Pricing strategies are no longer static or based on intuition in the hotel industry. Hotels and hospitality businesses are required to adopt advanced, data-driven strategies that can adapt in real time to changing demand, competitor action, and market trends.
This blog details Price Elasticity, providing in-depth analysis and actionable advice crafted through industry-based case studies and state-of-the-art AI deployment. It also delves into hotel pricing strategy, MLOps service, and how the basic economic principle of demand elasticity fits with MLOps, operationalizing machine learning to develop a scalable and automated price optimization engine that transforms hospitality revenue management.
What is Price Elasticity & Dynamic Pricing in Hospitality
It is the degree to which demand responds to price changes. For hotels, this implies understanding the impact of a percentage price change on booking volumes. Generally, an elasticity in price greater than 1 indicates that demand is very responsive to a 5% price rise, for instance, leading to more than a 5% fall in bookings. Having this knowledge enables hotels to set prices that maximize revenue without compromising occupancy.
Dynamic pricing in the hospitality industry takes advantage of this price elasticity by offering flexible room prices by adjusting the prices several times each day. These pricing adjustments depend on local events, seasonality, booking velocity, other hotel pricing, etc. Unlike a set price, a dynamic pricing strategy in hotels can help boost revenue by capturing when guests are ready to pay more at different times in their booking process.
For example, Marriott International uses special pricing methods that look at hotel demand forecasting, how people book rooms, and what big events are going on. Their AI-based system helps the company drive 5% annual revenue increase by continually optimizing room rates. (Source)
In the same way, OYO Hotels uses technology-driven dynamic pricing to rapidly respond to market and demand patterns. (Source)
Modeling Demand with Elasticity for Hotels
Accurate price elasticity modeling requires historical data to be collected and processed. These include occupancy rates, average daily rates (ADR), competitor pricing, booking, event calendars, and customizing seasonality. Models quantify changes in bookings against changes in price for different types of rooms, times, and some customer characteristics. More intricate methodologies include:
Segmented Elasticity Modeling
Segmented elasticity modeling determines price elasticity for different customer categories, which include business travelers, leisure guests, and groups. This stratification helps differential pricing based on weighted price, which is that business travelers and groups, to some extent, have a lower price and so are less responsive to pricing changes, whereas leisure travelers are more responsive to pricing controls.
Time-Series Analysis & Forecasting
Forecasting and time series are analytical approaches that predict demand based on historical pricing and booking data. These data sets identify seasonality, local events, and relevant holiday periods. Predicting demand elasticity in real time will place hospitality brands in an active position regarding pricing.
Price Optimization & Recommendation Engine
A price optimization engine leverages price elasticity models to suggest price adjustments that best attain RevPAR (Revenue per Available Room) and occupancy goals.
Key features:
- Real-time Data Feeds: Real-time data monitoring of booking, market, and competitor price changes.
- Constraints Management: Business rules and constraints define and control price thresholds.
- Personalization: Forecasted personalization analyzes client categories and their behavior.
A hospitality chain wanting to automate dynamic pricing and custom subscriptions may use an AI-driven price optimizer that sets recommended prices using elasticity models, as well as booking and competitor prices in real time.
MLOps Pipeline & Architecture for Price Elasticity Engines
Sustainable price optimization systems need automated MLOps machine learning operations that streamline training, deploying, monitoring, and updating models at scale.
Typical Architecture:
- Data Ingestion & Unification: Aggregate data from PMS (Property Management Systems), CRM, and market intelligence, along with other external sources.
- Feature Engineering: Define and extract key actionable variables like booking lead time, seasonality indices, and competitor pricing.
- Model Training: Use historical data and real-time data to train elasticity and forecasting models.
- Validation & Testing: Ensure models are general, not overly fitted to the training data.
- Deployment: Use APIs to integrate models into pricing engines and front-desk systems.
- Monitoring & Feedback Loop: Track changes in the model, accuracy, and business KPIs. Keep retraining the models all the time.
Such pipelines let hotels set prices automatically. This helps to lower the need for manual interventions and reduce operational costs.
Implementation Challenges, Risks & Best Practices
Though there are clear benefits in the price elasticity of prices with MLOps, there are also a few challenges that need to be fixed:
- Price Volatility and Customer Trust: Setting a reputation for consistent and seamless pricing positively affects customer goodwill. Moreover, overly inconsistent pricing can lead customers to alienation.
- Data Quality Issues: High-level demand prediction may be clouded by incomplete, disorganized, or missing data sets. Hence, a solid data plan for data handling is required.
- Rate Parity Across Channels: Price management for OTAs and direct, corporate, and corporate bookings requires system integrations.
- Competitor Reactions: Poorly positioned pricing strategies may trigger price wars for lower profit margins.
Best practices include a phased approach, beginning with small pilot initiatives. Ensure communication flows freely between the sales and marketing departments. The synergy between artificial intelligence and domain knowledge for market signal prediction.
Roadmap: From Pilot to Production to Scale
To apply scalable price optimization in hospitality, it is crucial to develop an intelligible, step-wise plan that includes structured roadmaps that transition from small-scale initiatives to large-scale.
- Pilot with Specific Segments: Initially, pilots should be with specific segments. Focus on a few properties or customer segments to validate elasticity models. Assess feedback to refine models.
- Refine Models with Feedback: Construct controlled revenue-impact experiments and A/B tests to inflict changes on revenue and observe the impacts. Assimilate with active revenue management systems and stakeholder expectations.
- Integrate with Existing Revenue Management Systems: Design closed-loop systems with developer-friendly APIs for unidirectional data flow and rule compliance.
- Automate Decision-Making: Automate decision processes with the appropriate controls. Progressively transition from human decision systems to total automation.
- Scale Across Portfolio: Then, models can be scaled across the entire enterprise, taking into account regional differences, brand tiering, and various levels of customization.
Conclusion
The integration of price elasticity and MLOps-driven dynamic pricing tools represents a new worldview in revenue management for the hospitality industry. The use of MLOps integrated automated systems enhances self-optimizing pricing systems, error-adjust and self-optimizing relearning pricing systems, the self-adjust and self-optimizing systems in the MLOps paradigm.
Tredence helps hospitality brands integrate automated systems into their operations, fostering profitability, operational efficiency, and hospitality market responsiveness. The implementation of revenue management integrated with artificial intelligence and economic theory allows hospitality leaders to succeed even during the most challenging times in the travel & hospitality services sector. Leverage Tredence’s industry know-how and cutting-edge AI innovation to build your travel analytics solutions to stay competitive in a fast-evolving industry.
FAQs
1. What is the price elasticity of demand?
Price elasticity measures and examines shifts in demand for a product in response to changes in price. It tells how much demand responds to a price change.
2. What is the formula for price elasticity?
Price Elasticity = (% Change in Quantity Demanded) ÷ (% Change in Price).
3. What is dynamic pricing in hotels?
Dynamic pricing involves changing a hotel room's price in relation to anticipated demand, competitor prices, and a variety of other criteria in the market to maximize revenue.
4. How does demand elasticity work in hospitality/hotels?
It estimates the changes in volume of bookings as a result of price changes. High elasticity means that small price changes will increase or decrease the volume of bookings considerably, and helps in a more consistent value-based price systematic approach.
5. What is MLOps in pricing?
It describes the practice of automating the pricing of a good or service, bringing order to the deployment, oversight, and automatic price updates to the model based on predetermined conditions set for price adjustments. This should be done in real time and will eliminate time lags, thus improving the overall efficiency and effectiveness of pricing.
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Editorial Team
Tredence
