The Grand AI Handbook
The Grand AI Handbook
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15-Minute Internal Demo: Marketing Insight Agent

15-Minute Internal Demo: Marketing Insight Agent

Product Walkthrough for Non-Technical Stakeholders

🎯 DEMO PURPOSE

Audience: Internal team (non-technical: business stakeholders, managers, product owners, leadership)

Goal: Show what the platform does, how it works from a user perspective, and what business problems it solves

Tone: Honest, collaborative, focused on capabilities and impact (not technical implementation)

⏱️ DEMO STRUCTURE (15 Minutes)

Segment 1: What Problem Are We Solving? (2 minutes)

Opening Context:

“The platform is to help businesses identify which customers are about to leave and automatically creates personalized campaigns to keep them.”

The Business Problem:

  1. Customer Churn is Expensive
    • Acquiring new customers costs 5-7x more than retaining existing ones
    • Companies lose 20-30% of customers annually
    • By the time you notice churn, it’s too late
  2. Manual Marketing is Slow
    • Creating personalized campaigns takes hours per customer
    • Segmentation is often based on gut feeling, not data
    • Generic “spray and pray” campaigns waste budget
  3. Data Sits Unused
    • Companies have tons of customer data
    • But no way to turn it into actionable insights
    • Reports show what happened, not what will happen

Our Solution: This platform automatically: ✅ Predicts which customers will churn (before they do) ✅ Calculates each customer’s future value ✅ Creates intelligent customer segments ✅ Generates personalized campaigns with AI ✅ Sends automated emails

Key Message:

“So it’s a complete action platform. From prediction to execution, all in one place.”

3B. Dashboard - Business Intelligence (2 minutes)

Navigate to Dashboard Tab

What You See First:

  • Top KPI Cards (4 metrics):
    • Total Revenue - All order amounts in selected period
    • Active Customers - Unique customers who ordered
    • Total Orders - Number of orders placed
    • Average Order Value - Revenue divided by orders

“These give you an instant snapshot of your business health.”

Interactive Charts Below:

Basic Analytics Section:

  1. Orders per Month (line chart, full width)
    • Monthly order volume trends
    • Each line represents a different year
    • Identify seasonal patterns and year-over-year growth
  2. Order Status (bar chart, left side)
    • Distribution of order statuses
    • Completed vs Cancelled breakdown
    • Spot fulfillment issues and cancellation rates
  3. Category Mix (pie chart, right side)
    • Sales distribution by product category
    • See which categories drive the most revenue
    • Optimize inventory based on top performers
  4. Top Categories (bar chart, left side)
    • Top 10 product categories ranked by revenue
    • Focus marketing efforts here
    • Identify your cash cows
  5. AOV Trend (line chart, right side)
    • Average order value over time
    • Track effectiveness of upselling campaigns
    • Monitor pricing strategy impact

Advanced Analytics Section:

  1. RFM Distribution (parallel coordinates, full width)
    • Recency, Frequency, Monetary analysis
    • Each line represents a customer segment
    • See behavior patterns across all three dimensions
  2. Segment Distribution (pie chart, left side)
    • Customer count by segment
    • VIPs, Steady Buyers, Onboarders, Churn Risks, Dormant
    • See where your customers cluster
  3. Revenue by Segment (bar chart, right side)
    • Total revenue generated by each segment
    • VIPs contribute disproportionate revenue
    • Prioritize retention for high-value segments
  4. Top SKU Momentum (8 mini-charts, full width)
    • Trends for your top 8 products by revenue
    • Each product gets its own trend chart
    • Identify rising stars and declining products
  5. Top Selling Stores (bar chart, left side)
    • Top 10 stores ranked by revenue
    • Identify highest-performing locations
    • Understand regional sales patterns
  6. Customer Repeat Rate (donut chart, right side)
    • Repeat customers vs one-time buyers
    • Higher repeat rates = better loyalty
    • Track customer retention health

The Interactive Part (DEMONSTRATE THIS):

  • Click the date filter dropdown at the top
  • Change from “Last 30 Days” to “Last 90 Days”
  • Watch ALL 11 charts update instantly
  • Change to “Last 7 Days” - watch it update again

“This is the power of real-time analytics. No waiting for reports. Ask a question, get an answer immediately. All 11 charts respond to your filters in seconds.”

