Win Rate

Overview

Win Rate measures the percentage of sales opportunities that result in closed won deals out of all qualified opportunities that reach a decision. This fundamental sales metric reflects the effectiveness of your sales team, sales process, and product-market fit.

A strong win rate indicates effective sales execution, compelling value propositions, and competitive positioning. It's a key indicator of sales team performance and an important factor in sales forecasting accuracy.

Formula:
Win Rate = (Number of Closed Won Deals / Total Closed Deals) × 100

30%+

Excellent

20-30%

Good

15-20%

Average

<15%

Needs Improvement

Industry Context

Win rates vary significantly by industry, deal size, and sales model. Enterprise B2B typically sees 15-25%, while transactional B2B may achieve 25-35%. Consider your specific context when benchmarking.

Why It Matters

Sales Effectiveness: Direct measure of sales team's ability to close deals
Forecast Accuracy: Essential for reliable pipeline and revenue forecasting
Resource Allocation: Helps prioritize high-probability opportunities
Competitive Position: Indicates how well you compete against alternatives
Process Optimization: Identifies areas for sales process improvement
Rep Performance: Benchmarks individual and team performance

How to Measure It

Calculate win rate by dividing closed won deals by total closed deals (won + lost), with analysis across multiple dimensions for optimization insights.

Basic Win Rate Analysis

-- Calculate win rates with comprehensive segmentation
WITH opportunity_outcomes AS (
  SELECT 
    opportunity_id,
    opportunity_name,
    deal_size,
    lead_source,
    industry,
    assigned_rep,
    created_date,
    closed_date,
    stage,
    close_reason,
    competitor,
    CASE 
      WHEN deal_size < 25000 THEN 'Small'
      WHEN deal_size < 100000 THEN 'Medium' 
      ELSE 'Large'
    END as deal_category
  FROM opportunities
  WHERE stage IN ('Closed Won', 'Closed Lost')
    AND created_date >= '2024-01-01'
    AND closed_date IS NOT NULL
)
SELECT 
  'Overall' as segment,
  COUNT(*) as total_opportunities,
  SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) as won_deals,
  SUM(CASE WHEN stage = 'Closed Lost' THEN 1 ELSE 0 END) as lost_deals,
  ROUND(100.0 * SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) / COUNT(*), 1) as win_rate_percent,
  ROUND(AVG(CASE WHEN stage = 'Closed Won' THEN deal_size END), 0) as avg_won_deal_size,
  ROUND(SUM(CASE WHEN stage = 'Closed Won' THEN deal_size ELSE 0 END), 0) as total_won_revenue

UNION ALL

SELECT 
  deal_category as segment,
  COUNT(*) as total_opportunities,
  SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) as won_deals,
  SUM(CASE WHEN stage = 'Closed Lost' THEN 1 ELSE 0 END) as lost_deals,
  ROUND(100.0 * SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) / COUNT(*), 1) as win_rate_percent,
  ROUND(AVG(CASE WHEN stage = 'Closed Won' THEN deal_size END), 0) as avg_won_deal_size,
  ROUND(SUM(CASE WHEN stage = 'Closed Won' THEN deal_size ELSE 0 END), 0) as total_won_revenue
FROM opportunity_outcomes
GROUP BY deal_category

UNION ALL

SELECT 
  lead_source as segment,
  COUNT(*) as total_opportunities,
  SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) as won_deals,
  SUM(CASE WHEN stage = 'Closed Lost' THEN 1 ELSE 0 END) as lost_deals,
  ROUND(100.0 * SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) / COUNT(*), 1) as win_rate_percent,
  ROUND(AVG(CASE WHEN stage = 'Closed Won' THEN deal_size END), 0) as avg_won_deal_size,
  ROUND(SUM(CASE WHEN stage = 'Closed Won' THEN deal_size ELSE 0 END), 0) as total_won_revenue
FROM opportunity_outcomes
GROUP BY lead_source
ORDER BY win_rate_percent DESC;

