Dynamic Pricing Simulator
Simulate a real-time pricing algorithm. Adjust prices automatically based on inventory scarcity, competitor moves, and demand surges.
Live Inputs
Lower inventory = Higher Price (Scarcity)
24-Hour Price Forecast
Algorithm Logic Visualization
How inputs drive the output price.
Scarcity Multiplier
As inventory drops (see Input), the price curve shifts upwards to preserve remaining stock for high-paying customers.
Competitor Matching
The grey line shows competitor price. Notice how your price tracks it but maintains a premium/discount based on demand.
Demand Peaks
The price naturally peaks at noon and evening (simulated rush hours). This captures consumer surplus during high-intent windows.
Execution Steps
Set your 'Base Price' (the anchor).
Adjust 'Inventory Level'. Lower inventory triggers scarcity pricing (higher prices).
Set 'Competitor Price'. The algorithm reacts to stay competitive.
Use the 'Demand Factor' slider to simulate high-traffic events (e.g., Black Friday).
Watch the chart to see how the algorithm adjusts price hour-by-hour.
Pro Strategy
- Don't just use dynamic pricing to raise prices. Use it to lower prices during off-peak hours to maintain sales velocity.
- Always set a 'Floor' price to protect your margin.
- Be transparent. Customers accept surge pricing for travel/rides, but hate it for concert tickets if it feels arbitrary.
Core Concepts
Surge Pricing
Raising prices during periods of peak demand (e.g., Uber during rush hour) to maximize revenue and manage supply.
Inventory Velocity
The speed at which stock is selling. High velocity often triggers price increases to prevent stocking out too early.
Price Floor/Ceiling
Hard limits set in the algorithm to prevent prices from going too low (unprofitable) or too high (brand damage).
What is Dynamic Pricing Simulator?
Dynamic Pricing (or Algorithmic Pricing) uses real-time data inputs to adjust prices on the fly. This simulator uses a rule-based logic model combining Scarcity (Inventory), Demand (Time/Traffic), and Competition to calculate the optimal price at any given hour.
Best For
- • Managing perishable inventory (airline seats, hotel rooms, food).
- • Clearing excess stock at the end of a season.
- • Maximizing profit during short-term demand spikes.
Limitations
- • Can lead to 'price wars' if algorithms automatically match competitors downward.
- • Requires accurate real-time data integrations.
- • Can damage customer trust if volatility is too extreme.
Alternative Methods
Time-Based Pricing
Simple Happy Hour logic (e.g., 50% off after 5pm) without complex algorithms.
Segmented Pricing
Different prices for different users (Student discount) rather than time.
Industry Applications
See how this methodology generates real revenue uplift in different sectors.
Ride-Sharing Giant
Not enough drivers during rainy rush hours.
Implemented Surge Pricing (Dynamic) to incentivize more drivers to get on the road and reduce low-value ride requests.
Ticket Resale Platform
Tickets selling out instantly to scalpers.
Used dynamic pricing to start high and lower slowly (Dutch Auction).