algo-price-dynamiclisted

# Dynamic Pricing ## Overview Dynamic pricing adjusts prices in real-time based on demand signals, time, inventory, and competitive conditions. Common in airlines, hotels, ride-sharing, and e-commerce. Objective: maximize revenue (or profit) subject to capacity/inventory constraints. ## When to Use **Trigger conditions:** - Pricing perishable inventory (hotel rooms, airline seats, event tickets) - Implementing demand-responsive pricing for e-commerce - Building surge pricing or time-based pricing systems **When NOT to use:** - For one-time pricing decisions (use Van Westendorp or conjoint) - When price changes are impractical (regulated markets, long-term contracts) ## Algorithm ``` IRON LAW: Dynamic Pricing Requires REAL-TIME Data Stale data produces prices optimal for PAST conditions, not current ones. Three data streams must be current: 1. Demand signal (bookings, searches, cart additions) 2. Inventory/capacity status 3. Competitive prices (where applicable) Update frequency: minutes for ride-sharing, hours for hotels, daily for retail. ``` ### Phase 1: Input Validation Collect: current demand indicators, remaining inventory/capacity, time until expiration/event, competitor prices, price floor/ceiling constraints. **Gate:** Real-time data feeds connected, business rules defined. ### Phase 2: Core Algorithm **Rule-based:** If demand > threshold, increase price by X%. Tiered rules by inventory level. **Demand-curve based:** 1. Estimate demand curve at current condi