A data-driven approach to elevating airport experiences: Insights from Ontario International Airport’s journey mapping analysis
Bibliographic record
Abstract
At Ontario International Airport (ONT), elevating the customer experience goes beyond increasing operational efficiency. By using a comprehensive, data-driven approach to creating a customer journey map, ONT gains a clear and comprehensive understanding of a passenger’s experience throughout the airport. This paper identifies key problems, areas of success and opportunities for improvement, leading to informed decision making and strategic investments that elevate overall customer satisfaction. Readers will learn the purpose and application of a customer journey map, including how to identify critical touchpoints and visualise the user experience from start to finish. The paper also introduces behavioural science tools, such as retina-scanning glasses, which help benchmark current experiences and uncover gaps in service. Additionally, it highlights the role of data-driven insights in influencing future design strategies, considering spatial features, emerging trends, policy changes and technology integration. The paper describes how to analyse and prioritise recommendations across each touchpoint, fostering cross-organisational consensus and guiding strategic investments in enhancing the airport experience. This knowledge is vital for professionals looking to innovate in service and infrastructure improvements, ensuring a world-class experience for airport passengers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".