Fetcherr's dynamic pricing model uses AI algorithms tailored to each airline's customer demographics. These algorithms are trained on several years of bookings, flight schedules, availability, fares, events, weather, capital markets, and macroeconomic and microeconomic data points from global markets and various verticals. The model predicts purchasing behavior in the air travel market within the context of an airline customer's business, enabling airlines to offer dynamic pricing that reflects real-time demand and market conditions.
Airlines are expected to earn around $6.14 per passenger, according to the International Air Transport Association (IATA). Despite the record revenue of $996 billion projected for the industry this year, the margins remain razor-thin with total expenses for airlines estimated to reach $936 billion.
Fetcherr's CEO identifies several challenges in adopting dynamic pricing, including traditional, outdated infrastructure and rule-based systems that limit real-time adjustments and swift market adaptation. Additionally, consumer aversion to dynamic pricing and potential legal challenges in some countries could hinder adoption.