The airline industry is highly competitive, therefore we have to be client-centric to stand out from our competitors and provide a pleasant experience for our customers. A satisfied customer results in greater retention and loyalty. Moreover, retaining customers is much more cost-effective than acquiring new ones. Hence, it is important that we make customer satisfaction our top priority.
As the Analytics team, we examined data from a customer satisfaction survey. Our goal was to understand what features drive satisfaction and what expectations customers have when flying with us. To learn more about what customers value, we ran a classification model and listed the top 5 most important features. We also predicted satisfaction with machine learning techniques such as Logistic Regression and Random Forest Classifier, obtaining a 0.96 accuracy. The purpose of this analysis was to find actionable steps to improve the comfort and the overall experience for our passengers. Here are the recommendations our team developed:
Recommendation 1: The airline should improve the following features: online boarding, inflight wifi service, and inflight entertainment, as these are the top three factors that drive satisfaction.
Recommendation 2: Advance the inflight service in Eco class (e.g., various snacks, drinks, movies, etc) since most of the flyers in this Class were dissatisfied with the airline.
Recommendation 3: Reinforce relationships with loyal customers and offer more rewards and increase benefits to boost satisfaction (e.g., better seat selection, discounted in-flight wifi).
Feedback for Future Analyses: Incorporate additional objective measures into further analyses (e.g., ticket price, amount of time from purchase to departure, flight occupancy).
Below is the Presentation and the Notebook with the code for the project.
A special thank you to my teammates: Nawei Zhang, Stephen Davis and Steven Tong. I learned so much from you this semester.
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