The Predictive Airliner (2024)

2019, The Predictive Airline

The Predictive Airliner is an airline that utilizes the latest technology to deliver an exceptional personalized experience to each and every passenger it flies. Today, technology such as AI, Machine Learning, Augmented Reality, IoT, Real-time stream processing, social media, streaming analytics and wearables are altering the Customer Experience (CX) landscape and airlines need to jump aboard this fast moving technology or run the risk of being left out in the cold. The Predictive Airliner reveals how these and other technologies can help shape the customer journey. The book details how the five types of analytics—descriptive, diagnostic, predictive, prescriptive, and edge analytics—affect not only the customer journey, but also just about every operational function within an airline. An IoT-connected airline can make its operations smart. Data collected at multiple company and customer touch points can be utilized to increase customer satisfaction, as well as make the airline more profitable. The book lays out a blueprint for airlines to use to build a better overall operation. By utilizing AI, machine learning, and deep learning airlines can monitor the health of their airplanes, ensure employee satisfaction, and deliver an award-winning customer experience every time. Analytical processes like decision trees, k-means clustering, logistic regression and neural networks are explained in detail, with specific use cases detailing how they are used profitably in the aviation industry. Edge analytics, sentiment analysis, clickstream analysis, and location analysis are seen through a customer intelligence lens to ensure passengers are treated in a personalized way that will not only increase loyalty but turn passengers into apostles for the airlines they chose to fly on. Connected devices can help with inventory optimization, supply chain management, labor management, waste management, as well as keep the airline’s data centers green and its energy use smart. Social media is no longer a vanity platform, but rather it is a place to both connect with current customers, as well as court new ones. It is also a powerful branding channel that can be utilized to both understand an airline’s position in the market, as well as a place to benchmark its position against competitors. The Predictive Airliner reveals how airlines can utilize this channel in a multitude of ways to connect with customers, as well as help in moments of crisis. Today, technology moves at break-neck speed and it can offer the potential of anticipatory capabilities, but it also comes with a confusing variety of technological terms--Big Data, Cognitive Computing, CX, Data Lakes, Hadoop, Kafka, Personalization, Spark, etc., etc. The Predictive Airliner will help airline executives make sense of it all, so that he or she can cut through the confusing clutter of technological jargon and understand why a Spark-based real-time stream processing data stream might be preferable to a TIBCO Streambase one, or none at all. The final chapter explains how an airline can utilize the concept of the customer journey as a roadmap to increase customer satisfaction. This book will help airline executives break through the technological clutter so that they can deliver an unrivaled customer experience to each and every passenger who steps aboard their planes.

Application of Big Data Systems to Airline Management

— The business world is in the midst of the next revolution following the IT revolution – the Big Data revolution. The sheer volume of data produced is a major reason for the big data revolution. Aviation and aerospace are typical areas that can apply big data systems due to the scale of data produced, not only by the plane sensors and passengers, but also by the prospective passengers. Data that need to be considered include, but are not limited to, aircraft sensor data, passenger data, weather data, aircraft maintenance data and air traffic data. This paper aims at identifying areas in aviation where big data systems can be utilized to enhance operational performances improve customer relations and thereby aiding the ultimate goal of increased profits at reduced costs. An improved management model built on a strong big data infrastructure will reduce operation costs, improve safety, bring down the cost and time spent on maintenance and drastically improve customer relations. Keywords— include at least 5 keywords or phrases

Airline Applications of Business Intelligence Systems

Airline industry is characterized by large quantities of complex, unstructured and rapid changing data that can be categorized as big data, requiring specialized analysis tools to explore it with the purpose of obtaining useful knowledge as decision support for companies that need to fundament their activities and improve the processes they are carrying on. In this context, business intelligence tools are valuable instruments that can optimally process airline related data so that the activities that are conducted can be optimized to maximize profits, while meeting customer requirements. An airline company that has access to large volumes of data (stored into conventional or big data repositories) has two options to extract useful decision support information: processing data by using general-purpose business intelligence systems or processing data by using industry specific business intelligence systems. Each of these two options has both advantages and disadvantages for the airline companies that intend to use them. The present paper presents a comparative study of a number of general-purpose and airline industry specific business intelligence systems, together with their main advantages and disadvantages.

