Type
AI Segmentation and
recommendation
Industry
Recognized as the world’s best leisure airline, Air Transat has built a strong reputation by delivering memorable travel experiences and adopting a deeply customer-centric approach.
Over time, the airline has accumulated a rich transactional and CRM database, generating strong potential to improve marketing activation and personalized merchandising capabilities.
Historically, merchandising relied primarily on rigid, pre-established business rules. While these mechanisms enabled effective campaign management, they mainly created the necessary conditions and opened the door to the adoption of more scalable methods focused on dynamic data processing.
The variability of segmentation strategies from one campaign to another highlighted an opportunity to introduce a more consistent framework in order to continuously optimize performance. Structured primarily around travel programs or geographic regions, recommendations hinted at the potential for more granular analysis at the destination level.
Air Transat wanted to establish a lasting analytical foundation to better leverage its customer data.
The goal was not only to improve targeting, but to evolve merchandising toward a predictive and measurable approach, prioritizing marketing activations based on their real revenue-generation potential, while ensuring operational simplicity for the teams.
Prioritize high
revenue-generation potential activations
Focus activations on the destinations with the highest purchase probability for each customer.
Transform customer segmentation
Replace one-off, reactive campaigns with a structured and measurable approach over time.
A recommendation engine was developed to analyze booking history, CRM data, seasonality, and commercial priorities.
For each customer segment, this engine identifies the most relevant destination at the right moment.
Segments and recommendations are associated with a score, enabling the evaluation of their potential and quality, and providing a clear analytical foundation for marketing activation.
With this solution, marketing teams can prioritize customers with the highest potential, align campaigns with commercial priorities, and analyze activation performance over time.
Decisions based on real purchase potential
Clear visibility into incremental impact
Advanced personalized merchandising at scale
The depth of our data is a strategic asset. With this AI solution, we can better leverage it to structure our merchandising, sharpen the relevance of our campaigns, and gain a clearer view of the impact generated.
This is an important step forward in how we create more useful, personalized, and relevant customer interactions.
Garcí Iñigo
Vice President Marketing & Loyalty
Air Transat
Today, Air Transat’s entire customer base is structured into optimized segments, maximizing the relevance of offers and their contribution to revenue generation.
These activations enable the measurement of CRM campaign performance through marketing indicators (tracking open and activation rates) and financial indicators (conversion rates, revenue per customer).
Beyond immediate gains, the project enabled a more sophisticated approach to segmentation and recommendation, established model governance, and strengthened internal artificial intelligence capabilities.
This project enabled Air Transat to move from fragmented personalization to AI-driven, structured, and governed marketing activation.
Beyond current campaigns, the organization now has a segmentation and recommendation engine capable of evolving with its commercial priorities and supporting large-scale personalization, aligned with its marketing objectives.
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