1 00:00:00,000 --> 00:00:00,550 2 00:00:00,550 --> 00:00:01,730 DANIEL: Hey, everyone. 3 00:00:01,730 --> 00:00:03,640 Thanks so much for joining us. 4 00:00:03,640 --> 00:00:08,109 For all of those online, welcome, as well. 5 00:00:08,109 --> 00:00:10,570 We're so happy for you to be here. 6 00:00:10,570 --> 00:00:12,760 Today we're going to be doing a quick tech 7 00:00:12,760 --> 00:00:16,300 talk on how we do time predictions at Uber 8 00:00:16,300 --> 00:00:19,450 and how Uber works as a company as a whole. 9 00:00:19,450 --> 00:00:22,870 This is meant to be a pretty casual talk, so feel free to interrupt us 10 00:00:22,870 --> 00:00:24,070 with any questions. 11 00:00:24,070 --> 00:00:27,310 But, to kick things off, let's do a couple of intros. 12 00:00:27,310 --> 00:00:28,380 My name's Daniel. 13 00:00:28,380 --> 00:00:33,210 I'm a product manager at Uber, working on Uber Eats Marketplace team. 14 00:00:33,210 --> 00:00:35,490 EVAN: And I'm Evan. 15 00:00:35,490 --> 00:00:38,010 I'm currently focused on the marketplace, 16 00:00:38,010 --> 00:00:42,610 how we incentivize our drivers, but have done rotations on the Share Rides team, 17 00:00:42,610 --> 00:00:45,176 as well as our hourly rental business. 18 00:00:45,176 --> 00:00:46,050 ALLIE: And I'm Allie. 19 00:00:46,050 --> 00:00:50,760 I'm a university recruiter at Uber, focusing a lot 20 00:00:50,760 --> 00:00:52,950 on hiring Harvard students this year. 21 00:00:52,950 --> 00:00:57,060 So I will be doing a little bit of recruiting slides 22 00:00:57,060 --> 00:00:59,130 towards the end of the tech talk. 23 00:00:59,130 --> 00:01:02,810 But I will also be here afterwards to answer any recruiting questions. 24 00:01:02,810 --> 00:01:04,290 So just take it away. 25 00:01:04,290 --> 00:01:05,180 DANIEL: Cool! 26 00:01:05,180 --> 00:01:07,640 All right, let me back up a couple slides. 27 00:01:07,640 --> 00:01:08,640 OK, sweet. 28 00:01:08,640 --> 00:01:13,380 So the topic of our talk today will be "Please Be on Time." 29 00:01:13,380 --> 00:01:18,130 So we'll be talking about how we think about time predictions, 30 00:01:18,130 --> 00:01:20,640 when you're Uber's arriving, when your food's arriving, 31 00:01:20,640 --> 00:01:25,100 and how we portray all that information to you. 32 00:01:25,100 --> 00:01:29,550 So, real quick, for agenda, we'll do a little bit of a deeper dive 33 00:01:29,550 --> 00:01:30,310 into who we are. 34 00:01:30,310 --> 00:01:31,187 EVAN: [LAUGH] 35 00:01:31,187 --> 00:01:34,020 DANIEL: Then we'll start off actually with a couple trivia questions 36 00:01:34,020 --> 00:01:35,680 about Uber at Harvard. 37 00:01:35,680 --> 00:01:37,260 And we have a couple swag items. 38 00:01:37,260 --> 00:01:42,030 So please, for the people in the room, speak up and we'll toss you a T-shirt. 39 00:01:42,030 --> 00:01:42,780 EVAN: Participate. 40 00:01:42,780 --> 00:01:44,950 You in the back, participate. 41 00:01:44,950 --> 00:01:46,560 [LAUGH] 42 00:01:46,560 --> 00:01:48,540 DANIEL: And then I'll dive into the models 43 00:01:48,540 --> 00:01:51,640 that power Uber Eats delivery times. 44 00:01:51,640 --> 00:01:55,050 And then we'll talk a bit about the more experiential side 45 00:01:55,050 --> 00:02:00,810 of how we display arrival times on the shared-rides products. 46 00:02:00,810 --> 00:02:04,470 And then Allie will give a overview of Uber opportunities. 47 00:02:04,470 --> 00:02:07,650 Hopefully all of you guys will want to join the team, after the talk. 48 00:02:07,650 --> 00:02:11,790 And then we're leaving like 20, 25 minutes at the end for Q&A, 49 00:02:11,790 --> 00:02:14,340 because we really want this to be interactive. 50 00:02:14,340 --> 00:02:17,170 That being said, for those of you in the room, 51 00:02:17,170 --> 00:02:21,031 please stop us as we go along if you have questions. 52 00:02:21,031 --> 00:02:21,530 Cool. 53 00:02:21,530 --> 00:02:25,370 So, back in our day, which was not that long ago-- 54 00:02:25,370 --> 00:02:27,860 I graduated in 2017-- 55 00:02:27,860 --> 00:02:30,710 I was concentrating on mechanical engineering. 