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How to Build a Dynamic Pricing Model with Extracted Flight Data?

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Have you ever wondered why it feels like airline ticket prices change every time you check? That's the consequence of dynamic pricing, a method that has become the norm for how airlines determine ticket prices. Prices might "go up and down for a variety of factors, customer demand, how many seats left, what competitors are charging," and even what time of year it is. To stay ahead of these rapidly changing conditions, airlines employ dynamic pricing models that draw on real-time data and cutting-edge technology to adapt pricing on the fly. This approach helps them make the most of every seat on every flight. As a result, ticket prices for the same flight route fluctuate on average multiple times per day. It provides businesses with the opportunity to implement a Data-driven strategy to interpret and predict patterns in price fluctuations.

As the amount of publicly available flight information has increased through APIs, Web Scraping, and third-party datasets, it has become easier for anyone to build predictive models using the same logic airlines use to set prices. These models can be valuable to travel agencies, comparison shopping tools, and revenue management professionals by enabling them to forecast future pricing better and make informed decisions on price points, offers, and marketing spend. When creating your own Dynamic Pricing Model, carefully implement it using high-quality data, practical feature engineering, and a strategic selection of machine learning algorithms. This blog will guide you step-by-step through the development of a Dynamic Pricing Model using extracted flight data to empower you in developing a more adaptable Dynamic Pricing Model for your business.

What Is Dynamic Pricing, and Why Does It Matter?

Dynamic pricing is a tactic that allows businesses to price their goods according to the status of the current market. Dynamic pricing is crucial for the airline industry. Airlines face huge demand fluctuation based on seasonality, supply-demand patterns, customer behaviors, and external factors. Airlines sometimes change ticket prices dozens of times a day, depending on how many seats are available on the plane, how popular the flight is, and — increasingly — how often the airline would like to adjust its prices in response to what competitors are charging 13 months before departure. For instance, once flights start to fill up and travel seasons near, ticket prices increase. On the opposite end of the continuum, ticket prices decrease seasonally when the airline aims to fill as many seats as possible during periods of low demand.

Dynamic pricing models leverage historical data and algorithms to identify ticket price trends and predict future price movements. Dynamic pricing models do not rely on "gut feelings"; instead, they use historical flight data and current flight schedules to predict pricing behavior. As a result, dynamic pricing models help identify the optimal times to purchase flight tickets, increase revenue for travel agents, and better compare competitors. Understanding dynamic pricing is key to building an accurate pricing model, as it illustrates how airlines respond to market signals and which factors most impact airline ticket prices.

Why Should You Use Extracted Flight Data for Pricing Models?

Downsized flight data supports influencing the estimate of how the price behaves to comply with the carriers' actions. Prices of flights in full commercial conditions can be gathered in real-time for multiple routes and airlines, thus discovering the level at which airlines adjust prices depending on the day, month, or season when travel is performed; the number of remaining days to the departure date; and the actions of competitors. Extracted Flight Data is required to provide the necessary information for developing an accurate Dynamic Pricing Model (DPM), as a DPM relies on accurate information from the actual marketplace to determine how airline ticket prices fluctuate.

Extracted flight data enables the analyst to examine how airline ticket prices interact with other variables that affect ticket prices. An example of this would be evaluating how a specific airline responds to increased demand compared to another airline operating in the same market. Extracted flight data also allows the analyst to investigate how airline ticket prices change before major travel holidays or on weekends, compared to other periods. Extracted flight data will enable the creation of highly advanced predictive models, comparative analyses, and automated pricing systems.

Extracted flight data enables travel agencies and online booking platforms to make pricing and promotion decisions for airline-related travel services, thereby increasing their profitability. Correctly extracted flight data enables travel agencies and online booking platforms to forecast future airline ticket prices, build effective sales strategies, and determine when to create promotional offers for specific sale items. Extracted flight data provides transparency into airline behavior, actionable insights for strategic decision-making, and enables analysts to make immediate pricing adjustments.

