We are proud to announce that DynamO has developed a one-of-a-kind simulation platform that models the demand for dynamically priced movie tickets in different scenarios, and also showcases what benefits our dynamic pricing engine offers.

In order to demonstrate the advantages and behavior of DynamO’s dynamic pricing engine, we have developed a unique simulation tool that is now available for tests to any interested party at request. In brief, the simulation works as follows:

1. Reading input:
   • Backend variables (not modifiable by the user):
     • hall size and layout
     • population size and composition (i.e. population categories)
   • User input (Fig. 1):
     • static ticket price,
     • boundary conditions for dynamic pricing (Initial, Minimum, and Maximum prices), and
     • expected properties of the show (defined as
       the combination of the Expected popularity of a given movie and the Time of the screening)
   • Historical data:
     • relative popularity of seat positions
     • reference hall saturation curve
   • Input from the pricing engine:
     • prices at the different seat positions
2. Evaluation of the momentary purchasing probability for each population category
   1. Establishing the saturation curves for each population category (the maximum depends on popularity of the movie and the time of screening; the shape depends on popularity of the movie)
   2. Creating the function of purchasing probability for each population category
   3. Discretization, and evaluation of the momentary purchasing probability for each population category
3. Seat selection (defining the position of the seat an individual from a given population category is willing to purchase based on seat position popularity and momentary price)
4. Calculation of hall occupancy, average price of sold tickets, extra profit, new price of unsold seats, etc.
5. Visualization of results (Fig. 2 and Fig. 3)

Output data is presented in two distinct views. The Screening view (Fig. 2) shows the average price of sold tickets, revenue, hall occupancy, and temporal evolution of demand for a single screening, and enables the comparison of outcomes obtained by applying static and dynamic pricing.

The Aggregated view (Fig. 3) averages the outcome of 100 simulation runs, and not only highlights the average prices, average hall occupancy, and the average extra profit from dynamic pricing, but also the popularity of different seats in a cinema hall.

Apart from this publicly available part of DynamO’s simulation tool, in our next blog post we will also introduce some of the results yielded by our deep analysis on a large set of data that we obtained via systematically screening the whole parameter space.

Until then, stay tuned and drop us a mail if you are interested in trying out our simulation tool: info@dynamopricing.com.

DynamO’s team of experts can help you to get the most out of dynamic pricing. DynamO has been established in the recognition that the service sector’s increasing interest in dynamic pricing in now coupled with an increasing acceptance among end customers. Our vision is to make dynamic pricing as widespread and common in the service sector as credit cards and online shopping in everyday life.