10 Biases that Afflict Hotel Revenue Managers

10 Biases that Afflict Hotel Revenue Managers

Elon Musk recently took to Twitter to declare that cognitive biases “should be taught to all at a young age.”  He referenced an infographic that listed the 50 most common biases. These biases affect us most in the decisions that we make on a daily basis. It, therefore, follows that these biases play a major role in Hotel Pricing and Revenue Management.

In the absence of collaborative data analysis, decisions made under uncertainty become highly subjective activities. The most relevant example in Hotel Revenue Management is the process of selecting the rate that has the best chance of optimizing revenue for any given night. In the vast majority of properties, this task remains a highly judgmental exercise. Even in properties that claim to use a “scientific” approach, the analysis applied is mostly algebraic or dependent on some “rule-of-thumb” set, which means that the effects of variance or “chance” are not being properly quantified to make a truly informed decision.

Revenue Managers must be keenly aware that any RM decision done without a thorough analytical interpretation of what is actually happening is likely to be subject to the unintentional application of some very human, but very dangerous biases. I thought it would be useful to highlight my list of the 10 most common biases applied in RM.

1. Confirmation bias (i.e. wishful thinking) is the tendency of people to favor information that confirms their beliefs. For example, we focus on spikes in the booking pattern that seems to verify our guesstimate of where the final occupancy will end up or how the month will close. These spikes may, in fact, be statistically insignificant.

2. Ambiguity bias is the tendency to avoid options for which we have little experience. This is often seen in the reluctance of some hotels to charge a rate that is outside of a defined “comfort” range. For some, this range may be defined by always staying below a certain competitor or within a percentage of the rates charged the previous year.

3. Bandwagon effect is the tendency to do or believe things just because others do. When everyone in the strategy meeting believes that “this will be a tough summer”, your rates will inevitably go low and stay low.

4. Anchoring bias is the tendency to rely too heavily, or “anchor,” on one piece of information. For example, you may perceive all “event” dates as strong demand days and should therefore be priced differently from all non-event dates when, in fact, some non-event dates may show similar demand patterns and can be priced just as aggressively as event dates. An additional hazard of this bias is that you run the risk of overestimating the performance of event dates.

5. Overconfidence is the excessive faith in one’s own abilities to make judgements. Enough said.

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6. Semmelweis Reflex is the act of automatically rejecting facts without thought or real consideration. Often managers “stick to their guns” because they have of a gut feeling about the outcome when all the evidence points to the contrary. This is especially true among senior managers who want others to respect their natural intuition.

7. Illusion of Control is the tendency to underestimate the magnitude of uncertainty because we believe that we have more control over events than we actually do. This is experienced by most in RM when they make aggressive rate changes but nothing significant materializes in occupancy.

8. Averages bias is the over-reliance on averages as a metric because we feel it captures the “essence” of what is going on. In most cases, you should be taking into consideration the significant changes in variations and deviations. A common example is the practice of rate-setting by adjusting rates by a percentage of the average comp set prices. Remember, if the lowest rate in the comp set drops by $50 and the highest increases by $50, the average is still the same, but the market dynamics have completely changed.

9. Illusory correlation bias is when we perceive a close relationship among two variable that are in fact uncorrelated or only loosely correlated. A classic example is a myth that higher total occupancy drives profit and outlet spend. Today, most sophisticated hotels have realized that outlet spend is driven by attracting the right customer, not just by increasing total occupancy. Furthermore, the “right customer” base may, in fact, be only a portion of the total customer population.

10. Recency bias is the tendency to focus on the most recent data and regard older data as irrelevant. For example, most Pace reports are based on comparisons to previous year information when, in some instances, it may make more sense to compare the current year to a peak performance year, a three-year average, or a similar performing year. In fact, if you are consistently ahead on Pace, you may be fist-bumping each other while simultaneously undermining your performance standards.

This is not meant to be a comprehensive list, but more a review of the biases that I have encountered when working with clients. Keep in mind that I did not count the frequency of each bias as I witnessed them, therefore the creation of the above list was also subject to my own biases.