Dynamic Pricing
Dynamic pricing is a pricing strategy that adjusts the prices of goods and services in real time based on variable factors such as demand, supply, competitive conditions, and time of day. While it has long been used in airline ticket and hotel room pricing, the spread of AI has expanded its adoption across a wide range of industries, including retail and food service.
Differences from Fixed Pricing
Traditional fixed pricing is determined by "cost + margin" and, once set, remains unchanged for an extended period. Dynamic pricing overturns this assumption by raising prices during high-demand periods or seasons and lowering them during slow periods, thereby maximizing revenue.
While this approach has decades of history in the airline industry, the barriers to implementation were high. This is because demand forecasting, competitor monitoring, and price simulation all need to run in real time.
Changes Brought About by AI
Advances in machine learning models have significantly reduced the cost of implementing dynamic pricing. An approach in which historical sales data, weather, event information, and competitor prices are fed into a model to predict optimal pricing has become commonplace.
In Thailand's hotel industry, the adoption of demand forecasting models that combine OTA (Online Travel Agency) pricing data with in-house reservation status is progressing. Balancing room occupancy rate and Average Daily Rate (ADR) is a key management challenge, and AI-driven price optimization is attracting attention as a means of alleviating this trade-off.
Pitfalls When Implementing
If prices fluctuate too frequently, there is a risk of eroding customer trust. When the price of the same product changes within a matter of hours, a psychological response of "I don't know when to buy" can set in, dampening the desire to purchase.
Furthermore, when competitors use the same algorithm, the risk of "algorithmic collusion"—where each party references the other's prices and triggers a race to the bottom—has also been pointed out. In practice, designing systems that combine price floor settings with rule-based constraints becomes critical.
Related Terms
Predictive Maintenance
Predictive maintenance is a method that uses AI to analyze sensor data and operation logs, predicting equipment failures in advance to enable planned maintenance.
AI Digital Twin
A system that integrates AI into digital replicas of physical assets or processes to perform real-time analysis, prediction, and optimization.
BPO (Business Process Outsourcing)
BPO refers to a form of outsourcing in which a company delegates specific business processes to an external specialized vendor. AI Hybrid BPO, which combines BPO with automation leveraging AI, has been attracting significant attention in recent years.
