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The IATA Data and Analytics Task Force (IDATF) is dedicated to addressing industry priorities related to Data and Advanced Analytics, with the goal of providing valuable support to the airline community.

The IATA Data and Analytics Task Force (IDATF) consists of airline representatives in the field of Data Management, Business Analytics, Data Science, Digital Transformation,Innovation and Enterprise Optimization. 
Each year the group sets an agenda to tackle identified actionable priorities were Data and Advanced Analytics can be used to support the airline community.

The first series of the DTAC Data Analytics Task Force was conducted from April to July 2022 featuring the active involvement of a group of airlines.

Customer Segmentation Analytics

Today airlines use customer information from direct bookings to classify travelers into personas based on purchase travel 
patterns, excluding Personal Information (PI). Currently customer data could be in silos within airlines and there is no industry 
wide overview. The traditional passenger segmentation into Business, Leisure and Visiting Friends and Relatives (VFR) 
groupings has been disrupted by the pandemic and there is a need of new common language for the industry to identify new 
common passenger segments (digital nomads). A new segmentation is needed for airlines and partners in the travel 
ecosystem to deliver a smooth the end-to-end travel experience and for airlines to optimize their personalization strategies.


Solution: Identify new personas (segmentation) at an industry level based on travel patterns and customer behavior, 
considering demographics and additional data sources beyond sales data. Size new markets and monitor evolution of trends.
Ultimately establish common attributes for passenger segments and develop catalogues/taxonomy for common use.

  • Purpose
  1. Identification of new personas (new segments): Utilize additional data sources (beyond transactional) to classify 
    segment using a ML-driven approach.
  2. Adjust the product offering to new personas: sizing of new markets, cater for relevant post-pandemic services 
    and predict the impact of external conditions on travel patterns.
  3. Enable consistent delivery of service: a common taxonomy can enable the delivery of a consistent customer
    experience among airlines by driving alignment with value chain partners.

Deliverables

  1. Ambition: Identify airline activities where Data & Analytics can bring tangible benefits and efficiencies
  2. Case for Change: List common pain points and challenges
  3. Monitoring progress: Live inventory of AI user cases (best practices)
  4. Roadmap: Develop an Airline Data & Analytics Roadmap to fast-track adoption & scale up AI/ML supported by IATA.

Areas of activity

Data and Analytics impact across Airline Business Dimensions

  • Commercial Capabilities: Building Offers and Distribution
  • End-to-end Customer Journey: Digital Identity, Information and Service, and Cargo Flow
  • Cross-Functional Planning and Execution: Scheduling, Day of Operations and Disruption Management
  • Flight Operations: Flight Planning and Operations; and Aircraft Maintenance
  • Ground Operations: Baggage Flow, Aircraft Turnaround

Group Discussions

  • How can Data & AI support innovation, value creation and operational efficiency?

            Review value of Automation and AI through Airline Business Dimensions. Innovation and Customer Experience. Value Creation. Improve Operational Efficiencies. Metrics used to track success. Industry Standards.

  • What prevents airlines to achieve further automation?

            Identify common challenges on Technology & data, Business Processes, Skills & Organization

  • How much progress have airlines achieved in adoption of AI the last 5 years?

            Industry inventory of AI User Cases, Maturity Assessment.

  • What steps can we take at industry level to fast-track adoption of analytics?

            Industry collaboration platforms. Data Advocacy. Certifications. Training.

Example of Airline Activities with AI components:

Revenue Management & Network Planning – Fraud Detection - Air Safety and Aircraft Maintenance –Customer Feedback Analysis – Crew management – Fuel efficiency optimization – Inflight sales & Cabin waste management – In Airport Self Services/Contactless – Flight Management and Autonomous systems