The core focus was on developing customer value in two ways: first, by building a business case through adjustments to the allocation of loyalty points, and second, by analyzing customer value potential at an individual level.

Data-driven insights guided our decisions. The foundation was strong: millions of transaction records provided behavioral data, while monthly surveys among tens of thousands of travelers provided attitudinal data. This combination made the analyses highly robust, enabling accurate predictions of both customer potential and the broader strategic business case.

The business case for strategic adjustments in points allocation was tested through simulation, with ticket price and distance flown as key variables. To assess customer value potential, we developed predictive models covering upsell, cross-sell, and cross-flight value, along with product-level responsiveness. This allowed us to design and implement individualized customer strategies.

The main challenge was to bring less data-driven parts of the organization along on the journey of marketing intelligence. The task was to make the abstract world of analytics tangible enough for management and staff to fully believe in its value. On the data side, a key pain point was that individual motivations for flying with KLM were not directly available. We solved this through a related initiative – AskAtlas.

KLM embraced the new points allocation model, while predictive models and business rules were successfully integrated into campaign execution. After KLM’s own subsequent business analysis, the intelligence layer delivered additional value amounting to several tens of millions of euros