56 00:02:30,710 --> 00:02:35,210 I lived in Currier, and I was memorizing shuttle schedules. 57 00:02:35,210 --> 00:02:39,440 I actually found out last night that we were giving this talk in this building, 58 00:02:39,440 --> 00:02:43,070 so I should have actually updated this because this is quite nostalgic for me. 59 00:02:43,070 --> 00:02:46,100 I was also a student manager for HSA, so I 60 00:02:46,100 --> 00:02:48,800 was working in the basement of this building, 61 00:02:48,800 --> 00:02:55,160 basically packing T-shirts into mailers and shipping them out all day. 62 00:02:55,160 --> 00:02:55,790 EVAN: Cool. 63 00:02:55,790 --> 00:02:57,700 And, again, I'm Evan. 64 00:02:57,700 --> 00:02:58,670 I studied CS here. 65 00:02:58,670 --> 00:03:01,610 I spent a lot of time in the MAC, playing basketball, 66 00:03:01,610 --> 00:03:03,200 and lived in Dunster House. 67 00:03:03,200 --> 00:03:06,020 That was exactly my window. 68 00:03:06,020 --> 00:03:09,735 And it's cool being back on campus. 69 00:03:09,735 --> 00:03:10,630 DANIEL: Sweet. 70 00:03:10,630 --> 00:03:13,420 OK, so let's start off with some trivia. 71 00:03:13,420 --> 00:03:15,190 Uber at Harvard. 72 00:03:15,190 --> 00:03:16,630 All right, so the first one-- 73 00:03:16,630 --> 00:03:21,329 which house, do you think, had the most Uber Eats orders in the last year? 74 00:03:21,329 --> 00:03:24,370 EVAN: And you have to provide some sort of justification for it, as well. 75 00:03:24,370 --> 00:03:27,481 76 00:03:27,481 --> 00:03:27,980 DANIEL: Yes. 77 00:03:27,980 --> 00:03:28,290 EVAN: [LAUGH] 78 00:03:28,290 --> 00:03:31,373 AUDIENCE: It's going to be [INAUDIBLE] because it's not close to anything. 79 00:03:31,373 --> 00:03:33,110 DANIEL: [LAUGH] 80 00:03:33,110 --> 00:03:34,340 [VOCALIZATION] 81 00:03:34,340 --> 00:03:35,681 Mather. 82 00:03:35,681 --> 00:03:37,930 [LAUGHTER] 83 00:03:37,930 --> 00:03:39,680 EVAN: But, thanks for the participation. 84 00:03:39,680 --> 00:03:41,810 Would you like a notebook or a T-shirt? 85 00:03:41,810 --> 00:03:43,060 AUDIENCE: I'd love a notebook. 86 00:03:43,060 --> 00:03:45,800 DANIEL: All right, sweet. 87 00:03:45,800 --> 00:03:48,735 Does this mean Mather has the words, like, [INAUDIBLE] quality? 88 00:03:48,735 --> 00:03:49,980 [LAUGHTER] 89 00:03:49,980 --> 00:03:52,340 All right, next one. 90 00:03:52,340 --> 00:03:54,450 Which house has the most pickups? 91 00:03:54,450 --> 00:03:56,986 So which house do most people want to get away from? 92 00:03:56,986 --> 00:03:57,860 EVAN: That was quick. 93 00:03:57,860 --> 00:03:58,770 That was quick response time. 94 00:03:58,770 --> 00:03:59,728 DANIEL: This guy knows. 95 00:03:59,728 --> 00:04:00,570 AUDIENCE: Lowell? 96 00:04:00,570 --> 00:04:02,820 DANIEL: Lowell House? 97 00:04:02,820 --> 00:04:03,320 Adams. 98 00:04:03,320 --> 00:04:04,010 [LAUGHTER] 99 00:04:04,010 --> 00:04:05,799 Lowell was, oh, pretty low, actually. 100 00:04:05,799 --> 00:04:06,840 EVAN: This was actually-- 101 00:04:06,840 --> 00:04:09,298 I thought this was cool, because it was-- there's obviously 102 00:04:09,298 --> 00:04:10,507 all sorts of noise in these. 103 00:04:10,507 --> 00:04:12,340 And these are basically, over the past year, 104 00:04:12,340 --> 00:04:15,880 we're looking at the time series of when people are-- based on their pickup 105 00:04:15,880 --> 00:04:17,996 and drop-off request locations. 106 00:04:17,996 --> 00:04:21,079 But this was, like, Adams, the house that everyone wants to get away from. 107 00:04:21,079 --> 00:04:23,287 And then people in Winthrop kind of stay in Winthrop. 108 00:04:23,287 --> 00:04:25,910 So I like this one. 109 00:04:25,910 --> 00:04:26,604 DANIEL: Cool. 110 00:04:26,604 --> 00:04:27,520 Do you want a T-shirt? 111 00:04:27,520 --> 00:04:28,705 AUDIENCE: I'll take a T-shirt, yeah. 112 00:04:28,705 --> 00:04:29,530 DANIEL: Sweet. 113 00:04:29,530 --> 00:04:32,000 EVAN: OK, the house that most people want to get away from. 114 00:04:32,000 --> 00:04:33,458 Which house has the most drop-offs? 115 00:04:33,458 --> 00:04:37,730 Or, no, sorry-- where do people want to go the most? 116 00:04:37,730 --> 00:04:38,630 AUDIENCE: Eliot? 