man vs machine revenue management

Tom Bacon does not dispute the value of analysts but argues that there is definitely a central role for machine learning in RM

Great revenue management analysts are invaluable. The 5+% increase in revenue attributed to a sophisticated RM system cannot be achieved without well-trained, highly experienced analysts who can properly maintain the system and intervene when appropriate.

This means that, potentially, groups of 50 individuals could be responsible for $1-$2 billion in incremental earnings a year at each of the larger airlines – $20-$40 million per analyst. Give these guys a raise!

Some airlines are experimenting with machine learning. Can the computer ‘learn’ from experienced analysts and replicate their interventions? At an Eyefortravel conference, easyJet, for example, explained how machine learning reverses the normal modelling process.

So the normal flow of big data analytics used in most RM systems goes like this.

Data => Computer => Programme => Output

Here, the system takes the data, builds a model around the data, and produces output (demand forecasts by fare and recommended inventory allocations by fare).

With machine learning, on the other hand we have:

[Data + Output (Analyst Intervention)] => Computer => Program

In this case the big data analytics approach is applied to analyst interventions themselves. So, the system can learn from the analysts and automatically adjust the demand forecasts/allocations without human intervention.

First, of course, we need to acknowledge that all analyst interventions – overriding the big data analytics modelling and recommendations – need to be performed with caution.

Academic studies have measured a negative value-add from many analyst interventions. Even experienced analysts can intervene too often – or their adjustments may be too large or too small.

The new programme must have a way to assess the actual value/benefit of the interventions or the machine could be learning poor practices!

Ultimately, machine learning needs to have a way to evaluate each intervention; in many cases, the machine will need to learn to ignore or offset interventions that don’t add value.

Read rest of the article at Eye for Travel