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dbt-audit-cpg-lead-magnet-outline

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dbt Audit Lead Magnet - Notion Implementation Outline

Purpose: Complete outline for “5 Signs Your CPG dbt Project Needs an Audit” lead magnet Target: CPG companies (100M ARR) with existing dbt projects Format: Single Notion page with interactive elements Time to Implement: 15-20 minutes

Notion Page Structure

Page Title

“5 Signs Your CPG dbt Project Needs an Audit”

Page Icon

🔍 (magnifying glass) or 📊 (chart)

SECTION 1: Hero & Lead Capture

Headline (H1)

5 Signs Your CPG dbt Project Needs an Audit

Subheadline (Body text, larger font)

Is your data infrastructure slowing down growth? Take the 2-minute diagnostic to see if you need an audit.

Lead Capture Form

Notion Implementation: Use Notion’s native form block or embed external form (Typeform, Google Forms, etc.)

Form Fields:

  1. Email (required, text input)
  2. Company Name (required, text input)
  3. Company Size (required, dropdown):
    • < $10M revenue
    • 50M revenue
    • 100M revenue
    • $100M+ revenue
  4. dbt Project Size (optional, dropdown):
    • < 50 models
    • 50 - 200 models
    • 200 - 500 models
    • 500+ models

Form CTA Button Text: “Get My Results”

Form Success Message: “Thanks! Scroll down to see your results and the 5 signs.”

SECTION 2: The 5 Signs

Notion Implementation: Use Toggle blocks for each sign (expandable sections)

Sign 1: Your dbt runs are cutting into business hours

Toggle Title: ⏰ Sign #1: Your dbt runs are cutting into business hours

Toggle Content: Your dbt infrastructure runs for 4+ hours. Your team needs data by 6 AM. You’re cutting it close—and if something breaks, you’re in trouble.

CPG-Specific Example:

“Our ingestion starts at midnight, takes 1-2 hours. Then dbt runs for 4 hours. By 5-6 AM, our marketing team is already looking at dashboards. There’s no room for error. No room to add more retail data sources.”

Why This Matters:

  • No buffer for failures or retries
  • Can’t add new data sources (Target POS, Walmart eComm)
  • Can’t increase refresh cadence during promotional spikes
  • Team loses trust when data isn’t ready

Quick Check: Is your dbt runtime > 2 hours? If yes, this is a red flag.

Sign 2: You can’t trust your inventory numbers

Toggle Title: 📦 Sign #2: You can’t trust your inventory numbers

Toggle Content: Your inventory models are inconsistent. Finance says one number, operations says another. You’re making decisions on data you don’t trust.

CPG-Specific Example:

“We have inventory data from Shopify, Amazon FBA, Ware2Go, and retail partners. The numbers never match. We spend hours reconciling discrepancies. Is it a data quality issue? A model logic problem? We don’t know.”

Why This Matters:

  • Inventory decisions affect cash flow and operations
  • Retail data lags (Target/Walmart POS arrives 2-3 weeks late)
  • Settlement reports don’t match order dates (Amazon)
  • Can’t confidently answer “How much inventory do we have?”

Quick Check: Do you spend > 2 hours/week reconciling inventory discrepancies? If yes, this is a red flag.

Sign 3: New team members take weeks to ramp up

Toggle Title: 👥 Sign #3: New team members take weeks to ramp up

Toggle Content: Your analytics engineer joins the team. They’re lost. 600-line models. No documentation. Broken tests that have been failing for years. They can’t contribute for weeks.

CPG-Specific Example:

“Emily joined as our analytics engineer. The infrastructure was a huge mess—600-line files, broken tests, no documentation. She was always wanting to bang her head against the wall. It took her weeks just to understand how revenue was calculated across e-commerce, retail, and wholesale.”

Why This Matters:

  • Slows down team velocity
  • Knowledge transfer is painful
  • New hires can’t debug issues independently
  • Business logic is buried in code, not documented

Quick Check: Do new team members take > 2 weeks to contribute? If yes, this is a red flag.

Sign 4: You’re patching the same issues every quarter

Toggle Title: 🔄 Sign #4: You’re patching the same issues every quarter

Toggle Content: Black Friday breaks your pipelines. Promotional spikes cause failures. You fix it, but it breaks again next quarter. You’re not fixing the root cause—you’re patching symptoms.

