By Keren Fanan
Lack of data is one of the main challenges in the corporate mobility industry. The source for the problem is down to the fragmentation in the mobility space. Many suppliers cover only specific parts of the full needs of businesses; there’s no single technology that connects it all.
The business travel sector is sitting on a treasure chest of opportunity. With better usage of data and artificial intelligence (AI), customer-centred transportation services can anticipate rider behaviour and traffic patterns, optimise routes and processes – and cut costs.
What is Artificial Intelligence?
AI is intelligence demonstrated by machines instead of humans, and the fuel that powers AI is data. Good quality data, and lots of it.
The more accurate the data, the better chance an AI engine will be able to identify the multiple variables at play, learn about them, improve them, and achieve success.
You might not think it, but we’re all heavy AI users in our day-to-day lives; when we watch movies, listen to music, search Google – it’s all in-built with AI capabilities.
But not when we travel.
Spend-and-reimburse is still the method of choice for many corporations. Forty-four per cent (42%) of enterprises manually or semi-manually reimburse employees.
Imagine a world in which we can track every single data point that is related to our travel habits. If that happens, business ground transportation can be, for once, managed by machines. And machines turn data into savings.
The benefits of using data in corporate travel
Corporate travel is an industry that yearns for change and digital transformation. Business travel processes have stayed the same over the last few years, mostly manual, with minimal usage of data. Similar to any old-fashioned or archaic area – it holds a massive opportunity.
Data is knowledge, and knowledge, as we all know, is power. Without data, we are all making powerless and misinformed decisions.
Here’s how businesses can transform corporate travel with data:
1. Understand rider behavior
Data helps organizations understand how their riders behave – giving an insight like no other. This 360-degree view on the rider enables prediction and resolve travel issues both efficiently and effectively.
2. Identify employees habits
To optimise travel programmes for spend and cost, we first need to identify employee habits. Some habits should be adopted by all, some should be tuned to a more efficient one.
For example, data shows that 15-20% of business taxi trips are up to one mile long.
This insight is an opportunity to introduce greener initiatives for short trips, or outline what essential travel looks like.
3. Predict price changes, peak times, and heavy traffic
Imagine being able to predict price changes and heavy traffic? Being able to optimise traveller schedules easily, making the most of travel budget. Being able to predict and outsmart heavy traffic confidently means employee’s day becomes more productive.
Predicting price changes throughout the day means companies can sidestep those pricey hours and make even more cost savings.
Not many people know but at some hours of the day, ride fare can inflate by 40-60%. People pay significantly more, on the exact same ride.
How Gett is using data to generate savings?
So we’ve talked about the importance of data in a corporate travel setting, but the truth is that Gett can help businesses generate savings, already today. Check out the scenarios below.
1. Business meeting in the crowded city center?
Gett’s system can tell you the ideal time to schedule business meetings.
Low traffic -> Better hours to travel -> Leads to better time to arrange business meetings
2. Heading to the airport?
Share the rides, enjoy the company, and save money
Gett has developed route optimisation algorithms use snapping, routing, and ETA.
3. Pickup in a massive business center?
Suggested pickup locations save employees time. Gett uses two models to improve pickup accuracy:
Static Pickup Points: Based on historical data and road graph features, the system analyses the best places for pickup in order to minimise driver-passenger meeting friction.
Dynamic Pickup Points: dynamic suggestion of pickup points – taking into account current supply positions and passenger destinations, to increase utilisation and reduce ride cost.
It’s normal now, because we did it first
There is no question that data and machine learning will continue shaping the way we travel.
Some days it will be obvious – AI assistants might help us book a car or a flight, check into hotels, or order meals in languages we don’t speak. In most of the time, it will be hidden in algorithms that help keep our daily lives simple.
If we change the mindset today – and use technology to collect the missing data, in a couple of years it would be hard to imagine how it used to be before.
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