What Type of Flight Data Should You Collect?

A reliable dynamic pricing approach relies on acquiring an appropriate set of data related to flights. The fundamental characteristics of the flights are the most basic of details, and all analysis model work can build upon them. Price features show the evolution of fare pricing over time by giving the model information about known volatility in fare pricing.

Seasonal markers also add to depth and enhance prediction, partnered with other demand signals and external context. With a blend of datasets, your model will be shown the full image of airline pricing behaviour and can surface hidden relationships and patterns. The more comprehensive and organized your data is, the better a dynamic pricing model you can receive for the real market conditions, and accordingly, it can adjust to your changing market prices.

Base Fare and Price Elements

Seasonal Indicators indicate recognised patterns in fluctuations based on holidays, school breaks, festivals, and peak times of the year. Booking lead time and flight path popularity are two demand-driven indicators that can affect fare variance. If these components are incorporated into the model, they further illuminate natural pricing cycles by indicating when naturally air ticket prices rise or fall, and allow forecasting of airlines' prices and identifying repeating demand-signifiers.

External Influencing Factors

External Influencing Factors are factors outside the airline's control that also affect pricing decisions, including weather disruptions, significant events, fuel prices, and airport congestion. The model uses this knowledge in a broader sense to provide a more complete picture of the 'external forces' that potentially affect an airline's pricing decisions, thus providing better context when airlines experience unusual pricing movement, and it is difficult to understand the stimulus behind them.

Competitor Signals and Market Signals

Competitors' prevailing fares, promotions, flash-sale events, and availability at the flight route level all indicate continued market movement by providing insight into how airlines respond to competitive pricing and how they set prices relative to competitive positioning. Market Signals afford greater predictability, particularly during fare wars and sudden promotional shifts (significant) in airline pricing approaches.

How Can You Extract Reliable Flight Data?

Accurate flight information is critical to creating a dynamic pricing algorithm that reflects actual market conditions. Application Programming Interfaces (APIs) provide structured access to real-time pricing and flight schedules, whereas web scraping allows flexibility in collecting non-API, customizable fields. Additional datasets collected from third parties include long-term historical pricing information that is useful for creating seasonal pricing models. Automated collection methods allow consumers of pricing models to utilize and analyze the most current airline behavior and pricing trends.

The following elaborates on both the Flight Information Collection Process and the Dynamic Pricing Algorithm Development Process.

API Access to Flight Pricing Data

APIs provide a structured way to obtain flight pricing data that is easy to integrate into a pricing model. Examples of API use include utilising API documentation to identify the upper limit of flight prices that can be charged for any specific ticket type via an API, and to determine whether each API's pricing limit is consistent.

Web Mining Techniques to Extract Flight Information

Web Data Mining is the process of gathering customer flight information using methods that cannot be achieved through APIs. Dynamic pricing and available seats may be examples of information that web mining could collect. Additionally, collecting flight information through web scraping could pose some challenges relating to technical and legal issues with anti-bot systems. However, web scraping enables the collection of large amounts of flight-related data from many sources, which may be beneficial to pricing analysts.

Using Historical Flight Pricing Data from Third-Party Vendors for Analysis

Third-party vendor historical flight pricing data is an integral part of building and training a pricing model. Third-party data sets usually have significantly more historical information than typical printed documentation. Also, third-party datasets commonly include flags for average pricing and highlight periods of higher price volatility driven by seasonal fluctuations. When combined with the above information, third-party datasets eliminate the need to manually gather flight price history and provide an excellent base for developing and using a Statistical Price Prediction Model.

Automating the Flight Pricing Data Collection Process

Automated flight pricing data collection pipelines ensure that regular processes are in place to validate and store newly acquired flight pricing data continuously. Automatic collection scheduling removes inconsistencies in data collection practices and enables continuous training of pricing models with timely, accurate flight pricing data.

How Do You Clean and Prepare Flight Data?

