What determines which properties are recommended?

Aidaptive selects and ranks inventory based on guest behavior, preferences, and real-time market conditions. The goal is to drive higher conversions, increased direct bookings, and better guest experiences by serving the most relevant options.

Data Collection & Guest Profiling

The engine continuously gathers and processes data from multiple sources, including:

• Guest behavior: Searches, clicks, time spent on listings, abandoned bookings.
• Past bookings: Previous stays, preferred room types, loyalty data.
• Contextual signals: Device type, location, time of day, seasonality, and real-time travel trends.
• Market trends & demand signals: Room availability, website traffic patterns, pricing.

Machine Learning & Heuristic Models for Personalization

The recommendation engine uses a combination of machine learning (ML) models and heuristic models to generate personalized recommendations.

These models learn patterns from guest behavior and inventory data to predict the most relevant listings:

• Collaborative Filtering

  • Suggests inventory based on similar users' behaviors.
  • Example: "Guests who booked this downtown single family home also looked at these nearby options."

• Content-Based Filtering

  • Matches guest preferences with listing attributes (e.g., ocean view, pet-friendly, breakfast included).
  • Example: If a guest prefers beachfront condominium properties, the system prioritizes those.

• Context-Aware Models

  • Adapts recommendations based on seasonality, booking window, or location.
  • Example: Last-minute travelers see listings with immediate availability aligned with their preferences.

• Popularity-Based Heuristics

  • Prioritizes properties that are trending based on recent bookings and searches.
  • Example: "This beachfront property has been booked 10 times in the last 30 days."

• Demand Surge Detection

  • Identifies listings with growing interest (e.g., due to an upcoming event or holiday).
  • Example: A city-center property sees a spike in demand due to a conference, so AI promotes it to guests searching within those dates.

• Exceptional Value for Cost

  • Surfaces listings with the best price-to-value ratio, factoring in guest reviews, amenities, and discounts.
  • Example: A 4-star property with a flash sale might be highlighted over a similarly priced 3-star option.

• Inventory Balancing Heuristics

  • Dynamically adjusts recommendations to optimize room occupancy and revenue.
  • Example: If a property has excess availability for a specific season, the AI might prioritize it in recommendations.

• Guest Lifetime Value (LTV) Optimization

  • Personalizes recommendations based on a guest’s long-term revenue potential.
  • Example: High-value repeat guests might see recommendations for premium properties.