About Google Flight’s New CO2 Display

Google Flights now displays a carbon emission (CO2) estimate for most flights.

Folks, this is a game changer because it brings flight-related emissions data to the shopping table on a scale that reaches millions of flight shoppers.

Google Flight display for Seattle to London, one way on Nov.1st 2021

Not only is Google Flight’s CO2 data credible and easy to understand, it is easy to see which flight is better or worse, and by how much. This has big implications, namely:

• Leisure and unmanaged business travelers will become accustomed to seeing this data. Many will use it to select their flights, making CO2 data part of the “consumer experience”.
• Business travelers who shop first on Google Flights before booking in the OBT/TMC channel will get used to seeing this data. Many will use it to select their flights.
• Travel managers will be expected by travelers and their executives to provide booking tools that provide flight-specific CO2 data.
  • This will create a standards problem, unless the booking tools use the same CO2 model as does Google Flights. More on this below.
• Airlines will feel pressure to improve their CO2 metrics as they appear in Google Flights, especially if Google is successful at making its CO2 model a free resource to others, as it says it plans to do.
• Unfortunately, it will reinforce the belief that premium seats, e.g., business class, should be allocated more CO2 per passenger than economy seats. This belief is wrong, and leads to more flight emissions, not less.

Insights About Google Flight’s CO2 model

Google’s CO2 model calculations are based on the European Environmental Agency (EEA) framework 3.1a, published in 2019, and some of its related data sources, including the BADA flight emissions model, which may use 2016 aircraft and engine data. The EEA framework is described here. Google apparently uses the Tier 3A approach, described on page 25. Google also uses data from other sources, including those from airlines and other sources to obtain the aircraft models and seating configurations.

Some things that caught my eye about Google’s approach:

More connections don’t necessarily mean more emissions. Note in the exhibit above that a 2-stop flight (circled in blue) has less emissions than the one-stop flight below it.

There seems to be no adjustment made for a flight’s passenger load factor or cargo weight, nor for the actual in-flight routing or actual gate-to-gate time. Factoring in these variables would make the model more accurate, IF the model was given each flight’s actual data. This data is practically impossible to get pre-flight. Other flight CO2 models may include these variables by making good-faith estimates, but they are still estimates, and probably a good example of providing false precision. Google’s approach is good enough.

That said, Google uses a traditional method for allocating more CO2 to premium economy, business and first class seats, based on the square footage of these types of seats. I’ll take another tilt at the windmill by saying again this is fundamentally wrong, and harmful to the climate.

Moving on. A flight’s emissions are shown as better or worse based on the other flights’ emissions for the same O&D, the same route, and the same cabin, and the same day(s) of travel. If a flight’s emissions are within 5%, plus or minus, of the median emission value, it is marked as “average”. Users can sort the itineraries based on CO2, so it makes it easy to pick the least-emitting flight.

Google says it plans to make its model available to the travel industry for free via the Travelyst coalition. It would be even better if Google published an API tomorrow to let anyone consume their calculations. The quicker the travel industry coalesces around flight and hotel CO2 calculation methods, the better. Until then, expect travelers to be confused about seeing significantly different emission estimates for the same flight. Confusion leads quickly to distrust, which is not what we want on this issue.

Innovation Opportunities

A few quick thoughts on how this platform could be put to even better use:

• Change the basis of allocating CO2 from the seat’s size, to the amount of CO2 emitted for the weight of the seat, its passenger and the passenger’s luggage, per square foot.
• Publish an API so anyone could consume these calculations (as mentioned above).
• Integrate this CO2 data into your corporate booking tool.
• Use the API and your TMC’s historical booking data for your program to set a 2019 baseline of flight emissions. Better, have the bright folks at Thrust Carbon or eco.mio do the heavy lifting, and enable ways to make offsetting easy and ethical.
• Update the analytical modeling to use more current data on engine emissions.
• For those concerned about the accuracy of the CO2 estimates, lobby for the airlines to publish for every flight the
  • Amount of fuel consumed;
  • Cargo weight;
  • Passenger and baggage weight;
  • Passenger load factor (wishful thinking).

Flight emissions will only grow in importance. It’s good to see Google enabling better decisions on this front.

The Hourly Cost of Air Travel

Road warriors, by definition, do a lot of traveling. All their airline tickets add up to some pretty big expenses, as do the hours they spend inside airplanes.

Why not take those two pieces of data and show what it costs business folks to fly per hour? Let’s face it, talking about price per mile might be great for aviation pros, but it’s not great for briefing management about travel expenses.

ARC’s Definitive Data, Air Clarity’s Innovative Analysis

Air Clarity, my firm’s air spend benchmarking tool, crunched a few million airline tickets from ARC’s corporate ticket database to get the answers. Since ARC stores all travel agency tickets sold in the U.S. on most every airline (excluding Southwest and a few other low cost airlines), this data is as good as it gets.

