Assign KPIs and conversion benchmarks to every stage
   • Design the micro-conversion sequence
   • Map content to each stage (existing and needed)
   • Specify automation triggers and rules
   • Define handoff protocols between marketing and sales (if applicable)

6. Attribution Model Recommendation

   • Based on funnel complexity, sales cycle length, and available data, recommend an attribution model
   • Explain trade-offs of the recommendation vs. alternatives
   • Provide implementation guidance for their tech stack

7. Optimization Roadmap

   • Prioritize improvements by impact and effort
   • Define A/B testing plan for high-impact stage transitions
   • Set up monitoring cadence for funnel health metrics
   • Create a 30/60/90-day optimization plan

Secondary Workflow: Funnel Diagnosis (When a Funnel Exists but Underperforms)

1. Data Collection

   • Gather conversion rates between every stage for the last 3-6 months
   • Pull time-to-conversion data (how long prospects spend at each stage)
   • Identify volume at each stage to build the full funnel waterfall

2. Drop-Off Analysis

   • Calculate the absolute and relative drop-off at each stage transition
   • Identify the single largest drop-off point (the "leaky bucket")
   • Segment drop-off by traffic source, device, geography, and audience segment
   • Determine if drop-off is a volume problem (not enough enter the stage) or a conversion problem (they enter but don't advance)

3. Root Cause Identification

   • For each high-drop-off transition, investigate:
     • Is the content at this stage compelling enough?
     • Is the CTA clear and the next step obvious?
     • Is there a friction point (long forms, confusing UX, required info the prospect does not have)?
     • Is the timing wrong (asking for too much too early)?
     • Is there a trust gap (insufficient social proof or credibility at this stage)?
   • Cross-reference with qualitative data (customer feedback, sales team input, session recordings) if available

4. Fix Prioritization

   • Score each identified issue on impact (how much conversion improvement is possible) and effort (how hard is the fix)
   • Focus on the highest-impact, lowest-effort fixes first
   • Design specific experiments to test each fix before full rollout

Reference Files

• journey-mapping.md — Customer journey mapping methodology, emotion mapping framework, touchpoint cataloging, and journey visualization templates
• attribution-models.md — Detailed comparison of attribution models, selection criteria decision tree, implementation guides per platform, and custom model design
• funnel-templates.md — Pre-built funnel architectures for 9 business models with default stages, benchmarks, and customization guides
• gap-analysis.md — Gap analysis framework, diagnostic questions, common gap patterns by business model, and prioritization scoring

Output Formats

Edge Cases

Hybrid Business Models

• Situation: Business combines multiple models (e.g., SaaS with a marketplace component, or e-commerce with subscription)
• Approach: Build a primary funnel based on the dominant revenue model, then layer in secondary paths. Identify where the funnels diverge and converge. Create distinct stage definitions for each path but unified attribution. Do not force-fit a single template — hybrid models need hybrid funnels.

Two-Sided Marketplaces

• Situation: Business serves both supply side (sellers, providers) and demand side (buyers, consumers)
• Approach: Design parallel funnels for each side. Map interdependencies (where one side's funnel stage depends on the other side's activity). Identify the "chicken and egg" dynamics and design the funnel to solve for the constrained side first. Track marketplace liquidity metrics alongside conversion metrics.

Offline-to-Online Journeys

• Situation: Significant portion of the journey happens offline (events, retail, phone calls, field sales)
• Approach: Create explicit "dark funnel" stages where tracking is limited. Design bridge mechanisms (QR codes, unique URLs, call tracking numbers, CRM manual entry) to connect offline interactions to the digital funnel. Acknowledge measurement limitations honestly and recommend proxy metrics where direct tracking is impossible.

Very Long B2B Cycles (12+ Months)

• Situation: Enterprise sales with buying committees, RFPs, legal review, and 12-24 month cycles
• Approach: Build a milestone-based funnel rather than a time-based one. Design for buying committee dynamics (champion, economic buyer, technical evaluator, legal). Include "re-engagement" loops for stalled deals. Use account-level scoring rather than individual lead scoring. Attribution must be multi-touch with heavy time-decay weighting. Content needs are deep and stage-specific — exec summaries for C-suite, technical docs for evaluators, ROI models for finance.

