Dynamic Ticket Pricing - Powered by AI

Our system introduces dynamic pricing to optimize ticket sales, adapting prices based on the “time of visit.” This approach allows for varied base prices (e.g., for adults and children) while dynamically adjusting ticket prices for each visitor using a range of influencing factors.

Factors Influencing Pricing

1. Park Data:
We analyze key data points from your location to dynamically adjust prices:

• Historical Data: Predict sales based on previous years, months, and weeks.

• Occupancy Rates: Prices remain stable or increase when occupancy exceeds a certain threshold, like 90%.

• Sale Interval: Longer intervals between bookings suggest low demand, prompting a price decrease, while shorter intervals indicate high demand, allowing for a price increase.

• Peak Dates: Adjust pricing for holidays and special events.

• Weather Forecasts: Adapt pricing based on expected weather conditions.

• Price Constraints: Safeguards ensure prices remain within predefined minimum, maximum, and median levels.

• Manual Adjustments: Retain control by manually setting prices for specific slots.

• Holidays: Automatically take into account national holidays most relevant to your leisure location.

2. Profiling Data:
Dynamic pricing also considers individual user behavior and booking patterns:

• Booking Timing: Incentivize last-minute buyers with discounts or adjust pricing for early planners likely to purchase.

• User IP Data: Track frequent visits over time to gauge interest and adjust pricing accordingly.

• Purchase History: Reward loyal customers with high spending history through tailored discounts, fostering a dedicated fanbase.
  
  

The Power of Booqi.me Data

At Booqi, we leverage extensive anonymous data from customer purchases across our platform to enhance our AI-powered dynamic pricing model. This data includes details such as the date and time of sales, visit schedules, total purchase amounts, types of leisure locations, and group sizes. By aggregating and analyzing this information across all Booqi clients—while strictly maintaining user privacy—we gain valuable insights that benefit everyone in the network.

Here’s how we use this data in our dynamic pricing model:

• Ticket Sales Trends: Identify fluctuations in ticket sales for specific days across our client base.

• Website Traffic Patterns: Detect changes in visitor traffic to client websites.

• Historical Trends: Act on historical trends from previous years, before they happen.

For example, if a significant number of bookings occur for a specific date or even a particular time slot, we can dynamically adjust ticket prices to reflect the increased demand. This aggregated anonymous data significantly enhances the precision of our pricing calculations, demonstrating the collective strength of Booqi.me.

When to Change Prices

Dynamic pricing changes can occur at various intervals:

• Hourly adjustments.

• After each new order.

• A specified number of times per day.

Developing an Effective Dynamic Pricing Strategy

Our data scientists work closely with your team to design a dynamic pricing strategy tailored to your audience and location. We start by analyzing all available parameters to identify which are most relevant to your business. Once identified, we prioritize these parameters based on their impact. For instance, a leisure park located outdoors may experience different effects from rainy weather compared to an indoor location.

1. Setting Price Points

After prioritizing the parameters, we establish three key price points: the median price and standard deviation for your business. Price points will be based on a normal distribution that is used in most statistical analyses. For example, if your regular weekday price is €10 and increases to €12.50 during vacations, the dynamic range might set the maximum price at €12.50, the minimum at €8.50, and the normal price at €10.

2. A Real-World Example: An Art Museum

Let’s consider an art museum. After analysis, the museum identifies the following parameters as the most relevant, ranked by importance:

1. Holidays: Museums typically see a significant rise in visitors during vacations.

2. Weather Forecast: Rainy weather often leads to an increase in indoor activity visitors, while sunny weather can reduce attendance.

3. Sale Intervals: Tracking sales intervals helps predict visitor trends and optimize pricing accordingly.

3. Leveraging Collective Data

In addition to these parameters, our system harnesses insights from trends across our collective network. This comprehensive data analysis ensures that prices automatically adjust within the predefined range—minimum, normal, and maximum—based on real-time conditions.

This seamless integration of data-driven insights and automated pricing is the true power of our software, helping you optimize revenue while meeting the needs of your audience.