Passenger Experience Intelligence Dashboard – British Airways Service Quality, Fleet & Satisfaction Optimisation Study

This portfolio project analyses British Airways passenger review data to evaluate service quality, customer satisfaction, and operational consistency across aircraft types, geographic markets, traveller segments, and time periods. Data processing and transformation are performed using Microsoft SQL Server, followed by exploratory analysis and interactive visualisation in Tableau to support executive-level reporting and insight generation.

Table of Contents

• Target Users
• Understanding the Data
• Data Analysis Framework
• Analytical Focus & Key Business Questions
• Key Insights & Strategic Action Recommendations
  • Interactive Dashboard
  • Question 1 – Overall Satisfaction Trends
  • Question 2 – Aircraft-Level Experience Variation
  • Question 3 – Geographic Performance Differences
  • Question 4 – Service Quality Drivers
  • Question 5 – Segment-Specific Satisfaction Dynamics

Target Users: Executive Leadership, Operations & Customer Experience Management, Business Stakeholders

This analysis is designed for airline leaders, operations managers, and customer experience professionals seeking to improve service quality and passenger satisfaction through data-driven insights. It delivers actionable intelligence across customer sentiment trends, service quality drivers, fleet-level experience variation, geographic performance differences, and traveller segmentation, enabling informed decisions on service optimisation, fleet investment prioritisation, and experience-led growth strategy.

Understanding the Data

The data for this project consists of two sperate spreadsheets: ba_reviews.csv, Countries.csv

ba_reviews Table Column Name Data Type header Text author Text date Date place Text content Text aircraft Text traveller_type Text seat_type Text route Text date_flown Date recommended Text trip_verified Text rating Number seat_comfort Number cabin_staff_service Number food_beverages Number ground_service Number value_for_money Number entertainment Number

Countries Table Column Name Data Type Country Text Code Text Continent Text Region Text

Data processing is performed in MS SQL Server using the 'ETL_pipiline_for_Tableau.sql' script. For more information, please read through the 'Data Analysis Framework' section below. This results in the following datasets: 1) ba_reviews_cleande.csv; and 2) Countries_cleaned.csv. The cleaned datasets are used for the Exploratory Data Analysis (EDA) conducted in this project.

Data Analysis Framework

The following set of cleaning and transformation steps was implemented in this project:

• Atomatic database population using BULK INSERT
• Applying TRIM to remove unwanted whitespaces from all columns text columns
• Duplicate checking with SELECT and removal with DELETE from all columns
• Stardising date format using DATETIME
• Replace semi-colons with commas using UPDATE

Next stages involve: 1) data visualisation by producing dynamic, interactive dashboards in Tableau using GEOGRAPHIC MAPS, LINE and HORIZONTAL BAR CHARTS and multipl PARAMETERS, FILTERS and CALCULATED MEASURES; and 2) data storytelling by delivering a comprehensive analysis report using MS PowerPoint. The output of both steps is available in the GitHub repository under 'Tableau_Portfolio_Project.twb' and 'Stakeholder report.pptx', respectively.

Analytical Focus & Key Business Question

This section contains questions important to people from different teams in the British Airways company, including 1) Corporate Strategy Teams; 2) Fleet Planning, Engineering, and Operations Leadership; 3) Network Planning, and Commercial Teams; 4) Customer Experience; and 5) Marketing & Business Intelligence teams. According to them, these questions are worth exploring to identify key satisfaction drivers, discovering structural service gaps, and highlighting fleet-level and regional performance variation.

List of business-related enquiries and their relevance:

1. How is British Airways performing overall, and how has customer satisfaction changed over time? => Enables executive leadership to monitor brand health, assess the impact of strategic initiatives, and course-correct based on long-term customer satisfaction trends.

2. Which aircraft types receive the highest and lowest customer ratings? => Supports fleet and operations leadership in prioritising refurbishment, retirement, and capital investment decisions based on customer experience impact.

3. How do ratings differ by geography or country? => Empowers regional and commercial leaders to identify market-level performance gaps and deploy targeted service and commercial interventions.

4. Which aspects of the flight experience are rated highest or lowest? => Guides customer experience and product owners in prioritising high-impact improvements across the end-to-end passenger journey.

5. Do ratings vary based on traveller type, seat type, or time period? => Enables marketing, revenue, and product strategy teams to refine segmentation, optimise pricing, and tailor service propositions to maximise customer value and loyalty.

