Dynamic Pricing Interactive
Surge Pricing
Adjust prices dynamically when demand exceeds capacity. Balance revenue optimization with customer experience during peak periods.
⚡ How Surge Pricing Works
📈
Demand Spikes
More users want to bet than system can handle efficiently
💰
Prices Rise
Higher hold/worse odds during peak demand
⚖️
Balance
Demand drops to match capacity, revenue maximized
Market Parameters
50 200
50 300
80 200
-3 -0.5
⚡ Surge Status
Demand/Capacity 150%
Surge Multiplier 1.71x
Price Increase +71%
Surge Impact
Original Demand
180
After Surge Pricing
-11
Actually Served
-11
Normal Revenue
$100
Surge Revenue
$-19
+-119%
Daily Demand Pattern
Demand peaks during prime time games (6-9pm). Surge pricing activates when demand exceeds capacity.
🏀 Betting Surge Scenarios
Big Game
1.5-2.5x
High-demand matchup
Last Minute
1.3-1.8x
Bets close to kickoff
Breaking News
2-4x
Injury announcement
Promo Period
1.2-1.5x
Everyone using same offer
R Code Equivalent
# Surge pricing model
calculate_surge <- function(demand, capacity, power = 1.5) {
ratio <- demand / capacity
if (ratio <= 1) return(1)
return(1 + (ratio - 1)^power * 2)
}
apply_surge <- function(demand, capacity, elasticity = -1.5) {
surge <- calculate_surge(demand, capacity)
price_increase <- (surge - 1) * 100
demand_reduction <- price_increase * elasticity / 100
effective_demand <- demand * (1 + demand_reduction)
list(
surge = surge,
effective_demand = effective_demand,
served = min(effective_demand, capacity),
revenue = min(effective_demand, capacity) * surge
)
}
result <- apply_surge(180, 120, -1.5)
cat(sprintf("Surge: %.2fx, Revenue: $%.0f\n", result$surge, result$revenue))✅ Key Takeaways
- • Surge when demand exceeds capacity
- • Higher prices reduce demand to manageable level
- • Maximizes revenue during peak periods
- • Balance revenue vs customer experience
- • Communicate surge clearly (transparency)
- • Use elasticity to calibrate multiplier