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Description
Prowen Technologies is a modern hotel pricing intelligence and analytics platform built to help hotels improve revenue management through AI-driven market analysis and competitor tracking. The platform enables hotels to monitor rate parity across more than 15 OTAs in real time, instantly identifying pricing discrepancies and undercuts that may impact direct bookings. Prowen provides intelligent pricing recommendations powered by AI, allowing hotels to optimize room rates based on competitor pricing, local demand trends, seasonal events, and booking patterns. The solution also offers competitive analysis tools that help hotels track market positioning, OTA ratings, and pricing performance against comparable properties. Interactive dashboards provide real-time visibility into key performance metrics such as average direct rates, OTA undercuts, market rankings, and demand forecasts. Hotels can also generate professional PDF reports for stakeholders and streamline reporting processes that would otherwise require manual spreadsheet management. Demand and event calendar features help revenue teams anticipate spikes in local demand caused by conferences, festivals, marathons, and other regional events. Prowen is designed for both independent hotels and hotel chains, offering flexible pricing plans and scalable analytics capabilities that support smarter pricing decisions and increased revenue growth. By automating market intelligence and providing actionable AI insights, Prowen Technologies helps hotels improve operational efficiency, strengthen direct bookings, and maximize overall revenue performance.
Description
Wave combines cutting-edge AI with a personal touch, enabling modern revenue managers to escape the confines of traditional 'black box' revenue management systems. This innovative platform offers an unprecedented level of ease and control not typically available in existing RMS solutions. With its distinctive strategy builder tool, Wave equips revenue managers with customizable AI models tailored to their hotel's specific strategy, enhancing transparency in pricing suggestions and allowing hoteliers to focus on managing their establishments effectively. Additionally, Wave caters to those hotels that may not currently utilize an RMS but require a robust revenue management framework. It is designed for hotels of all sizes that struggle with the capacity and resources needed to analyze and act on their data effectively. Furthermore, the system automates demand forecasting through non-linear calculations, improving price elasticity assessments and promoting smarter decision-making in revenue strategies. Ultimately, Wave empowers hotels to optimize their revenue management processes like never before.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Agilysys Analyze
Agilysys Visual One
Execu/Suite
GuestTracker
IQware
Infor HMS
Lodgical Solution
MSI CloudPM
Opera
RdpGuard
Integrations
Agilysys Analyze
Agilysys Visual One
Execu/Suite
GuestTracker
IQware
Infor HMS
Lodgical Solution
MSI CloudPM
Opera
RdpGuard
Pricing Details
$1
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Prowen Technologies
Founded
2024
Country
India
Website
hotelanalytics.prowentech.com
Vendor Details
Company Name
SHR
Founded
1987
Country
United States
Website
shr.global/wave-revenue-management-system/
Product Features
Product Features
Revenue Management
Competitor Analysis
Dynamic Pricing
For Airlines
For Hospitality Industry
Forecasting
Inventory Control
Price Optimization
Recommendation Engine
Yield Management