Key Insights & Strategic Action Recommendations

This section presents a concise overview of the patterns and trends revealed through Exploratory Data Analysis (EDA) in relation to each of the questions posed above, followed by recommendations derived from the findings.

Interactive Dashboard

Question 1 Insights and recommendations

Key Insights:

• Overall customer satisfaction averages 4.2, indicating acceptable but underwhelming performance.

• Monthly ratings display high volatility, reflecting inconsistent service delivery.

• No sustained upward trend is visible, suggesting limited long-term impact from current improvement initiatives.

• Periodic declines imply vulnerability during peak operational stress periods.

• Stability without improvement signals competitive risk in a customer-driven market.

Based on this analysis, I recommend the following actions:

• Implement root-cause analysis on low-performing months to isolate operational drivers of customer dissatisfaction.
• Establish experience consistency programs focused on standardising service quality across routes, aircraft, and crews.
• Align frontline performance incentives with customer satisfaction metrics to reinforce accountability.
• Enhance operational resilience during peak periods through staffing optimisation, contingency planning, and predictive demand modelling.
• Introduce closed-loop customer feedback mechanisms to rapidly identify issues and validate improvement effectiveness.

Question 2 Insights and recommendations

Key Insights:

• Customer ratings vary significantly across aircraft types, indicating inconsistent onboard experience.

• Wide-body aircraft generally achieve higher satisfaction, particularly on long-haul routes.

• Lower-rated aircraft likely reflect outdated interiors and inferior cabin configurations.

• Experience inconsistency weakens brand coherence and customer trust.

• Fleet composition is a critical driver of satisfaction outcomes.

Based on this analysis, I recommend the following actions:

• Prioritise refurbishment of lowest-rated aircraft types to rapidly lift baseline experience.
• Incorporate customer satisfaction metrics into fleet investment decision-making.
• Standardise cabin experience guidelines across aircraft wherever operationally feasible.
• Accelerate retirement of persistently underperforming aircraft models.
• Use aircraft-level feedback to inform interior design and seat configuration strategy.

Question 3 Insights and recommendations

Key Insights:

• Customer satisfaction varies substantially by country, reflecting uneven service execution.

• Local operational conditions significantly influence customer perception.

• Some regions act as best-practice benchmarks, while others require targeted intervention.

• Geographic inconsistency undermines brand coherence.

• Localised improvement strategies are more effective than broad global initiatives.

Based on this analysis, I recommend the following actions:

• Conduct regional deep-dive diagnostics to isolate root causes of dissatisfaction.
• Deploy targeted operational improvement programs in lowest-performing markets.
• Replicate best practices from high-performing regions across the network.
• Strengthen local accountability structures for service quality delivery.
• Align airport partner performance metrics with customer satisfaction Key Performance Indicators (KPI).

Question 4 Insights and recommendations

Key Insights:

• Entertainment is the lowest-rated service dimension, representing a major experience gap.

• Food, beverage and seat comfort also significantly underperform in terms of expectations.

• Interpersonal service performs comparatively better.

• Onboard product quality now dominates satisfaction outcomes.

• Targeted improvements can generate high-impact experience gains.

Based on this analysis, I recommend the following actions:

• Prioritise inflight entertainment system upgrades, focusing on reliability and content breadth.
• Redesign onboard food and beverage strategy to align with premium customer expectations.
• Accelerate seat comfort enhancement initiatives, especially on long-haul aircraft.
• Re-evaluate value proposition and pricing alignment to improve perceived value for money.
• Introduce continuous monitoring of service dimension Key Performance Indicators (KPI).

Question 5 Insights and recommendations

Key Insights:

• Satisfaction varies significantly by traveller type and seat class.

• Premium segments consistently outperform economy segments.

• Economy dissatisfaction reflects comfort and value perception gaps.

• Segment differences influence loyalty and repeat purchase behaviour.

• Targeted interventions can unlock material revenue upside.

Based on this analysis, I recommend the following actions:

• Enhance economy cabin comfort and service interaction points within cost constraints.
• Develop differentiated experience strategies by traveller type.
• Refine segmentation-based pricing and bundling strategies.
• Target loyalty incentives toward high-risk, low-satisfaction segments.
• Continuously monitor satisfaction at segment level to drive agile improvements.