Use Case Example:

“If your CFO asks ‘What’s our revenue trend this quarter?’, you change the filter to ‘This Quarter’ and show them instantly. If they ask ‘Which stores are performing best?’, you point to the Top Selling Stores chart. If they want to know customer loyalty, you show the Repeat Rate. All in one dashboard. No Excel exports, no waiting for analysts.”

3C. Customer Intelligence - The Core Feature (4-5 minutes)

Navigate to Customer Intelligence Tab

IMPORTANT: There are TWO views in this section:

VIEW 1: Segment Analysis (30 seconds - Quick Overview)

What You See First:

  • Dropdown to filter by segment (All Customers, VIPs, Steady Buyers, Onboarders, Churn Risks, Dormant)
  • 6 analytical charts showing segment-level insights:
    1. CLV Distribution - “Who are your most valuable customers?”
    2. Value Contribution by Segment - “Which segments drive the most revenue?”
    3. Churn Risk Distribution - “How many high-risk customers in each segment?”
    4. Retention Curve - “How well do we retain customers over time?”
    5. Segment Growth - “Are our segments growing or shrinking?”
    6. Segment Mix Trends - “How is our customer mix evolving?”
  • At-Risk Customers table at the bottom

What to Say (while scrolling quickly through charts):

“This view gives us segment-level analytics - CLV distribution, value contribution, churn risk breakdown, retention curves, segment growth, and mix trends. These show how each customer group performs. But the real thing is when we drill into individual customers…”

ACTION: Scroll down to At-Risk Customers table

VIEW 2: Customer Drilldown (4 minutes - MAIN FOCUS)

What You See:

  • At-Risk Customers table showing high-value customers with high churn risk
  • Each row shows: Customer ID, Churn Risk %, CLV value, Segment

DEMONSTRATE: Pick High-Risk Customer

  1. Select “Churn Risks” from segment dropdown (if not already selected)
    • Table shows only at-risk customers
    • “These are critical - high value + high risk of leaving”
  2. Click on a Customer (e.g., customer with 75-90% churn risk and $2,000+ CLV)
    • Customer Drilldown View Opens

Customer Drilldown Screen - What You See:

Section A: Customer Profile Cards (4 KPIs at top)

  • Segment - Customer category (Churn Risk, VIP, etc.)
  • Churn Risk - 85% probability (red indicator)
  • CLV (90D) - $2,340 predicted value
  • Total Orders - Lifetime order count

“This is a $2,340 customer with 85% chance of leaving. Would you ignore that?”

Section B: AI Customer Analysis

Click: “Generate AI Analysis” button

While Loading (30-45 seconds):

“Our AI is analyzing this customer’s complete history - all purchases, timing patterns, product preferences, spending changes. It’s comparing to thousands of other customers to understand WHY they’re at risk and WHAT to do.”

When Complete - Shows:

  1. Risk Assessment - High/Medium/Low with color indicator
  2. Key Insights (numbered bullets):
    • ⚠️ “Order frequency decreased 40% in last 60 days”
    • ⚠️ “Average order value dropped from $280 to $150”
    • ⚠️ “Increased discount usage - showing price sensitivity”
    • ⚠️ “Last purchase 45 days ago (usually buys every 21 days)”
  3. Recommended Action:

    “Launch immediate retention campaign with personalized discount on preferred categories”

Value Prop:

“This analysis would take a marketing analyst 2-3 hours. The AI just did it in 45 seconds.”

