Machine Learning at Travelopia

At Travelopia, we offer personalised travel experiences from our high-end travel brands. It’s been a challenging market for a couple of years. During that time, we’ve been on our own journey, to make greater use of our data and better understand our guests.

Our initial attempt at using machine learning didn’t work out, because we were fixated on the technology. By creating a steel thread from prototype to end-user, and focussing on one use case at a time, we were able to dramatically improve the quantity and quality of data insights that we could use to power our business.

We’d like to share with you how we’ve changed Travelopia with machine learning. It’s not been about platforms, technology, or scale. It’s been about what our data definitions should be, what a team should look like, and how we should use data insights across our organisation.

Participants Takeaways:

• Focus on user outcomes, not machine learning technology
• Don’t worry about data platforms, worry about how your teams will self-organise
• A data inception allows you to intentionally design teams and workloads
• Making better use of your data insights will set up your organisation for success

Themes: Data & ML, AI, Machine Learning