Here’s what the price per flight hour looks like, based on the average hourly prices paid by roughly 2,100 corporate travel programs:

The quick answer: About $80 an hour for short haul (domestic) flights; about $110 an hour for long haul flights

Doesn’t that make for a much easier conversation about the cost of air travel?

For context, this study by American Express GBT, ARC and my firm found that the average road warrior earned about $80 an hour, assuming 2,000 work hours per year.

Travel managers, try talking to your business stakeholders about the price per hour of air travel, and see if that doesn’t make for more engaged discussions.

Custom Industry Peer Group Benchmarks

If you’re wondering what your company’s price per hour is, and how that compares to other firms in your industry, good news…tClara is organizing industry peer groups to help provide even better value from our Air Clarity benchmark data. Here are the groups we’re starting:

If you’re a travel manager interested in one of our industry peer groups, follow the group by signing up here…no cost, no obligation.

More information about Air Clarity’s benchmark reports for corporate travel managers, TMCs and airlines is here.

Some limitations and definitions around these price per hour  numbers:

What’s The Real Goal of a Travel Program?

 

Quick – name three metrics that travel managers care most about…and no, you can’t say savings, savings and savings.

Savings, for sure, maybe followed by discounts and policy compliance, or average ticket price/room/car rate.    These are time-tested, industry-accepted, common-sense metrics that are the foundation for status-quo management of the travel category.

(Going to GBTA’s Convention? See a related meet-up note at the end of this post)

Before you reject my call to demote these traditional metrics, consider the maxim “Measure what matters”.  Note that it isn’t “Measure what’s laying around, looking like it matters”.  It’s not “Measure what we’ve always measured”.

It’s the “what matters” part that’s the key.  That, and an evolved view of travel management’s mission.

Shouldn’t the goal of managing travel be to create the most value from whatever the travel budget is?  To create the biggest business impact, net of the cost?  Sure…which means we need to think about measuring said impact.

Manage Travelers and Their Taxes with Traveler Data

Sure, payroll taxes are not in scope of a travel manager’s job.  But there is a great way for travel managers and TMCs to add real value here by being proactive with traveler data.

Curious?

The Problem

Many countries and other taxing jurisdictions are aggressively seeking more payroll taxes from business travelers.  This means their pay could be docked for spending just one day in a foreign country on business.

This article reports that “more than 100 countries have joined The Global Forum on Transparency and Exchange of Information for Tax Purposes, which is working toward automated exchange of immigration reports, hotel stays and airline reservations. Better-informed enforcement bodies will have an easier time catching non-compliant firms and individuals.” (emphasis added)

The problem is not limited to international travel.  In the US, many states have some form of traveler tax, according to this 2013 Pew report.

The Solution

File this under “Being a proactive problem solver”.  These traveler tax issues place the burden of proof on the employer.  So travel managers should reach out to their payroll tax liability folks to discuss the types of travel data needed to manage these risks.

There is the historical travel angle, and the future travel angle.  The key is to analyze each traveler’s location and duration history, and calculate how many days the traveler has until triggering some type of tax liability.

Complex? OMG, yes.  Fortunately, there are at least two firms working specifically in this area.  Blackspark uses TMC booking data and is integrated with Concur, while Monaeo uses the traveler’s mobile phone data.

The point is that there are automated ways to feed traveler data into the equation.  This can keep travelers from triggering significant tax liabilities – and from refusing to travel for fear of losing more of their hard-earned pay.

TMCs, you should raise this issue with your clients.  If you get enough traction, why not incorporate pre-trip alerts to help manage these future tax liabilities?

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Survey Says: Biggest Analytical Pain Points Are…

* Quantifying savings (36%), and measuring the traveler experience (16%)

* Working with travel technology tools, e.g. self-booking, expense reporting and data reporting tools (34%), and the airline category (20%)

* Deciding how to structure the analysis (22%), getting good data (20%) and proving cause and effect from the data (20%)

* Meeting the analytical demands of Senior Executives (28%), Procurement (26%) and Finance (24%)

This comes from my recent survey of 50 anonymous and self-proclaimed travel buyers  – so take this as directionally interesting; not statistically significant.

It’s curious to me that this group is still struggling with

Travel Buyers, What’s Your Big Analytical Pain Point?

A lot of folks in the travel industry don’t enjoy the numbers side of the business nearly as much as they do the people side.  Fair enough, as the whole industry is built on the premise of building better interpersonal relationships.

But what is it about the analytical efforts that are really causing you the most pain?

Maybe if we understood those pain points better, our industry could do a better job of making the numbers side a bit easier on everyone.