Section C: Detailed Marketing Campaign (COVERED IN NEXT SECTION 3D)

Section D: Customer Summary

Two-Column Layout:

LEFT: Customer Information (JSON)

  • Complete customer data in structured format
  • Demographics, predictions, behavioral features

RIGHT: Order & Spending Timeline Chart

  • Monthly order count (bars)
  • Cumulative spending (line)
  • Visualizes purchasing patterns over time
  • Shows frequency changes and spending trends

Section E: Order History Table

  • Complete table of all orders
  • Columns: Order ID, Date, Amount, Status
  • Sorted by most recent
  • Scrollable list

Section F: Product Preferences & Purchase Mix

Product Purchase Mix Chart (bar chart)

  • Shows top 12 products customer bought
  • Bars sized by purchase frequency
  • Grouped by category
  • Use for: Product preferences, cross-sell opportunities, tailored recommendations

THE KEY INSIGHT:

“Look at this complete 360° customer view. Sarah Chen is worth $2,340 in the next 90 days, but there’s an 85% chance she’s about to stop buying from us. We can see her complete order history, what products she prefers, and her spending timeline. Traditional analytics wouldn’t catch this until she’s already gone. We’re seeing it NOW, while we can still do something about it.”

3D. AI Campaign Generation (2 minutes)

Still on Customer Profile - Scroll Down:

Find: “Detailed Marketing Campaign” Section

Click: “Generate Marketing Campaign” button

What Happens (30-60 seconds):

  • Enhanced loading screen
  • Progress indicators:
    • “Analyzing customer profile…”
    • “Generating multi-channel strategy…”
    • “Creating messaging templates…”
    • “Building implementation timeline…”
  • Auto-refresh notice: “Page will update when ready”

While Loading, Explain:

“Now the AI is creating a complete marketing campaign for Sarah. This includes email templates, social media copy, SMS messages, and a week-by-week implementation plan. Normally, a marketing manager would spend 2-3 hours creating this for one customer. The AI does it in under a minute.”

When Complete - Show the Results:

Campaign Ready Card Shows:

Status: ✅ Ready to Deploy

What’s Included:

  1. Multi-Channel Strategy - Email, Social Media, Direct channels
  2. Personalized Messaging - Templates tailored to Sarah’s behavior
  3. Implementation Timeline - When to send what
  4. Success Metrics - How to measure if it worked

Click: “View Recommendation” button

Opens: Complete Campaign Document (HTML) in New Tab

Walk Through the Document (30-45 seconds):

Point Out Key Sections:

  1. Executive Summary
    • Quick overview of the customer
    • Why they’re at risk
    • Campaign objective
  2. Email Campaign Template
    • Subject line: “We miss you, Sarah! 20% off your favorites”
    • Body with personalization:
      • “Hi Sarah,”
      • “We noticed you haven’t shopped in a while…”
      • “Here’s 20% off Electronics and Home & Garden”
      • Specific product recommendations
    • Call-to-action button
  3. Social Media Strategy
    • Facebook ad copy
    • Instagram post ideas
    • Targeted messaging
  4. SMS Campaign
    • Short text message template
    • Timing recommendation
  5. Implementation Timeline
    • Week 1: Send email + launch social ads
    • Week 2: Follow-up SMS if no response
    • Week 3: Final email with extended offer
    • Week 4: Evaluate results
  6. Success Metrics
    • Open rate target
    • Click-through rate target
    • Conversion goal
    • Revenue target

THE WOW MOMENT:

“This is a complete, ready-to-execute marketing campaign. Created in 60 seconds. Personalized to this specific customer. This document would normally take a marketing team 2-3 hours to create, and it wouldn’t be nearly as data-driven.”