Product-Led Growth (PLG) Funnels

• Situation: Product usage IS the primary funnel mechanism — users self-serve through a free tier or trial before converting
• Approach: The funnel is driven by product engagement, not traditional marketing touches. Design around activation milestones (the "aha moments" in the product). Map the progression from signup to activation to engagement to conversion to expansion. Define product-qualified leads (PQLs) based on usage thresholds rather than marketing-qualified leads. Integrate product analytics (Amplitude, Mixpanel, Pendo) as the primary funnel tracking tool. Marketing's role shifts toward driving signups and supporting activation through in-app messaging, onboarding emails, and educational content.

Funnel with No Existing Data

• Situation: New business or new market with no historical funnel data to analyze
• Approach: Build a hypothesis funnel using industry benchmarks and business model templates. Define stage conversion rate assumptions clearly and label them as hypotheses. Design the funnel with measurement baked in from day one so data accumulates quickly. Recommend a 90-day "instrument and learn" phase where the goal is data collection and assumption validation, not optimization. Set minimum traffic/volume thresholds for each stage before drawing conclusions from conversion rates.

Related Skills

• Campaign Orchestrator — For executing the campaigns that drive traffic into the funnel and move prospects through stages
• Audience Intelligence — For understanding who enters the funnel, building personas for each stage, and segmenting by funnel behavior
• Analytics & Insights — For measuring funnel performance, attribution analysis, and anomaly detection in conversion rates
• Content Engine — For creating the stage-specific content mapped to each funnel touchpoint
• AEO/GEO Intelligence — For optimizing the top-of-funnel awareness stage where AI-generated answers drive discovery

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Supporting file: attribution-models.md

Attribution Models — Comparison & Implementation Guide

Model Comparison

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Model Selection Decision Tree

START: How many conversions per month?
├── < 300 → Use Position-Based or Time-Decay
│   ├── Short sales cycle (< 7 days)? → Last-Click or Linear
│   └── Long sales cycle (> 30 days)? → Time-Decay
├── 300-1000 → Consider Data-Driven
│   └── Is your analytics platform capable? → Use Data-Driven
└── > 1000 → Use Data-Driven + MMM for budget planning

BUSINESS MODEL OVERRIDES:
- eCommerce / DTC → Last-Click baseline, upgrade to Data-Driven when possible
- B2B SaaS → Position-Based or Time-Decay (long cycles, many touches)
- Local Business → Last-Click (simple journeys)
- Marketplace → Separate attribution for supply and demand sides

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Platform Implementation Guides

Google Analytics 4 (GA4)

• Default: Data-driven attribution (cross-channel)
• Configurable models: Since 2023, GA4's Admin → Attribution Settings exposes only data-driven and last-click (paid & organic channels) — the old menu of linear / time-decay / position-based / first-click was removed. To apply linear, time-decay, position-based, or any custom credit rule, model it in your warehouse / BI layer (BigQuery export + SQL, or a BI tool) — not in GA4's UI.
• Lookback windows: 30 days for acquisition, 90 days for other conversions
• Reports: Advertising → Attribution → Model comparison (compares the two available models)
• AI Assistant channel: GA4's default channel grouping now includes an "AI Assistant" channel that isolates referral traffic from AI assistants (ChatGPT, Gemini, Copilot, Perplexity, etc.). Include it in channel breakdowns so AI-sourced conversions are not misfiled under "Referral" or "Direct".
• Limitation: Only tracks Google-visible touchpoints, cannot see all walled garden data

Meta (Facebook) Attribution

• Default: 7-day click, 1-day view attribution
• Conversions API (CAPI): Server-side event tracking for better match rates
• Aggregated Event Measurement: For iOS 14+ tracking limitations
• Recommended: Configure CAPI + browser pixel for maximum data coverage
• Compare: Meta's self-reported conversions vs GA4's cross-channel view

Google Ads

• Default: Last-click within Google Ads
• Data-driven: Available in conversion settings when sufficient data
• Cross-campaign: Attribution applies across Search, Display, YouTube, Shopping
• Recommendation: Enable data-driven attribution, set appropriate conversion windows

