Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/114694
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Fung, Clare | - |
| dc.creator | Lo, Ada | - |
| dc.identifier.uri | https://oer.lib.polyu.edu.hk/concern/works/vt150j78h | - |
| dc.language.iso | eng | - |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.subject | Customer relations -- Management -- Data processing | - |
| dc.subject | Airlines -- Customer services | - |
| dc.subject | Customer services -- Data processing | - |
| dc.subject | Artificial intelligence | - |
| dc.title | How Can AI Help Airlines Listen Better | - |
| dc.type | Case Study | - |
| dc.type | OER | - |
| dcterms.abstract | Airlines have traditionally used metrics like NPS and SCAT to assess customer service, but these often miss the complexities of customer feedback. With Large Language Models (LLMs), AI can analyze vast amounts of customer interactions from various channels, uncovering specific concerns and trends that generic categories overlook. AI enables more nuanced categorization and real-time monitoring at service touchpoints, helping airlines respond proactively. However, survey responses tend to be polarized, risking skewed AI-driven insights. To address this, airlines should supplement AI analytics with targeted outreach to gather more balanced and representative customer feedback for accurate decision-making. | - |
| dcterms.issued | 2025 | - |
| Appears in Collections: | Open Educational Resources | |
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