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New Inflight Service Model
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Transportation
- Professional Service
Applicable Functions
- Business Operation
Use Cases
- Predictive Replenishment
Services
- Data Science Services
- System Integration
The Challenge
A leading global airline introduced a new inflight service model for its flight attendants in a subset of its flights. The company sought to answer two key questions: Does the new service model drive an increase in revenue? Is passenger satisfaction improved as a result of the new service model? However, the airline struggled to eliminate the bias that existed in the group of flights that implemented the new service model. Flights where the new service model had been introduced generated higher than average revenue per flight. In addition, analysis was not straightforward because of the multitude of entities (e.g., flights, customers, employees) involved. These challenges made it difficult to understand performance of the new service model and if it should be expanded to additional flights.
About The Customer
The customer is a leading global airline that operates a vast network of flights across various regions. The airline is known for its commitment to customer service and innovation in the aviation industry. With a large fleet and a significant number of employees, the airline continuously seeks ways to enhance passenger experience and operational efficiency. The airline's focus on customer satisfaction and revenue growth drives its initiatives to test and implement new service models. By leveraging advanced analytics and business experimentation, the airline aims to stay competitive and maintain a positive brand image in a highly dynamic market.
The Solution
Using APT’s Test & Learn® software, the airline analyzed an in-market business experiment to determine the revenue and passenger satisfaction impacts of the new inflight service model. The software compared “test flights” in which the new service model was implemented to highly similar “control flights” that did not receive the new service model, in order to eliminate bias and accurately isolate the program’s incremental impact. APT’s rigorous test vs. control analysis showed that the new inflight service model drove a slight increase in both total revenue and passenger satisfaction. The software also enabled results to be broken out by different customer characteristics to provide a better understanding of how the effectiveness of the new service model varied. This deeper dive revealed that flights with a high percentage of shorter-tenured customers drove the greatest impact on revenue and satisfaction. The software then automatically analyzed hundreds of flight attributes to identify characteristics associated with a higher revenue lift from the new service model. Specifically, the software revealed that the new inflight service model drove a greater impact for longer-haul flights and for those with lower previous passenger satisfaction.
Operational Impact
Quantitative Benefit
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