117 00:04:38,630 --> 00:04:39,852 [INAUDIBLE] 118 00:04:39,852 --> 00:04:41,060 DANIEL: Do you live in Eliot? 119 00:04:41,060 --> 00:04:42,630 AUDIENCE: No, I'm a freshman-- 120 00:04:42,630 --> 00:04:43,610 [LAUGHTER] 121 00:04:43,610 --> 00:04:48,490 Because I know a lot of students-- like, I help [INAUDIBLE].. 122 00:04:48,490 --> 00:04:49,100 DANIEL: OK. 123 00:04:49,100 --> 00:04:50,720 EVAN: Well, I think you may be [INAUDIBLE] this year, 124 00:04:50,720 --> 00:04:51,850 because it's actually Currier. 125 00:04:51,850 --> 00:04:52,433 DANIEL: Uh-oh! 126 00:04:52,433 --> 00:04:54,280 [LAUGHTER] 127 00:04:54,280 --> 00:04:57,420 Gotta say, yeah, Currier was the best house. 128 00:04:57,420 --> 00:04:57,920 All right-- 129 00:04:57,920 --> 00:04:58,660 EVAN: Yeah, Winthrop's (LAUGHING) at the bottom. 130 00:04:58,660 --> 00:04:59,450 DANIEL: --one more. 131 00:04:59,450 --> 00:05:00,199 Oh, this is Evan's 132 00:05:00,199 --> 00:05:03,200 EVAN: OK, so which house has the most trips to Cumnock Field? 133 00:05:03,200 --> 00:05:08,860 This was between the hours of 6:00 and 10:00 PM, during the week. 134 00:05:08,860 --> 00:05:10,580 AUDIENCE: [INAUDIBLE] 135 00:05:10,580 --> 00:05:12,957 EVAN: Cumnock fields-- what year are you? 136 00:05:12,957 --> 00:05:13,790 AUDIENCE: Sophomore. 137 00:05:13,790 --> 00:05:15,774 EVAN: Sophomore, well, so it's-- 138 00:05:15,774 --> 00:05:19,360 [LAUGH] well, so the intent for this was, like, 139 00:05:19,360 --> 00:05:21,680 the house that is the most into IMs. 140 00:05:21,680 --> 00:05:24,634 So, who is taking the most Ubers to Cumnock. 141 00:05:24,634 --> 00:05:26,020 [INTERPOSING VOICES] 142 00:05:26,020 --> 00:05:26,690 EVAN: I got a couple Winthrops. 143 00:05:26,690 --> 00:05:28,110 AUDIENCE: It's got to be Winthrop. 144 00:05:28,110 --> 00:05:28,830 EVAN: It's got to be Winthrop. 145 00:05:28,830 --> 00:05:30,000 You would think so-- 146 00:05:30,000 --> 00:05:32,850 AUDIENCE: Cabot, I see there. 147 00:05:32,850 --> 00:05:37,000 EVAN: Maybe the secret to winning IMs is that you actually walk to the field. 148 00:05:37,000 --> 00:05:40,590 It could be like a some sort of heuristic for athletic prowess. 149 00:05:40,590 --> 00:05:41,550 I don't know. 150 00:05:41,550 --> 00:05:43,050 I don't know. 151 00:05:43,050 --> 00:05:47,340 DANIEL: All right, and, last one, which house travels to the airport the most? 152 00:05:47,340 --> 00:05:51,300 So, which house has the most worldly travelers? 153 00:05:51,300 --> 00:05:55,170 154 00:05:55,170 --> 00:05:55,670 Anyone? 155 00:05:55,670 --> 00:05:56,590 EVAN: You're going to get this right. 156 00:05:56,590 --> 00:05:57,120 I can feel it. 157 00:05:57,120 --> 00:05:57,828 AUDIENCE: Quincy. 158 00:05:57,828 --> 00:05:58,760 DANIEL: Quincy? 159 00:05:58,760 --> 00:06:00,065 EVAN: Oh! 160 00:06:00,065 --> 00:06:01,050 [LAUGH] Winner. 161 00:06:01,050 --> 00:06:02,110 DANIEL: Nice. 162 00:06:02,110 --> 00:06:05,656 All right, we'll get you guys T-shirts after this. 163 00:06:05,656 --> 00:06:06,520 All right. 164 00:06:06,520 --> 00:06:09,561 EVAN: And then, also, just one of the cool things about working with Uber 165 00:06:09,561 --> 00:06:11,320 is that we are working with so much data, 166 00:06:11,320 --> 00:06:15,160 and the visualization tools to make these sorts of maps and visualizations 167 00:06:15,160 --> 00:06:18,310 is really easy. 168 00:06:18,310 --> 00:06:20,980 This query was for over a week's time-- 169 00:06:20,980 --> 00:06:23,370 all of the pickups over Harvard's campus. 170 00:06:23,370 --> 00:06:26,890 And you can see that Uber has made its mark. 171 00:06:26,890 --> 00:06:31,480 And this is a combination of rides and deliveries. 172 00:06:31,480 --> 00:06:34,720 And also, this, I don't actually-- 173 00:06:34,720 --> 00:06:37,990 [LAUGH] this is, yeah, we're all one big, happy family. 174 00:06:37,990 --> 00:06:41,840 The Ubers has made friendships between the Quad and houses, the river houses. 