A data-driven approach for characterising revenues of South-Asian long-haul low-cost carriers per equivalent flight capacity per block hour

Abstract

This study sets out to investigate the revenue characteristics of long-haul low-cost carriers (LHLCCs) in the Southeast Asian market using a recently established metric that calculates revenue per equivalent flight capacity per block hour (REB). REB allows for the accurate comparison of airlines on different routes with varying cabin configurations, and stage lengths. The majority of the data was sourced from Sabre MIDT and OAG, while supplementary data were from other various sources. In addition to REB, the study investigates: How do LHLCCs yields compare to full-service network carriers (FSNCs)? Are LHLCCs positively impacted by smaller share of connecting passengers? Are LHLCCs positively impacted by ancillary revenues? Do LHLCCs benefit from higher load factors and seat densities?Results show that LHLCCs performed 26.6% less in overall REB compared to their FSNC counterpart despite LHLCCs generating 43.9% less yield. This is a result of less revenue diluting connecting passengers, higher average ancillary revenue per block hour, higher average load factors and higher average seat densities for LHLCCs. On a route-level, some LHLCC operations can equally perform or outperform competition’s revenue performance. Furthermore, the findings are mostly consistent with earlier REB research conducted on the North Atlantic market by Soyk et al. (2018). Revenue and operating characteristic showed the same trends in both markets, although to varying degrees.This study exposes, with high detail, the revenue generating inner-workings of the elusive long-haul low-cost model in its second largest market, and compares it on equal grounds to their full-service network competition. We learn that airlines can drive the airfare down while minimising the loss of revenue per flight capacity of the aircraft by adjusting for the numerous variables that directly impact it, such as seat density, cabin configuration, ancillary revenues, load factors, and percentage of connecting passengers.