AI dynamic pricing use cases for tours and attractions

Carla Vianna
Carla Vianna
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AI dynamic pricing use cases for tours and attractions

As tours and attractions seek ways to stay competitive through their experiences, they must also consider innovative ways to maximize revenue. And, artificial intelligence (AI) can help you drive new bookings, increase average order value, and repeat customers.  

In this article, we’ll explore some AI dynamic pricing use cases that tours and attractions can use to maximize profit and enhance the customer experience.

What is dynamic pricing?

First, let’s touch on the definition of dynamic pricing. Simply put, this strategy involves adjusting prices in real-time based on various factors, such as demand, supply, market conditions, and competitor pricing.

Dynamic pricing allows businesses to optimize revenue by setting prices more fluidly rather than sticking to fixed pricing models—hence the name “dynamic.” For travel businesses, this could mean adjusting the pricing of services, attractions, or excursions based on the season, time of year, time of day, and holidays.

An example would be a kayaking business that lowers the prices of its excursions during the winter seasons and raises them again during the warmer, more crowded summer season.

What is the role of AI in modern pricing strategies?

We’ve arrived in an era when AI is pivotal to modern pricing strategies. AI tools are now used to enhance decision-making processes and allow businesses to respond quickly to changing market dynamics.

In the travel industry, AI has made a massive breakthrough in research, navigation, customer service, and auto-responses using chatbots. Tools like ChatGPT have also made it easier for businesses to provide their guests with personalized travel assistance. Lately, AI dynamic pricing has become a fantastic tool for tourism businesses to price their services strategically to reach their maximum revenue potential.

These are a few of the benefits of using AI dynamic pricing:

  • Maximizing revenue and profit – AI algorithms analyze vast amounts of data to identify pricing opportunities and adjust prices accordingly—all in the name of boosting revenue and profit potential. This means your AI tool regularly weighs competitor prices, inventory levels, consumer behavior, and market trends to determine the best way to bank on the fluctuating market.
  • Efficient equipment and guide management – One of the most significant advantages of using an AI-driven pricing tool is that it can help you better allocate your resources, ensuring efficient use of equipment and guides. For example, a whale watching business can use AI to analyze booking forecasts and demand patterns to adjust prices dynamically. It can predict high-demand periods and allocate more guides—which helps maximize revenue by optimizing the use of available resources and ensures that customer demand is met effectively, leading to a more efficient and profitable operation.
  • Aligning pricing with demand – One practical approach is demand-based pricing, which uses forecasting models that analyze historical and real-time data to predict future demand for services. For example, a whale watching business owner would increase prices during high season to capitalize on the market. Then, the operator would decrease them during off-peak seasons to attract more customers.
  • Enhanced customer experience – Offering personalized pricing based on customer preferences and behavior can improve customer experience. AI can assist in analyzing customer data—including purchase history, browsing behavior, and demographics—to identify customer segments with different price sensitivities instead of using a one-size-fits-all approach.

What elements do you need to have in place for AI-driven dynamic pricing?

Let’s dive into the key elements you must establish for successfully implementing AI-driven dynamic pricing.

Real-time data collection 

Real-time data collection is the process of gathering, processing, and making data available as soon as it’s created, supporting near-instant decision-making for businesses. AI-driven dynamic pricing only works if you gather relevant data, such as historical sales data, competitor pricing, market demand trends, and customer feedback. Then, based on these data points, your pricing will fluctuate accordingly.

Machine learning models 

Machine learning models are computer programs that use machine learning algorithms to analyze and interpret data. They form the backbone of AI-driven dynamic pricing systems. These models analyze the collected data, identify patterns, and make predictions to optimize pricing decisions.

Real-time price adjustments

AI-driven pricing systems can adjust prices in real time based on changes in market conditions, demand fluctuations, and competitor pricing. This allows businesses to stay competitive and increase revenue opportunities.

Continuous learning and customer feedback loops

Continuous learning and customer feedback loops allow AI-driven tools to adapt and improve. Incorporating customer feedback and monitoring pricing performance will enable you to fine-tune your pricing strategies for better results.

Key challenges and ethical considerations when implementing dynamic pricing 

Let’s explore the key challenges and ethical considerations when implementing dynamic pricing.

Accurate and relevant real-time data inputs

One key challenge is ensuring the accuracy and relevance of real-time data inputs. Dynamic pricing considers current demand, competition, time of day, customer location, and more. 

Your pricing strategy will only be as successful as the data collected. In the whale-watching business example, they may experience pricing errors if the data used for dynamic pricing is outdated or irrelevant—like using last summer’s peak numbers to predict this winter’s booking volume.

Businesses need to consistently confirm the accuracy of the data being collected and regularly audit their sources. 

Transparent pricing for customers

In June 2017, during the London Bridge attack, Uber’s dynamic pricing algorithm caused rates to jump more than 200% in that area of town. Similar increases happened during the 2016 bombing in New York City, a 2017 taxi drivers’ strike in protest of U.S. anti-immigration policy, and a 2020 Seattle mass shooting.

In these cases, Uber’s dynamic pricing model worked in an economic sense, but the outcome was vastly inappropriate from a customer’s perspective. The surge pricing made it more difficult for people to escape emergency situations, which, understandably, was detrimental to the ride-sharing app’s reputation.