175 00:06:41,840 --> 00:06:43,180 Right, it's kind of-- 176 00:06:43,180 --> 00:06:45,620 again, this visualization is just really cool to me, 177 00:06:45,620 --> 00:06:50,470 but basically you see all of these trips between both the Quad and the river. 178 00:06:50,470 --> 00:06:52,810 And I don't actually know this, but my guess 179 00:06:52,810 --> 00:06:56,170 is most of that is happening kind of late nights, Friday, Saturday, Sunday. 180 00:06:56,170 --> 00:06:57,598 But I'd have to double-check that. 181 00:06:57,598 --> 00:06:59,082 [LAUGHTER] 182 00:06:59,082 --> 00:07:00,290 It's kind of like horoscopes. 183 00:07:00,290 --> 00:07:00,640 You've got to read into it. 184 00:07:00,640 --> 00:07:02,473 DANIEL: I was definitely one of those trips. 185 00:07:02,473 --> 00:07:03,290 EVAN: Yeah. 186 00:07:03,290 --> 00:07:03,850 DANIEL: Cool. 187 00:07:03,850 --> 00:07:08,650 And Uber's popularity isn't just at Harvard. 188 00:07:08,650 --> 00:07:12,460 As you guys have probably seen, in the news, every day, 189 00:07:12,460 --> 00:07:15,230 Uber is expanding all across the globe. 190 00:07:15,230 --> 00:07:19,840 So this graph on the right shows Uber's trip count 191 00:07:19,840 --> 00:07:22,670 in our first five big cities. 192 00:07:22,670 --> 00:07:26,170 So we started out in SF, back in 2010. 193 00:07:26,170 --> 00:07:28,780 And the cool thing here is, the growth is pretty much 194 00:07:28,780 --> 00:07:31,360 exponential in all of these cities. 195 00:07:31,360 --> 00:07:32,710 And the other cool thing is-- 196 00:07:32,710 --> 00:07:34,210 So we started out in SF. 197 00:07:34,210 --> 00:07:40,090 Then we went to New York, then Seattle, then Chicago, then DC. 198 00:07:40,090 --> 00:07:43,120 And you can actually see that the growth actually gets faster 199 00:07:43,120 --> 00:07:49,450 and the curve gets even steeper, as we launched more cities. 200 00:07:49,450 --> 00:07:52,300 And when you look at this on a global scale, 201 00:07:52,300 --> 00:07:55,360 Uber's definitely not just in the US. 202 00:07:55,360 --> 00:07:57,430 So this graph is actually, on a log scale, 203 00:07:57,430 --> 00:08:00,400 just showing the ridiculous amount of growth 204 00:08:00,400 --> 00:08:03,580 that we have all across the world. 205 00:08:03,580 --> 00:08:09,670 Uber being an international company is a pretty unique challenge for us. 206 00:08:09,670 --> 00:08:14,310 I often like to think that other companies, like Facebook or Google, 207 00:08:14,310 --> 00:08:16,350 the experience is pretty much the same. 208 00:08:16,350 --> 00:08:20,050 But, for Uber, we're really operating at the interface 209 00:08:20,050 --> 00:08:22,210 of the digital and physical worlds. 210 00:08:22,210 --> 00:08:25,840 And everything from regulation to the actual products that we're 211 00:08:25,840 --> 00:08:28,750 offering our riders and eaters is really, really different 212 00:08:28,750 --> 00:08:30,760 in all of these regions. 213 00:08:30,760 --> 00:08:34,450 That's one of the really fun parts about being a PM at Uber. 214 00:08:34,450 --> 00:08:37,772 EVAN: And then I'd just add that, in the US, it's easy for us to [INAUDIBLE],, 215 00:08:37,772 --> 00:08:39,730 like Uber and Lyft compete really aggressively. 216 00:08:39,730 --> 00:08:40,230 Right? 217 00:08:40,230 --> 00:08:44,200 But in each of these mega regions, it's, you have a really tough competitor. 218 00:08:44,200 --> 00:08:47,380 And, you know, in Asia it was-- and continues to be-- 219 00:08:47,380 --> 00:08:48,670 DiDi. 220 00:08:48,670 --> 00:08:51,280 In Europe, it's Cabify. 221 00:08:51,280 --> 00:08:53,440 In the Middle East, it's Careem. 222 00:08:53,440 --> 00:08:56,560 It's like, in all of these places, there is really intense competition. 223 00:08:56,560 --> 00:08:59,290 And how a company headquartered in San Francisco 224 00:08:59,290 --> 00:09:01,920 can compete with these players that are much more local 225 00:09:01,920 --> 00:09:04,880 continues to be a challenge. 226 00:09:04,880 --> 00:09:06,300 DANIEL: Cool. 227 00:09:06,300 --> 00:09:08,800 But we are doing pretty amazing. 228 00:09:08,800 --> 00:09:14,760 So, as of 2018, we're in over 650 cities, across the globe. 