Close the tab, return to platform

3E. Email Automation (1 minute)

Scroll to: “Email Marketing” Section

Show the Flow:

Step 1: Generate Email

  • Click “Generate Email” button
  • AI creates:
    • Personalized subject line
    • HTML email body
    • Customer’s name, preferred products, tailored offer

Step 2: Preview Email

  • Show the rendered email
  • Point out personalization:
    • “Hi Sarah,” (customer name)
    • “Your favorites: [Electronics, Home & Garden]” (actual preferences)
    • “20% discount on your next purchase” (tailored offer)
    • Branded design, logo, colors

Step 3: Send Email

  • Click “Send Email”
  • Confirmation: “Email sent to sarah.chen@email.com”
  • Status: “Delivered”

Step 4: Track History

  • Show email history table
  • Columns: Date, Subject, Status, Recipient
  • All emails sent are tracked

THE COMPLETE LOOP:

“From insight to action in under 5 minutes:

  1. AI identified Sarah is at risk (85% churn)
  2. AI explained WHY she’s at risk (decreased frequency, price sensitivity)
  3. AI created a complete campaign (multi-channel strategy)
  4. AI generated and sent personalized email (ready to go)

This entire workflow - from data to execution - used to take days. Now it’s minutes.”

Segment 4: Business Impact & Use Cases (2-3 minutes)

Real-World Scenarios:

Scenario 1: Retention Campaign

  • Before: Marketing team manually reviews customer list once a month, identifies at-risk customers by gut feeling, creates generic “we miss you” campaigns
  • After: Platform identifies at-risk customers daily, AI generates personalized retention campaigns automatically, send targeted emails immediately
  • Impact: 20-30% improvement in retention rate, 40x faster execution

Scenario 2: VIP Customer Management

  • Before: VIP list maintained manually, everyone gets same “thank you” email, no differentiation
  • After: Platform automatically identifies VIPs, creates personalized upsell campaigns, monitors behavior for early warning signs
  • Impact: Increased VIP lifetime value by 15-25%, better customer experience

Scenario 3: New Customer Onboarding

  • Before: Generic welcome email series, same for everyone, no behavior tracking
  • After: AI identifies new customers, tracks engagement, adjusts campaigns based on behavior, personalized product recommendations
  • Impact: 30-40% higher conversion from first to second purchase

ROI Numbers:

Time Savings:

  • Manual campaign creation: 2-3 hours per customer
  • AI campaign creation: 60 seconds per customer
  • Efficiency Gain: 40-120x faster

Revenue Protection:

  • Average at-risk customer value: $1,500-3,000
  • Typical retention campaign success: 20-30%
  • Revenue Saved per Recovered Customer: $300-900

Scale:

  • Manual process: Analyze 5-10 customers per day
  • AI platform: Analyze 1,000+ customers per day
  • Scale Improvement: 100-200x

Budget Efficiency:

  • Generic campaigns: 1-2% response rate
  • Personalized AI campaigns: 5-8% response rate
  • Campaign ROI: 3-4x better

Segment 5: What Makes This Different? (1 minute)

Comparison to Traditional Tools:

Traditional Analytics Our Platform
Shows past behavior Predicts future behavior
Generic segments (RFM) Intelligent segments (Churn × Value)
Manual campaign creation AI-generated campaigns
Batch reports (weekly) Real-time dashboards
Multiple disconnected tools One unified platform
Insights only Insights + Execution

Key Differentiators:

  1. Predictive, Not Just Descriptive
    • We tell you who WILL churn, not who already churned
  2. Dual Prediction System
    • Most tools predict either churn OR value
    • We predict BOTH and combine them intelligently
  3. AI-Powered Campaigns
    • Not just templates
    • Personalized content based on 120+ behavioral signals
  4. End-to-End Platform
    • From data upload → insights → campaign → email
    • Everything in one place
  5. Real-Time Intelligence
    • Interactive dashboards, not static reports
    • Ask questions, get answers immediately

Segment 6: Who Should Use This? (1 minute)

Target Industries:

  • E-commerce (online retailers)
  • Retail chains (multi-location stores)
  • Subscription businesses (SaaS, memberships)
  • Restaurants/hospitality
  • Any business with repeat customers

Target Company Size:

  • 1,000+ customers
  • Multiple products/categories
  • Suffers from customer churn
  • Has customer purchase data

Target Users:

  • Marketing managers
  • Customer success teams
  • Retention specialists
  • Business analysts
  • CMOs/Marketing VPs

Use Cases:

