Dynamic pricing

Dynamic pricing is a strategy that changes prices in response to demand, timing, inventory, or other market conditions. It ranges from frequent automated adjustments to prices that vary by customer or transaction.

Price elasticity of demand: Price elasticity of demand measures how strongly the quantity demanded changes when a product’s price changes. Elasticity estimates how a price adjustment may affect sales and revenue.
Airline revenue management: Airline revenue management allocates seats and sets fares to earn revenue from flights with fixed capacity and departure times. Airlines adjust fares as seats sell and departure approaches.
Personalized pricing: Personalized pricing offers different prices to different customers based on individual information or predicted willingness to pay. It varies by customer, unlike changes based only on shared market conditions.
Consumer surplus: Consumer surplus is the difference between what buyers are willing to pay and what they actually pay. Changing prices can redistribute the gains from a transaction between buyers and sellers.
Revenue management: Revenue management uses demand forecasts and pricing or inventory controls to maximize revenue from limited, perishable capacity. Dynamic pricing is one of revenue management’s main ways to match prices to demand.
Surge pricing: Surge pricing raises prices temporarily when demand for a service exceeds available supply. Ride-hailing platforms use it to respond to local imbalances between trip requests and drivers.
Price discrimination: Price discrimination occurs when a seller charges different prices for the same good or service for reasons beyond cost differences. Dynamic prices can enable price discrimination, but they do not necessarily target individual customers.
Algorithmic pricing: Algorithmic pricing uses software models to recommend or set prices from data and business rules. Automated adjustments can make pricing faster while raising questions about accountability.
Demand forecasting: Demand forecasting estimates future demand using historical data, current signals, and predictive models. Forecasts help set prices before expected changes in demand occur.
Hotel revenue management: Hotel revenue management adjusts room prices and availability to maximize returns from fixed nightly capacity. Room rates vary with booking pace, season, and expected occupancy.

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