Competitive Win Rate Analysis

-- Analyze win rates by competitor presence
WITH competitive_analysis AS (
  SELECT 
    opportunity_id,
    stage,
    deal_size,
    competitor,
    CASE 
      WHEN competitor IS NULL OR competitor = '' THEN 'No Competition'
      ELSE competitor
    END as competition_type
  FROM opportunities
  WHERE stage IN ('Closed Won', 'Closed Lost')
    AND created_date >= '2024-01-01'
),
win_loss_by_competitor AS (
  SELECT 
    competition_type,
    COUNT(*) as total_deals,
    SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) as won_deals,
    SUM(CASE WHEN stage = 'Closed Lost' THEN 1 ELSE 0 END) as lost_deals,
    ROUND(100.0 * SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) / COUNT(*), 1) as win_rate,
    ROUND(AVG(deal_size), 0) as avg_deal_size
  FROM competitive_analysis
  GROUP BY competition_type
)
SELECT 
  competition_type,
  total_deals,
  won_deals,
  lost_deals,
  win_rate,
  avg_deal_size,
  RANK() OVER (ORDER BY win_rate DESC) as win_rate_rank
FROM win_loss_by_competitor
WHERE total_deals >= 5  -- Minimum sample size
ORDER BY win_rate DESC;

Win Rate Trends and Patterns

-- Track win rate trends over time
WITH monthly_win_rates AS (
  SELECT 
    DATE_TRUNC('month', closed_date) as month,
    assigned_rep,
    COUNT(*) as total_deals,
    SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) as won_deals,
    ROUND(100.0 * SUM(CASE WHEN stage = 'Closed Won' THEN 1 ELSE 0 END) / COUNT(*), 1) as win_rate
  FROM opportunities
  WHERE stage IN ('Closed Won', 'Closed Lost')
    AND closed_date >= '2023-01-01'
    AND assigned_rep IS NOT NULL
  GROUP BY 1, 2
  HAVING COUNT(*) >= 3  -- Minimum deals for statistical relevance
),
trend_analysis AS (
  SELECT 
    month,
    assigned_rep,
    total_deals,
    won_deals,
    win_rate,
    LAG(win_rate) OVER (PARTITION BY assigned_rep ORDER BY month) as prev_month_win_rate,
    AVG(win_rate) OVER (
      PARTITION BY assigned_rep 
      ORDER BY month 
      ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
    ) as three_month_avg_win_rate
  FROM monthly_win_rates
)
SELECT 
  month,
  assigned_rep,
  total_deals,
  win_rate,
  ROUND(three_month_avg_win_rate, 1) as three_month_avg,
  ROUND(win_rate - prev_month_win_rate, 1) as month_over_month_change,
  CASE 
    WHEN win_rate >= 25 THEN 'High'
    WHEN win_rate >= 15 THEN 'Medium'
    ELSE 'Low'
  END as performance_tier
FROM trend_analysis
WHERE month >= CURRENT_DATE - INTERVAL '12 months'
ORDER BY month DESC, win_rate DESC;

Calculation Guidelines

Include only qualified opportunities that reached a decision. Exclude deals that are still open, withdrawn, or never properly qualified to get accurate win rate calculations.

Best Practices

1. Consistent Opportunity Definition

Define clear qualification criteria (BANT, MEDDIC, etc.)
Only include deals that reached a genuine decision
Exclude unqualified leads from win rate calculations
Maintain consistent stage definitions across team

2. Segmentation Analysis

Track by deal size and complexity
Analyze by lead source and channel
Monitor competitive win rates
Segment by industry and use case

3. Win Rate Improvement Strategies

Better Qualification: Focus on winnable opportunities
Competitive Intelligence: Understand and counter competitor positioning
Value Articulation: Strengthen business case and ROI messaging
Sales Training: Improve objection handling and closing skills

4. Loss Analysis

Conduct systematic win/loss interviews
Track common loss reasons and patterns
Identify coachable vs. structural issues
Share insights across sales team

5. Forecasting Integration

Use historical win rates for probability scoring
Adjust forecasts based on deal characteristics
Factor in competitive intelligence
Update win rate assumptions regularly