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How Sydney Airport Reduced Congestion and Improved Customer Experience
One of Australia’s busiest airports faced challenges managing long queues and congestion at the terminals due to post-COVID labour shortage.
The Opportunity
Sydney Airport, Australia’s busiest international gateway, faced significant challenges managing long queues and congestion at terminals due to post-COVID labour shortages – requiring a data-driven approach to transform operations.
The Delivery
Agile Insights delivered a comprehensive solution spanning Initiate, Design, Build and Run phases – developing a near real-time data model, Power BI operational reports, and a centralised app serving as a one-stop location for all key operational reporting.
The Outcome
Faster passenger processing, reduced wait times, improved retail and dining revenue, data-driven decision-making, and on-time delivery of key reports during peak seasons – significantly enhancing the overall customer experience.
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About Sydney Airport
Sydney Airport is Australia’s primary international airport, serving millions of passengers annually. As a critical hub for domestic and international travel, the airport is committed to delivering seamless operations and an exceptional traveller experience.
The Challenge

Sydney Airport faced significant operational challenges following the COVID-19 pandemic:

  • Understanding passenger demographics and behaviour to personalise services.
  • Integrating real-time vehicle traffic monitoring for better ground transportation services.
  • Implementing real-time monitoring to optimise operations and improve customer satisfaction.
  • Analysing trends and patterns for accurate forecasting and resource optimisation.
Client Testimonial
"Agile Insights has helped Sydney Airport overcome significant operational challenges. Their data-driven solutions have been instrumental in enhancing our customer experience and improving overall efficiency."
Sydney Airport

Our Solution

Agile Insights delivered a comprehensive solution to address Sydney Airport’s challenges, structured across four phases:

Initiate
Conducted workshops with operational leaders to understand current processes. Collaborated with the data team to understand data sources and backend architecture. Identified key data points and transformations required, recommending tools and technologies to streamline processes and capture missing data.
Design
Identified key focus areas and defined relevant KPIs for quick operational decision-making. Designed a comprehensive data model supporting near real-time, historical, and forecast data. Prepared mockups to cater to user requirements and define report design.
Build
Iteratively improved the proposed solution in collaboration with stakeholders. Developed reports in parallel with the data team to ensure data accuracy. Optimised reports using Power BI functionalities including composite models, streaming datasets, auto page refresh, and hybrid models.
Run
Prepared an app serving as a one-stop location for all key operational reports. Delivered reports on time after thorough testing with key stakeholders. Completed handover with end-to-end documentation and client workshops.
Why It Worked

The solution was executed over a period of three months through four structured phases – Initiate, Design, Build and Run – ensuring close collaboration with Sydney Airport’s operational leaders and data team throughout. Each phase built on the last, with iterative stakeholder feedback incorporated at every stage to ensure accuracy and relevance. End-to-end documentation and client workshops ensured a smooth handover and lasting capability within the airport’s team.

The Impact

  • Faster processing times enabled Sydney Airport to efficiently handle more passengers, greatly improving the overall customer experience.
  • Reduced waiting times led to shorter queues, improving passenger satisfaction.
  • Faster processing allowed more time for shopping and dining, increasing retail and restaurant sales.
  • Improved data transparency and reports enabled faster, data-driven decision-making.
  • Understanding of factors affecting on-time performance improved operational efficiency.
  • Strengthened collaboration between operations and data teams for better problem-solving.
  • On-time delivery of the solution ensured key reports were available during peak seasons, enhancing the customer experience.

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