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HappyOrNot

How daa uses real-time feedback to boost service quality and revenue across Dublin and Cork airports

“HappyOrNot has completely changed how we interpret passenger feedback. Instead of waiting for quarterly reports, we now have continuous signals we can act on the same day. It has helped us focus our efforts where they make the biggest impact.”

Niall Kierans
Business Data Analyst – Transformation, Airport Operation, daa
Industry: Transportation
Area: Ireland

2.25m

passenger feedback responses collected annually

3-point NPS growth 

 in washroom experience

80% reduction

in SQM related fines

22-place climb

in Ireland’s Most Reputable Companies ranking (2024)

Managing a high-growth hub under strict service quality rules

daa operates major international airports in Ireland, including Dublin and Cork. Together, Dublin and Cork welcome over 40 million passengers a year, connecting travellers across Europe, the US, and the Middle East.

The environment is large-scale, high-footfall, and time-sensitive. As a regulated entity under the Irish Aviation Authority (IAA), daa must meet strict Service Quality Measures (SQMs) on queue times, washroom standards, staff friendliness, and overall passenger experience. Missing those targets, even for a quarter, can trigger significant financial penalties and reputational damage.

Leadership wanted a clearer, more continuous picture of how passengers felt at critical moments in their journey, so they could act before SQMs were at risk and avoid missing opportunities to improve both the passenger journey and commercial performance.

What real-time passenger signals reveal

daa partnered with HappyOrNot in 2013 and initially introduced 8 Smiley Terminals™ at check-in, washrooms, security, retail, and boarding gates. By 2025, the HappyOrNot deployment includes 62 Smiley Terminals and 55 Smiley Touch™ devices across Dublin and Cork airports, covering Airport Operations and lounges in Dublin, and Security, Food & Beverage (F&B), Retail, and Washrooms in Cork. This comprehensive footprint delivers continuous, actionable data to drive improvements and meet regulatory targets.

Each response is time- and location-stamped, building a rich dataset that, over time, has revealed:

  • Behavioural and operational patterns: sentiment softens over the day, with sharper dips during staff breaks and shift changes at peak times.
  • Infrastructure and layout effects: low-capacity washrooms in high-traffic areas score lower, and performance varies by terminal, zone, and partner area. This clearly shows where to reconfigure and invest.
  • Demographic insight: using the AI-powered Demographics feature on Smiley Touch, daa unlocked who is most vocal in the data. Male passengers make up 58% of responses, despite representing 43% of travellers, and are more likely to give very positive or very negative scores than female passengers.
  • Engagement depth: around 80% of respondents use follow-up questions and 22% leave a written comment. Unhappy passengers are twice as likely to elaborate, giving operational teams rich context behind low scores.

Insights are shared through regular performance reviews and integrated into Power BI dashboards, so teams can overlay experience data with operational metrics like flight schedules, staffing, and throughput and decide where to focus effort.

“HappyOrNot has revealed patterns we hadn’t previously identified — like how low-capacity washrooms consistently receive lower satisfaction scores, especially in high-traffic areas. Having this data has been crucial in building strong cases for infrastructure investment with senior leadership. It’s also helping us make smarter decisions when planning future facilities,” says Niall Kierans.

2 people looking at integrated data analytics on tablet

From insight to action on the ground

Crucially, HappyOrNot data does not just stay in dashboards. It drives action on the ground. daa uses real-time alerts and live views to notify frontline managers when scores drop below agreed thresholds. If satisfaction dips in a particular washroom block, security lane, or retail area, local teams can investigate immediately.

Examples of actions triggered by the data include:

  • Rapid fixes: dispatching cleaning teams, restocking facilities, or opening additional lanes when scores indicate an emerging issue
  • Tactical changes: adjusting rosters, re-timing cleaning schedules, or tweaking queue layouts in known peak periods
  • Strategic decisions: using long-term patterns to justify investments in reconfiguration, capacity increases, or partner performance improvement

The feedback loop now underpins an ongoing rhythm of monitoring, planning, and improvement. Operational, commercial, and partner teams all work from the same set of experience signals, making it easier to prioritise changes that matter most to passengers.

“Our frontline teams rely on live alerts from HappyOrNot to respond quickly when something is not right. It keeps everyone aligned on the passenger experience and gives staff a sense of pride and ownership in their work,” says Niall.

Sharper SQM performance, happier passengers, stronger commercial returns

The combination of continuous feedback and targeted action has delivered clear results.

“Since implementing HappyOrNot, we have seen a clear uplift in passenger satisfaction, especially in areas where we have made targeted improvements based on feedback,” says Niall. “Our regulatory compliance has improved, leading to a significant reduction in fines. The Passenger Advisory Group, which reports to the Airport Board, has acknowledged visible improvements across the airport. And perhaps most notably, Dublin Airport climbed 22 places in Ireland’s Most Reputable Companies in 2024, a strong signal from our passengers that their experience is improving.”

  • Quantitatively: NPS scores for washrooms have improved year-on-year since 2023, mirroring gains in IAA Service Quality Measure (SQM) results. Despite handling 4% more passengers in 2025 (around 1.2 million additional travellers), daa increased washroom NPS from 55 in 2024 to 58 in 2025, showing stronger passenger perception under higher pressure.
  • Financially: Real-time HappyOrNot data has guided targeted interventions that significantly reduced SQM-related fines. This directly protects revenue and demonstrates clear ROI from proactive feedback management. Airports Council International (ACI) research shows that a 1% rise in passenger satisfaction correlates with a 1,5% increase in retail and F&B spending, underscoring the commercial upside of daa’s satisfaction gains.
  • Qualitatively: The data has reinforced a culture of ownership and pride across teams. Passengers increasingly recognise visible efforts from airport staff, and insights have supported higher morale, more consistent service across terminals and partner-operated areas, and stronger cross-departmental collaboration around a smoother, more enjoyable passenger journey.

A new lens on the passenger journey

For daa, HappyOrNot has done more than improve individual touchpoints. It has changed how the organisation thinks about the passenger journey.

“Using HappyOrNot shifted our perspective. We realised that what we measure, and how quickly we act on it, really matters. HappyOrNot gave us a lens on the passenger journey that we simply did not have before, and it has helped us prioritise improvements that truly enhance the travel experience.”

As passenger numbers continue to grow, daa sees this feedback loop as essential for:

  • Staying ahead of regulatory requirements
  • Protecting and enhancing the airport’s reputation
  • Supporting sustainable commercial growth while keeping passengers at the centre of every decision

About daa

daa is a global airport and travel retail group with business in 15 countries around the world. It operates Dublin and Cork airports in Ireland and has additional international operations in retail and airport management. Together, Dublin and Cork airports handle over 40 million passengers annually, connecting travellers to destinations across Europe, North America, the Middle East, and beyond. To learn more, visit daa.

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