Design Strategy

To mitigate the incorrect assumptions associated with the independent demand model, a discrete choice model was created. First, an assortment set was defined as all alternatives available to a customer searching on a particular channel for a given departure date on a certain number of days before departure. Then, using the multinomial logit model, the probability of customers choosing each of the alternatives in that assortment set can be calculated, summing to 1. This is completed following the idea of utility maximization, as customers are more likely to choose an alternative that provides them greater utility, or satisfaction. A predicted utility for each alternative is calculated using values for different attributes (price, departure time, refundability, upgradability, number of stops) and the customer weights corresponding to the attributes. Customer weights, in the form of a beta vector, are predicted using a maximum likelihood estimation equation. Arrival rates, or the estimated arrivals per day written in the form of a lambda vector, are the other variable predicted using maximum likelihood estimation. Multiplying the probability of a customer choosing a specific alternative by the estimated number of arrivals to that alternative provides an estimated number of people who choose each alternative in the assortment set.