229 00:09:14,760 --> 00:09:16,950 We are in 77 countries. 230 00:09:16,950 --> 00:09:20,520 And, just as of June, I believe, this year, we just 231 00:09:20,520 --> 00:09:23,550 hit 10 billion rides, around the world. 232 00:09:23,550 --> 00:09:27,570 To put that into perspective, we actually hit 5 billion 233 00:09:27,570 --> 00:09:28,660 less than a year ago. 234 00:09:28,660 --> 00:09:34,890 So in one year we've gone from 5 billion and doubled that amount to 10 billion. 235 00:09:34,890 --> 00:09:37,161 So this may all sound pretty simple. 236 00:09:37,161 --> 00:09:37,660 Right? 237 00:09:37,660 --> 00:09:42,090 Like, when you call an Uber from the Quad or the river, you tap a button, 238 00:09:42,090 --> 00:09:46,460 you get in that Uber, you chat with the driver, you listen to some music, 239 00:09:46,460 --> 00:09:49,060 and then suddenly you're at your destination. 240 00:09:49,060 --> 00:09:51,750 But there's a lot that can go wrong-- 241 00:09:51,750 --> 00:09:54,450 again, especially in this space where we're 242 00:09:54,450 --> 00:09:57,960 interacting with not just software on your phone 243 00:09:57,960 --> 00:10:00,090 but we're actually moving people. 244 00:10:00,090 --> 00:10:02,490 We're moving things around the world. 245 00:10:02,490 --> 00:10:06,250 And there's just so much that can go wrong with that. 246 00:10:06,250 --> 00:10:10,800 So, like I said, for the ride side you might be used to opening your app, 247 00:10:10,800 --> 00:10:14,400 requesting a car, getting in that car, getting to your destination. 248 00:10:14,400 --> 00:10:16,830 For those of you who may have tried Uber Eats-- 249 00:10:16,830 --> 00:10:18,930 similar sort of simple flow. 250 00:10:18,930 --> 00:10:21,930 You tap a button, you choose that burger or burrito you want, 251 00:10:21,930 --> 00:10:25,210 and it arrives 20 to 30 minutes later. 252 00:10:25,210 --> 00:10:28,170 But even in this second half of requesting 253 00:10:28,170 --> 00:10:32,490 a car to getting in that car, or ordering food and getting that food, 254 00:10:32,490 --> 00:10:37,090 there's a lot of logistics problems that we think about, day in, day out. 255 00:10:37,090 --> 00:10:43,290 One of them is figuring out when exactly that ride or food will arrive. 256 00:10:43,290 --> 00:10:45,400 This isn't a simple problem. 257 00:10:45,400 --> 00:10:48,370 And so there's the sort of mathematical part of it-- 258 00:10:48,370 --> 00:10:51,280 how do we actually create models to predict it? 259 00:10:51,280 --> 00:10:53,460 But there's also the experiential component of, 260 00:10:53,460 --> 00:10:56,670 how do we convey that information to you and make it 261 00:10:56,670 --> 00:11:00,220 meaningful to you as a eater or rider? 262 00:11:00,220 --> 00:11:05,010 So I'm going to focus my part of the talk on Uber Eats. 263 00:11:05,010 --> 00:11:08,500 So, just a show of hands-- who has tried Uber Eats or is familiar with it? 264 00:11:08,500 --> 00:11:10,870 OK, awesome, OK, cool, cool. 265 00:11:10,870 --> 00:11:13,660 I thought was going to have to explain the app. 266 00:11:13,660 --> 00:11:17,290 So, real quick-- so, like, you know, you open the app. 267 00:11:17,290 --> 00:11:21,140 I think the parts of this flow that you're used to is, you open the app, 268 00:11:21,140 --> 00:11:25,840 you place your order, and then you're sort of used to seeing the delivery. 269 00:11:25,840 --> 00:11:29,080 But there's a ton of intermediary steps that 270 00:11:29,080 --> 00:11:33,910 happen, both on the restaurant side, on the courier side, 271 00:11:33,910 --> 00:11:35,930 and on the eater side. 272 00:11:35,930 --> 00:11:41,630 And a lot of this is based on knowing exactly when the food at the restaurant 273 00:11:41,630 --> 00:11:42,860 will be ready. 274 00:11:42,860 --> 00:11:46,070 So, for the marketplace side, this allows 275 00:11:46,070 --> 00:11:48,650 us to know exactly when to dispatch a courier-- 276 00:11:48,650 --> 00:11:51,560 making sure that that courier doesn't arrive at the restaurant 277 00:11:51,560 --> 00:11:54,980 too late or too early. 