If you are a travel buyer, please take 2 minutes to answer five quick questions here:

Travel Buyers: This Quarter’s Travel Data Pain Points?

The survey is anonymous, and meant to shed some directional light on the problems.

I’ll publish the results here and on LinkedIn.

Please share this as you see fit.  Thank you!

Why Data Predicting Trumps Data Reporting

Quick – name a travel metric that your CFO would pay a lot of money to predict reasonably well.

Next year’s travel savings? Maybe. Next year’s travel spend? Maybe.  But he won’t pick your firm’s future travel policy compliance rate, or next year’s average airfare, or your traveler’s future satisfaction with your online booking tool.

My point is that while firms spend a lot of money on travel data reporting, the core value that those pretty dashboards deliver is not very high – not in the grand scheme of your firm’s business.

Here’s why: Data reports are the result of the 20th-century management dictum “Measure what matters”.

Within the boundaries of a travel program there are dozens of things that matter.  And so we’ve figured out how to measure them. In order to report them.  In order to manage them.

But all those dials and gauges and stop lights really do is simply give you signals.  Signals that you have to interpret to keep your travel program  between the white lines of your side of the road.

What those travel dashboards don’t do is tell you how to get to a better travel program.

They can’t, because they have two big flaws:

• Data reports are stuck in the past
• Travel data reports are stuck in the world of travel

So that’s the data-driven solution to getting better travel programs – deal with the future, and deal with data well outside of travel.

Enter Predictive Analytics

The key is linking travel’s impact to business outcomes.  Outcomes that matter on a much bigger scale, like sales, customer satisfaction, employee attrition, health/safety costs, etc.

This will fundamentally change the way you view and manage a travel program.

Instead of seeking to minimize travel costs, you’ll be trying to maximize sales, or perhaps minimize employee turnover – by putting a travel program in place that clearly contributes to those goals.

If sales are trending down, or employee turnover is trending up, what should you, the travel manager do to help fix these problems?

Obviously, you’ll need some new lights on your dashboard – lights driven by data from Sales and HR.  More importantly, you’ll need to know how to impact those non-travel metrics.

That’s where predictive analytics comes in.  You need to have a data-driven understanding of how things like cabin policy and hotel tiers impact bigger, non-travel metrics like employee productivity, health and safety, and attrition.

You need to predict with confidence that by changing a variable in the travel policy, it will cost $X and improve the non-travel metric by Y%.

You’ll do this in one of two ways.  If your travel program is big enough, you’ll be able to mine your own data and build these models.  If your program is too small to offer enough data, you’ll depend on benchmarks and case studies from the larger firms.

Either way, you’ll find yourself importing non-travel data into your travel dashboards, and exporting pro-active, fact-based advice on how to drive to your firm’s bigger goals.

Management theories evolve.  Dictums change.  It’s time to move on from measuring what matters to predicting what matters.

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Solving the Blah-Blah Travel Data Problem

Travel managers routinely rank travel data as one of their most important issues. Yet AirPlus recently reported that 56% of North American travel managers surveyed said they would not be willing to pay for better travel data.

OK, so let’s assume those buyers are reasonable folks.  How do we explain, then solve this conundrum?

Those buyers must not see much value – provable, hard dollar value – in “better” travel data.  I get that.  This industry has put up with mediocre travel data for so long that we’re used to ordinary, low-value, blah-blah data and data reporting.

Travel Data and Airline Sourcing Education Decks

You gotta love the 40 good folks who gave up a beautiful Sunday in San Diego to talk travel data.  I delivered a 6-hour workshop for GBTA on this topic, and am proud to report that not one person fell asleep!  Here’s the handout file we used. Topics included:

• Sources and uses of travel data
• Boring data reports and stupid statistics
• Making good data-driven presentations
• Key concepts needed for travel analyses
• Using derivative data to answer seven key questions
• GBTA’s KPI Resource document (as a handout; we didn’t have time to discuss it)

I’m back in the analytics game with tClara

Many of you know that I sold my last company, Travel Analytics,  back in 2006.  I had a 5-year non-compete, and filled my time doing a fair amount of speaking and training, and of course writing here on whatever caught my eye in the travel industry.

Along the way I met some great guys at Diio, the aviation data intelligence firm. They know aviation data inside and out, and have an amazing group of really smart software engineers who – and this is vital – are very focused on providing great customer service.

Long story short, we’ve joined forces to offer on-demand analytics for the corporate travel industry.  Our firm is tClara, pronounced tee-CLAIR-ah, a riff on “clarity”.  You can check out our site here.

Our sweet spot is delivering customized reports in three areas: Airline category analysis, travel policy decision support, and trip friction scoring and benchmarking.