NB: This is an article by Tom Bacon, an airline veteran and industry consultant in revenue optimisation
Reasonable doubt is a legal concept, not a business concept. US juries are instructed to review the evidence and find a defendant ‘not guilty’ if there is reasonable doubt that he committed the crime. Thus, the jury can conclude that he ‘probably’ or ‘likely’ committed the crime but, if there is reasonable doubt, they still must find the defendant ‘not guilty’.
As the saying goes: “It is better for ten guilty defendants to go free than to sentence one innocent citizen”. In the legal system, we assess upside (convict all criminals) versus downside (jail the wrong guy) and opt to be conservative.
This concept applies to risk taking in business. In general, we are taught to make decisions based on ‘most likely’. However, in many cases, when the upside of an initiative is much different from the downside, we need to exercise ‘reasonable doubt’ with regard to the ‘most likely’ scenario. Comparing upside versus downside in a business situation often justifies pursuing a strategy even if in the ‘most likely’ or ‘probable’ scenarios, it won’t work!
RM systems already measure upside versus downside, and estimate an entire probability distribution so in theory they would already quantitatively and scientifically assess ‘reasonable doubt’, and not act solely based on ‘most likely’.
· ‘Most likely’ demand for a $1,000 passenger may be less than one. However, RM systems may still save a seat for such a high fare passenger based on a less-than-50% chance that he will show up. The upside of ensuring a seat is available may be too compelling.
Nevertheless, RM systems are often slow to respond to market changes and are not designed to capture major turning points.
· In many cases, ‘most likely’ is based on conventional views or linear extrapolations that may not hold given the complex, frequently non-linear, dynamics of the market.
So, ‘reasonable doubt’ should form the basis for constant testing. Tests, by definition, limit the downside and if they identify an otherwise overlooked demand opportunity can lead to broader application, and broader success.
Take these two examples:
What if…there is a larger mix of high fare passengers than is represented in the historic database?