OpportunityIQ MVP Scanner

Identifies revenue opportunities by matching clients against pre-defined scenarios and calculating potential revenue.

When to Use This Skill

Use when the user asks to:

•"Scan my clients for opportunities"
•"Find revenue opportunities in my book"
•"Run OpportunityIQ on my client data"
•"Identify sales opportunities"
•"Generate my weekly opportunity report"

Inputs Required

•
Client Data: Google Sheets link with tabs:
- •Client_Master: Client demographics, portfolio totals, cash balances
- •Products: FIAs, life insurance, annuities with details
- •Holdings: Investment positions for concentration analysis
•
Scenario Selection (defaults to MVP scenarios):
- •FIA Surrender Ending
- •Cash Drag Opportunity
- •Concentrated Position

Reference Materials

For detailed guidance on specific topics, consult these reference files:

•Data Structure: See data_structure_guide.md for comprehensive guide on handling multi-source data with client identifiers
•Classifications: See expanded_classifications.md for 15+ classification categories with detailed explanations
•Implementation: See implementation_guide.md for step-by-step deployment roadmap
•Product Reference: See product_reference_system.md for maintaining product/carrier reference tables
•Decision Guide: See decision_guide.md for choosing between different approaches
•Starter Scenarios: See starter_scenarios.md for 12 fully-completed scenario examples

Templates

Use these templates for creating new scenarios:

•Excel Template: scenario_template.xlsx - Excel workbook with Scenario Library, Classification Reference, and Example Scenarios
•Markdown Template: scenario_template.md - Markdown version optimized for LLM processing

Process Workflow

Step 1: Load & Validate Data

python

# Load Google Sheets data
client_master = load_sheet(url, 'Client_Master')
products = load_sheet(url, 'Products')
holdings = load_sheet(url, 'Holdings')

# Validate required columns exist
required_columns = {
    'Client_Master': ['Client_ID', 'Client_Name', 'Total_Portfolio', 'Cash_Balance', 'Cash_Yield'],
    'Products': ['Client_ID', 'Product_Type', 'Current_Value', 'Surrender_End_Date', 'Cap_Rate'],
    'Holdings': ['Client_ID', 'Current_Value', 'Percent_of_Portfolio']
}

# Report any missing data

Step 2: Match Scenarios

Scenario 1: FIA Surrender Ending

Criteria:

•Product_Type = 'FIA'
•Surrender_End_Date within 12 months from today
•Cap_Rate < 5.5%

Revenue Calculation:

python

revenue = product['Current_Value'] * 0.05  # 5% commission

Output Template:

code

Client: {client_name}
Type: FIA Replacement Opportunity
Details: {product_name} FIA with ${current_value:,} approaching surrender end in {months} months. Current cap rate of {cap_rate} is below market average of 5.5-6.5%.
Revenue Estimate: ${revenue:,}
Action: Schedule review meeting to discuss current product performance and illustrate replacement options with higher crediting rates.

Scenario 2: Cash Drag

Criteria:

•Cash_Balance > $50,000
•Cash_Yield < 3.0%

Revenue Calculation:

python

revenue = client['Cash_Balance'] * 0.01  # 1% AUM fee

Output Template:

code

Client: {client_name}
Type: Cash Repositioning Opportunity
Details: ${cash_balance:,} in cash earning {cash_yield}. Money market funds currently yielding 4.5-5.5%.
Revenue Estimate: ${revenue:,} annually
Action: Quick call to reposition to higher-yielding money market or short-term treasury fund.

Scenario 3: Concentrated Position

Criteria:

•Any single position > 20% of portfolio
•Total_Portfolio > $500,000
•Accredited_Investor = 'Yes'

Revenue Calculation:

python

# Assume 30% of concentrated position can be repositioned
reposition_amount = position['Current_Value'] * 0.30
# Alternative investment typically 5% upfront
revenue = reposition_amount * 0.05

Output Template:

code

Client: {client_name}
Type: Concentration Risk / Diversification
Details: {ticker} position of ${position_value:,} represents {percentage}% of portfolio. Significant single-stock concentration risk.
Revenue Estimate: ${revenue:,}
Action: Schedule portfolio review to discuss diversification into alternative investments or sector diversification.

Step 3: Consolidate & Rank

python

# Collect all opportunities
all_opportunities = []
all_opportunities.extend(fia_opportunities)
all_opportunities.extend(cash_opportunities)
all_opportunities.extend(concentration_opportunities)

# Deduplicate: One opportunity per client (keep highest revenue)
client_best_opp = {}
for opp in all_opportunities:
    client_id = opp['client_id']
    if client_id not in client_best_opp:
        client_best_opp[client_id] = opp
    elif opp['revenue'] > client_best_opp[client_id]['revenue']:
        client_best_opp[client_id] = opp

# Sort by revenue (highest first)
sorted_opportunities = sorted(
    client_best_opp.values(),
    key=lambda x: x['revenue'],
    reverse=True
)

Step 4: Generate Report

Report Format:

code

═══════════════════════════════════════════════════
  OPPORTUNITYIQ MVP SCAN RESULTS
═══════════════════════════════════════════════════

Generated: {current_date_time}
Clients Scanned: {total_clients}
Opportunities Found: {total_opportunities}
Total Revenue Potential: ${total_revenue:,}

