Facing Up Fare War: Generating Competitive Price Models With Gene Expression Programming

In the airline industry, the Revenue and Pricing teams generally spend a considerable amount of time analysing and interpreting the actions of their competitors. Most of the time the analysts have to use their analytical skills to create ad-hoc methods to interpret or find patterns in the fares. In this field, it is key to automate the process, avoid human errors, and add new features that provide accurate fares. Considering this, a gene expression programming algorithm is proposed to carry out this process, returning an interpretable rule set which acts as a recommender system to ease the daunting process done by the pricing teams manually. The proposal was applied to a real scenario with the information provided by the Air Canada airline for five months in Canadian markets. The experimental analysis revealed, by means of non-parametric statistical tests, that the proposed gene expression programming algorithm was key to getting the appropriate features and, hence, accurate and highly interpretable results. The proposal obtained extremely accurate results (around 96% in both accuracy and F1 measure) with a reduction of around 50% in the rule set in many cases.