Revenue management systems forecast demand by O&D (origin-destination) pairing by flight, by day and by fare level 300 days out. There is, quite literally, a stunning amount of big data and complex analytics behind all this.
What is more, given the challenge of any algorithm to accurately predict the future from historic performance, the system calculates uncertainty (forecast variance) around each forecast and optimises pricing based on both the forecast and the uncertainty of the forecast.
Greater certainty (less uncertainty) justifies more inventory associated with that fare level; less certainty justifies less inventory for that demand level.
Lessons from the fashion industry
A professor at Harvard, Dr Kris Ferreira, in studying the fashion industry, another industry famous for oft-times unpredictable behavior by its customers, recommends another approach to dealing with inherent uncertainty.
In addition to measuring forecast uncertainty and incorporating it in planning, an explicit process needs to be implemented for ‘learning’. Rather than wait for actual performance to come in and the model adjusted accordingly, she suggests fashion companies should implement numerous price changes early in the cycle, explicitly to learn and thus to better assess demand.
Of course, often with fashion, price comes down over time – early in the cycle, prices are relatively high and prices come down only as inventory is depleted or the selling season comes to an end. Nevertheless, she observes that forecast misses often drive extremely heavy markdowns late in the process.
She recommends retailers test market responses to price changes early in the cycle – and continuously thereafter. She argues that a series of small tests across the buying cycle can provide better insight into demand than a baseline forecast that doesn’t adjust until it’s too late, and where the only option is heavy discounting.
How does this apply to airline pricing? Perhaps, like in fashion, airlines need to implement a system of more frequent price changes – both fare levels and inventory allocations – before most of the demand has already occurred.