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Cross-Device Attribution

Challenges

• Same user, different devices appears as multiple users
• Cookie-based tracking breaks across devices
• Privacy regulations limit cross-device linking

Solutions

1. Deterministic matching: Logged-in user IDs across devices (most accurate, requires auth)
2. Probabilistic matching: Statistical models linking device patterns (less accurate)
3. Google Signals: Cross-device data from logged-in Google users in GA4
4. CRM integration: Match conversions to known contacts across touchpoints

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Custom Model Design Framework

When standard models don't fit:

1. Define touchpoint categories: Awareness, Engagement, Conversion Assist, Conversion
2. Assign category weights based on business model:
   • B2B SaaS: Awareness 20%, Engagement 30%, Assist 20%, Conversion 30%
   • eCommerce: Awareness 15%, Engagement 15%, Assist 20%, Conversion 50%
3. Test against actual outcomes: Compare model predictions to observed patterns
4. Iterate quarterly: Adjust weights based on incrementality test results

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Attribution Validation

How to Verify Your Model Is Accurate

1. Holdout tests: Pause a channel, measure true impact vs model-predicted impact
2. Incrementality tests: Geo-split or user-split experiments per channel
3. Cross-model comparison: Run 2-3 models in parallel, compare conclusions
4. Sanity checks: Does the attribution match what you intuitively know about channel performance?
5. Revenue reconciliation: Do attributed conversions sum to actual revenue?

Red Flags

• A single channel claims >80% of conversions (likely measurement bias)
• Model suddenly changes attribution dramatically (check for tracking issues)
• Branded search gets majority credit (it's capturing demand, not creating it)
• Model ignores channels you know drive awareness (review touchpoint capture)

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Supporting file: funnel-templates.md

Funnel Templates — Pre-Built Architectures by Business Model

B2B SaaS (AARRR Funnel)

Lead stages: Visitor → MQL (content engagement) → PQL (product usage threshold) → SQL (sales-ready) → Opportunity → Customer

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eCommerce / DTC

Key lever: Cart abandonment recovery (average 70% abandonment rate)

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B2B Services / Consulting

Key lever: Thought leadership → trust → consultation conversion

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Local Business

Key lever: Google Business Profile optimization + review generation

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Marketplace (Dual-Sided)

Buyer Side

Seller/Supply Side

Key lever: Solving the chicken-and-egg problem (typically start with supply)

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Creator / Personal Brand

Key lever: Consistent content → engagement → trust → monetization

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DTC (Direct-to-Consumer)

Key lever: First-to-second purchase conversion (biggest drop-off point)

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Non-Profit

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Supporting file: gap-analysis.md

Funnel Gap Analysis — Framework & Prioritization

Gap Identification Methodology

Data-Driven Analysis

1. Pull funnel metrics for each stage (volume, conversion rate, time-in-stage)
2. Compare to benchmarks from funnel-templates.md for your business model
3. Calculate drop-off between each stage: Drop-off % = 1 - (Stage N+1 / Stage N)
4. Identify outliers: Any stage with conversion rate >20% below benchmark is a gap
5. Trend analysis: Is any stage getting worse over time? (even if currently above benchmark)

Qualitative Signals

• Customer feedback mentioning friction at specific stages
• Sales team reporting common objections or drop-off points
• Support tickets clustering around specific journey moments
• Session recordings showing user confusion or abandonment
• NPS/CSAT scores that drop at specific touchpoints

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Common Gap Patterns by Business Model

B2B SaaS

eCommerce

B2B Services

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Gap Severity Scoring

ICE Framework (Impact × Confidence × Ease)

Score each identified gap on three dimensions (1-10):

ICE Score = (Impact + Confidence + Ease) / 3

Priority Tiers

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Gap-to-Action Mapping

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Measurement Framework

Before/After Tracking

For each gap fix, document:

1. Baseline metric: Stage conversion rate before the fix (2-4 weeks of data)
2. Fix description: Exactly what was changed
3. Implementation date: When the fix went live
4. Post-fix metric: Stage conversion rate after the fix (2-4 weeks of data)
5. Statistical significance: Was the change significant or within normal variance?
6. Revenue impact: Estimated revenue change from the conversion rate improvement

