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How DoorDash and Zipcar pivoted during the pandemic with the help of A.I.

November 08, 2021 00:00 AM UTC
- Updated November 09, 2021 01:37 AM UTC

A conversation with Alok Gupta of DoorDash and Freedom Dumlao of Zipcar.

Transcript
So how has door dash pivoted during the pandemic? And I guess the role that a I played um as you thought about all of this, one of the things we saw when the pandemic started was an increased demand by people home for food delivery etcetera. And at the same time it was coupled with have fewer people commuting less traffic on the road. So what that meant was we had a surge in demand and all of our food um prediction times for how long it would take to deliver an item. Got a little confused because it's far quicker to travel the road. So we have to readjust all of our models to instead of training on say the last three months of data, shrink it to the last two days or two weeks of data. And so it's a scramble to go through all of our models. What do we need to update? Um That was one thing, the second thing was there was a a really big demand from users to access grocery and convenience items from home more than than there had been. So we had to accelerate some of our ai there in problem areas. We haven't really done much before in the past like predicting inventory, recommending substitution sort of problems that you don't see in the food delivery, but you see in the grocery delivery space freedom. How about you? How how I guess how did the demand change during the pandemic? And then as we came out of the pandemic and power you able to leverage ai for that. Yeah it was it was a pretty dramatic shift, you know, similarly to what you were speaking to look all of a sudden the traffic disappeared. Some of that was people just simply were staying home. So at the very beginning of the pandemic, nobody was driving, nobody was doing anything. Um, and then all of a sudden everyone was driving again, at least everyone was getting into zip cars again for us. Uh That came as a shock because it was sort of all at once. There wasn't anything that really helps us to predict that that was about to happen. And then once we saw it happening, we noticed right away that the the types of drives and the driving behavior that our members had were completely different right before the pandemic, people would wake up in the morning, maybe they would take a Zipcar to the office or to do their daily chores. Um, but people had all decided to, you know, people were all forced to work from home, uh which meant that they were spending more time where they maybe had an opportunity in the middle of the day to do something different. Uh, So the models for how people were using cars dramatically changed during the pandemic. And even now, even as we've seen some offices reopening, we're still seeing lots of people who are still staying at home. And so that the way that they use our our cars has changed. Uh, fortunately before the pandemic, we've made some pretty big investments in some systems that helped us to predict what we would need in terms of positioning vehicles to make sure that every member had access to a vehicle when they needed it. Uh so we were able to tune that system once we started seeing how people were driving again and get those predictions to be back on target so that members can get access to vehicles when they needed them again. So this is very interesting because a lot of machine learning models are very data hungry. But if you can't use historical data, how do you gather, I guess. How do you get enough data to train these machine learning models accurately? I think the first one doesn't you accept at the beginning there's gonna be some noise and um, elevated levels of inaccuracy and you sort of use that cost of that budget to explore the data space maybe more actively than you would otherwise to then build up your training set, then you can then take advantage of and train the model. But yeah, there's no shortcut in the beginning of a lot of trial and more more error than usual for us, we were able fortunately to to leverage some comparable models. So for example holidays and vacation weeks and things like that helped us to bridge quite a bit. But you know, we did have to sort of drop that time period of like the bizarre, nobody's leaving their house anymore time period. All of that was basically useless for us as we restarted. So we had to sort of skip that period in time. Hello, can you share some of the more creative uses of ai that you've seen within door dash? Yeah. So one thing we see with a lot of our restaurants are merchants on the platform. They're opening and closing hours can be quite dynamic and if we send an order when they're not open, it's a poor experience for everybody. So um one of the functionalities, our dashes have the dashes are the folks that door dash that pick up the item from the restaurant, deliver it to the customer is when they go to a restaurant and they see it closed. They take a photo to verify that it's closed and to help us process and just our timing. We used that bank of photos and we saw that was actually pretty good information. They're using computer vision, we could actually read the updated opening and closing times and use that to dynamically update the restaurants. How is it um And other use cases based on some of our best performing dashes, we're able to improve our hyper local understanding of restaurants and residents to try and surmise what are the best routes or places to park and then routes to walk to get inside the restaurant inside an apartment building and it may seem trivial, but shaving 10 seconds here, five seconds there really adds up to improve the experience with the dash to the customer, the merchant, everyone, and so we're going that sort of into that level of detail.