AI-Driven Dynamic Pricing System for Park Avenue Hospitality

Challenge

In the competitive hospitality industry, optimizing room rates based on demand, competitor pricing, and various external factors is critical for maximizing revenue. Park Avenue Hospitality faces challenges in adjusting their room rates dynamically to reflect real-time occupancy, seasonal fluctuations, local events, and competitor activities. The need for a scalable and intelligent pricing system to enhance revenue management and improve pricing strategies is more urgent than ever.

Key Challenges:

Real-Time Rate Adjustments:
Accurately adjusting prices based on fluctuating occupancy levels, competitor activity, and external events.
Competitor Price Monitoring:
Continuously tracking competitor rates across multiple online travel agencies (OTAs) and direct booking channels.
Event and Seasonal Pricing:
Applying premium pricing during high-demand periods (e.g., local festivals or seasonal events).
Forecasting Demand:
Predicting future demand trends to set optimal prices, especially during unpredictable market conditions like weather changes or economic shifts.

Solution

The proposed solution involves a two-phase implementation of an AI-driven dynamic pricing system that leverages industry best practices, including insights from leaders like Marriott and Four Seasons. The system will adjust room rates dynamically in response to real-time occupancy, competitor pricing, and event schedules, with a plan to enhance its capabilities through advanced forecasting and personalized pricing.

Phase 1: Core Dynamic Pricing Implementation

Objective: Deploy a foundational system that optimizes room rates based on real-time data inputs, including occupancy, competitor pricing, seasonal trends, and local events.

Key Features of Phase 1:

Competitor Benchmarking: Daily scraping of competitor rates using AWS Lambda and custom APIs to compare pricing across OTAs and direct channels.
Occupancy Tracking: Real-time integration with the Property Management System (PMS) to monitor room availability and adjust rates accordingly.
Event-Based Pricing: Integration with local event calendars to apply premium pricing during high-demand events like Shanghai Fashion Week or the Singapore Grand Prix.
Rule-Based Adjustments: Automatically adjust room rates by 10–15% when occupancy exceeds 70%, based on industry benchmarks by Altexsoft.

Deliverables for Phase 1:

Dynamic Pricing Dashboard:
1. Real-time heatmaps showing occupancy levels across all properties.
2. Side-by-side comparison of competitor rates.
3. Analysis of event impacts, including predicted demand spikes during local festivals and events.
API Integration: Seamless connection with Park Avenue’s PMS and channel managers (e.g., SiteMinder) for automatic pricing adjustments.
Alert System: Notifications sent to revenue teams when competitor rates fall below 5% of Park Avenue’s prices, enabling timely rate adjustments.

Timeline: 16 weeks to complete Phase 1.

Phase 2: Advanced Pricing Scenarios

Objective: Expand the system to incorporate more sophisticated pricing models, customer segmentation, demand forecasting, and personalized pricing options.

Key Features of Phase 2:

AI Demand Forecasting:
Use TensorFlow and historical data to predict demand up to 90 days in advance, helping to set optimal pricing based on anticipated market conditions.
Personalized Pricing:
Integrate with Park Avenue’s CRM (e.g., Salesforce) to offer personalized discounts and promotions to repeat guests or loyalty members, improving customer retention.
Open Pricing Engine:
Adjust prices for specific room types and views (e.g., premium pricing for ocean-view rooms) to account for demand variability by room category.
Price Elasticity Models:
Implement A/B testing frameworks to determine the most effective rate ranges by testing customer responses to different pricing strategies and maximizing revenue per customer segment.

Deliverables for Phase 2:

Advanced Pricing Engine:
Incorporating AI-driven demand predictions, customer segmentation, and dynamic adjustments for specific room types and views.
Personalized Pricing Platform:
Implement personalized discounts for loyalty members and repeat customers based on CRM data, driving higher customer retention rates.
Real-Time Demand Forecasting Dashboard:
An advanced dashboard to visualize predictive demand models and adjust pricing strategies accordingly.

Timeline: Phase 2 will be implemented after Phase 1, with an estimated duration of 12–16 weeks.

Key Benefits

Increased Revenue:
Real-time pricing adjustments based on occupancy, competitor rates, and event-based demand allow Park Avenue Hospitality to maximize revenue during peak periods.
Competitive Edge:
By continuously monitoring competitor pricing and integrating event schedules, Park Avenue can remain competitive, attracting more guests by offering the most attractive rates compared to nearby hotels.
Better Demand Forecasting:
Advanced AI-driven demand forecasting allows the company to anticipate booking trends, helping to set optimal rates well in advance, reducing the risk of underpricing or overpricing.
Enhanced Customer Experience:
Personalized pricing based on customer segmentation ensures that repeat guests and loyalty members receive better deals, improving customer satisfaction and loyalty.
Scalable Solution:
The phased implementation ensures that the system is scalable, with the flexibility to expand across multiple properties, integrate with existing systems, and handle increasing demand as Park Avenue grows.
Optimized Operational Efficiency:
Automating price adjustments and integrating real-time data feeds reduces the manual effort required from the revenue team, allowing them to focus on strategic decision-making and customer engagement.

Conclusion

This AI-driven dynamic pricing system will allow Park Avenue Hospitality to optimize room rates dynamically based on real-time occupancy, competitor activity, seasonal fluctuations, and local events. By leveraging advanced AI algorithms and predictive models, the system will help the company increase revenue, stay competitive, and enhance customer satisfaction. With a phased implementation approach, the solution is scalable, flexible, and adaptable to the growing needs of Park Avenue Hospitality.

Case Details