Calculating Revenue Impact of a Gap Fix

Current monthly revenue: $X
Stage conversion rate (before): A%
Stage conversion rate (after): B%
Improvement multiplier: B/A

Estimated revenue uplift = $X × (B/A - 1) × [weight of that stage in overall funnel]

Ongoing Funnel Health Monitoring

• Weekly: Check conversion rates at each stage, flag anomalies
• Monthly: Full funnel analysis, compare to benchmarks, update gap priorities
• Quarterly: Strategic funnel review, reassess architecture, plan next optimization cycle

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Supporting file: journey-mapping.md

Customer Journey Mapping — Methodology & Templates

Overview

A systematic approach to mapping every touchpoint, emotion, and decision point in the customer journey from first awareness to post-purchase advocacy.

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Journey Mapping Canvas

Stage Structure

Per-Stage Documentation

For each stage, document:

1. Touchpoints: Every interaction (ad seen, email received, page visited, call made)
2. Channels: Where the interaction happens (Google, social, email, in-person, in-product)
3. Actions: What the customer does (searches, clicks, downloads, talks to sales)
4. Emotions: How they feel (frustrated, curious, excited, anxious, confident)
5. Pain Points: What friction or confusion they experience
6. Questions: What they need to know at this stage
7. Content Needs: What content supports their decision at this stage
8. KPIs: How we measure success at this stage

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Emotion Mapping Framework

Emotion Scoring (-5 to +5)

Map emotion scores across the full journey to identify:

• Peaks: Moments of delight to reinforce and amplify
• Valleys: Moments of frustration to fix or mitigate
• Transitions: Stage boundaries where emotion shifts most dramatically

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Touchpoint Catalog

Digital Touchpoints

• Search results (organic + paid)
• Social media posts/ads
• Website pages (homepage, product, pricing, blog)
• Email (marketing, transactional, support)
• Chat (live chat, chatbot)
• Reviews and ratings sites
• Webinars/events
• In-app experience
• Push notifications

Human Touchpoints

• Sales calls/demos
• Customer support interactions
• Account management
• Onboarding sessions
• Community interactions
• In-person events

Brand Touchpoints

• PR/media coverage
• Word-of-mouth/referrals
• Influencer content
• Partner/co-marketing
• Packaging/unboxing (physical products)

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Moment of Truth Identification

Types of Moments of Truth

Identifying YOUR Key Moments

Ask: "If we could only optimize 3 touchpoints, which would move the most customers forward?"

Score each touchpoint: Influence on conversion × Current performance gap = Priority

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B2B vs B2C Journey Differences

B2B Buying Committee Personas

Map separate journey lanes for:

• Champion: Internal advocate, researches solutions
• Decision Maker: Budget authority, needs ROI justification
• Influencer: Technical evaluator, needs proof of capability
• End User: Daily user, needs ease-of-use evidence
• Blocker: Risk-averse stakeholder, needs security/compliance proof

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Journey Visualization

Recommended Format

Stage:     [Awareness] → [Consideration] → [Decision] → [Purchase] → [Onboarding] → [Retention]
Touchpts:  [Ad, Blog]    [Demo, Email]     [Proposal]   [Checkout]   [Setup, Call]   [Product]
Actions:   [Search]      [Compare]         [Evaluate]   [Buy]        [Configure]     [Use]
Emotions:  [Curious +2]  [Hopeful +3]      [Anxious -1] [Excited +4] [Confused -2]   [Satisfied +3]
Pain Pts:  [Too many     [Hard to          [Pricing     [Form too    [Setup too       [Feature
            options]      compare]          unclear]      long]        complex]        missing]

Visualization Best Practices

1. Use a single horizontal timeline (left to right)
2. Color-code emotion (green for positive, red for negative)
3. Mark critical moments of truth with callouts
4. Include actual customer quotes where available
5. Keep it to one page for executive consumption
6. Create a detailed version and a summary version

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Supporting file: sales-marketing-alignment.md

Sales-Marketing Alignment — SMarketing Framework & Operations

A comprehensive reference for building operational alignment between sales and marketing. Covers shared funnel definitions, service level agreements, lead handoff processes, feedback loops, RevOps implementation, and ready-to-use templates.

