Pricing Strategies Using AI: Dynamic Pricing and Consumer Behavior in Cabs and Flights

Abstract

In the digital age, pricing has evolved from static labels to dynamic algorithms, especially in industries like cab services and air travel, where demand and supply fluctuate rapidly. This research explores the role of Artificial Intelligence (AI) in enabling dynamic pricing mechanisms on platforms such as Uber, Ola, MakeMyTrip, and Goibibo. These platforms rely on real-time data to adjust prices based on variables like user behavior, travel urgency, traffic conditions, and time of booking. While this model benefits companies through revenue optimization and efficient resource allocation, it also raises significant concerns regarding fairness, transparency, and customer trust. The need to balance profitability with user satisfaction makes it essential to study how consumers perceive and respond to AI-driven pricing in these sectors.

The study follows a quantitative research approach, utilizing structured questionnaires to collect data from active users of cab and flight booking platforms between the ages of 18 and 45. The core aim is to assess consumer attitudes toward fluctuating prices—whether they find them justifiable or manipulative—and to examine their behavioral responses, such as delaying bookings, switching to competitor apps, or abandoning purchases altogether. Data collection was conducted online using Google Forms, and Microsoft Excel was employed for statistical analysis. Key variables analyzed include perceived fairness, trust in algorithms, urgency of travel, and frequency of usage. The findings provide insight into how different demographics (such as age and income) affect pricing sensitivity and decision-making in high-demand scenarios.

The results suggest that while many users appreciate the convenience and speed of booking services with AI-enabled platforms, they often feel skeptical when prices surge unexpectedly without clear reasoning. Trust and transparency emerged as crucial factors in shaping user loyalty and continued engagement with these platforms. This study offers valuable recommendations for companies to make dynamic pricing more consumer-friendly—through clear communication, personalized offers, and transparent algorithm behavior. By addressing consumer concerns, businesses can not only improve customer satisfaction but also ensure long-term retention in an increasingly competitive marketplace.