278 00:11:54,980 --> 00:11:57,100 And this is really important for us. 279 00:11:57,100 --> 00:12:01,060 As Uber, we think about this three-sided marketplace-- 280 00:12:01,060 --> 00:12:04,316 the restaurant, the eater, and the courier. 281 00:12:04,316 --> 00:12:06,940 It's really important that we get these time predictions right. 282 00:12:06,940 --> 00:12:09,520 That way, we can control things like supply and demand 283 00:12:09,520 --> 00:12:12,590 and prevent surge in our logistics systems. 284 00:12:12,590 --> 00:12:15,370 For you guys, as the eaters, this is super-important 285 00:12:15,370 --> 00:12:19,270 that we get this right so we can provide fast delivery times 286 00:12:19,270 --> 00:12:24,320 and that we're able to provide ETD estimates of when your food will arrive 287 00:12:24,320 --> 00:12:28,390 and it won't, like, jump a lot while you're waiting for your food. 288 00:12:28,390 --> 00:12:31,980 For couriers, this is important because we 289 00:12:31,980 --> 00:12:34,020 don't want couriers to arrive at a restaurant 290 00:12:34,020 --> 00:12:37,300 and wait there for 10 minutes while the restaurant's preparing the food. 291 00:12:37,300 --> 00:12:37,800 Right? 292 00:12:37,800 --> 00:12:39,399 That's not the best use of their time. 293 00:12:39,399 --> 00:12:41,190 So we really want to make sure that they're 294 00:12:41,190 --> 00:12:43,770 maximizing their earning potentials. 295 00:12:43,770 --> 00:12:46,590 And finally, on the restaurant side, you know, 296 00:12:46,590 --> 00:12:49,410 I've been to restaurants both here in the US, 297 00:12:49,410 --> 00:12:53,100 and I've traveled abroad to a lot of our restaurant partners. 298 00:12:53,100 --> 00:12:56,614 They really don't like it when couriers are waiting around in the restaurant. 299 00:12:56,614 --> 00:12:58,530 They're trying to manage their front of house, 300 00:12:58,530 --> 00:13:00,900 and they have a bunch of people also filing in. 301 00:13:00,900 --> 00:13:03,490 It's just not a great experience. 302 00:13:03,490 --> 00:13:09,910 So this problem really touches everyone in the entire Uber Eats ecosystem. 303 00:13:09,910 --> 00:13:14,280 So the first problem is, we don't really know when the food is done. 304 00:13:14,280 --> 00:13:18,450 No one's standing in the restaurant and telling us, the food is done; 305 00:13:18,450 --> 00:13:20,070 you should send a courier now. 306 00:13:20,070 --> 00:13:21,280 We don't have a camera. 307 00:13:21,280 --> 00:13:22,710 We don't call the restaurant. 308 00:13:22,710 --> 00:13:24,450 The restaurant doesn't call us. 309 00:13:24,450 --> 00:13:29,830 And so there's no ground truth on validating our predictions. 310 00:13:29,830 --> 00:13:32,750 A second problem is, we also don't have a ton 311 00:13:32,750 --> 00:13:37,110 of good signals into knowing when that food will be ready at the restaurant. 312 00:13:37,110 --> 00:13:40,865 So we're working with very limited number of data points. 313 00:13:40,865 --> 00:13:43,400 314 00:13:43,400 --> 00:13:47,720 To give you a sense of how this is impacting eaters and restaurants, 315 00:13:47,720 --> 00:13:51,500 real quick-- so each restaurant in the Uber Eats ecosystem 316 00:13:51,500 --> 00:13:56,060 has a tablet in the restaurant that shows them all their ongoing orders. 317 00:13:56,060 --> 00:13:59,510 And so, for this one, for Michelle, there 318 00:13:59,510 --> 00:14:04,250 will be a due time, which is when the restaurant can expect a courier 319 00:14:04,250 --> 00:14:06,260 to show up and pick up the food. 320 00:14:06,260 --> 00:14:09,800 And it'll tell them how long that is from now. 321 00:14:09,800 --> 00:14:12,470 And then, on the eater side, hopefully maybe some of you guys 322 00:14:12,470 --> 00:14:15,530 are familiar with this, right after you place your order we show you 323 00:14:15,530 --> 00:14:18,034 that to-the-minute time prediction. 324 00:14:18,034 --> 00:14:20,640 325 00:14:20,640 --> 00:14:24,480 So one approach that we can take, to tackle this problem, 326 00:14:24,480 --> 00:14:27,960 is predicting when that food will be ready, 327 00:14:27,960 --> 00:14:33,630 creating a prediction system that will inform us when we should dispatch 328 00:14:33,630 --> 00:14:37,022 a courier to the restaurant. . 