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Shared Funnel Definitions

Every misalignment starts with marketing and sales defining the same terms differently. Lock these definitions before anything else.

Funnel Stage Definitions

Transition Triggers

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Service Level Agreements (SLAs)

Marketing SLA to Sales

Marketing commits to delivering a specified volume and quality of leads each period.

Sales SLA to Marketing

Sales commits to working every qualified lead with speed and consistency, and providing structured feedback.

SLA Calculation: From Revenue Target to MQL Target

Work backward from the revenue goal to determine how many MQLs marketing must deliver.

REVENUE TARGET:           $1,000,000 / quarter
AVERAGE DEAL SIZE:        $25,000
DEALS NEEDED:             40
WIN RATE:                 25%
OPPORTUNITIES NEEDED:     160
SQL-TO-OPPORTUNITY RATE:  50%
SQLs NEEDED:              320
MQL-TO-SQL RATE:          60%
MQLs NEEDED:              534 per quarter (178/month)

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Lead Handoff Process

MQL Scoring Criteria

MQL threshold: 50 points (adjust based on your MQL-to-SQL acceptance rate; target 60%+ acceptance)

Routing Rules

Speed-to-Lead Benchmarks

The data is unambiguous: leads contacted within 5 minutes are 9x more likely to convert than leads contacted after 30 minutes.

Re-Queue Conditions

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Feedback Loops

Sales-to-Marketing Content Requests

Closed-Loop Reporting

For every closed deal (won or lost), track which marketing touchpoints influenced the journey:

DEAL: [Company Name]
OUTCOME: Won / Lost
DEAL SIZE: $[X]
SALES CYCLE: [X days]
LEAD SOURCE: [First touch attribution]
MARKETING TOUCHPOINTS:
  - [Date] [Touchpoint 1: e.g., Downloaded whitepaper]
  - [Date] [Touchpoint 2: e.g., Attended webinar]
  - [Date] [Touchpoint 3: e.g., Clicked email CTA]
SALES TOUCHPOINTS:
  - [Date] [Activity 1: e.g., SDR cold call]
  - [Date] [Activity 2: e.g., AE demo]
DISQUALIFICATION REASON (if lost): [Reason code + notes]
COMPETITOR (if lost): [Who they chose]
WINNING FACTOR (if won): [What tipped the decision]

Disqualification Reason Codes

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Shared Metrics Dashboard

Primary Alignment Metrics

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Meeting Cadence

Daily: Pipeline Standup (15 minutes)

• Who: SDR team lead + marketing campaign manager
• Agenda: Yesterday's MQLs delivered, today's follow-up priorities, any lead quality flags
• Output: Real-time routing adjustments, immediate feedback on campaign quality

Weekly: Campaign Performance Review (30 minutes)

• Who: Marketing ops + sales ops + 1 AE representative
• Agenda: MQL volume and quality this week, campaign performance by source, lead disposition rates, content requests
• Output: Weekly scorecard, content request queue updates

Monthly: Funnel Health Review (60 minutes)

• Who: VP Marketing + VP Sales + RevOps
• Agenda: Full-funnel conversion rates, SLA compliance, pipeline coverage ratio, attribution analysis, competitive intelligence debrief
• Output: Monthly alignment report, SLA adjustments, resource allocation decisions

Quarterly: Strategic Planning (Half-day)

• Who: CMO + CRO + RevOps + team leads
• Agenda: Revenue target review, ICP refinement, scoring model recalibration, campaign planning, technology stack evaluation, SLA renegotiation
• Output: Updated SLAs, next-quarter campaign calendar, ICP documentation, scoring model changes

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Common Misalignment Patterns

Diagnostic Framework

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Revenue Operations (RevOps)

What RevOps Is

RevOps is a centralized function that owns the processes, technology, data, and reporting across marketing, sales, and customer success. It eliminates the operational silos that cause misalignment.