  • Retention campaigns for at-risk customers
  • VIP customer nurturing
  • New customer onboarding
  • Win-back dormant customers
  • Cross-sell/upsell campaigns

Segment 7: Next Steps & Discussion (1-2 minutes)

Current Status: ✅ Platform is functional end-to-end ✅ AI integration working (H2O GPTe) ✅ Email automation operational ✅ All core features demonstrated today ⚠️ Some enhancements still needed (listed below)

What’s Working Well:

  • Data upload and processing
  • Predictions and segmentation
  • Dashboard analytics
  • AI campaign generation
  • Email sending

Known Limitations / Future Improvements:

  • Currently CSV upload only (could connect directly to databases)
  • Email open/click tracking not yet implemented
  • No A/B testing framework yet
  • Single user (could add teams/roles)
  • Limited to email channel (could add SMS, push notifications)

Potential Next Steps:

Option 1: Internal Pilot

  • Use with our own customer data
  • Test campaigns on small segment
  • Measure actual retention improvement
  • Refine based on learnings

Option 2: External Beta

  • Find 2-3 pilot customers
  • Offer free trial
  • Gather feedback
  • Validate market fit

Option 3: Feature Enhancement

  • Add most-requested features
  • Improve UI/UX based on feedback
  • Add more AI capabilities
  • Build integrations (Shopify, Salesforce, etc.)

Questions for Discussion:

  1. What’s the priority: pilot, beta, or features?
  2. Who should be the target customer?
  3. What’s the biggest concern or risk?
  4. What additional features are must-haves?
  5. What’s the timeline expectation?

Segment 8: Open Q&A (2-3 minutes)

Common Questions:

Q: “How accurate are the predictions?” A: “Our models achieve 95%+ accuracy based on historical data. The churn prediction means if we say 80% risk, 8 out of 10 times that customer will actually churn if no action is taken.”

Q: “What if we don’t have all the data fields?” A: “The system is flexible. It needs basic customer and order data. Everything else is optional. The more data, the better predictions, but it works with minimal data too.”

Q: “Can this integrate with our existing systems?” A: “Right now it works with CSV uploads. We can build integrations to CRMs (Salesforce, HubSpot), e-commerce platforms (Shopify, WooCommerce), or any system with an API.”

Q: “How long does setup take?” A: “Upload data → Wait for processing (5-10 minutes) → Start using. That’s it. No complex configuration.”

Q: “What’s the AI cost?” A: “The AI (H2O GPTe) charges per usage. Roughly $0.10-0.50 per campaign generated. At scale, could be optimized with caching.”

Q: “Can it send to thousands of customers at once?” A: “Yes, for bulk sending we’d recommend using a proper email service (SendGrid, Mailchimp) instead of Gmail. The platform can integrate with those.”

Q: “What about privacy/security?” A: “Customer data stays on your servers. AI calls are secure API connections. We can add encryption, access controls, audit logs as needed.”

Q: “How customizable are the campaigns?” A: “Very. The AI generates a draft, then you review and edit before sending. You have full control over messaging, branding, and timing.”

📋 DEMO PREPARATION CHECKLIST

Before the Demo:

Data Setup:

  • Load sample customer data (realistic names, values)
  • Make sure predictions are generated
  • Identify 2-3 good example customers:
    • One high-risk VIP (for dramatic effect)
    • One steady buyer (for upsell example)
    • One onboarder (for growth campaign)

Platform Setup:

  • Start the application
  • Verify all tabs load
  • Test filter interactions
  • Pre-generate ONE campaign (backup in case AI is slow)
  • Have email setup ready (optional - can just show preview)

Presentation Setup:

  • Close unnecessary browser tabs
  • Increase font size for visibility
  • Test screen sharing
  • Have backup screenshots (in case app crashes)
  • Prepare customer examples on paper (names, values, risk %)
  • Charge laptop, bring charger

🎤 PRESENTATION TIPS FOR NON-TECHNICAL AUDIENCE

Do:

