Talking tech with AWS – reworking the approach to revenue management

The fundamentals of revenue management

Revenue management is the backbone of the travel business and is quickly becoming more prevalent in the hospitality industry.

Over the years the travel industry has developed sophisticated systems for forecasting demand, managing inventory, and responding to competitors' prices in the market. Revenue managers across travel and hospitality have been using advanced mathematical models to predict demand and set pricing, using historical data.

However, the disruption in travel and subsequent changes in traveller behaviour over the past two years have made those methods less effective.

Revenue management was invented in the early 1980s by Robert Crandall, American Airlines CEO, and it is routinely used today by hotels and airlines to maximise their profitability. Given that there is a limited number of seats in a plane or rooms in a hotel, the objective is to sell as many seats/rooms so to maximise revenue.

By the time the plane takes off, or the night passes, ideally no seat or room goes unsold, and they were all sold at the highest possible price there was demand for. It is all a supply and demand balance that is now underpinned by huge data lakes and complex algorithms.

But as market conditions have become more uncertain - and historical seasonal data trends less reliable - historical rules-based pricing algorithms have become less effective.

Increasingly there is a need for dynamic pricing techniques that scale and adapt to market conditions but maintain the appropriate business risk mitigation controls to ensure that the customer experience is not impacted.

AWS (Amazon Web Services) provides some background about all the stages of revenue management, from data collection to marketing, in this detailed ebook: Foundations of Revenue Management

Dynamic pricing and the role of machine learning and artificial intelligence

Combining data reveals many levels of sophistication to the revenue management system. In fact, revenue management views can be applied to the core product (e.g. seat or room), or to the ancillary, or to the bundle of it, notes AWS.

"It can be assessed before you know the customer, before purchase, or during the sale, when you know more about the customer. It can then be evaluated in real-time (e.g. Do I offer à-la-carte, or a bundle? Do I price separately or jointly?), greatly increasing the complexity of the problem", it explains.

As such, more traditional mathematical models have been compounded with new techniques and machine learning and artificial intelligence algorithms.

Paul Armstrong, lead architect at Enterprise AWS, explains in the ebook that businesses should modernise, augment, complement, and advance their revenue management system.

"This will make it more flexible and agile, easing the burden of adapting to the ever-changing landscape", Mr Armstrong says.

Recalibrating the revenue management model

Although old models may use old and outdated data, meaning they are not calibrated to the new reality, they do not need to be thrown away, according to Mr Armstrong.

"As such, they need to be adaptable to changing trends and need to run faster, scale up and down based on the data they can access, and be executed multiple times a day - possibly in real-time", he says.

The cloud offers such flexibility to modernise systems.

The data that was historically used by revenue management models - such as past demand, customer demographic, customer behaviour, competitor responses and more - is now just a subset of the data that can be used to recalibrate these models.

Running in the cloud makes ingestion of new data quick and economical. There is plentiful first-party data (e.g. click stream on your website), second-party data (e.g. what your affiliate sales are seeing), and third-party data (e.g. weather, events/holidays, social interactions, etc.), that can now be ingested and processed", says Mr Armstrong.

This enables the adapted model to be more comprehensive and consider other "factors" in determining the right price, he adds.

The 'right product' at the 'right time' to the 'right audience'

Using these machine-learning and artificial intelligence models will help businesses to accurately identify patterns on real-time data, which both analysts and the historical models did not consider, and to define clearly more "customer segments," or "price granularities" that enable travel suppliers to offer the "right product" at the "right price" to the "right audience", says AWS.

AWS suggests five pathways to execute a modern revenue management strategy:

• meet with your revenue management team, do an initial strengths, weaknesses, opportunities and threats (SWOT) analysis;
• identify data sources for a 360 degree view of the customer, and review your architecture;
• speed up your personalisation and ancillary revenue strategy with total revenue management;
• deploy machine learning / artificial intelligence progressively to predict, assist, automate, and accelerate decisions;
• feed back, iterate, and scale, tracking the influenced metrics and bottom line impact.

You can find out more in the AWS ebook: Modern Revenue Management in the Cloud

About AWS Travel and Hospitality…

AWS Travel and Hospitality is the global industry practice for Amazon Web Services (AWS), with a charter to support customers as they accelerate Cloud adoption.

Companies around the world, across every segment of the travel and hospitality industry - and of every size - run on AWS.

This includes industry leaders like Airbnb, Avis Budget Group, Best Western, Booking.com, Choice Hotels, DoorDash, Dunkin' Brands, Expedia Group, Korean Air, McDonald's, Ryanair, SiteMinder, Sysco, Toast, United Airlines and Wyndham Hotels.

These companies, and many others, are transforming their businesses by leveraging technology to enhance customer experiences and increase operational efficiency.

For more information visit: AWS Travel and Hospitality