329 00:14:37,022 --> 00:14:38,730 So one hypothesis-- you know, if you were 330 00:14:38,730 --> 00:14:42,150 to think about this sort of as a machine-learning-driven prediction 331 00:14:42,150 --> 00:14:48,300 system, is that we can rely on how restaurants behave, with the tablet. 332 00:14:48,300 --> 00:14:51,760 So for instance, if they delay the order and tell us, 333 00:14:51,760 --> 00:14:56,980 hey, you dispatched someone but we weren't quite ready yet, 334 00:14:56,980 --> 00:14:59,230 that probably means our prediction wasn't quite right. 335 00:14:59,230 --> 00:15:02,145 So we can take that restaurant input as a signal. 336 00:15:02,145 --> 00:15:04,780 337 00:15:04,780 --> 00:15:08,320 Another hypothesis is, we can look at how couriers behave 338 00:15:08,320 --> 00:15:11,080 once they arrive at the restaurant. 339 00:15:11,080 --> 00:15:15,700 So this was a trip, picking up, I think, like, burritos from El Jefe's. 340 00:15:15,700 --> 00:15:20,720 You can see this courier arrive, and he ends up waiting around a lot. 341 00:15:20,720 --> 00:15:23,170 He's kind of driving around. 342 00:15:23,170 --> 00:15:25,930 He might be parking, but you can also tell that he's, like, 343 00:15:25,930 --> 00:15:28,630 loitering around for a while. 344 00:15:28,630 --> 00:15:32,620 We can use this also as an input to say, hey, this courier arrived, 345 00:15:32,620 --> 00:15:34,810 ended up waiting for 15 minutes. 346 00:15:34,810 --> 00:15:37,710 The food probably wasn't ready yet-- 347 00:15:37,710 --> 00:15:41,520 again, a hypothesis. 348 00:15:41,520 --> 00:15:45,751 So, if you combine all these hypotheses, they begin to become features. 349 00:15:45,751 --> 00:15:46,250 Right? 350 00:15:46,250 --> 00:15:49,040 And so you can start building out a prediction model, 351 00:15:49,040 --> 00:15:50,820 using all these features. 352 00:15:50,820 --> 00:15:53,630 So one is when the food is done from the restaurant. 353 00:15:53,630 --> 00:15:58,700 Another is the courier wait time at the restaurant, after they've arrived. 354 00:15:58,700 --> 00:16:01,760 Some other ones that you might use are live order count-- you know, 355 00:16:01,760 --> 00:16:05,980 how busy is this restaurant, in real time? 356 00:16:05,980 --> 00:16:09,360 Another one might be basket size, as a proxy 357 00:16:09,360 --> 00:16:11,830 for how many items are in that order. 358 00:16:11,830 --> 00:16:14,900 If you're ordering, like, the 12 pizzas that we 359 00:16:14,900 --> 00:16:16,650 have for this event, that's probably going 360 00:16:16,650 --> 00:16:21,570 to take a little longer to prepare than a burger for one person. 361 00:16:21,570 --> 00:16:24,440 And then, finally, time of day and day of week. 362 00:16:24,440 --> 00:16:28,700 So you can probably imagine El Jefe's is probably pretty busy on a Friday night, 363 00:16:28,700 --> 00:16:31,010 after people are, like, coming out of parties. 364 00:16:31,010 --> 00:16:35,240 And then Zinneken's might be a popular spot on Sunday mornings-- 365 00:16:35,240 --> 00:16:37,890 someone who wants brunch. 366 00:16:37,890 --> 00:16:41,700 So we combine all these features into this model. 367 00:16:41,700 --> 00:16:45,400 And we're constantly iterating on this prediction model. 368 00:16:45,400 --> 00:16:49,260 It's what fuels the millions of Uber Eats trips on a weekly basis 369 00:16:49,260 --> 00:16:51,390 that we do all around the world. 370 00:16:51,390 --> 00:16:57,380 And we're constantly striving to reduce the error in our models. 371 00:16:57,380 --> 00:17:00,100 This is a quick screenshot of all the cities. 372 00:17:00,100 --> 00:17:03,460 So we're now in over 290 cities, across the world. 373 00:17:03,460 --> 00:17:05,800 And it's little machine-- 374 00:17:05,800 --> 00:17:10,180 [LAUGH] I mean, it's, like, little models like these that you might think 375 00:17:10,180 --> 00:17:14,329 is almost, like, a piece that you might be working on. 376 00:17:14,329 --> 00:17:16,420 But in the real world, this is actually impacting 377 00:17:16,420 --> 00:17:20,650 millions of rides, millions of deliveries, every day, 378 00:17:20,650 --> 00:17:22,930 around the world. 