Continental Airlines Flies Sky High with Business Intelligence

Real-time data warehousing and business intelligence (BI), supporting an aggressive Go Forward business plan, have helped Continental Airlines transform its industry position from “worst to first” and then from “first to favorite.” With a $30M investment in hardware and software over six years, Continental has realized conservatively over $500M in increased revenues and cost savings in areas such as marketing, fraud detection, demand forecasting and tracking, and improved data center management. Continental is now recognized as a leader in real-time business intelligence based upon its scalable and extensible architecture, prudent decisions on what data are captured in real-time, strong relationships with end users, a small and highly competent data warehouse staff, a careful balance of strategic and tactical decision support requirements, its understanding of the synergies between decision support and operations, and changed business processes that utilize real-time data.

Artificial Intelligence potential within airlines: a review on how AI can enhance strategic decision-making in times of COVID-19

Journal of Airline and Airport Management, 2021

Purpose: Airline strategy relies on the competitive environment analysis and the management of resources. Artificial Intelligence (AI) algorithms are being increasingly deployed throughout several industries. COVID-19 has further stressed a sector where firms have historically struggled to sustain profitability.The purpose is to explore the potential of AI applications regarding strategic decision-making in airlines in times of crisis and to depict a roadmap to encourage scholars and practitioners to jointly implement these tools within corporations.Design/methodology/approach: This study firstly reviews the state-of-the-art regarding transport organization trends with focus on airline strategy and finance as well as AI tools, supported by the collaboration of a former airline digitalization strategist. Secondly, the potential of the latter to be applied in those functions is analyzed, considering different Machine Learning (ML) methods and algorithms.Findings: Some applications or ...

Revolution of Artificial Intelligence and the Internet of Objects in the Customer Journey and the Air Sector

2020

Artificial intelligence (AI) is a discipline interested in the processes and methods that allow a machine to perform tasks related to human intelligence. It offers many opportunities related to problem solving, quick decision-making, increasing efficiency and reducing costs. Because of its so various fields of application, artificial intelligence is at the heart of the new industrial revolution. Algeria aims to present its AI strategy by 2020. In this paper, we are interested in defining AI, its potential fields of application, and in particular, its influence in the customer journey and position of RFID (Radio-Frequency Identification) in the chain; application in the aviation sector and its relationship to the Internet of Things are also described through examples.

Designing with Meaningful Data: Deep personalisation in the air travel context

DRS2018: Catalyst, 2018

Factor Determinants of Customer Satisfaction with Airline Services Using Big Data Approaches

Jurnal Pendidikan Ekonomi Dan Bisnis (JPEB), 2020

Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience

Society of Visual Informatics, 2024

Ensuring customer loyalty is crucial for the success of any airline service provider in today's competitive environment. This study employs machine learning techniques to predict the likelihood of customers revisiting airline services, emphasizing the role of emotional connections in fostering loyalty. By analyzing feedback comments and satisfaction ratings, we explore how sentiments expressed by customers correlate with their propensity to return. Using sentiment analysis and features extracted through the Linguistic Inquiry and Word Count (LIWC) methodology, we categorize sentiments into various dimensions, integrating these with user experience (UX) elements for a comprehensive predictive model. Our methodology includes a robust data collection process, involving an initial survey of 17,000 valid responses and a follow-up survey one year later. We evaluate multiple classifiers, including Decision Tree, Random Forest, and XGBoost, through five-fold cross-validation. Results reveal that XGBoost achieves the highest accuracy of 85% in predicting return visits, highlighting the predictive power of machine learning in understanding customer behavior. These findings offer significant insights for airlines, enabling them to tailor services and strategies to enhance customer satisfaction and loyalty. Our study underscores the importance of sentiment analysis and UX in predicting customer loyalty, providing a roadmap for future research and practical applications in the airline industry.

Oneworld Alliance: A Data Analysis on Airline Customer Reviews

Shifting the airline business model: Current struggles of national carriers to become profitable passenger experience companies

2012

National flag carriers are struggling for survival, not only due to classical reasons such as increase in fuel and tax or natural disasters, but largely due to the inability to quickly adapt to its competitive environment – the emergence of budget and Persian Gulf airlines. In this research, we investigate how airlines can transform their business models via technological and strategic capabilities to become profitable and sustainable passenger experience companies. To formulate recommendations, we analyze customer sentiments via social media to understand what people are saying about the airlines.