Micromobility companies that aim to turn a profit have to juggle running a tight ship, adhering to constantly shifting local regulations and getting as many riders on vehicles as possible. Boston-based Zoba, a micromobility fleet optimization startup, has developed a platform to help with that.
Zoba runs a behind-the-scenes logic layer of an operator’s fleet management software that uses AI to help decide where every vehicle in a fleet should be positioned, how to incentivize users with dynamic pricing and how to optimize operational efficiency.
“The way we approach this problem is we have a core platform that we’ve built, which is basically designed to help us understand your current market conditions, so we use machine learning to understand where demand will be for the systems, how that would be impacted by prevailing weather conditions and all the other sorts of very dynamic factors that happen in the market,” Joseph Brennan, co-founder of Zoba, told TechCrunch. “Then we feed that information into an optimization engine that is purpose-built to help us solve for these very large, complex optimization problems fast enough for them to be operationally relevant.”
Zoba’s tech is plugged into the back end of over 150,000 vehicles across 150 cities globally, mainly in the U.S. and Europe. The startup works with very large operators, including Spin, and on Tuesday announced the close of a $12 million Series A round led by NTTVC, with participation from existing investor CRV.
Zoba intends to use the funds to scale its go-to-market functions, like customer support, sales and marketing, as it expands into new markets like India and Latin America. It also wants to expand into new verticals like delivery and maybe even autonomous ride-hailing in the future, says Brennan.
“We really think of ourselves as a deep tech company,” Brennan said. “The product is not done, it’s never going to be done. And so we will continue to invest heavily in terms of widening what we view as a pretty large technical lead between us and anyone else in this problem space.”
Zoba’s system is informed by historical data provided by the operators, such as where vehicles have been ridden or when they’ve been in maintenance. This information is then used to reconstruct demand histories in a way that can not only detail where rides have been in the past, but also predict where they will be in the future.
Zoba also collects environmental information about the city that might drive near-term fluctuations in demand, like road networks and weather data.
“Weather is a really critical one,” said Brennan. “We’re using Tomorrow.io weather data, which is the best in class weather data that’s very granular and accurate, because we need to know for the next two hours, is it going to drop five degrees in temperature? Is it going to start to rain? This is the core of what’s flowing into our system.”
Most in-house fleet management platforms are less data driven and more intuition-based, with in local markets guessing at where demand might be, says Brennan.
“What we are trying to do is take that data which is an incomplete, heavily censored data set, and find some sort of truth about what demand actually looks like using models that we’ve been developing for years,” he said.
The company says by implementing its platform, shared micromobility companies have seen ridership increase by 20% to 50%, which helps to cover overhead costs like the vehicles themselves, warehouses and labor. Zoba also says it’s improved labor productivity by 20% by managing deployments, rebalances and battery swaps, and helps operators be better city partners by helping to manage zone constraints and keep vehicles off sidewalks.
“Zoba is truly unique in this market, in that they’ve spent a lot of time thinking about the industry’s problems, then building a new solution from the ground up to address fleet optimization,” said Ben Bear, CEO of Spin, in a statement. “Instead of relying on historical trends to determine demand, Zoba gives the ability to predict where our customers will need our services beforehand. That truly enables us to increase our revenues while reducing operational costs.
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