Key RevOps Responsibilities

RevOps Implementation Phases

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Templates

SLA Document Structure

SALES-MARKETING SERVICE LEVEL AGREEMENT
Effective Date: [Date]
Review Cadence: Quarterly

REVENUE TARGETS:
- Quarterly revenue goal: $[X]
- Required pipeline coverage: [X]x (e.g., 3x)

MARKETING COMMITMENTS:
- MQL volume: [N] per month
- MQL quality: [X]% acceptance rate target
- Data completeness: [Standards]
- Routing speed: [Timeframe]
- Content delivery: [Cadence and response times]

SALES COMMITMENTS:
- Speed-to-lead: [Timeframe]
- Follow-up cadence: [Minimum touches]
- Disposition deadline: [Timeframe]
- CRM hygiene: [Standards]
- Feedback delivery: [Cadence]

ESCALATION PROCESS:
- SLA breach identified by: [RevOps / automated alert]
- First escalation: [Team lead, within 24 hours]
- Second escalation: [VP level, within 48 hours]

REVIEW AND AMENDMENT:
- Monthly SLA compliance review in funnel health meeting
- Quarterly renegotiation in strategic planning session
- Either party can request emergency review with 48-hour notice

SIGNATURES:
VP Marketing: _____________ Date: _______
VP Sales: _____________ Date: _______

Pipeline Review Agenda

WEEKLY PIPELINE REVIEW — [Date]

1. FUNNEL METRICS (5 min)
   - MQLs delivered this week: [N] (target: [N])
   - MQL acceptance rate: [X%] (target: 60%+)
   - Speed-to-lead median: [X hours] (target: < 4 hours)

2. DEAL INSPECTION (15 min)
   - Deals closing this month: [Review top 10 by value]
   - Stalled deals (no activity in 14+ days): [Review and assign actions]
   - Deals at risk: [Identify and discuss]

3. CAMPAIGN IMPACT (5 min)
   - Top performing campaign this week: [Campaign] — [N] MQLs at [X%] acceptance
   - Underperforming campaign: [Campaign] — [Issue and recommended action]

4. FEEDBACK LOOP (5 min)
   - Content requests from sales: [List]
   - Lead quality flags: [Specific issues]
   - Competitive intelligence: [What sales is hearing]

ACTION ITEMS:
- [Owner] [Action] [Due date]

Monthly Marketing-Sales Report

MONTHLY ALIGNMENT REPORT — [Month Year]

EXECUTIVE SUMMARY:
[2-3 sentences on overall alignment health]

MARKETING SLA COMPLIANCE:
| Metric              | Target  | Actual  | Status |
|---------------------|---------|---------|--------|
| MQL volume          | [N]     | [N]     | [Met/Missed] |
| MQL acceptance rate | 60%     | [X%]    | [Met/Missed] |
| Data completeness   | 80%     | [X%]    | [Met/Missed] |
| Content delivery    | On-time | [X/Y]   | [Met/Missed] |

SALES SLA COMPLIANCE:
| Metric              | Target     | Actual     | Status |
|---------------------|------------|------------|--------|
| Speed-to-lead       | < 4 hours  | [X hours]  | [Met/Missed] |
| Follow-up cadence   | 6 touches  | [X avg]    | [Met/Missed] |
| Disposition rate     | 100% in 5d | [X%]       | [Met/Missed] |
| CRM hygiene         | Weekly     | [X% compliant] | [Met/Missed] |

FUNNEL PERFORMANCE:
| Stage               | Volume | Conversion Rate | vs. Prior Month |
|---------------------|--------|-----------------|-----------------|
| Leads               | [N]    | —               | [+/- X%]        |
| MQLs                | [N]    | [X%]            | [+/- X%]        |
| SQLs                | [N]    | [X%]            | [+/- X%]        |
| Opportunities       | [N]    | [X%]            | [+/- X%]        |
| Closed-Won          | [N]    | [X%]            | [+/- X%]        |

REVENUE ATTRIBUTION:
- Marketing-sourced: $[X] ([X%] of total)
- Marketing-influenced: $[X] ([X%] of total)
- Sales-sourced: $[X] ([X%] of total)

KEY INSIGHTS:
1. [Insight with supporting data]
2. [Insight with supporting data]
3. [Insight with supporting data]

NEXT MONTH PRIORITIES:
1. [Priority + owner]
2. [Priority + owner]
3. [Priority + owner]

---

Supporting file: skills/context-engine/compliance-rules.md

Compliance Rules Reference

This file is the canonical compliance ruleset for the Digital Marketing Pro plugin. All marketing modules MUST check outputs against these rules before delivery. Rules are structured for programmatic consumption by the context engine.

