If you’re still relying on a rules-based pricing system to manage your hotel’s rates, you’re not alone but you might be leaving serious revenue on the table.
NB: This is an article from TakeUp
Subscribe to our weekly newsletter and stay up to date
As guest demand becomes more dynamic and market conditions shift by the hour, hotel revenue managers are rethinking their approach to pricing. The conversation has moved from static rules to intelligent, adaptive systems. And at the center of that shift is AI pricing.
In this post, we’ll break down the key differences between rules-based pricing software for hotels and AI pricing tools, and offer a direct hotel revenue management software comparison so you can decide what’s right for your property. Spoiler: AI doesn’t just represent the future of revenue management, it’s already delivering better results today.
What Is Rules-Based Pricing in Hotels?
Rules-based pricing software relies on predefined conditions to determine room rates. These rules might include things like:
- “Increase rate by $20 if occupancy > 80%”
- “Match competitor pricing within $5”
- “Offer 15% discount 14+ days before arrival”
While these systems may seem logical and give a sense of control, they depend entirely on inputs defined by the user. That means every scenario must be anticipated in advance and every change in demand requires manual updates. In theory, it’s automation. In practice, it’s often just outsourcing spreadsheet logic into a slightly friendlier interface.
Rules-based systems can work for simple pricing needs, but they fall short in volatile markets or complex booking patterns. Their biggest flaw? They don’t learn or adapt. They react only when a preset rule is triggered, and by then, it may already be too late.
How AI Pricing Tools Work for Hotels
By contrast, AI pricing tools for hotels use machine learning models to analyze hundreds of data points in real time: market trends, booking pace, competitor pricing, search demand, lead times, and more. These systems don’t just follow instructions, they discover patterns, test hypotheses, and update pricing dynamically to optimize for revenue and occupancy.
What makes AI systems different:
- Real-time responsiveness: AI adapts pricing the moment demand shifts, not after a rule gets triggered.
- Continuous learning: The system improves over time, understanding what price points convert best for your specific property and guest segments.
- Demand forecasting: AI predicts future booking behavior, so you can price with confidence days, weeks, or months in advance.
- Minimal manual input: You’re no longer burdened with maintaining a massive library of if-this-then-that rules.
The result? You make smarter pricing decisions, faster and without having to touch rates every day.
