Solving the Revenue Management Challenge: Transforming Tour Package Pricing

In the dynamic world of holiday package pricing for travel and hospitality companies, revenue management poses a significant challenge. Traditional demand forecasting models often fall short, struggling to reflect competitive price differences and adapt to rapidly changing market conditions. In this blog post, we’ll explore the complexities of revenue management in the context of tour package pricing and how innovative solutions are reshaping the industry.

Challenges in Existing Demand Forecasting Models

  1. Ineffectiveness: Traditional models fail to provide accurate forecasts, especially for package products with multiple components.
  2. Competitive Price Differentials: They often overlook the critical factor of competitive pricing differences, which can significantly impact demand.
  3. Complexity: The conflicting demand for each component within a package further complicates forecasting.
  4. Slowness to Adapt: These models are slow to respond to changes in the competitive landscape or market situations, resulting in missed opportunities.
  5. Challenging Assumptions: The assumption that price increases close to the arrival date is always effective is not always accurate.

The Goals: Automated Recommendation Processing

To tackle these challenges, the goal is to enable automated recommendation processing in revenue management. This approach aims to:

  1. Quantify Strategic Objectives: Provide a framework to quantify strategic objectives and apply them consistently.
  2. Define Rules: Define rules for various scenarios, eliminating the need to review each recommendation manually.
  3. Empower Revenue Management: Shift the focus from micro-management to strategy development.

Tour Package Pricing Overview

A successful revenue management solution consists of several key components:

  1. Representative Package Model: Selects essential packages to optimize markup/margin by flight, property-key room type, and room night.
  2. Price Response Model: Develops a ‘win’ probability curve based on price and price differentials compared to key competitors.
  3. Component Optimization Model: Utilizes Mixed Integer Optimization (MIP) to generate optimal price recommendations for flight and hotel components. It simulates how these changes impact representative packages.
  4. Aggregate Markup Maximization: The best component prices are determined by maximizing the expected probability of winning customers over competitors, leading to aggregate markup optimization.

What Do We Optimize?

The solution optimizes a combination of flight and hotel mark-ups to achieve the overall optimal price. Factors to consider include:

  1. Cross-Network Effects: Increasing flight mark-up affects all packages at a destination, while hotel mark-up impacts all points of origin for the same destination.
  2. Length of Stay: Flight and hotel markup effects may vary based on the length of stay, which competitor pricing is not linearly dependent upon.
  3. Balancing Act: Recommendations must balance between increasing margins and the impact on different package components.

Example of Post-Optimization Rules

To fine-tune recommendations, post-optimization rules are applied:

  1. Volume and Profitability: Rules are defined to index responses to deviations from target volume or profitability.
  2. Margin Adjustments: Pricers apply rules to adjust margins up or down from the optimized value to achieve volume or profitability objectives.
  3. Special Cases: Rules may also apply for bulk overrides, promotions, room type variations, and controlling distribution channel availability.

Solution Benefits

The implementation of this automated solution brings significant benefits to hoteliers, resort operators, and package tour providers:

  1. Demand Flexibility: It caters to industries with challenging demand forecasting and less dependence on days to arrival.
  2. Dynamic Competitor Pricing: Rapid pricing responses are enabled, considering competitor pricing dynamics.
  3. Tailored Strategies: Different product lines can be managed with unique strategies, addressing risk and non-risk inventory effectively.
  4. Consistency: Maintains consistent decision-making and reduces the reliance on specific revenue management skills.


The challenges of revenue management in the tour package industry are met with innovative solutions that prioritize automation, data-driven decision-making, and optimization. By leveraging technology and advanced modeling, businesses can not only navigate the complexities of demand forecasting but also thrive in a competitive landscape. The transformation of revenue management is reshaping the industry, empowering businesses to achieve their strategic objectives and maximize profitability.

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