A notoriously low margin business, travel and tourism have been one of the most severely affected industries by the pandemic. While the world slowly opens again, the situation appears to be constantly evolving. Uncertainty remains the biggest barrier, with government advice and individual restrictions to certain countries playing havoc with how travel and tourism businesses can resume operations – most forced to do so at a drastically reduced capacity.
Whether you’re an airline, cruise line, hotel, or tour operator, making the most out of every single booking will be more important than ever – especially in the coming months and years. The adoption of data science and analytics will be key to the recovery of this sector, playing a pivotal role in helping these businesses understand how to reduce costs and generate the maximum revenue while sticking to strict new rules.
The reality is the travel and tourism industry is sitting on a goldmine of data. Sadly, only a fraction of it is used today. When used, it’s primarily used in a descriptive way – condensing large amounts of data into more easily digestible takeaways. While this may provide decision-makers with a level of understanding about business outcomes, the information often arrives too late and lacking any creative insights to action properly.
Now more than ever, the industry needs to know more and be able to predict more about what could happen and what will happen, from a strategic to execution perspective. However, making this move, relies on the companies themselves enabling analytics across the business, building a culture of analytics so data-curious workers can extract these insights across the entire analytic process. All-to-often, we see data analytics bogged down in time-consuming data preparation rather than being able to explore data insights, something that can be easily fixed by automating more of these analytic processes.
It also requires travel and tourism businesses to embrace intuitive, self-service technology, which will allow the entire organisation to predict and react to changing demand and make critical data-driven decisions.
There are lots of practical ways that data and analytics can help travel businesses streamline their business, from fare price automation and crew staff scheduling to fuel consumption efficiency.
For example, by using a data-led method, one major North American airline has saved almost $100m in fuel efficiency by dramatically increasing its fuel forecasting efficiency. Employing more than 24,000 pilots and flight attendants, the company has also improved the accuracy of its crew scheduling forecasts, enabling it to save hundreds of thousands of dollars in extra costs that previously arose from the failure to anticipate daily changes.
Similarly, a UK budget airline has been able to improve its route planning accuracy to save fuel and fly more efficiently. The carrier, which usually runs 1,400 flights per day in peak summer, is now able to continuously analyse flight data monitoring to pinpoint extra efficiencies in taxiing, take-off, cruising, and landing.
Another airline is using a similar method to take care of pricing and demand analysis, based on market dynamics and competitor pricing. This has enabled it to reduce its analytical process down from 19 hours to just under 1 hour. As a result, the airline is able to build statistical and predictive modelling to ensure that it’s pricing every single flight at the correct value and not losing money or running flights with too many empty seats.
These are just a few examples of how a self-service analytics platform can help airlines. Other real-world use cases include revenue forecasting, enhanced decision-making for targeted overbooking, cleaning rotation, and scheduling – the latter being especially important right now. While most airlines face a similar range of challenges, a data-driven platform can also be tailored for niche use cases.
The benefits of cultivating a culture of data and analytics can help many areas of the travel and tourism industry, including airports, hotels and restaurants. Whether its automating seating and room prices or optimising staff and supplies for extra cleaning and hygiene rotation scheduling, data will help these businesses to build themselves back up and enable them to stay more resilient in future.
To do this, however, requires finding the right mix of democratising data, automating business processes, and elevating human ingenuity to turn every data worker into a discoverer of marginal profitability.