  • ✅ Use simple language (avoid: “model inference”, “feature engineering”)
  • ✅ Focus on WHAT it does, not HOW it works
  • ✅ Tell stories (“Meet Sarah, a $2,300 customer about to leave…”)
  • ✅ Show real numbers ($2,340 value, 85% risk, $300-900 saved)
  • ✅ Pause for questions anytime
  • ✅ Relate to their experience (“Like when a customer ghosts you…”)
  • ✅ Use analogies (“The AI is like a marketing analyst that never sleeps”)

Don’t:

  • ❌ Say “machine learning algorithm”
  • ❌ Mention “scikit-learn” or “HistGradientBoosting”
  • ❌ Show code or architecture diagrams
  • ❌ Use technical jargon (“API”, “RAG”, “inference pipeline”)
  • ❌ Rush through the demo
  • ❌ Assume they understand terms like “churn” (explain briefly)

Replace Technical Terms: | Instead of… | Say… | |————–|——–| | “ML model” | “AI prediction system” | | “Feature engineering” | “Analyzing behavior patterns” | | “Inference” | “Making predictions” | | “API integration” | “Connect to other systems” | | “Batch processing” | “Analyzing all customers” | | “Real-time dashboard” | “Live, up-to-date reports” |

🎯 KEY MESSAGES TO EMPHASIZE

Message 1: Predictive, Not Reactive

“We tell you WHO will churn BEFORE they do, not after it’s too late.”

Message 2: Automated Personalization

“What used to take hours per customer now takes seconds. And it’s better because it’s based on data, not gut feeling.”

Message 3: Complete Solution

“From insight to action in one platform. No juggling multiple tools.”

Message 4: Measurable Impact

“Save $300-900 per retained customer. That adds up fast with hundreds of at-risk customers.”

Message 5: Easy to Use

“Upload data, get insights, send campaigns. No data science degree required.”

🗣️ OPENING & CLOSING STATEMENTS

Opening (After Introductions):

“Before we dive in, quick context: This platform solves a problem every business faces - customer churn. Losing customers is expensive, and by the time you notice they’re gone, it’s too late. What if you could predict who’s about to leave, understand why, and automatically create personalized campaigns to keep them? That’s what I’m going to show you today. It takes data you already have and turns it into action. Let’s take a look…”

Closing:

“So that’s the platform. From prediction to execution, all in one place. The key takeaway: we’re not just showing you reports about what happened. We’re predicting what will happen and helping you do something about it automatically. The difference between a customer leaving and staying is often just a well-timed, personalized message. This platform makes that possible at scale. What questions do you have?”

📊 DEMO FLOW SUMMARY

1. Problem Statement (2 min)
   "Churn is expensive, manual marketing is slow"

2. How It Works (2 min)
   "Upload data → AI predicts → View insights → Generate campaigns → Send emails"

3. Upload Demo (1 min)
   "Show how easy data upload is"

4. Dashboard (2 min)
   "Real-time business intelligence"

5. Customer Intelligence (5 min) ⭐ MAIN FOCUS
   - Show high-risk customer
   - Generate AI analysis
   - Generate campaign
   - Show email

6. Business Impact (2 min)
   "ROI numbers, time savings, revenue protection"

7. Differentiators (1 min)
   "What makes this unique"

8. Next Steps & Q&A (2 min)
   "What do we do with this?"

💡 BACKUP PLAN

If AI Generation is Slow:

  • Have a pre-generated campaign ready
  • Switch to that example
  • Say “I ran this earlier, here’s what it generated…”

If Something Breaks:

  • Have screenshots of every key screen
  • Switch to screenshot walkthrough
  • Say “Let me show you what this looks like…”

If Questions Get Off Track:

  • Acknowledge the question: “Great question…”
  • Defer if needed: “Let’s discuss that after, I want to make sure we see the full demo first”
  • Redirect: “Let me show you something related to that…”

Remember: This is an internal demo, so it’s okay to say:

  • “This part needs improvement”
  • “We’re still working on…”
  • “Great feedback, let’s add that to the roadmap”

Be honest, be collaborative, show the value. You got this! 🚀