Note: We at Locale.ai are committed to making the geospatial industry more robust and mainstream. This is part 2 of the series “Ask an Expert” where we engage with geospatial experts 1:1 on interesting ideas, discuss geospatial techniques and tools and talk about the industry! You can read Part 1 here.
Utsav Agarwal is now working in an early-stage fintech startup, Flat White, aimed to enable millions of Indian households to meet their credit, savings and investments needs by better monetizing their assets.
Prior to this, he worked with Glovo and Uber in their expansion teams and started up with #nwplyng, a free, fun & simple way to share music with friends on Facebook, Twitter & Foursquare.
In this interview, we will talk about his learnings at Uber and Glovo and why he thinks all logistics, commerce, mobility or social networks need to carry out geospatial analytics. We will also cover the gaps the Covid Pandemic has uncovered for supply chain companies and the steps they need to take in the future to avoid a situation like this.
To set some context, can you talk a little bit about your background. How did you make a shift from studying music management to working in an early stage startup, Flat White Capital?
Utsav: It’s been an interesting journey - not one without its own challenges and highs & lows.
I’m a Mechanical Engineer by academics; special emphasis on academics as it’s a degree I’ve, not the knowledge. As a teenager, I got really into indie rock and the sub-culture that came along with it. In my 1st year of engineering, I got an opportunity to manage a thrash metal band - which I gladly accepted. I enjoyed it so much that I decided to pursue the passion further after graduating. That led to the Music Business certificate from UCLA.
Ironically, it’s my time in LA that opened my eyes to the tech startup ecosystem. I witnessed the meteoric growth of Foursquare and Groupon during my time in LA.
In 2011, I moved back to India for a myriad of reasons, and that’s when I decided to switch to tech. I figured the best way to learn is to startup and throw myself into the deep end - so a music sharing app called nwplyng was born, that fit perfectly with my past experience in the music industry and new found passion in tech.
Here is a write-up about his experience and lessons. Check it out here:
Long story short, nwplyng was a failure, however it gave me a jump start in the industry - the necessary learnings and network. That led to me joining Uber, then Glovo and now Flat White Capital. As for investing, it’s more of a side hustle - a passion born out of unraveling the financial jargon, giving back to the startup ecosystem and learning the broad principles of wealth management.
At Uber, you were primarily responsible for expansion in Indian cities and internationally. How did you make those decisions and what kind of tools or products did the team use?
Utsav: While I can’t publicly talk about all the tools & products, here’s a bit of colour around Uber’s expansion team: It was an extremely agile team, with an exhaustive playbook in place by the time I joined, built over 4 years of global expansion.
There were many data points that went into selecting a city / country to launch - with another city in an already operational country being much easier than a new country altogether. We did a bunch of preliminary work studying the potential supply available, customer demographics, penetration of smartphones, mobile data & digital payments, airport & railway station traffic, presence of major corporates, local taxes, Motor Vehicles Act for the regulatory aspect, and a lot more.
Another important factor that guided us was the eyeballs data - users that opened the app in that particular city / country - this served as the proxy for the inherent demand built up in the city by either new users downloading the app & signing up, or existing users from operational cities visiting there and expecting the Uber service.
Once we had factored these parameters in, we travelled to the locations with highest priority to dig deeper - meet with supply partners and understand the business from them. All of this went into a report, post which a decision was taken - based on product market fit and our risk appetite (which in 2014 & early 2015 was endless)!
Fast forward to Glovo- You were one of the early employees to join in! How did you see the technical landscape evolve at Glovo? What kind of tools did the team use there and how did they operate?
Utsav: Something I didn’t realize while leaving Uber was the tech supremacy they had in their products & internal tools and quality of engineering talent to ship that out of SF. However, I also knew that in bits and atoms businesses, with a strong atoms component, one can compete against superior tech through better localization and throwing people at the problem. That’s how Ola & Grab competed against Uber in this part of the world.
I joined Glovo when they were operational in 4 countries, & left at 23 operational countries in 17 months - out of which 10 were under my region. As one would expect, during this phase, there were tectonic shifts in our modus operandi, especially when it came to tech.
In terms of strength, the team went from ~ 20 to 100+ engineers, PMs, data scientists and designers. In terms of tools, we leaned on external plug & play products much more than I had seen at Uber - it made sense to do so rather than build it inhouse with our limited resources.
It’s only towards the end of my stint there, that Glovo created their own geo-spatial tool i.e. 3+ years after inception of the startup.
Moving Capital (where you are an investor) has invested in several mobility and on-demand companies including Bird, Lime, Shuttl and Dunzo. From your experience at Uber, how do you think all these companies with ground operations can use data to improve their unit economics?
Utsav: Bits and atoms startups are a recent phenomenon, hence use of data in such businesses is an ever evolving science - one that can prove to be a moat to drive efficiency and improve unit economics.
What I’ve learnt at Uber & Glovo is the importance of looking at the city at a granular level - in polygons (or clusters) based on one’s knowledge of the city. And thereafter, measuring the key metrics by polygon to compare results and take decisions accordingly.
Take Swiggy for example and city as Mumbai. If they were looking at data by polygons, and came across a certain area (let’s say Khar West) with a high density of restaurants, however low order volume, they could run a promo for deliveries limited to that area. They don’t do this currently, however can implement to improve their unit economics - their current promo methodology is more around city wide promos like SwiggyIt that offers a certain % off based on the restaurant.
In the above scenario, what’s needed isn’t only the ability to track data by polygons, but also the data infrastructure to run a promo by a polygon(s) of choice.
This is what the Covid Pandemic has also taught us, right? We need more robust supply chains, better tracking, monitoring, analysis and infrastructure to avoid the crunch of logistics.
Utsav: Absolutely! Empty aisles and stock outs might make it seem like there is shortage of supplies and food. But, the providers of staples would tell you that there isn't. There is just a logistics crunch, especially in the US.
This is a situation that no one, not even Amazon's models couldn't predict this unprecedented panic-fueled purchases. The traditional supply chains were efficient in stocking supplies in just the right amount, and now when demand is sky rocketing, the system is falling.
“We do not see a supply shock in the sense of the availability,” said one UN economist to Bloomberg. “But there could be a supply shock in terms of logistics, not being able to move it from point A to point B.” — The Morning Brew, 22nd March
This proves that businesses need to invest in developing the right geospatial tools to map their dependencies, track and monitor their inventory 24X7 and even, optimize turnaround times. The advantages to being able to rapidly shift production among suppliers, factories, and countries will prove to be a significant return on investment.
Businesses need to focus on the foundation and build a solid scalable infrastructure, so that the layer on top can be plugged in easily. This way people don’t waste their time rewiring the code or running the same analysis time and again. Without scalable infrastructure, analysis often grinds to a halt.
Operational Analytics at Locale
At Locale, we are building an “operational” analytics platform that uses geospatial data of users, vehicles, or riders. Supply and operations teams in last-mile delivery companies can monitor what’s going on on the ground and take more tactical decisions. Product and analytics teams can use it to do historical analysis and take more strategic decisions.
Our mission is to bring the same level of granular analytics as Uber and Grab to every company collecting location data. We want to enable business teams in on-demand companies to get operational insights within a minute, not hours or weeks later!