CPG-Specific Example:

“Every Black Friday, our dbt runs fail. We patch it. Then product launches cause the same issues. We’re not fixing the architecture—we’re putting bandaids on broken infrastructure. DAG cycles, DRY violations, no incremental strategies.”

Why This Matters:

  • Reactive, not proactive
  • Technical debt compounds
  • Can’t handle scale (promotional spikes, new channels)
  • Architecture doesn’t support growth

Quick Check: Do you fix the same pipeline issues multiple times per year? If yes, this is a red flag.

Sign 5: Your retail data pipelines are breaking

Toggle Title: 🏪 Sign #5: Your retail data pipelines are breaking

Toggle Content: Target POS data arrives late. Walmart eComm data doesn’t match your internal numbers. Retail partner data is inconsistent. You can’t get a unified view of revenue across channels.

CPG-Specific Example:

“We have revenue from Shopify, Amazon, Target POS, Walmart eComm, and wholesale partners. Each has different definitions. Settlement reports don’t match order dates. We can’t answer ‘What’s our total revenue?’ without manual reconciliation.”

Why This Matters:

  • Multi-channel complexity requires clean architecture
  • Revenue definition chaos (e-commerce vs. retail vs. wholesale)
  • Retail data requires special handling (lags, formats)
  • Can’t scale to new retail partners

Quick Check: Do you have > 3 revenue data sources that don’t reconcile? If yes, this is a red flag.

SECTION 3: Interactive Diagnostic

Notion Implementation: Use Checkbox blocks for questions. Use Notion formulas for auto-scoring (see formula section below).

Diagnostic Header

H2: Quick Self-Assessment

Body Text: Check all that apply. Your score will appear below.

Diagnostic Questions

Question 1 (Checkbox): ☐ My dbt runtime is > 2 hours

Question 2 (Checkbox): ☐ I have models with > 500 lines of code

Question 3 (Checkbox): ☐ I have tests that have been failing for > 6 months

Question 4 (Checkbox): ☐ I don’t have documentation on how revenue is calculated

Question 5 (Checkbox): ☐ My retail data pipelines (Target/Walmart) cause issues regularly

Question 6 (Checkbox): ☐ I can’t debug inventory discrepancies quickly (< 30 minutes)

Question 7 (Checkbox): ☐ New team members take > 2 weeks to contribute to dbt projects

Question 8 (Checkbox): ☐ I have DAG cycles in my dbt project

Question 9 (Checkbox): ☐ I have DRY violations (repeated code/logic across models)

Question 10 (Checkbox): ☐ My dashboards aren’t ready by 9 AM on most days

Scoring Section

Notion Implementation: Use Notion formula property or manual calculation

Formula Logic:

  • Count checked boxes
  • 0-3 checked: “Healthy” (green callout)
  • 4-6 checked: “Needs Attention” (yellow callout)
  • 7+ checked: “Critical - Audit Recommended” (red callout)

Scoring Results (Use Callout blocks):

Result: Healthy (0-3 checked)

Callout Type: Info (blue/green)

Content: ✅ Your dbt project is in good shape

You have some areas to improve, but your infrastructure is manageable. Consider:

  • Documenting your revenue models
  • Setting up incremental strategies for long-running models
  • Establishing naming conventions

Next Step: Book a 15-minute call to discuss optimization opportunities.

Result: Needs Attention (4-6 checked)

Callout Type: Warning (yellow)

Content: ⚠️ Your dbt project needs attention

You have several red flags that are slowing down your team and affecting data quality. Consider:

  • Getting an audit to identify root causes
  • Prioritizing fixes by impact
  • Building a roadmap for improvement

Next Step: Get your free dbt audit roadmap. We’ll identify what’s broken and prioritize fixes.

Result: Critical - Audit Recommended (7+ checked)

Callout Type: Error (red)

Content: 🚨 Your dbt project needs an audit

You have multiple critical issues affecting data quality, team velocity, and business decisions. Your infrastructure is likely:

  • Slowing down growth
  • Creating technical debt
  • Reducing team confidence in data

Next Step: Get your free dbt audit roadmap. We’ll audit your entire project and deliver a prioritized fix list in 3-4 weeks.

SECTION 4: CTA

CTA Header

H2: Get Your Free dbt Audit Roadmap

CTA Body

Body Text: Stop debugging 600-line models. Stop wondering if your data is trustworthy. Get a clear roadmap to fix it.

We audit your entire dbt project—models, tests, dependencies, architecture—and deliver a prioritized roadmap in 3-4 weeks.

