Abstract: An elasticity tester to initiate a price elasticity experiment during an experiment period, the price elasticity experiment to test a price elasticity for bookings made during the experiment period for a travel property for a day of arrival and adjusts a recommended price to an adjusted price for the travel property during the experiment period according to user-defined test configuration parameters. The elasticity tester further measures actual bookings made during the experiment period for the travel property for the day of arrival at the adjusted price and compares the actual bookings made at the adjusted price to forecasted bookings for the travel property at the recommended price.

Abstract: A pricing rules engine identifies a plurality of potential competitors for a travel property and identifies one or more rule conditions. The pricing rules engine performs a pricing correlation on the plurality of potential competitors according to the one or more rule conditions and determines, based on a result of the pricing correlation, at least one of the plurality of potential competitors to be included in a competitor set. When an offer price exceeds a rule threshold, the pricing rules engine adjusts a sub-rate price discount for the travel property according to rates charged by competitor travel properties in the competitor set. When the capacity for a given room type satisfies the rule threshold, the pricing rules engine adjusts a price modifier for the given room type according to rates charged by competitor travel properties in the competitor set.

Abstract: A travel alert manager receives travel data from a plurality of data sources and identifies an alert data type from the travel data. The travel alert manager generates an alert for the alert data type based on a primary alert condition, the alert to provide a notification that the travel data will affect a travel property. The travel alert manager determines a priority of the alert and issues the alert if alerts matching the priority of the alert are authorized by system settings.

Abstract: A travel sessionizer captures raw event data from a user device, the raw event data representing user shopping behavior, and parses the raw event data into one or more travel sessions. Each of the one or more travel sessions represents shopping behavior for a single user corresponding to a specified date range. The travel sessionizer denormalizes the one or more travel sessions to organize travel session data by individual property days, where each individual property day represents travel session data corresponding to a separate property on a given day. The travel sessionizer then provides the individual property day data to a profit optimization module for further processing.

Abstract: A profit optimization module identifies historical transaction data associated with a travel property. The historical transaction data includes bookings made for a same day of the week as the day of arrival during a forecast period for a plurality of previous weeks. The profit optimization module also identifies lost business data associated with the travel property from the historical transaction data. The lost business data includes at least one of a regret or a denial. The profit optimization module forecasts a demand for bookings at the travel property on a day of arrival, wherein the demand for bookings is based on at least in part on the historical transaction data and the lost business data.