Demystifying how Pricepoint's AI-Driven Pricing works

Pricepoint is an advanced AI-powered dynamic pricing software designed specifically for hotels. Its primary goal is to increase a property's revenues, specifically focusing on the revenue per available room (RevPAR). The software achieves this by creating a unique pricing strategy for each room type and future date, striking a balance between average daily rate (ADR) and occupancy.

To generate pricing recommendations, Pricepoint follows a three-step process:

1. Historical Analysis: Pricepoint examines historical data for any relevant trends or seasonality. While historical data serves as a starting point, Pricepoint recognizes that each day, month, or week is unique and therefore uses historical data as a reference rather than the sole driver.

2. Continuous Learning: Pricepoint learns from every new booking, cancellation, or modification to adjust its forecasts. This dynamic learning process takes into account changing customer behavior, demand patterns specific to the year, and the availability of rooms.

3. Comparative Analysis: Pricepoint considers several factors when creating specific pricing recommendations. These factors include the number of available rooms for sale, the remaining number of days before the target date, and the booking pace compared to similar days. The booking pace analysis is crucial in determining pricing variations, even if the number of available units remains constant. For instance, the recommended prices for an upcoming Friday with 10 rooms for sale would differ based on whether the bookings occurred recently, consistently over the past weeks, or months ago.

It's important to note that Pricepoint prioritizes the unique situation, characteristics, and booking dynamics of each property when generating recommendations. While considering market competitiveness, Pricepoint's pricing decisions are not driven by the actions of competitors. This is due to several reasons:

1. Diverse Comparison Set: Assembling a relevant competitor set can be challenging in the online sales landscape, as properties of different classes may appear side by side. Therefore, Pricepoint focuses on optimizing prices based on the property's individual dynamics rather than direct competition.

2. Advanced Pricing Intelligence: Pricepoint's customers are typically price leaders, equipped with better analysis and strategies that align with their property's specific needs. Rather than being price takers, they set the trend in the market, and others may follow their lead.

3. Unique Property Situations: Each property has its own circumstances at any given time. Factors such as recent booking losses or high occupancy levels influence pricing decisions. Pricepoint takes into account these unique situations, ensuring that pricing adjustments are tailored to maximize profits and market potential.

4. Collaborative Market Optimization: Pricepoint recognizes that the hotel industry is not a zero-sum game where one property must "win" against its competitors. By optimizing prices collectively, properties with good products can thrive alongside each other, capturing the most value from the market.

In summary, Pricepoint's AI-powered dynamic pricing software offers hotels the ability to maximize revenues by generating unique pricing strategies. By leveraging historical data, continuous learning, and customized analysis, Pricepoint ensures that each property's pricing recommendations align with their specific needs and market dynamics.