───────────────────────────────────────────────────

OPPORTUNITY #1 - ${revenue:,}

Client: {client_name} ({client_id})
Type: {scenario_name}

Details:
{detailed_description}

Recommended Action:
{next_steps}

───────────────────────────────────────────────────

[Repeat for each opportunity]

───────────────────────────────────────────────────

SUMMARY BY TYPE:
• FIA Replacement: {fia_count} opportunities - ${fia_total:,}
• Cash Repositioning: {cash_count} opportunities - ${cash_total:,}
• Concentration/Diversification: {conc_count} opportunities - ${conc_total:,}

PRIORITY ACTIONS:
1. High Priority (>$5,000): {high_priority_count} opportunities
2. Medium Priority ($2,000-$5,000): {medium_priority_count} opportunities
3. Quick Wins (<$2,000): {quick_win_count} opportunities

NEXT STEPS:
□ Review each opportunity for accuracy
□ Prioritize top 3-5 for immediate outreach
□ Note any false positives to refine criteria
□ Decide: Expand to more scenarios or scale client base?

Error Handling

Handle common data issues gracefully:

python

# Missing data
if 'Surrender_End_Date' not in products.columns:
    print("⚠️ Warning: Surrender_End_Date not found in Products sheet. Skipping FIA scenario.")

# Invalid dates
try:
    surrender_date = pd.to_datetime(product['Surrender_End_Date'])
except:
    print(f"⚠️ Invalid date format for client {client_id}, skipping this product")

# Empty dataframes
if holdings.empty:
    print("⚠️ No holdings data found. Skipping concentration scenario.")

# Division by zero
if total_portfolio > 0:
    percent = position_value / total_portfolio
else:
    percent = 0

Output Examples

Example 1: FIA Opportunity

code

OPPORTUNITY #1 - $24,350

Client: John Smith (C001)
Type: FIA Replacement Opportunity

Details:
Allianz 222 FIA with $487,000 approaching surrender end in 7 months. Current cap rate of 4.5% is significantly below current market rates of 5.8-6.5%. Client has held product for 6.5 years through low-rate environment.

Recommended Action:
1. Schedule 45-minute review meeting within next 2 weeks
2. Run side-by-side illustration: Current product vs. best available replacement
3. Review surrender schedule details to confirm no penalties
4. Prepare 1035 exchange paperwork if client approves
5. Expected timeline: 30-60 days to completion

Revenue Estimate: $24,350 (5% commission on $487,000)
Confidence: High

Example 2: Cash Drag Opportunity

code

OPPORTUNITY #3 - $1,500

Client: Mary Johnson (C002)
Type: Cash Repositioning Opportunity

Details:
$150,000 in cash earning 0.5% in traditional savings account. Money market funds currently yielding 4.5-5.5%. Client is losing approximately $6,000 annually in opportunity cost. No upcoming liquidity needs identified.

Recommended Action:
1. Quick 15-minute call to client
2. Explain opportunity cost (earning $750/year vs. $7,500/year)
3. Recommend repositioning to money market fund
4. Execute trade same day if approved
5. No-brainer quick win

Revenue Estimate: $1,500 annually (1% AUM fee)
Confidence: High

Validation Checklist

After running scan, validate results:

• All opportunities have valid Client_IDs
• Revenue estimates are > $0
• Descriptions are specific (not generic)
• Recommended actions are actionable
• No obvious false positives (clients who don't match criteria)
• Revenue estimates seem realistic for your practice

Customization Options

User can adjust:

Revenue Commission Rates:

python

COMMISSION_RATES = {
    'FIA_replacement': 0.05,      # Default: 5%
    'Life_insurance': 0.01,       # Default: 1% of face value
    'AUM_fee': 0.01,              # Default: 1%
    'Alternative_product': 0.05,  # Default: 5%
}

Threshold Values:

python

THRESHOLDS = {
    'cash_minimum': 50000,                    # Minimum cash balance
    'cash_yield_threshold': 3.0,              # Yield below which to flag
    'fia_cap_rate_threshold': 5.5,            # Cap rate below market
    'concentration_percentage': 20,            # Portfolio concentration %
    'concentration_minimum_portfolio': 500000, # Minimum portfolio for concentration
}

Urgency Weighting (if implementing ranking):

python

URGENCY_WEIGHTS = {
    'Immediate': 1.3,
    'Near-term': 1.2,
    'Time-sensitive': 1.3,
    'Strategic': 1.0,
}

Expansion Path

To add more scenarios:

•Define matching criteria
•Add revenue calculation logic
•Create output template
•Test with sample data
•Add to scenario rotation

Future scenarios to consider:

•Tax loss harvesting (seasonal)
•RMD planning (pre-age 73)
•Life insurance coverage gaps
•Beneficiary reviews
•Estate planning triggers
•Portfolio rebalancing triggers

Notes

•Privacy: Client data never leaves your Google Sheet. Skill only reads and analyzes.
•Accuracy: Revenue estimates are conservative. Actual results may vary.
•Frequency: Recommend running weekly (every Sunday night for Monday delivery).
•Scale: Tested with 10-50 clients. Can handle up to 500 clients per scan.

Success Metrics

Track these over time:

•Opportunities identified per scan
•False positive rate (opportunities that aren't real)
•Conversion rate (opportunities → actions)
•Revenue generated (closed deals from opportunities)
•Time saved (vs. manual review)