Once collected, flight data has to be preprocessed to make it accurate and uniform. This flight data is typically gathered in a form that is very "noisy", including: missing values, duplication of records, and inconsistent formatting of dates, airports, and currencies. So data cleaning here might mean getting rid of duplicate entries, reformatting dates, fixing airport codes, and checking that currencies are consistent throughout. Missing values, furthermore, should be imputed with statistical methods or deleted if they decrease accuracy.

After having cleaned up the data, it's now time to derive useful features (independent variable) from the raw dataset. Feature engineering is a key component in building a successful pricing model. By determining these days, you can know on what day airlines offer the lowest fares by knowing how many of them are left before the flight takes off.

The establishment of indicators for peak demand outside the holiday period (weekends, high travel season) will also allow the determination of "when" demand is most noticeable. For example, both last year's average for a given route and its price volatility indicate variability on that route, so that these two pieces of information may respectively be used as inputs to influence pricing prediction models.

In addition to the indicators mentioned above, other features can be created as well, such as how much cheaper or more expensive competitors' prices are compared to the airline being priced, what times of the day the flight will take place (during the day, night, etc.), or whether or not the flight has a layover, i.e. whether or not there is one or more connections required to reach the destination. Creating these features increases the amount of data available to the model, allowing deeper patterns in airlines' pricing behaviour to be uncovered.

Which Algorithms Should You Use for Dynamic Pricing Models?

Your goals determine which algorithm(s) to use to either predict or optimise price levels. Most traditional forecasting algorithms, such as random forests, gradient boosting, and neural networks, produce fair price predictions based on past trends and patterns. Optimisation-oriented algorithms, such as reinforcement learning, simulate decision-making processes and suggest the best prices to maximise revenue. Ensemble-based modelling approaches tend to deliver better predictive accuracy and require a robust evaluation methodology to determine which model(s) demonstrate the highest overall performance, interpretability, and computational expense.

• Predictive Models: Both XGBoosting and random forest models provide insight into how price changes relate to a variable or feature in determining fare prices.
• Price optimisation algorithms: Reinforcement learning enables price adjustments through a series of reward-optimised decisions throughout the pricing process.
• Complex relationship patterns found by Neural Networks: Neural network models have a unique ability to detect non-linear relationships between variables and fare price data that could otherwise go unnoticed with other predictive model methodologies.
• Ensemble Method: By combining several different algorithms, you can reduce overfitting and achieve greater consistency in predicting the outcome of a test situation, regardless of its nature.

How Do You Build a Dynamic Pricing Model Step-by-Step?

Creating an efficient dynamic pricing system requires a clearly defined, structured approach. The development process will begin with determining how the dynamic pricing system aligns with the business objectives. The objectives can be defined as one of three categories (i.e., predicting seasonal ticket prices, determining the best ticket prices to charge, and tracking competitors' prices).

After defining the purpose of the dynamic pricing model, the following phases must occur:

Step 1: Define the Pricing Objective

Clearly define whether the pricing model will focus on predicting air ticket prices, suggesting optimal selling prices (i.e., dynamic pricing) for air tickets, or monitoring competitors' pricing to support specific business objectives.

Step 2: Collect the Data

You can collect data on airline ticket prices using APIs (Application Programming Interfaces) or web scraping, and store it in a way that scales access and analysis (such as an SQL database or in the cloud).

Step 3: Prepare the Data

To create a high-quality, reliable dataset for developing a pricing model, you will need to remove duplicate records, correct missing-value errors, and format the dataset appropriately.

Step 4: Create Features

As part of developing your dynamic pricing model, during this stage, you will create practical and relevant features that will be used in your model (booking windows, seasonal indicators, competitor price differential, historical averages), which is an essential step since having well-developed features leads to a more robust predictive dynamic pricing model for flight prices.

