The September 2023 SBL case study is based on the airline industry, so the examiner may draw some examples of real-life scenarios when setting questions. The following article relates to a critical topic in the SBL syllabus, “Big Data”. In the exam, you don’t need to describe theories behind “Big Data” model but use the concepts to generate ideas and paragraphs for the briefing paper or report.
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ACCA SBL in real life: How is “Big Data” used in the budget airline industry?
Big data is transforming the operations and strategies of budget airlines, empowering them to optimize performance, enhance customer satisfaction, and make data-driven decisions. Let’s explore specific examples of how budget airlines can leverage big data across various aspects of their business using the prescriptive, descriptive, and predictive analytics approaches:
Operational Efficiency and Aircraft Maintenance:
Example – Wizz Air: This European budget airline could implement data analysis of aircraft sensor data and maintenance records to predict maintenance needs accurately. By proactively addressing maintenance issues, Wizz Air can reduce unexpected downtime and enhance operational efficiency.
Customer Experience Enhancement and Personalization:
Example – JetBlue: A US-based budget airline like JetBlue could employ prescriptive analytics to analyze customer preferences and behavior. By tailoring services and offers based on this data, JetBlue can create a personalized travel experience that resonates with passengers and fosters loyalty.
Customer Experience Enhancement:
Example – easyJet: As a UK-based budget airline, easyJet could monitor social media channels to gain real-time insights into customer sentiment. By promptly addressing customer concerns and leveraging positive feedback, easyJet can enhance its reputation and build stronger customer relationships.
Example – Jetstar Airways: As a budget airline based in Australia, Jetstar Airways could analyze competitor data to inform its market positioning. By understanding competitor pricing, routes, and customer satisfaction, Jetstar Airways can adjust its strategies to maintain a competitive edge.
Demand Forecasting and Pricing Optimization:
Example – Ryanair: This Irish budget airline can analyze historical booking patterns, flight routes, and seasonal trends to predict future demand accurately. By using predictive analytics, Ryanair can adjust its pricing strategies and flight schedules in real-time to maximize revenue and minimize unsold seats.
Route Planning and Expansion:
Example – AirAsia: As a leading budget airline in Southeast Asia, AirAsia can utilize predictive analytics to analyze passenger preferences, regional demand, and profitability of potential routes. This data-driven approach would help AirAsia identify lucrative new routes and optimize existing ones to cater to specific travel trends.
Fuel Efficiency and Cost Savings:
Example – Southwest Airlines: As a prominent low-cost carrier in the US, Southwest Airlines could leverage predictive analytics to analyze flight data, weather conditions, and aircraft performance. By optimizing flight paths and making real-time adjustments, the airline can achieve substantial fuel savings and minimize operational costs.
Fraud Detection and Security:
Example – IndiGo: This Indian budget airline could utilize predictive analytics to identify patterns of fraudulent activities such as credit card fraud or ticket scalping. By implementing robust fraud detection mechanisms, IndiGo can protect both its revenue and customers’ financial information.
By categorizing the applications of big data in budget airlines under prescriptive, descriptive, and predictive analytics, we highlight the diverse ways in which data-driven insights can enhance various aspects of the airline industry. From operational efficiency and customer experience to pricing strategies and security measures, each approach offers unique benefits that contribute to the overall success and competitiveness of budget airlines.