What you get:

  • Complete infrastructure assessment
  • Prioritized fix list (ranked by impact)
  • Architecture recommendations (DRY, DAG cycles, modularity)
  • Performance analysis (bottlenecks, optimization strategies)
  • Documentation & testing plan
  • Implementation roadmap

Results: 50%+ runtime reduction. Modular code. Working tests. Scale with confidence.

CTA Buttons

Primary CTA (Button/Link): [Get Your Free dbt Audit Roadmap] → Link to landing page

Secondary CTA (Button/Link): [Schedule a 15-minute call] → Link to booking page

Notion Formatting Instructions

Page Setup

  1. Create new Notion page
  2. Set page title: “5 Signs Your CPG dbt Project Needs an Audit”
  3. Add page icon: 🔍 or 📊

Section 1: Hero

  • Use H1 for headline
  • Use large body text for subheadline
  • Add form block (Notion native or embed)
  • Style: Centered or left-aligned

Section 2: The 5 Signs

  • Use Toggle blocks for each sign
  • Toggle title: Use emoji + bold text
  • Toggle content: Use body text, callouts for examples
  • Visual hierarchy: H2 for section header, toggles for each sign

Section 3: Diagnostic

  • Use H2 for “Quick Self-Assessment”
  • Use Checkbox blocks for each question
  • Use Callout blocks for results (Info/Warning/Error)
  • Add divider between questions and results

Section 4: CTA

  • Use H2 for header
  • Use body text for description
  • Use bullet list for “What you get”
  • Use button blocks or linked text for CTAs
  • Style: Prominent, centered or left-aligned

Visual Enhancements

  • Use dividers between major sections
  • Use callout blocks for emphasis (examples, results)
  • Use emojis strategically (not overused)
  • Maintain consistent spacing

Formula for Auto-Scoring (Advanced)

If using Notion database for diagnostic:

  1. Create database with checkbox properties for each question
  2. Add formula property: if(prop("Q1") + prop("Q2") + prop("Q3") + prop("Q4") + prop("Q5") + prop("Q6") + prop("Q7") + prop("Q8") + prop("Q9") + prop("Q10") <= 3, "Healthy", if(prop("Q1") + prop("Q2") + prop("Q3") + prop("Q4") + prop("Q5") + prop("Q6") + prop("Q7") + prop("Q8") + prop("Q9") + prop("Q10") <= 6, "Needs Attention", "Critical"))
  3. Use formula result to show appropriate callout

Simpler Alternative: Manual calculation instructions for users (“Count your checked boxes and see result below”)

Content Notes for Designer

Tone

  • Direct, technical, but approachable
  • CPG-specific examples throughout
  • Outcome-focused (not feature-focused)

Visual Hierarchy

  • Hero section: Most prominent
  • 5 Signs: Expandable, scannable
  • Diagnostic: Interactive, engaging
  • CTA: Clear, action-oriented

CPG-Specific Elements

  • Inventory data quality
  • Retail data pipelines (Target/Walmart)
  • Multi-channel revenue complexity
  • Promotional spike handling
  • Revenue definition chaos

Lead Capture Strategy

  • Form at top (before content) for maximum conversion
  • Form also accessible at bottom (after diagnostic)
  • Capture: Email, Company Name, Company Size, dbt Project Size

Implementation Checklist

  • Create Notion page
  • Add page title and icon
  • Build Section 1: Hero & Lead Capture (form)
  • Build Section 2: The 5 Signs (toggle blocks)
  • Build Section 3: Interactive Diagnostic (checkboxes)
  • Build Section 4: CTA (buttons/links)
  • Add visual enhancements (dividers, callouts, emojis)
  • Test form submission
  • Test diagnostic checkboxes
  • Verify all links work
  • Review CPG-specific examples
  • Final design polish

Quick Reference: CPG-Specific Pain Points

  1. Inventory Data Quality: Multiple sources (Shopify, Amazon FBA, Ware2Go, retail), reconciliation issues
  2. Retail Data Pipelines: Target/Walmart POS data lags, settlement report mismatches
  3. Multi-Channel Attribution: E-commerce, retail, wholesale revenue definitions don’t match
  4. Promotional Spikes: Black Friday, product launches break pipelines
  5. Revenue Definition Chaos: “Sales” means different things to different teams

Last Updated: 2026-01-30 Status: Ready for Designer Implementation Estimated Build Time: 15-20 minutes

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