Step 5: Splitting Data into Training and Test Sets

The complete dataset must be split into training and test sets (typically 80%/20%) to allow unbiased evaluation of the dynamic pricing model's performance and reduce the risk of overfitting.

Step 6: Model Training

By applying algorithms such as Random Forest and/or Gradient Boosting, you can determine pricing patterns in prepared datasets and ultimately produce a price prediction from the input data provided to the model.

Step 7: Evaluating the Model's Performance

After creating the dynamic pricing model, you will evaluate its ability to accurately predict airplane ticket prices using metrics such as RMSE, MAE, and R².

Step 8: Deploying the Model

Publicize pricing models (APIs, web apps, or cloud services) that enable automated or scheduled price forecasts in response to live resource analysis.

How Do You Integrate Dynamic Pricing into Business Workflows?

Through its Integration Component, Pricing can be transformed from a traditional pricing model to an integrated pricing solution within the organization. The use of automation allows Pricing data to be automatically updated without manual entry, creating an efficient process for providing teams with real-time access. Automated Pricing provides sales channels (Booking Engines or Sales Platforms) with access to the most current Pricing recommendations when creating quotes. Additionally, the Demand Forecast will impact marketing and inventory decision-making. The ability to provide ongoing support enables organizations to respond to Risk and Opportunity more quickly through the use of Pricing Solutions.

• Automated Price Updates: Automated data pipelines for automatic integration of forecasts themselves back into your software on the agreed timescale.
• Real Time Price Monitoring: Portals allow all parties to see variances between expected prices and actual prices, along with suspect pricing.
• Integration to Business: Pricing data will interface with an organization's booking software, CRM software, and yield management software.
• Forecasts to Market: Forecasts will help identify when to schedule promotions, budget for marketing campaigns, and identify peak sale periods for travel.

What Challenges Will You Face While Building a Pricing Model?

Creating a Dynamic Pricing model creates several challenges. The rapid pace at which airlines adjust ticket prices requires vast amounts of accurate data and the ability to collect and maintain it. In addition, most websites impose some form of access restriction, which can affect web scraping capabilities; thus, it is essential that airlines, in addition to complying with all legal requirements, also take steps to avoid being blocked by such restrictions using technological means.

In addition, due to the rapid changes in airline pricing strategy, demand patterns, and market factors, the model will likely need to be retrained frequently to remain valid. External factors like fuel prices, weather events, and global incidents can affect pricing. A robust planning system and a dynamic pricing model help users address the challenges that come with dynamic pricing. This system allows businesses to adjust to changes in demand for their products or services.

What Future Trends Will Shape Dynamic Pricing?

As we look to the future and consider how to adjust pricing, there are many opportunities for businesses due to technology, including Artificial Intelligence (AI), Machine Learning (ML), and real-time data analysis. Airlines are also interested in building personalized pricing based on customer behavior and preferences into the system to be able to provide individual offers (discounts, etc.) for any particular customer.

Dynamic pricing systems will also be able to incorporate and utilise live competitor pricing data immediately. Also, Predictive Analytics will help customers determine the best time to book their flights and support their booking decisions. Finally, airlines will be able to develop long-term pricing based on additional external data, such as weather patterns or other events affecting travel. As our ability to process information continues to improve, airlines will transition dynamic pricing into intelligent adaptive systems, enhancing their ability to optimize revenue and improve the overall customer experience.

Conclusion

Dynamic pricing models using historical flight data enable accurate predictions of airline pricing trends and improve the development of airline revenue strategies. By leveraging high-quality historical data, robust feature engineering, and Advanced Machine Learning, organizations can optimize pricing decisions in real time. Dynamic price perfecting will be refined through automation and unique forecasting, as technology continues to advance. Teaming up with a quality data provider like Scraping Intelligence enables companies to access volumes of reliable historical flight data, based on which authentic dynamic pricing models are created. Organisations will profit from the use of suitable tools, which can be used to develop further and improve dynamic pricing models to increase profits and sustain competitiveness within a travel context.