Revenue Operations Agent

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Overview

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What the Agent Needs to Accomplish

• Forecast revenue across multiple time periods
• Analyze deal progression and conversion rates
• Monitor pipeline health and identify risks
• Track quota attainment and performance
• Identify trends and patterns in revenue data
• Generate revenue reports and insights

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How Pylar Helps

• Unified Revenue View: Combining deals, contracts, billing, and historical data
• Real-time Analytics: Querying current revenue metrics and forecasts
• Multi-Source Integration: Joining CRM, billing, and finance data
• Advanced Calculations: Complex revenue calculations and forecasting
• Trend Analysis: Identifying revenue patterns and trends

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Without Pylar vs With Pylar

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Without Pylar

• ❌ Data scattered across CRM, billing, and finance systems
• ❌ Manual revenue calculations and forecasting
• ❌ Complex data aggregation across systems
• ❌ Time-consuming report generation
• ❌ Limited real-time insights
• ❌ Difficult to track quota and performance

• 4-5 different system integrations
• Custom revenue calculation logic
• Manual forecasting models
• Complex data aggregation
• ~6-8 weeks development time

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With Pylar

• ✅ Single endpoint for all revenue data
• ✅ Automated revenue calculations
• ✅ Real-time forecasting
• ✅ Unified quota tracking
• ✅ Easy to update calculations
• ✅ Built-in analytics

• Connect 4-5 data sources (1.5 hours)
• Create revenue views (3 hours)
• Build MCP tools with AI (2 hours)
• Connect to agent builder (15 minutes)
• Total: ~7 hours

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Step-by-Step Implementation

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Step 1: Connect Data Sources

1. Connect CRM (Deals, opportunities, contracts)
2. Connect Billing System (Invoices, subscriptions, payments)
3. Connect Finance System (Revenue recognition, financial data)
4. Connect Sales Activity (Quota, performance data)

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Step 2: Create Revenue Views

CREATE VIEW revenue_forecast AS
SELECT 
  DATE_TRUNC('month', close_date) as forecast_month,
  SUM(amount * (probability / 100)) as weighted_revenue,
  SUM(amount) as best_case_revenue,
  COUNT(*) as deal_count,
  AVG(probability) as avg_probability
FROM crm.deals
WHERE close_date >= CURRENT_DATE
GROUP BY DATE_TRUNC('month', close_date);

CREATE VIEW quota_tracking AS
SELECT 
  rep_name,
  quota_amount,
  SUM(closed_won_amount) as attained_amount,
  (SUM(closed_won_amount) / quota_amount) * 100 as quota_attainment_percent,
  SUM(weighted_pipeline) as pipeline_value,
  (SUM(weighted_pipeline) / quota_amount) * 100 as pipeline_coverage_percent
FROM sales.rep_performance
GROUP BY rep_name, quota_amount;

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Step 3: Create MCP Tools

• get_revenue_forecast(months_ahead: number, include_breakdown: boolean)

• get_quota_tracking(rep_name: string, period: string)

• analyze_pipeline_health(threshold: number)

• analyze_revenue_trends(period: string, metric: string)

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Example Agent Interactions

• Weighted Forecast: $1.2M
• Best Case: $1.8M
• Pipeline Coverage: 3.2x quota
• Top Rep: Sarah Johnson (145% of quota)“

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Outcomes

• Forecast Accuracy: 30% improvement
• Quota Visibility: Real-time tracking
• Pipeline Health: 25% better pipeline management
• Report Efficiency: 85% reduction in report time

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Next Steps

• Sales Assistant Example
• Financial Analyst Example