Empowering airline teams to uncover passenger sentiment trends and drive smarter decisions with clarity and speed
Empowering airline teams to uncover passenger sentiment trends and drive smarter decisions with clarity and speed
Empowering airline teams to uncover passenger sentiment trends and drive smarter decisions with clarity and speed
To comply with non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views & facts of the company.
Project Overview
AERENA is a modular digital platform developed by AERQ to transform the in-flight experience through digital engagement, operational empowerment, and data-driven intelligence. It offers airlines tools to manage media, apps, UI customisation, fleet health, and more.
Within AERENA, I led the UX design for the Analytics module—a core product empowering airlines to gain actionable insights from passenger sentiment data. The Analytics module supports stakeholders including airline operations managers, customer experience analysts, and marketing leads in transforming feedback into strategic decisions.
Duration
3 months
My Role
UX Designer
Contribution
Problem Definition, Research & Analysis, Component Design, Mid & High-Fidelity Design
Team
Data Analyst; Data Engineer; Product Owner
Problem Statement & Design Goals
Airlines gather vast amounts of feedback across flights, classes, and timeframes, but traditionally struggle to translate this data into clear, actionable insights without heavy analytical effort.
Passenger sentiment trends
How might we help airline teams quickly understand passenger sentiment trends?
Flexible segmentation
How might we enable flexible segmentation by flight class, nationality, or trip purpose?
Make complex data intuitive
How might we present complex data in an intuitive, non-technical format?
Design Goals
Prioritise clarity, actionability, and usability.
Create a modular, scalable dashboard experience.
Reduce cognitive load while preserving analytical depth.
Research & Analysis
Through domain research and interviews with internal experts and airline stakeholders, I identified key user needs:
Better understanding of NPS drops
Difficulty correlating NPS drops with specific routes, classes, or trip types.
Consistent data interpretation
Fragmented tools causing inconsistent data interpretation.
Deeper investigation and quick grasp
Demand for fast, at-a-glance health checks and deeper investigative capabilities.
We developed three primary personas: Airline Ops Manager, CX Analyst, and Marketing Lead.
Design Process
Information Architecture
The Analytics experience was divided into two key layers:
Overview: High-level KPIs, current NPS scores, quick trends.
Deep Dive: Sentiment breakdowns, per-class analysis, detailed survey feedback.
Overview
Deep Dive
Overview
Deep Dive
Wireframes & Interaction Models
Initial wireframes focused on modular layouts, progressive filtering, and contextual drill-downs. Particular care was given to the positioning and clarity of filters affecting global and local views.
Component Design
I developed a system of reusable UI components, including filter panels, NPS visualisations, sentiment trend graphs, and survey breakdowns. Special attention was given to scale and consistency across use cases.
Data Visualisation Decisions
Donut charts: For NPS summaries and comparative deltas.
Stacked bar charts: For class segmentation of sentiment categories.
Trend lines and growth bars: For historical NPS performance.
Heatmaps: For detailed survey response distributions.
Final Solution
NPS Report – Overview
Donut visual summarising current NPS with past-period comparison.
Flight Route, Trip Purpose, and Nationality filters clearly scoped.
Timeline and growth views enabling granular exploration.
NPS Report – Deep Dive
Customer Sentiment Categories over time (Promoters, Passives, Detractors).
NPS per Compartment view highlighting Business, Economy, Premium Economy.
Onboarding Survey
Response volume and response quality breakdowns.
Survey result summaries linked to key entertainment and travel satisfaction areas.
Full-blown Survey
Visual alerts for dissatisfaction hotspots.
Detailed insights per question, per flight class, and comparative analysis tools.
Design Rationale
Global vs local filter clarity to manage scope.
Intuitive hierarchy balancing KPIs with drill-down opportunities.
High-contrast visual cues for critical insights.
Tooltips and microinteractions enhancing discoverability without noise.
Wrap up
Outcomes
Surfaced compartment-specific issues, enabling targeted service improvements.
Enhanced cross-functional collaboration between marketing, operations, and CX teams.
Reflection & Takeaways
This project solidified key lessons in:
Balancing complexity and clarity when designing enterprise SaaS experiences.
The critical importance of progressive disclosure in data-heavy environments.
Designing for both strategic decision-making and operational actionability.
