Car rental and ground transportation technology specialist Mobacar believes that building machine learning and artificial intelligence into its distribution platform is creating a new business model for the sector.
The business is based in Ireland. It picked up some funding in September 2015 which reportedly valued it at €20 million.
Tnooz threw some questions at Mobacar's chief technology officer David Gregg about his role at the business, what technologies and trends from outside travel are worth keeping an eye on and how to ensure that innovation is at the heart of the company culture.
Briefly, tell us what do you do?
The car rental and ground transportation industry is stale. My job is to deliver the technology to shake up the sector by using the latest innovations in artificial intelligence and cloud computing, among others, to build a new business model
And that isn’t buzzword bingo – at Mobacar we have a proven approach to delivering a relevant transportation product every time to end customers, built on unique algorithms, powered by a global and scalable cloud infrastructure which we can deliver with minimal cost and resources.
So what do I do? Well my title is "chief technology officer" but I like to think my job is to make sure that someday soon you will all have something positive to say on this key part of the travel experience.
What is the most difficult initiative or strategy you've had to implement, and why?
At Mobacar we made a deliberate decision to invest a significant amount of time and money in our ‘Retail Intelligence’ – the machine learning algorithm which separates us from pretty much every other car rental site you can think of. Creating a booking engine which combined the global supply across multiple modes of transportation with this artificial intelligence was both challenging and rewarding
Our peers have a simple approach to pre-selections for customers - six people won’t fit in a mini for example – but we take a different approach, driven by customer’s actual preferences.
Every customer interaction has deep learning artificial intelligence applied to create the best possible match for today and adapts over time to the individual customer’s preferences. For our data science PhDs this was a labour of love – for the technology team this was a whole new challenge to adjust our engine to behave in a dynamic fashion based on this algorithm which took plenty of innovation (read, late nights and coffee) and changes to our application architecture to deliver.
The reward was when we saw this drive significant uplifts for our customers in the first month of launch – that was a huge moment for us a company and vindication on us taking our time to get the product right before going out widely to market.
What are your top goals for your team?
In the short term, we will deliver some ‘first to market’ technologies in the first half of the year. But remaining true to our overarching approach of a culture of innovation while going the extra mile for our customers is a priority.
We actively challenge each developer to deliver several proof of concepts per quarter (regardless of if they make it into our final product or not); we challenge the team to continuously increase their skillset via proactive learning and research on new technology and trends (which must be shared with the wider group) and couple this with some of the standard KPIs you would expect of a technology organisation around code quality and response times to customers.
The other key aspect we have is instilling the ‘test and learn’ approach into our team. What we mean by that is that we see our goal as understanding the customer better. The website, mobile app etc is just a part of that, But the industry is plagued by websites performing thousands of A/B tests to tweak their websites – we believe less is more in this case.
Every customer interaction is a test to us, and helps us perfect the personalised experience for the customer, not just tweak a generic website. So we run data driven tests which give us much deeper insight and set specific goals for the team around running these experiments on a regular basis.
What is your ultimate vision in terms of travel tech/distribution?
Where to start with that question! As an outsider to the travel industry I was dismayed at the legacy technology and fragmented customer experience across travel. Some of the big names in technology have the ability to drive change at an industry level (I’m looking at you, Google).
A travel ecosystem powered by APIs for a richer set of connections is essential to deliver consumers' demands for seamless integration and consistency of experience across devices.
The other key trend is artificial intelligence, and I think ‘AI as a service’ will evolve. This is something we are already working on with our partners who want to take advantage of low cost computing power from the cloud, and the rich set of data from traditional and new data sources, to improve all aspects of the travel experience.
We are now ‘cognifying’ everything around us (to use the term from Kevin Kelly’s excellent book – ‘The Inevitable’). How we engage with the world will continue to evolve around the smartphone and voice search is a key trend to monitor.
What sector outside of travel do you think travel has the most to learn from, and why?
I’m going to cheat slightly here and reference Amazon vs a specific sector. It is a company for me which demonstrates how technology can transform industries –online retailing, cloud infrastructure or logistics. Its vision is to be “earth's most customer-centric company”.
Normally I'd roll my eyes at statements like that but with products such as the Kindle, the core amazon.com retail experience and the enterprise-based Amazon Web Services, I think it can fulfil its mission statement.
So what can travel learn from this? Quite a lot!
- Make better use of the huge set of customer data you have for an improved retail experience
- Embrace new technology - such as the cloud = to transform IT and look to artificial intelligence and new ways of customer engagement (such as Amazon Echo) to constantly drive innovation
- Give transparency in pricing and act with the customers' interests at heart – this will drive loyalty in what is an incredibly competitive sector
All of this needs an agile way of working, allowing for streamlined decision making. So while I could cite examples of good sectors, we are focused on game changers, as we believe that is what the industry needs, right now.
What is intelligent ground transportation and what does it mean for the industry?
Mobacar uses artificial intelligence to gain a deep understanding of customers, predict their specific mobility needs and fulfil this through our global ground transportation network in real time.
What this means is that each customer now gets the most relevant transportation option, at the most appropriate price delivered through a personalised booking experience. For our partners they can now retail at a customer level, driving increased bookings, revenue, customer retention and overall commercial performance.
As I mentioned above, artificial intelligence is the a game changer in travel, the ability to use the rich set of data available on customers to eliminate the frustration and focus on relevance. There are huge opportunities for airlines and OTAs to convert ground transportation into a serious revenue stream on the back of our approach.
What steps do you take to drive innovation in your group?
Everyone at Mobacar has a voice to contribute to how we innovate. We operate a few channels in Slack (which we use across the company for real time messaging and groups) to capture new ideas in both commercial and technical areas which we review on a regular basis as a management team.
We don’t kill innovation with paperwork – if we believe the idea will help us deliver a better customer experience we green light a proof of concept and have a scrum team dedicated to nothing but innovation and continuous improvement.
We also regularly review our product roadmap to ensure we stay fresh and react to what are partners are asking for and what our rich dataset is telling us from a customer behaviour perspective.
Our size gives us a huge advantage here – we make decisions quickly and deliver fast; innovation isn’t something we do on the side – it’s part of the job description for us.
NB: Image via Oneinchpunch for BigStock