How Data is Driving a Tech Revolution in Trucking: Q&A with Convoy CTO Dorothy Li
Data Science • Published on November 10, 2021
Data and tech are unlocking new solutions in freight at a moment when supply chains are more important than ever for the economy.
At the 2021 National Association for Business Economics’ annual Tech Economics Conference on November 8th, Convoy’s Chief Technology Officer, Dorothy Li, spoke in a fireside chat moderated by Convoy’s Director of Economic Research, Aaron Terrazas, discussing the future of data in trucking, the role of data science and economists, and why reducing waste in the $800B trucking industry is so critical.
Below is a summary of the conversation, edited for brevity.
Convoy isn’t exactly a household name. For those that are unfamiliar with what we’re doing, could you tell us a little bit about Convoy?
When people traditionally think about freight and shipping, a lot of us take it for granted, but it’s really the backbone of our economy, delivering the clothes we wear and the food we eat.
Convoy’s founders had backgrounds in supply chain and were frustrated with massive inefficiencies in the $800B trucking industry, and set out to do something about it.
Realizing there was an opportunity to use data and automation to help solve the many inefficiencies afflicting the freight industry, they launched Convoy in 2015.
Convoy is an open, fully connected digital freight marketplace. What that means is, for truck drivers, they use our Convoy mobile app to find loads to haul. At the same time, we use machine learning and automation to match these drivers with shipments from retailers and manufacturers, ensuring the most optimal trucking company is selected for each specific load.
At the end of the day we’re optimizing how millions of truckloads move around the country, helping retailers and manufacturers that ship products by truck operate more efficiently, which in turn helps truck drivers earn more, and reduces carbon emissions waste from empty miles.
Coming from a background at Amazon, I’m sure you were exposed early on in your career to economists playing an active role in the day-to-day business. More and more, we’re seeing that pattern accelerate at companies. Can you tell us about your view on the role of economists and data scientists in the modern business and tech era?
My first touchpoint with an economist was right after launching Amazon Prime. The whole premise of Prime was for Amazon to measure whether it increases wallet share of customers’ spend.
But you can’t evaluate the program just by subscription volume; you have to see whether it’s actually increased the lifetime value of the subscription. Same thing with Prime Video, this is a free add-on for existing Prime members, but the content it takes to supply Prime Video is extremely expensive to make, so how do you justify it from a business perspective?
You have to see the downstream impact. When someone signs up for a subscription, how does that impact the customer’s lifetime journey? So that was my first taste of working with economists to figure out the downstream impact of these programs.
To me, economists and data scientists are not just tracking data independently, they’re working together to make sense of the various events, finding correlations, and defining the incentives.
I think it’s fair to say economists love data. Can you tell us a little bit about the data Convoy collects, and how it feeds into business decision-making?
Data and insights are becoming the currency of the modern supply chain, with transparency being at the core of transforming this industry.
At Convoy, we gather thousands of data points in every phase of the shipment lifecycle. From when it’s loaded on trailers to when it’s unloaded at a facility, we know where a trailer is at every point, and we track this in a few ways.
Our carrier mobile application uses location tracking and geofencing to proactively notify when drivers may be running late so that shippers can better optimize their labor to receive the shipment. This also helps protect our drivers by providing proof of delivery and ensuring they receive payment for their haul.
With Convoy Go, we have a fleet of smart trailers outfitted with location sensors and internet connectivity that allow us to gather routing data and optimally rebalance them for future loads.
Another thing we collect are network insights that we gather from the facilities we service. You can think about these like an Amazon Reviews or an OpenTable for restaurants; this provides a forum for truckers to provide feedback on the quality of facilities: How congested is the facility? Are there basic services like restrooms available to drivers? This helps improve the quality of service for carriers.
You’re responsible for the Economics and Science & Analytics teams at Convoy, along with our Engineering and Product groups. Based on what you’ve seen at Convoy the past couple of months, what do you think are the most interesting or exciting economics or core data science questions that the team is tackling?
Some of the most interesting problems our economists are working on are market making and designing auctions.
When you’re automating manual matching of shippers to carriers, there is a need to make explicit the incentives of all the agents and carefully build scalable matching mechanisms.
We use our economists heavily to design these mechanisms through their work in auctions, contracting, and beyond.
Another thing our economists and data scientists are doing is designing experiments.
Our economists work in tandem with our data scientists to build the best experience possible for our shippers and carriers, constantly developing and testing new features for these users with a need to innovate quickly.
To wrap things up, from your perspective, what do you think are the most important skills that new economists and data scientists need to be successful both in tech and in working with software engineers that may have a different background than many of us do?
First and foremost, being able to problem-solve with business context is critical. This means having the business IQ to understand and know deeply what the problem is to determine what your customers are actually asking for.
At Convoy we practice loving problems not solutions; and really what that means is to not fall in love with any one solution, but to look at every problem uniquely and determine quickly which assumptions are critical and which are not.
Second is recognizing data is messy. The truth is, there is no such thing as clean data. We have to deal with a lot of data systems, so it’s important to be okay with that. Companies are always improving, data tools are getting better, but be okay with data being messy and being able to adapt to that.
Third but not least, start simple and iterate, get to 80 percent quickly.
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