Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

This study explores the enhancement of customer satisfaction in the airline industry, a critical factor for retaining customers and building brand reputation, which are vital for revenue growth. Utilizing a combination of machine learning and causal inference methods, we examine the specific impact of service improvements on customer satisfaction, with a focus on the online boarding pass experience. Through detailed data analysis involving several predictive and causal models, we demonstrate that improvements in the digital aspects of customer service significantly elevate overall customer satisfaction. This paper highlights how airlines can strategically leverage these insights to make data-driven decisions that enhance customer experiences and, consequently, their market competitiveness.

It's been a rough few months for the technology industry. Meta, Amazon, Google, Spotify, and Twitter have all laid off a sizable chunk of their workforce (the list goes on, too). Everybody is talking about how ChatGPT and other generative-AI chatbots are role-playing as Skynet, and the older tech giants are feeling out of step. But whereas Google and Microsoft are deep into the chatbot arms race, Meta looks like a late-aughts tech dinosaur. It's time to shake things up, to turn the ship around.

As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. This study aims to measure customer satisfaction by analyzing sentiments of Tweets that mention airlines using a machine learning approach. Relevant Tweets are retrieved from Twitter's API and processed through tokenization and vectorization. After that, these processed vectors are passed into a pre-trained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also perform lexical analysis on the collected Tweets to model keywords' frequencies, which provide meaningful contexts to facilitate the interpretation of sentiments. We then apply time series methods such as Bollinger Bands to detect abnormalities in sentiment data. Using historical records from January to July 2022, our approach is proven to be capable of capturing sudden and significant changes in passengers' sentiment. This study has the potential to be developed into an application that can help airlines, along with several other customer-facing businesses, efficiently detect abrupt changes in customers' sentiments and take adequate measures to counteract them.

Commercial airlines and other travel and transportation leaders are facing significant challenges in managing pricing, demand, and logistics in today's volatile environment. This summer's travel disruptions have laid bare the potential for intermittent hiccups in post-pandemic operations to have drastic effects on customer satisfaction and revenue opportunities. With travelers' patience wearing thin, airlines need to reinforce their people, processes, and technologies. By building artificial intelligence (AI) solutions into processes across their organizations, airlines can leverage their data, analysts, and revenue management opportunities to take advantage of new business fundamentals in this changing environment. "Artificial intelligence isn't replacing the airline data analyst's job," said Alex Mans, founder and CEO of FLYR Labs, a technology company driving commercial optimization for airlines.

Imagine you notice a sudden change in the weather. The sky is turning black, the clouds are rolling grey, the breeze is getting colder, and the birds are traveling back to their trees. What would your next thought be -That it's going to rain soon? We examine everything around us, use our previous knowledge, and then react to a situation accordingly. AI is being trained to work on similar lines.

With the emergence of cognitive technologies and artificial intelligence in the travel sector, this particular domain will have the opportunity of transforming itself from a merely commoditized service provider to an active and interactive trip coordinator. Let's be a bit specific. We are particularly dealing with airlines and air travel and transportations in this context. What does current market statistics have to say? However, that has not quite transformed the booking scenario and several allied aspects.

To understand the important dimensions of service quality from the passenger's perspective and tailor service offerings for competitive advantage, airlines can capitalize on the abundantly available online customer reviews (OCR). The objective of this paper is to discover company- and competitor-specific intelligence from OCR using an unsupervised text analytics approach. First, the key aspects (or topics) discussed in the OCR are extracted using three topic models - probabilistic latent semantic analysis (pLSA) and two variants of Latent Dirichlet allocation (LDA-VI and LDA-GS). Subsequently, we propose an ensemble-assisted topic model (EA-TM), which integrates the individual topic models, to classify each review sentence to the most representative aspect. Likewise, to determine the sentiment corresponding to a review sentence, an ensemble sentiment analyzer (E-SA), which combines the predictions of three opinion mining methods (AFINN, SentiStrength, and VADER), is developed. An aspect-based opinion summary (AOS), which provides a snapshot of passenger-perceived strengths and weaknesses of an airline, is established by consolidating the sentiments associated with each aspect. Furthermore, a bi-gram analysis of the labeled OCR is employed to perform root cause analysis within each identified aspect. A case study involving 99,147 airline reviews of a US-based target carrier and four of its competitors is used to validate the proposed approach. The results indicate that a cost- and time-effective performance summary of an airline and its competitors can be obtained from OCR. Finally, besides providing theoretical and managerial implications based on our results, we also provide implications for post-pandemic preparedness in the airline industry considering the unprecedented impact of coronavirus disease 2019 (COVID-19) and predictions on similar pandemics in the future.

The use of AI (Artificial Intelligence) technology in commercial aviation has brought some significant changes in the way flights are being operated today. The world's leading airline service providers are now using AI tools and technologies to deliver a more personalized traveling experience to their customers. From building AI-powered airport kiosks to using it for automating airline operations and security checking, AI will play even more critical roles in the aviation industry. The International Air Transport Association (IATA) and Airports Council International (ACI) have noticed AI's value too. Here, a look at how airlines are currently using AI, and emerging areas that show promising results for a better travel experience.

The Covid-19 pandemic had a ubiquitous effect upon every nation and every industry, and it seems no sector is free from its impact, although one we can all recognise as being impacted more than most is the travel sector. With the pandemic this year forcing most businesses to suddenly adapt to some kind of remote working, the need for digital transformation has grown significantly. In the travel sector, we've seen flights grounded around the world, shipping halted, and road transport disrupted permanently, which has brought tourism to a standstill. This has had a direct impact on all other parts of the travel sector, affecting consumers, sector workers, business, and the global economy. Airlines alone are expected to lose $84.3 billion in 2020 for a net profit margin of -20.1%.