It may sound a little obscure to apply the core principles of a Disney-Pixar film to revenue management, but guest columnist Tom Bacon has a bash

Inside Out, a recently released animation box office hit by Disney’s Pixar, depicts how the mind works through portrayal of competing emotions. In the first three days the film took $91.1 million at the box office.

Okay, so it goes without saying that a revenue management cast ‘inside out’ may not have the same appeal but I had a bash at showing what it might look like.

Pixar’s Inside Out:

List of characters and their stock responses for a revenue-management styled version of Inside Out:

Fear:  “What if seats go empty? Let’s take this low fare passenger now.” (risk of spoilage)

Hope:  “No. Let’s hang out awhile, I’m sure there’s a higher fare passenger out there.” (dilution risk)

Anxiety:  “Let’s not wait; if the no-show rate is high, I’ll still have empty seats.” (fall-away)

High anxiety:  “Yikes, it’s even worse if the no show’s don’t materialise and I’ve oversold.” (denied boarding)

Fairness:  “Everyone should pay for the features he or she selects.” (ancillary)

Greed:  “Let’s try to get the maximum ancillary out of everyone.” (nickle-diming)

Now let’s try to imagine a typical RM Inside Out Conversation

Fear:  “We aren’t achieving our target load factor. Hasn’t demand weakened versus our history-based model? We need to take some more bookings.”

High anxiety:  “I wouldn’t do that. Our oversales were totally manageable last month. The model understates the cost of overbooking if you take into account the high cost of passenger disruption at the gate.”

Anxiety:  “Actually, I agree with Fear. True, our no-show rate was as predicted but we had empty seats – we can definitely take more. All of our competitors are achieving much higher load factors than we are.”

Greed:  “I agree. Also, our models don’t account for ancillary very well. Even if we take more $99 passengers we can still get another $30-$50 in ancillary. It’ll be great!”

Fairness:  “Wait a minute, Greed. True, many low fare passengers are buying more features, but that really isn’t our goal. I think many passengers are still learning – and our service fees aren’t totally transparent. Many of our low fare customers feel ‘tricked.’ We shouldn’t adopt a strategy that depends on them continuing to be tricked.”

Hope:  “Everyone, calm down please. Rather than a weakening in demand, I think the booking curve has just shifted a bit. If we take too many low fare bookings now, we may squeeze out higher fare passengers. In fact, this is precisely our job – restricting low fare capacity in favor of expected high fare passenger demand. Let’s keep our yield focus and resist the temptation to go for load factor.”

The emotions of revenue managers

There are at least six competing factors or emotions borne by revenue managers:

• Taking bookings or holding out for higher fares
• Overbooking or avoiding denied boarding risk
• Growing ancillary through ‘choice’ or exploiting certain segments

Indeed, we seek to maximise revenue while acknowledging there is considerable uncertainty in demand by flight, in no-show behavior, and in customer ancillary buy-up.

Mathematics can only take us so far. In the end, we’re all measured on results. So did unit revenue increase? Empty seats, or too much low fare traffic, or denied boarding or lower ancillary revenue? ‘Statistical backup’ is rarely an acceptable excuse if you are really wrong. 

Therefore, there is a certain amount of emotion as we review model forecasts and recommendations. Questions arise:

• What if the statistical models don’t work?
• Don’t we have better information than the model?
• Can’t we add value by intervening?
• How do we best respond to last month’s (or last week’s, or yesterday’s) model shortfall?

The balance between reliance on the model and intervention is often difficult to achieve. In fact, most models are much smarter than the analyst – the ability to incorporate masses of data without showing bias for outliers or focusing too much on the most recent observations. So, proper intervention has three features:

• It explicitly addresses something not in the model (new information, refined objectives)
• It factors in each of the ‘RM emotions’, not just responding to one (they all exist; to listen to just one or two will likely lead to unsatisfactory results)
• It focuses on inputs before outputs.  Forecast parameters before Actual Forecasts; Forecasts before Inventory Controls.

It’s okay to second guess. In fact, it’s the job of the RM department to second-guess the model: the parameters may be outdated or the forecasts may be slow to incorporate the most recent trends. RM managers and analysts should continually question whether current trends are properly captured by the model and continually consider the possibility that the model is not working properly. 

But, likewise, they should make sure they’re tapping into each of the emotional responses, not just the one that seems loudest at any particular point in time. RM models often have imperfections but they exist along each of many different dimensions. Also, given the difficulty in ‘out-thinking’ a complex and sophisticated model, once the department has considered every perspective, it should still only act on a fraction of these inward-oriented enquiries. In other words, it’s important to set a high hurdle on intervention.

Tom Bacon has worked in the airline space for 25 years. He now consults to the industry revenue optimisation. Questions? Email Tom at or visit his website