ENABLING PERSONALISED MARKETING AND COMMS
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 guest’s needs.
Historically, research and data science have worked in silos (and occasionally as rivals) in businesses. But combining these disciplines across different solutions offers significant benefits – a deeper understanding of motivation and emotion with the understanding of behaviour means we make decisions with both evidence and context.
Our approach had three key components:
Use 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.
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.
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 with the customer’s specific needs and arrives at the time that needs arise.
Missions are used to shape on-site experience – through high-level proposition development, but also by providing site managers with data about the guests they are hosting before arrival. This enables better customer service as well as a more personalised on-site sales experience for those guests looking to book their next break.