Market Analysis Interactive

Competitive Pricing

Position your pricing relative to competitors. Find the sweet spot between volume (lower hold) and margin (higher hold).

Hold Rates (%)

2 8

📊 Your Position

Your Hold 5.0%

Market Avg 4.8%

vs Market +0.17%

⚖️ Market parity: Competing on other factors

Estimated Market Share

Lower hold → higher market share (inverse relationship)

Estimated Revenue ($1M Market)

You

$12.2K

Comp A

$12.2K

Comp B

$12.2K

Comp C

$12.2K

Revenue = Share × Market × Hold. Optimal hold maximizes revenue, not share.

🎯 Competitive Strategies

Price Leader

Lowest hold, highest volume

⚠️ Margin pressure

✓ Scale advantage

Premium

Higher hold, better product

⚠️ Volume loss

✓ Strong brand

Match

Stay at market average

⚠️ No differentiation

✓ Status quo

Segment

Different prices for segments

⚠️ Complexity

✓ Diverse users

R Code Equivalent

# Competitive pricing analysis
estimate_share <- function(holds) { 
  # Lower hold = higher share (inverse)
  inverse <- 1 / holds
  inverse / sum(inverse) * 100
}

optimize_hold <- function(competitor_holds, market_size = 1000000) { 
  test_holds <- seq(2, 8, by = 0.1)
  results <- sapply(test_holds, function(my_hold) { 
    all_holds <- c(my_hold, competitor_holds)
    shares <- estimate_share(all_holds)
    my_revenue <- shares[1] / 100 * market_size * my_hold / 100
    return(my_revenue)
  })
  
  optimal_idx <- which.max(results)
  list(optimal_hold = test_holds[optimal_idx], max_revenue = results[optimal_idx])
}

# Example
comp_holds <- c(4.5, 5.2, 4.8)
result <- optimize_hold(comp_holds)
cat(sprintf("Optimal: %.1f%%, Revenue: $%.0f\n", result$optimal_hold, result$max_revenue))

✅ Key Takeaways

• Lower hold → higher share, lower margin
• Optimal hold maximizes revenue, not share
• Monitor competitor pricing regularly

• Price leadership requires scale
• Premium requires differentiation
• Segment pricing for different user types