Warner Leisure had increased capacity and wanted to minimise any latency in occupancy by leveraging their (very loyal) customer base. There had been historic reliance on catalogues and a ‘one size fits all’ approach to marketing – something they wanted to change quickly to be more targeted and better meet their guests’ needs.
Our approach had three key components: Predictive, Segmentation and Experience.
In terms of Segmentation, guests may have one or several missions when they stay in a hotel – a romantic break, family gathering or a celebration. We can attribute historic stays using unsupervised machine learning to discover how they like to interact with the brand and build our marketing around likely future missions.
Firstly, we used machine learning to predict when guests are likely to make their next reservation and when they are likely to stay so that we can try and pull forward their next booking.
Finally, there’s the experience. If we know when customers are likely to stay and what their preferred missions are, we can align that with the features and availability of hotels. This means the marketing activity aligns to the customers specific needs and arrives at the time that need arises.