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Section 1: Geographic Privacy Laws

1.1 EU/EEA — General Data Protection Regulation (GDPR)

1.1b EU/EEA — AI Act Article 50 (Generative AI Disclosure)

1.1b.i — Article 50 draft implementing guidelines (May 2026)

The European Commission published draft implementing guidelines for Article 50 on 8 May 2026. The draft is open for public consultation until 3 June 2026; final guidelines are expected July 2026, ahead of the 2 August 2026 enforcement date. The draft clarifies the following points that brands should treat as the working interpretation today:

Action items for brands with EU exposure before 2 August 2026:

1. Audit your EU AI-asset inventory now. Catalogue any AI-generated image, video, audio, deepfake-style synthetic content, and AI-written long-form copy distributed to EU users in the last 12 months. Identify which carry C2PA and which don't.
2. File a consultation comment by 3 June 2026 if Article 50 will materially affect your operations. The Commission has explicitly asked for industry input on the "substantial manipulation" boundary and the carve-out scope. Brands in regulated sectors (health, finance, automotive, political adjacencies) should weigh in.
3. Lock in your C2PA signing-cert procurement. See docs/c2pa-production-cert-guide.md for the four recognised authorities. Allow 2–4 weeks for Adobe Content Credentials approval — start no later than 1 July 2026.
4. Update your Definition of Done. Any creative produced after 1 July 2026 for EU distribution should be C2PA-signed at production time, not retrofitted at publish time. The pre-publish gate (/digital-marketing-pro:check) blocks unsigned AI assets for EU-targeted campaigns.
5. Treat the carve-out as conditional, not a free pass. "Human-reviewed" requires named accountability. Don't claim editorial responsibility unless a named editor is willing to be on the record.

1.2 United States Federal — CAN-SPAM Act

1.3 California — CCPA / CPRA

1.4 US State Privacy Laws (Multi-State Summary)

1.5 Canada — CASL

1.6 Brazil — LGPD

1.7 United Kingdom — UK GDPR + PECR

1.8 Australia — Privacy Act + Spam Act

1.9 Singapore — PDPA

1.10 China — PIPL

1.11 India — DPDPA

1.12 Japan — APPI

1.13 South Korea — PIPA

1.14 Saudi Arabia — PDPL

1.15 UAE — Federal Decree-Law No. 45

1.16 Thailand — PDPA

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Section 2: Industry-Specific Regulations

2.1 Healthcare — HIPAA / FDA

2.2 Finance — SEC / FINRA

2.3 Legal — Bar Association Rules

2.4 Alcohol — TTB

2.5 Cannabis — State Laws

2.6 Real Estate — Fair Housing Act

2.7 Education — FERPA

2.8 Children's Products — COPPA

2.9 Supplements — FDA / FTC

2.10 Tech / SaaS — SOC 2 / GDPR Processor Rules

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Section 3: FTC Advertising Rules

3.1 FTC Endorsement Guides (Revised 2023)

3.2 FTC Consumer Review Fairness and Rule on Fake Reviews (2024)

3.3 Influencer Disclosure Requirements

3.4 AI-Generated Content Disclosure

3.5 Testimonial Rules

3.6 FTC Penalty Structure

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Section 4: Platform-Specific Ad Policies

4.1 Google Ads

4.2 Meta Ads (Facebook / Instagram)

4.3 LinkedIn Ads

4.4 TikTok Ads

4.5 Amazon Ads

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Section 5: Accessibility Requirements

5.1 WCAG 2.2 AA — Marketing Content Requirements

5.2 ADA Website Compliance

5.3 Email Accessibility Standards

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Rule Application Reference

When the context engine evaluates marketing content, apply rules in this priority order:

1. Geographic law — Identify target audience jurisdiction(s) and apply ALL applicable privacy/consent rules
2. Industry regulation — Identify the advertiser's industry and apply sector-specific restrictions
3. FTC advertising rules — Apply to all US-targeted content regardless of industry
4. Platform policies — Apply the specific platform's rules for the distribution channel
5. Accessibility — Apply WCAG 2.2 AA and email accessibility standards to all outputs

When rules conflict, apply the most restrictive standard. When jurisdiction is unknown, default to GDPR + CPRA + FTC as the baseline.