By shaping AERENA Analytics, I contributed to AERQ’s broader mission of digitally transforming the in-flight experience and empowering airlines to operate smarter, faster, and closer to their passengers.
Project Overview
AERENA is a modular digital platform developed by AERQ to transform the in-flight experience through digital engagement, operational empowerment, and data-driven intelligence. It offers airlines tools to manage media, apps, UI customisation, fleet health, and more.
Within AERENA, I led the UX design for the Analytics module—a core product empowering airlines to gain actionable insights from passenger sentiment data. The Analytics module supports stakeholders including airline operations managers, customer experience analysts, and marketing leads in transforming feedback into strategic decisions.
To comply with non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views & facts of the company.
Duration
3 months
My Role
UX Designer
Contribution
Problem Definition, Research & Analysis, Component Design, Mid & High-Fidelity Design
Team
Data Analyst; Data Engineer; Product Owner
Problem Statement & Design Goals
Airlines gather vast amounts of feedback across flights, classes, and timeframes, but traditionally struggle to translate this data into clear, actionable insights without heavy analytical effort.
Passenger sentiment trends
How might we help airline teams quickly understand passenger sentiment trends?
Flexible segmentation
How might we enable flexible segmentation by flight class, nationality, or trip purpose?
Make complex data intuitive
How might we present complex data in an intuitive, non-technical format?
Design Goals
Prioritise clarity, actionability, and usability.
Create a modular, scalable dashboard experience.
Reduce cognitive load while preserving analytical depth.
Research & Analysis
Through domain research and interviews with internal experts and airline stakeholders, I identified key user needs:
Better understanding of NPS drops
Difficulty correlating NPS drops with specific routes, classes, or trip types.
Consistent data interpretation
Fragmented tools causing inconsistent data interpretation.
Deeper investigation and quick grasp
Demand for fast, at-a-glance health checks and deeper investigative capabilities.
We developed three primary personas: Airline Ops Manager, CX Analyst, and Marketing Lead.
Design Process
Information Architecture
The Analytics experience was divided into two key layers:
Overview: High-level KPIs, current NPS scores, quick trends.
Deep Dive: Sentiment breakdowns, per-class analysis, detailed survey feedback.
Overview
Deep Dive
Overview
Deep Dive
Wireframes & Interaction Models
Initial wireframes focused on modular layouts, progressive filtering, and contextual drill-downs. Particular care was given to the positioning and clarity of filters affecting global and local views.
Component Design
I developed a system of reusable UI components, including filter panels, NPS visualisations, sentiment trend graphs, and survey breakdowns. Special attention was given to scale and consistency across use cases.
Data Visualisation Decisions
Donut charts: For NPS summaries and comparative deltas.
Stacked bar charts: For class segmentation of sentiment categories.
Trend lines and growth bars: For historical NPS performance.
Heatmaps: For detailed survey response distributions.
Final Solution
NPS Report – Overview
Donut visual summarising current NPS with past-period comparison.
Flight Route, Trip Purpose, and Nationality filters clearly scoped.
Timeline and growth views enabling granular exploration.
NPS Report – Deep Dive
Customer Sentiment Categories over time (Promoters, Passives, Detractors).
NPS per Compartment view highlighting Business, Economy, Premium Economy.
Onboarding Survey
Response volume and response quality breakdowns.
Survey result summaries linked to key entertainment and travel satisfaction areas.
Full-blown Survey
Visual alerts for dissatisfaction hotspots.
Detailed insights per question, per flight class, and comparative analysis tools.
Design Rationale
Global vs local filter clarity to manage scope.
Intuitive hierarchy balancing KPIs with drill-down opportunities.
High-contrast visual cues for critical insights.
Tooltips and microinteractions enhancing discoverability without noise.
Wrap up
Outcomes
Surfaced compartment-specific issues, enabling targeted service improvements.
Enhanced cross-functional collaboration between marketing, operations, and CX teams.
Reflection & Takeaways
This project solidified key lessons in:
Balancing complexity and clarity when designing enterprise SaaS experiences.
The critical importance of progressive disclosure in data-heavy environments.
Designing for both strategic decision-making and operational actionability.
By shaping AERENA Analytics, I contributed to AERQ’s broader mission of digitally transforming the in-flight experience and empowering airlines to operate smarter, faster, and closer to their passengers.
