Pricepoint considers market context, but its pricing decisions aren't driven by what competitors are doing. There are several reasons for this:
1. Comparison Sets Are Inherently Unreliable
Assembling a genuinely relevant competitor set is difficult in the online sales landscape, where properties of very different classes can appear side by side in search results. Rather than anchoring to a comparison set that may not be accurate, Pricepoint focuses on optimizing prices based on your property's own dynamics.
2. Each Property Has Its Own Unique Situation
Recent booking activity, current occupancy levels, and other property-specific circumstances all shape what the right price is at any given moment. Pricepoint accounts for these individual dynamics directly, rather than adjusting in reaction to what other properties are charging.
3. Pricing Isn’t a Zero-Sum Game
The hotel industry isn't a contest where one property must "win" against others for the same guest. By optimizing pricing based on real demand rather than undercutting each other, well-positioned properties can thrive alongside one another and capture more value from the market.
4. Pricepoint Is Built for Price Leadership, Not Price Following
Rather than reacting to what competitors charge, Pricepoint is designed to help properties set pricing based on their own data, demand, and strategy — positioning them as price leaders rather than price takers. The goal is for your pricing to reflect your property's own value and market dynamics, rather than simply following the market.
5. Competitor Pricing Overlooks What Makes Your Property Unique
Matching competitor rates ignores the things that may justify a different price for your property — location, guest experience, or specialized services. Pricepoint's pricing accounts for your property's own characteristics, rather than pricing you as if you were interchangeable with nearby competitors.
👉 For the specific factors Pricepoint uses to calculate pricing, see Why Your Prices Change: The Key Factors Behind Pricepoint’s AI-driven Pricing Recommendations