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Reframing Passenger Experience Strategy: A Predictive Model for Net Promoter Score Optimization
Subject area: Science,Engineering and Technology · Area of research: Airline Strategy
Abstract
In today?s highly competitive aviation landscape, enhancing the passenger experience has emerged as a strategic imperative for airlines seeking sustained customer loyalty and brand differentiation. This study proposes a predictive model aimed at optimizing the Net Promoter Score (NPS) a widely adopted metric for gauging customer satisfaction and loyalty by reframing traditional passenger experience strategies through data-driven insights. Utilizing machine learning algorithms and passenger feedback analytics, the model identifies key experiential variables including in-flight services, digital touchpoints, crew responsiveness, and airport facilities that significantly influence NPS outcomes. The research draws on a comprehensive dataset comprising customer survey responses, flight operations data, and sentiment analysis of unstructured text from online reviews. A multivariate regression analysis and supervised learning models such as Random Forest and XGBoost were employed to determine feature importance and predict NPS with high accuracy. Results demonstrate that proactive interventions in service personalization, real-time responsiveness, and seamless end-to-end travel integration can significantly uplift NPS. Furthermore, the study introduces a dynamic passenger experience matrix that enables airline managers to allocate resources strategically based on predicted NPS fluctuations across passenger segments. This reframing moves beyond reactive service improvements and enables a forward-looking, predictive approach to passenger experience management. The model's implementation framework is adaptable across various airline categories low-cost, hybrid, and full-service and can support real-time decision-making through integration into customer relationship management (CRM) platforms. By combining technological intelligence with human-centric design, this approach empowers airlines to not only meet but anticipate evolving passenger expectations. The findings offer critical implications for aviation strategists, customer experience professionals, and digital transformation leaders in the travel industry, laying a foundation for predictive experience management that aligns with operational goals and enhances long-term brand equity.
Keywords
Passenger Experience, Net Promoter Score (NPS), Predictive Analytics, Airline Strategy, Customer Loyalty, Machine Learning, Digital Transformation, Aviation, Sentiment Analysis, Experience Optimization.
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How to cite this paper
@article{1709387,
author = {Maida Nkonye Asata, Daphine Nyangoma, Chinelo Harriet Okolo},
title = {Reframing Passenger Experience Strategy: A Predictive Model for Net Promoter Score Optimization},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {5},
pages = {208-227},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709387.pdf},
abstract = {In today?s highly competitive aviation landscape, enhancing the passenger experience has emerged as a strategic imperative for airlines seeking sustained customer loyalty and brand differentiation. This study proposes a predictive model aimed at optimizing the Net Promoter Score (NPS) a widely adopted metric for gauging customer satisfaction and loyalty by reframing traditional passenger experience strategies through data-driven insights. Utilizing machine learning algorithms and passenger feedback analytics, the model identifies key experiential variables including in-flight services, digital touchpoints, crew responsiveness, and airport facilities that significantly influence NPS outcomes. The research draws on a comprehensive dataset comprising customer survey responses, flight operations data, and sentiment analysis of unstructured text from online reviews. A multivariate regression analysis and supervised learning models such as Random Forest and XGBoost were employed to determine feature importance and predict NPS with high accuracy. Results demonstrate that proactive interventions in service personalization, real-time responsiveness, and seamless end-to-end travel integration can significantly uplift NPS. Furthermore, the study introduces a dynamic passenger experience matrix that enables airline managers to allocate resources strategically based on predicted NPS fluctuations across passenger segments. This reframing moves beyond reactive service improvements and enables a forward-looking, predictive approach to passenger experience management. The model's implementation framework is adaptable across various airline categories low-cost, hybrid, and full-service and can support real-time decision-making through integration into customer relationship management (CRM) platforms. By combining technological intelligence with human-centric design, this approach empowers airlines to not only meet but anticipate evolving passenger expectations. The findings offer critical implications for aviation strategists, customer experience professionals, and digital transformation leaders in the travel industry, laying a foundation for predictive experience management that aligns with operational goals and enhances long-term brand equity.},
keywords = {Passenger Experience, Net Promoter Score (NPS), Predictive Analytics, Airline Strategy, Customer Loyalty, Machine Learning, Digital Transformation, Aviation, Sentiment Analysis, Experience Optimization.},
month = {November},
}