Advanced Revenue Management & Pricing Strategy
Introduction
This intensive, advanced-level training program delves into revenue management and pricing strategy complexities, equipping participants with cutting-edge techniques and strategic frameworks to maximize revenue and profitability. This program builds upon foundational knowledge and focuses on advanced analytics, dynamic pricing, competitive strategies, and practical application through case studies and simulations.
How you will benefit
- Develop and implement sophisticated dynamic pricing strategies.
- Utilize advanced forecasting methods, including machine learning techniques.
- Optimize revenue across multiple channels and customer segments.
- Analyze and respond to competitive pricing dynamics.
- Implement revenue management systems effectively and leverage data analytics.
- Apply revenue management principles in a variety of business contexts.
Who should attend
This advanced course is designed for experienced revenue management professionals, pricing managers, analysts, and business leaders seeking to refine their expertise and drive significant revenue growth. Prior experience in revenue management or a related field is required.
What you will cover
- Beyond demographics – behavioural, psychographic, and value-based segmentation.
- Algorithms, automation, and responsiveness to market changes.
- Tailoring prices to individual customer profiles and preferences.
- Advanced techniques for measuring and predicting price sensitivity.
- Case Study: Developing a dynamic pricing strategy for an airline/hotel/e-commerce platform.
- Gathering and analyzing competitor pricing data.
- Understanding competitive interactions and strategic responses.
- Managing pricing conflicts effectively.
- Case Study: Analyzing a competitive pricing scenario and developing a response strategy.
- ARIMA modelling, exponential smoothing, and advanced techniques.
- Multiple regression, non-linear regression, and model selection.
- Introduction to relevant algorithms (e.g., neural networks, random forests).
- Incorporating economic indicators, seasonality, and other external influences.
- Hands-on Exercise: Building a forecasting model using real-world data.
- Influencing customer behaviour through pricing.
- Balancing supply and demand.
- Optimizing revenue across different distribution channels.
- Case Study: Developing a demand management plan for a seasonal business.
- Balancing online, offline, and mobile channels.
- Measuring the impact of different channels on revenue.
- Resolving pricing discrepancies and channel competition.
- Case Study: Developing a multi-channel revenue management strategy.
- Advanced methods for estimating customer value.
- Targeting high-value customers with personalized offers.
- Maximizing long-term profitability.
- Case Study: Developing a CLTV-based revenue management strategy.
- Choosing the right system for your business.
- Connecting RMS to relevant data sources.
- Leveraging RMS capabilities for efficiency and insights.
- Hands-on Exercise: Working with a simulated RMS environment.
- Key Performance Indicators (KPIs) for Revenue Management: Tracking progress and identifying areas for improvement.
- Data Visualization & Reporting: Communicating revenue management insights effectively.
- Predictive Analytics for Revenue Management: Anticipating future trends and opportunities.
- Case Study: Analyzing revenue data and developing actionable recommendations.
Schedule
Vancouver
$6,000
09 & 10 Oct 2025
Live Online
$6,000
13 & 14 Nov 2025
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