Updated : Feb 9, 2025
Summary:
- PriceLabs leverages scraped data from platforms like Airbnb, Vrbo, and Booking.com, fueling Market Dashboards, Dynamic Pricing, and Neighborhood Data for a comprehensive global overview.
- PriceLabs tackles the challenge of distinguishing booked and blocked dates using block removal logic, enhancing accuracy in discerning genuine bookings.
- Leveraging Airbnb as a critical reference, PriceLabs deduces bookings on other platforms, showcasing adaptability and versatility.
- The latest Breakthrough Release introduces the Hyper-Local Pulse (HLP) algorithm, ensuring unmatched precision and adaptability in dynamic pricing, marking a giant leap for property managers globally.
- Market Dashboards ingeniously infers critical booking details by analyzing changes in listing calendars, offering insights into Booking Window, Length of Stay, and more.
Dynamic pricing has become a game-changer in the ever-evolving landscape of property and vacation rentals. PriceLabs stands out among the frontrunners in this arena with its innovative breakthrough release. The release isn’t just an upgrade; it’s a giant leap for property managers and revenue management teams. To truly understand the power behind PriceLabs, let’s delve into where it gets its data, how it processes it, and the groundbreaking refinements introduced in the latest Breakthrough Release.
Where does PriceLabs Data come from?
PriceLabs relies on scraped data from Airbnb, Vrbo, and Booking.com, and direct data from KeyData to compile a comprehensive overview of listings. This approach fuels Market Dashboards, Dynamic Pricing, and Neighborhood Data. PriceLabs captures valuable information by exploring the public pages of each listing, including changes to the calendar, availability fluctuations, price adjustments, and other relevant details. This method ensures a robust and expansive dataset, providing insights for any location globally.
Data Processing: Inferring Insights from Changes
In dynamic pricing, not all booking data is available from a listing’s calendar. Market Dashboards, Dynamic Pricing, and Neighborhood Data employ clever methods to infer critical information such as Booking Window, Length of Stay, Booked Date, and Booked Price. PriceLabs analyzes changes in a listing’s calendar over time and identifies consecutive unavailable dates as potential bookings. The scraping frequency allows for a well-determined booked date, contributing to a more comprehensive understanding of each booking.
Block Removal: Mastering Challenges
A common challenge with scraped data is distinguishing between booked dates and dates blocked by the owner. Market Dashboards, Dynamic Pricing, and Neighborhood Data address this challenge using block removal logic. PriceLabs analyses factors such as Length of Stay, Booking Window, Market Occupancy, and extreme price variations. This helps to determine if a listing is genuinely booked or if the dates are intentionally blocked.
Cross-OTA Booking Deduction: Airbnb as the Key
Even when a property is listed on multiple OTAs, Market Dashboards, Dynamic Pricing, and Neighborhood Data can deduce bookings on other platforms as long as the property is also listed on Airbnb. The logic lies in the fact that bookings on other OTAs or direct websites lead to blocked dates on Airbnb. These features, leveraging their block detection mechanism, can identify whether a date is booked, regardless of the channel through which the booking was made.
Dynamic Pricing Analysis: Decoding the Scores
Dynamic Pricing Analysis adds another layer of insight for property managers. The “Dynamic Pricing” column in listing comp sets reflects day-to-day price variation and changes since the last check. Scores range from 0 to 1, where 1 indicates significant daily price variation and constant changes. The scores are categorized into bins—None, Low, Moderate, and High—offering property managers a quick reference to understand the pricing dynamics of their comp set.
- None: Listings with scores between 0.0 – and 0.1, indicating rare price variations.
- Low: Scores between 0.1 – 0.25, where listings may change prices for holidays and events, but overall prices remain constant weekly.
- Moderate: Scores between 0.25 – 0.5, indicating varying prices weekly, but with slight variance and infrequent updates.
- High: Scores above 0.5, where listings’ prices account for day-of-week and holiday/event demand changes, updating every few days, potentially using dynamic pricing software.
The Breakthrough Release: Refining the Algorithm
PriceLabs’ Breakthrough Release is not merely an upgrade; it’s a leap into the future of revenue management. Fueled by cutting-edge data science and user feedback, this release is tailored for medium to large property management companies. With over 20 new features and tools, it promises substantial global benefits for property and revenue managers.
The Hyper Local Pulse (HLP) Algorithm: Precision Redefined
At the core of the Breakthrough Release stands the Hyper Local Pulse (HLP) algorithm, a dynamic pricing innovation designed to optimize rates and maximize revenues. HLP leverages hyper-local data sets, ensuring unparalleled precision and adaptability in pricing strategies. Within three months of adopting HLP, new PriceLabs users experienced an average 26% boost in Revenue per Available Night (RevPAR).
HLP’s Key Features and Benefits:
- Hyper-local Relevance: HLP leverages hyper-local comp-sets for precise adjustments. This ensures unmatched accuracy in adapting to seasonality and specific days.
- Event and Holiday Pricing: HLP is enriched with a four-way event detection system. It considers pacing from the previous year, early demand signals, competitor pricing, and hotel price indications. This results in improved event and holiday pricing.
- Rapid Market Adaptability: The swift response mechanisms guarantee optimal responsive seasonal and event pricing. It allows property managers to seize the full potential of market dynamics.
- Time-saving: Significant reduction in the need for manual pricing interventions, streamlining operations, and enhancing efficiency.
- Transparency: PriceLabs’ data science team provides detailed insights into how the algorithm works, ensuring transparency and understanding for users.
Conclusion:
In conclusion, the science behind PriceLabs data is a journey from meticulous data gathering and processing to the revolutionary refinement of the algorithm in the latest Breakthrough Release. Property managers worldwide can now harness the power of dynamic pricing with unparalleled accuracy and efficiency. As the future of revenue management continues to evolve, PriceLabs remains at the forefront, leading the way into a new era of success for property managers and revenue teams.