As a result, Uber now offers upfront pricing estimates and allows users to compare prices before booking rides.

While most tours and attractions won’t find themselves in this dire of a situation, you should still strive for transparency in your pricing practices, and clearly communicate the factors influencing prices.

A positive way for tours and attractions to stay transparent is by announcing price specials and changes before a new season approaches. An example of this would be announcing an early bird special before prices skyrocket as the peak season approaches. 

Addressing potential discriminatory pricing concerns

Dynamic pricing algorithms have been criticized for potentially perpetuating discriminatory pricing practices. For example, one study found racial bias in ride-sharing pricing algorithms, where higher fares were attached to drop-offs in minority neighborhoods compared to predominantly white neighborhoods.

Unfortunately, when machine learning is applied to social data, algorithms aren’t advanced enough to understand historical injustices and social biases that may be present. To mitigate this, businesses should regularly audit their pricing algorithms for bias and implement measures to ensure fairness and equity.

This may include diversifying data sources, refining algorithms to minimize bias, and conducting regular reviews to identify and address any discriminatory pricing patterns.

Balancing profit goals with customer trust

Balancing profit goals with customer trust is essential for long-term success in dynamic pricing. U.S.-based airlines have faced backlash for deceptive pricing practices, such as hidden fees and misleading fare displays. Yet dynamic pricing has always been a part of the industry, with ticket prices rising and falling based on supply and demand.

Now, airlines are considering dynamic pricing on add-on fees, like checked luggage. JetBlue recently announced that the price of check-in luggage will go up during peak travel times if it’s within 24 hours of departure.

To maintain customer trust while pursuing profit goals, businesses should prioritize transparency and integrity in their pricing practices. This may involve disclosing all fees upfront, clarifying price changes clearly, and proactively overriding pricing surges when necessary. For example, airlines still have the power to lower prices during disasters. Several U.S. carriers offered $19 fares for a 40-minute evacuation flight from Maui to Honolulu to help those fleeing from wildfires.

All businesses must find the strategic balance between making a profit and ensuring their customers’ satisfaction.

Regulatory compliance

Regulatory compliance issues can also pose significant obstacles. A recent example is a lawsuit against Ticketmaster and its parent company, Live Nation, for allegedly violating antitrust laws through ticket pricing practices.

The lawsuit alleges Ticketmaster and its parent company were anti-competitive during the ticket sale for Taylor Swift’s “The Eras Tour,” imposing higher prices on fans in the presale, sale, and resale markets.

Tours and attractions need to stay informed about relevant laws and regulations governing pricing practices in your industry to ensure compliance and mitigate legal risks.

Overcoming consumer backlash

Consumer backlash is a common risk associated with dynamic pricing, especially when customers perceive pricing practices as unfair or deceptive. 

The fast-food chain Wendy’s recently made headlines after announcing it would experiment with dynamic pricing in 2025. The restaurant said it planned to test AI-enabled menu changes and suggestive selling based on factors like the weather. The backlash was instant: Customers took the announcement to mean that prices would increase during peak times, and  #BoycottWendys began trending on social media.

Wendy’s soon followed up saying that the restaurant didn’t plan to instill surge pricing in that manner. Instead, the move was intended to allow the restaurant to change menu offerings throughout the day and offer discounts during slower periods.

To avoid this situation, travel businesses should prioritize transparency, fairness, and responsiveness in their pricing practices. This may involve soliciting customer feedback, promptly addressing concerns about their excursions and experiences, and clearly communicating pricing changes.

How will dynamic pricing evolve? 

Dynamic pricing is poised to evolve further with technological advancements and changing consumer preferences. Here are some potential future trends in dynamic pricing:

  • Personalized pricing – Dynamic pricing systems may become increasingly sophisticated, offering personalized pricing tailored to individual customer preferences and behavior. 
  • Predictive analytics – AI-driven systems may leverage predictive analytics to proactively anticipate future demand trends and adjust prices.
  • Dynamic bundling – Businesses may explore dynamic bundling strategies, combining multiple products or services into customized packages with dynamic pricing based on customer preferences and demand.

We’ll also see more tours and attractions leverage dynamic pricing to improve the customer experience. A typical negative experience at a theme park often involves enduring long lines for rides, food, and restrooms, which is why Walt Disney World in Orlando transitioned from a manual to an algorithmic dynamic pricing structure in 2018.

The new system increased the prices for multiday tickets but reduced prices for off-peak dates. This encouraged guests to plan their trips well in advance or during less busy periods to benefit from lower prices. The aim was to make customer flow more predictable, reduce the volatility in staff and resource needs, and significantly improve the guest experience by allowing them to enjoy more attractions with less waiting.

Disney kept single-day ticket prices constant across its four parks, setting prices between $109 and $129 regardless of the season. This strategy provided clear cost expectations for visitors and helped Disney gather data on visitor behavior to refine its service offerings.


AI-driven dynamic pricing offers significant opportunities for tourism and travel businesses to optimize pricing strategies, escalate revenue, and enhance customer satisfaction. The goal is to retain business with transparency and consideration for consumers. However, implementing dynamic pricing requires careful consideration of ethical practices and regulatory compliance. There is a balance between revenue and the happiness of your clientele.


Writer Carla Vianna

Carla Vianna

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