Next project →
Project Overview
AERENA is a modular digital platform developed by AERQ to transform the in-flight experience through digital engagement, operational empowerment, and data-driven intelligence. It offers airlines tools to manage media, apps, UI customisation, fleet health, and more.
Within AERENA, I led the UX design for the Analytics module—a core product empowering airlines to gain actionable insights from passenger sentiment data. The Analytics module supports stakeholders including airline operations managers, customer experience analysts, and marketing leads in transforming feedback into strategic decisions.
Duration
3 months
My Role
UX Designer
Contribution
Problem Definition, Research & Analysis, Component Design, Mid & High-Fidelity Design
Team
Data Analyst; Data Engineer; Product Owner
Problem Statement & Design Goals
Airlines gather vast amounts of feedback across flights, classes, and timeframes, but traditionally struggle to translate this data into clear, actionable insights without heavy analytical effort.
Passenger sentiment trends
How might we help airline teams quickly understand passenger sentiment trends?
Flexible segmentation
How might we enable flexible segmentation by flight class, nationality, or trip purpose?
Make complex data intuitive
How might we present complex data in an intuitive, non-technical format?
Design Goals
Prioritise clarity, actionability, and usability.
Create a modular, scalable dashboard experience.
Reduce cognitive load while preserving analytical depth.
Research & Analysis
Through domain research and interviews with internal experts and airline stakeholders, I identified key user needs:
Better understanding of NPS drops
Difficulty correlating NPS drops with specific routes, classes, or trip types.
Consistent data interpretation
Fragmented tools causing inconsistent data interpretation.
Deeper investigation and quick grasp
Demand for fast, at-a-glance health checks and deeper investigative capabilities.
We developed three primary personas: Airline Ops Manager, CX Analyst, and Marketing Lead.
Design Process
Information Architecture
The Analytics experience was divided into two key layers:
Overview: High-level KPIs, current NPS scores, quick trends.
Deep Dive: Sentiment breakdowns, per-class analysis, detailed survey feedback.
Overview
Deep Dive
Overview
Deep Dive
Wireframes & Interaction Models
Initial wireframes focused on modular layouts, progressive filtering, and contextual drill-downs. Particular care was given to the positioning and clarity of filters affecting global and local views.
Component Design
I developed a system of reusable UI components, including filter panels, NPS visualisations, sentiment trend graphs, and survey breakdowns. Special attention was given to scale and consistency across use cases.
Data Visualisation Decisions
Donut charts: For NPS summaries and comparative deltas.
Stacked bar charts: For class segmentation of sentiment categories.
Trend lines and growth bars: For historical NPS performance.
Heatmaps: For detailed survey response distributions.
Final Solution
NPS Report – Overview
Donut visual summarising current NPS with past-period comparison.
Flight Route, Trip Purpose, and Nationality filters clearly scoped.
Timeline and growth views enabling granular exploration.
NPS Report – Deep Dive
Customer Sentiment Categories over time (Promoters, Passives, Detractors).
NPS per Compartment view highlighting Business, Economy, Premium Economy.
Onboarding Survey
Response volume and response quality breakdowns.
Survey result summaries linked to key entertainment and travel satisfaction areas.
Full-blown Survey
Visual alerts for dissatisfaction hotspots.
Detailed insights per question, per flight class, and comparative analysis tools.
Design Rationale
Global vs local filter clarity to manage scope.
Intuitive hierarchy balancing KPIs with drill-down opportunities.
High-contrast visual cues for critical insights.
Tooltips and microinteractions enhancing discoverability without noise.
Wrap up
Outcomes
Surfaced compartment-specific issues, enabling targeted service improvements.
Enhanced cross-functional collaboration between marketing, operations, and CX teams.
Reflection & Takeaways
This project solidified key lessons in:
Balancing complexity and clarity when designing enterprise SaaS experiences.
The critical importance of progressive disclosure in data-heavy environments.
Designing for both strategic decision-making and operational actionability.
By shaping AERENA Analytics, I contributed to AERQ’s broader mission of digitally transforming the in-flight experience and empowering airlines to operate smarter, faster, and closer to their passengers.
To comply with non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views & facts of the company.
charlesjaja.dny@gmail.com
charlesjaja.dny@gmail.com
2025 Charles Jaja
2025 Charles Jaja
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