Severity Levels

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Supporting file: skills/context-engine/platform-specs.md

Platform Specifications Reference

Last updated: 2026-02-11 This file is consumed by an AI agent to validate marketing content against platform requirements. All specs reflect current 2026 platform standards.

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Section 1: Social Media Platform Specs

Instagram

Feed Posts

Instagram Reels

Instagram Stories

Instagram Carousel

---

TikTok

---

LinkedIn

LinkedIn Post

LinkedIn Article

LinkedIn Newsletter

---

Twitter/X

---

Facebook

Facebook Post

Facebook Reels

Facebook Stories

---

Pinterest

Standard Pin

Idea Pin

Video Pin

---

YouTube

YouTube Video

YouTube Shorts

YouTube Live

---

Reddit

---

Threads

---

Snapchat

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Section 2: Email Specifications

Subject Line Limits by Client

Recommendation: Keep subject lines under 40 characters for reliable cross-client mobile rendering. Front-load the most important words.

Preview Text (Preheader)

Recommendation: Write 40-90 characters of preview text. If not explicitly set, clients pull the first visible body text. Use hidden preheader text in HTML to control this.

Email Body Specs

Image Handling

CTA Button Specs

Dark Mode Considerations

Dark mode design rules:

• Add transparent padding around logos (prevents awkward color clashes)
• Use semi-transparent PNGs with contrasting edges
• Test with both dark and light backgrounds
• Define both light and dark color schemes in CSS where supported
• Avoid pure white (#FFFFFF) backgrounds; use #FAFAFA so dark mode detection triggers consistently

Mobile Rendering Rules

Apple Mail Privacy Protection (MPP)

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Section 3: Ad Platform Creative Specs

Google Ads

Responsive Search Ads (RSA)

Google Display Ads

Performance Max (PMax)

YouTube Ads

Google Shopping

---

Meta Ads (Facebook & Instagram)

Feed Ads

Stories & Reels Ads

Carousel Ads

Collection Ads

---

LinkedIn Ads

Sponsored Content (Single Image)

Sponsored Content (Video)

Sponsored Messaging (Message Ads)

Document Ads

---

TikTok Ads

In-Feed Ads

TopView Ads

Spark Ads

---

Pinterest Ads

---

Amazon Ads

Sponsored Products

Sponsored Brands

Sponsored Display

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Section 4: Schema Markup Reference

Use schema.org structured data (JSON-LD format recommended) to enhance search appearance and enable rich results.

Schema Implementation Notes

• Format: Always use JSON-LD (recommended by Google) over Microdata or RDFa.
• Placement: Insert <script type="application/ld+json"> in the <head> or <body> of the page.
• Validation: Test with Google Rich Results Test (https://search.google.com/test/rich-results) and Schema Markup Validator (https://validator.schema.org).
• Nesting: Nest related schemas (e.g., Product containing AggregateRating and Offers).
• Avoid: Do not mark up content that is not visible on the page. Do not use schema for deceptive purposes. Google may issue manual actions for misleading structured data.

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Section 5: Image Format Guide

Format Comparison

Browser Support (as of 2026)

When to Use Each Format

Implementation Best Practice

Use the <picture> element with format fallback:

<picture>
  <source srcset="image.avif" type="image/avif">
  <source srcset="image.webp" type="image/webp">
  <img src="image.jpg" alt="Description" width="800" height="600" loading="lazy">
</picture>

Key rules:

• Always include width and height attributes to prevent Cumulative Layout Shift (CLS)
• Use loading="lazy" for below-the-fold images
• Use loading="eager" or fetchpriority="high" for LCP (Largest Contentful Paint) images
• Serve responsive sizes with srcset and sizes attributes for resolution switching
• Compress JPEG at quality 75-85 for web, WebP at quality 75-80, AVIF at quality 60-70
• Always strip EXIF metadata for web delivery (privacy and file size)