Hospitality
Increasing Revenue with Personalization and Dynamic Pricing
The post-COVID-19 return to travel promises to be one of fits and starts, with states, regions, and countries at various and evolving stages of recovery. While lower guest volumes will persist for some time, hotels can use technology to increase their share of the available revenue through personalization and dynamic pricing offer creation. Putting guests in control and creating relevant digital conversations with them will not only build additional trust but will also increase the speed of the recovery and help boost market share for the hotels.
Rethinking B2C Guest Booking Automation Requirements
Ability to support dynamic occupancy-based pricing
Creation of personalized offers based on tier, persona and/or past purchase history
Creation of dynamic cost optimization packages to maximize transactions (i.e., early check-in, late check-out options)
Provide flexible guarantee and cancellation policies by region or segment
Generate offer of additional insurance for gaps in the cancellation policy
Communication of relevant guest health and safety requirements during the booking process i.e., temperature checks
Must-Have Technologies
2- way, real-time PMS/CRS integration to Booking Engine
RMS property management solution with access to integrated real-time demand
Flexible rule engine approach allowing configuration authoring to provide greater flexibility in changing environments
The ability to support both shopping cart and packages
Integration with Air (live) and Charter
Centralized content repository for brand-related requirements
APIs to integrate with health status updates from government health authorities
Customer Engagement Intelligence capabilities to integrate real time analytics, personas, etc. to predict intent and preferences as well as communicate relevant content
Automated re-booking, refunds, cancellation, and crediting decision making based on changing check-in scenarios
Customer Engagement Intelligence integration for unknown visitors to predict intent and preferences to present relevant offers