AWS: 21st Century forecasting for the travel & hospitality industry
Forecasting is a decision-making tool that helps businesses cope with the impact of the future’s uncertainty by examining historical data and trends. Businesses across every industry need to forecast components of their operations, and that is particularly important, and challenging, in times of uncertainty.
Actionable forecasts in the Travel and Hospitality industry bring particular value: how many guests will arrive and when, how to staff accordingly, adjusting inventory positions, modelling the impact of promotions/events on business performance, optimising pricing/revenue management, and projecting revenue/cash flow.
Forecast too high and the business will be inefficient with resources: likely buying too much product, facing potential spoilage or waste, while also missing opportunities to invest capital elsewhere.
Forecast too low and the company will have missed sales opportunities while decreasing customer experiences and satisfaction levels.
Despite unpredictability, accurate forecasting is critical for Travel and Hospitality companies. A white paper, AWS Travel & Hospitality, explores the resources (a mix of talent and experience aligned by a strong culture), procedures (that support the continuous improvement of the forecasting process) and technology (that enables the capture of genuine patterns and relationships in data, while removing noise) that will assist travel and hospitality companies improve their forecasting to operate better in the 21st century.
Notably, it serves to review improvements in forecasting, the use of machine learning and artificial intelligence, and metrics, to assess a forecast's value, as Wesley Story, enterprise strategist at AWS explains.
Disruptive moments in life have yielded innovations
“Travel and Hospitality companies have faced many disruptive events over the years. The industry has survived extreme weather, financial crises, acts of terror, business events like deregulation, and of course pandemics. Yet the industry has proven to be resilient over time. These moments of extreme pressure have yielded innovations that improved operational efficiencies, travellers and guests’ experiences, and expanded offerings.
“Airline deregulation in the United States in 1978, for example, stimulated the creation of loyalty programs that are still in place today. A few years later, you see loyalty programs emerging in hotel and restaurant chains as well.
“Loyalty programs didn’t just reward customers for their dedication to a particular hotel or airline brand. They also delivered volumes of data once considered unfathomable. This data combined with yield management could arguably have birthed the practice of revenue management.
Revenue management transforms from emerging to best practice
“Revenue management, an emerging practice in the 1980s, became a best practice for efficient operators by the 1990s.
"Fast forward 30 years and the rudimentary forecasting spreadsheets have been replaced by robust systems and processes. As revenue management expanded and matured as a practice, it also increased the importance of forecasting and expanded the scope from sales, labour, and inventory forecasts to multi-dimensional forecasts feeding downstream activities.
Forecasts become ‘heartbeat for the entire value chain’
“These forecasts become more than internal processes, but the heartbeat for the entire value chain. Accurate forecasting has never been easy, but it became a consistent process for most, even through “typical” events (think holidays, conferences, sports, and normal bad weather). However, with the pandemic, it’s never been harder when you consider the extreme variances of the inputs.
Monthly cycles are now weekly, weekly cycles now daily
“The conversations I’ve had with peers in the industry inform me that this isn’t an isolated challenge. It is also not isolated to the travel and hospitality industry. Based on one industry customer group I work with, forecasting cycles that were once monthly are now weekly, weekly cycles are daily, and in some critical cases you may be reforecasting throughout the day as you monitor the dynamic inputs.
Using technology to ease the pain of forecasting
“However, this is putting strain on the forecasting processes and systems as they just weren’t designed to work this way. It’s stimulating many to look towards technology and advanced techniques to address the pain.
“In a recent study conducted by leading travel and hospitality media outlet Skift, 78% of travel and hospitality executives said digital transformation was more important now than ever. Digital Transformation is a broad topic that can mean different things to different people.
Enterprise agility, time to market, and cost reduction now the focus
“Based on the conversations we have as Enterprise Strategists with AWS customers, the prevailing business outcomes sought are enterprise agility, time to market, and cost reduction. By building on AWS, companies are able to save valuable expenses and respond quickly to these ever-changing market dynamics.
“While I can’t predict the future, I do know AWS, along with our partners, will be there to help customers build for what’s next. Innovations, born out of disruption, will impact and improve the way we fly, sleep, eat, and experience the world in the years ahead.”
The role of machine learning and artificial intelligence
Travel and hospitality companies are looking to transform their businesses and adapt to whatever the next trends will be. “Given the changing dynamics in the world, improving and expanding forecasting should be a high priority for the foreseeable future”, says Mr Story.
Technologies such as machine learning and artificial intelligence can support the process, primarily in two modes: passive and active decisioning. In passive decisioning, human interpretation or action is typically still required in the process. Whereas in active decisioning, cognitive frameworks drive decision-making without human intervention.
“At this point, more use cases tend to use passive decisioning”, explains Mr Story, “but I expect that to change as companies gain confidence in their cognitive models in domains such as dynamic pricing and customer targeting.”
“While we can’t predict the future, when these things are done, we can get a lot closer,” adds Mr Story.
Author: Wesley Story, Enterprise Strategist, AWS, works with executives to share experiences and strategies for how the cloud can enable them to increase speed and agility while devoting more of their resources towards their customers.