Airline Loyalty Personalisation

Develop a cross-channel airline loyalty personalisation solution for a personalised marketing experience for a customers.

Creation and implementation of software in 3 months.
MVP of a next-best-offer delivery engine based on predictive models developed by data scientists.
Improved relevance and offers to users due to improved data feeds and a contextual engine that derived contextual insights.
Work alongside internal teams to integrate the offers into 3 new channels across the group brands.

Existing Analytics capabilities primarily used to better understand customer behaviour through propensity modelling. However, there was no ability to embed propensity to purchase modelling directly into ranking product offers within marketing campaigns.
An inconsistent personalised experience across channels and products.
Scaling research or implement proof of concept quality airline loyalty personalisation infrastructure to the full loyalty program member base.
Inability to provide relevance of offers based on context (channel, location, time, device, browsing history, previous offers).

Translated the MVP to product grade data pipelines and a processing engine.
Establish an architecture roadmap to embed the MVP, test, learn and increment the architecture to scale the MVP.
Quickly identify, cleanse and ingest streams of contextual information to overlay contextual information to the relevance and ranking of next-best-offer.
Establish an operating model to rollout changes to predictive models based on further research by the data science team.

Myndful can bridge the gap between technology and the business As we have deep understanding of the loyalty business and technolog ecosystem to ensure that the solution is fit for purpose based on program objectives.
Myndful can take the output of data science teams and translate it to high quality and scalable solutions quickly.
Myndful can embed new solutions into the broader technology ecosystem quickly as we have deep experience in platforms integration.
Myndful have a deep understanding of the marketing technology ecosystem and technical expertise in data platform and integration so were selected to deliver the end-to-end platform integration architecture, delivery of a contextualised next-best-offer engine in combination with data scientists, and integration of the engine into the broader airline loyalty personalisation technology ecosystem.

Untapped potential of making real-time and data-driven decisions on determining the next best offer to present to members in the context of how they interact with the organisation.
Untapped potential of applying these decisions to more products consistently across the group.
Untapped potential of increasing omnipresence of offers across brands channels.

Any predictive model is only as good as the data that you feed it. You should assess the feasibility of provide access to data, data quality measures, feature extraction, and the sparsity and density of the data sets.
Start simple – propensity modelling provides a solid predictive foundation for personalisation. The more complex the model and experimentation ecosystem, the more engineering effort need to embed models at scale.
Managing the catalog of offers and content blocks to present personalised offers at scale is challenging. As content templates become more personalisable and the representation across different devices and channels needs to be catered for, more input is required from marketing and copywriting teams.