Reducing Carbon Footprint and Cost in Meetings & Events Travel

TroopTravel
Oct 31 · 5 min read
Imagine you are organizing a meeting of 300 people from 35 different origins. Where do they meet?

Meetings and events planners are under continuous pressure to reduce cost. As if this is not enough, in recent months, they have been tasked to reduce carbon footprint as well. For many this sounds like an impossible venture. Luckily, a service like TroopTravel can help.

Reducing the carbon footprint of one meeting by 187t — equivalent to the annual CO2 emissions of up to 46 people

We have helped plenty of companies to optimize their planning around their meetings and events.

This article is around a real customer case where we helped a Fortune 500 company to reduce the carbon footprint of one of their meetings by 187t. To put things into perspective, according to MyClimate.org the average carbon footprint of a person varies somewhere between 4t and 15t of CO2 emissions per annum depending on individual lifestyles.

This means that the customer reduced the carbon footprint of the meeting by an amount equal to the annual CO2 emissions of up to 46 people. This can quickly add up if you think about the hundreds to thousands of meetings and events large multinational companies are organizing each year.

Reducing cost for meetings and events travel

The other great “side-effect” of this specific case is that the customer was as well able to reduce the total cost for hotels and flights by more than $70,000. Purely through an analysis on which destination would be a good place to meet.

CASE STUDY: 300 people coming from 35 different origins. Where do they meet?

Imagine you are organizing a meeting for 300 people coming from 35 different locations distributed all over the world. How would you find the best meeting location and what factors would you consider?

300 people from 35 origins with 20% of the travelers coming from Chicago. Where do they meet?

This is actually a real case of one of our customers. They were organizing an event for 300 people coming from 35 different origins spread all over the world. Out of these 35 locations there was one origin (Chicago, IL|US) where 60 attendees (20% of the group) were based. The interest of the customer was to optimize the destination selection specifically around carbon footprint and cost.

Before we ran this event through our Big Data analysis platform we were expecting Chicago to come out as the destination with the least carbon footprint and potentially as well the cheapest one. In the end, there would be 60 people less travelling which must have a remarkable impact on carbon footprint and cost.

Saving 22% on carbon footprint despite more people travelling by air

When we saw the results of the analysis we were very surprised to see that you could actually reduce the carbon footprint by 187t despite flying 50 more people to Boston than to Chicago.

Reducing CO2 emissions by 22% despite 50 people more flying to Boston than Chicago

The results were very unexpected and we started to look deeper into the data to fully understand the reasoning behind this.

Saving $70,000+ by flying 50 more people to Boston than Chicago

Interestingly, the cost analysis brought a similar surprise to us. Boston was not only better in terms of carbon footprint, our customer would also be able to save more than $70,000.

Saving $70,000 by moving 290 people to Boston rather than 240 to Chicago

Both results around carbon footprint and cost seemed to be completely counter-intuitive. Since we really wanted to understand the reasons for this, we started to analyse the data further.

It made sense that the hotel cost in Chicago is lower than in Boston

First, we looked at the hotel cost.

We saw that Chicago would roughly be $100,000 cheaper. This kind of made sense because first of all, there were 50 fewer people a day staying at the hotel and secondly, the average hotel pricing level during that period was slightly lower in Chicago than Boston.

Hotels in Chicago were about $100,000 cheaper because of 50 people less staying in a hotel over 5 days

Next, we started to look at the flight cost. What we saw there was very surprising.

You can save almost $170,000 with 50 more people flying

Apparently, it would be around 43% cheaper ($170,000) flying 50 more people to Boston than to Chicago. At first, this felt like it did not make any sense at all.

Why should it be 43% cheaper to fly 290 people to Boston than 240 to Chicago?

In order to make sense of this, we continued digging deeper into the data. At last, we found the reason for this very unexpected result.

The travel policy can massively impact your destination decision

Apparently, the travel policy was responsible for the massive reduction and saving potential on carbon footprint and cost on the air side. Our customer has a travel policy which allows travelers to fly business class if the flight is longer than 6 hours.

The travel policy is responsible for the massive savings on the air side.

Apparently, Boston was so well geographically located for this group that only 29 travelers would be able to fly business class versus 135 people if the meeting would happen in Chicago.

Traveling in business class has a remarkable higher carbon footprint due to the additional space the traveler is using in comparison to a traveler in economy class on the same flight.

The massive saving on the number of business class tickets compensated for the $100,000 additional hotel cost as well as created the saving potential of more than $70,000.

Incredible results, right?

So, when do you start optimizing your meetings and events planning using Big Data?

When do you start optimizing your meetings and events planning?

TroopTravel is an award-winning Big Data analytics platform which helps events planners to take informed decisions around cost, carbon footprint, ease of travel, security and much more. The platform is currently used by several Fortune 500 companies. More information at www.trooptravel.com

Using Big Data to find the best meeting locations for globally distributed teams

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