379 00:17:22,930 --> 00:17:26,349 All that being said, we are not always perfect. 380 00:17:26,349 --> 00:17:30,730 And so, in addition to building out these models, 381 00:17:30,730 --> 00:17:33,000 we also have to know how to properly convey 382 00:17:33,000 --> 00:17:38,310 that information to eaters, especially when there's uncertainty involved-- 383 00:17:38,310 --> 00:17:39,380 like with time. 384 00:17:39,380 --> 00:17:43,020 And so Evan's going to talk a little bit about how we reveal uncertainty 385 00:17:43,020 --> 00:17:46,050 to our customers on the ride side. 386 00:17:46,050 --> 00:17:47,010 EVAN: [INAUDIBLE] 387 00:17:47,010 --> 00:17:47,970 DANIEL: Yeah. 388 00:17:47,970 --> 00:17:50,480 EVAN: Cool. 389 00:17:50,480 --> 00:17:52,970 So, yeah, so, part of the work that I've done 390 00:17:52,970 --> 00:17:58,560 is much more how we show these models to our shared-ride riders in particular. 391 00:17:58,560 --> 00:18:02,750 But I'm just going to start walking you through why this problem, to me, 392 00:18:02,750 --> 00:18:07,900 is a really cool one and gets at a lot of the human-computer interaction work 393 00:18:07,900 --> 00:18:10,660 that I was thinking about at school. 394 00:18:10,660 --> 00:18:12,770 The two high-level-- the so-what's, here, 395 00:18:12,770 --> 00:18:16,470 is one is that probabilities are really difficult to communicate. 396 00:18:16,470 --> 00:18:19,300 So, when Daniel's showing you, like, right your food, 397 00:18:19,300 --> 00:18:21,800 if the courier ends up getting there too early and lingering 398 00:18:21,800 --> 00:18:24,950 and maybe the restaurant's late, what all of this comes down to 399 00:18:24,950 --> 00:18:26,850 is there's some time range. 400 00:18:26,850 --> 00:18:29,960 And in that range, your food will come. 401 00:18:29,960 --> 00:18:32,300 But people, myself included, we're not very 402 00:18:32,300 --> 00:18:34,880 good at intuiting what it means if there's 403 00:18:34,880 --> 00:18:39,530 a 50% something arriving at this time, versus a 70% chance of it arriving 404 00:18:39,530 --> 00:18:40,460 at another time. 405 00:18:40,460 --> 00:18:43,400 It's like, how we weigh those probabilities and the actions we take 406 00:18:43,400 --> 00:18:48,680 is just a super-difficult thing to build products around. 407 00:18:48,680 --> 00:18:53,420 And the second is that our customers are super-sensitive when things go wrong. 408 00:18:53,420 --> 00:18:55,220 Part of this-- and everyone's been there. 409 00:18:55,220 --> 00:18:58,370 It's like, you're ordering a ride into Boston, and you're waiting outside, 410 00:18:58,370 --> 00:18:59,470 and it's cold-- 411 00:18:59,470 --> 00:19:03,170 you're the street, it's cold, it's snowing, you didn't wear a jacket. 412 00:19:03,170 --> 00:19:06,710 It's like, there are visceral experiences that our customers have 413 00:19:06,710 --> 00:19:09,470 that, very quickly, they can go, like, to, 414 00:19:09,470 --> 00:19:12,434 like, there's an "Uber sort of sucks," here, moment. 415 00:19:12,434 --> 00:19:14,100 And so those are happening all the time. 416 00:19:14,100 --> 00:19:16,808 And we don't want to be building products that people think suck. 417 00:19:16,808 --> 00:19:19,430 So the question is, how do we manage these expectations 418 00:19:19,430 --> 00:19:22,010 in ways that are productive? 419 00:19:22,010 --> 00:19:24,620 And on that note of the sorts of scenarios 420 00:19:24,620 --> 00:19:29,180 where it matters, say you order like a huge basket for dinner, 421 00:19:29,180 --> 00:19:32,859 or say you came here expecting there to be pizza and the pizza wasn't here. 422 00:19:32,859 --> 00:19:35,150 Like, everyone would be grumpy, and no one would really 423 00:19:35,150 --> 00:19:36,960 care about listening to our talk. 424 00:19:36,960 --> 00:19:38,442 You know, we lose you, right there. 425 00:19:38,442 --> 00:19:39,900 So the pizza's is really important. 426 00:19:39,900 --> 00:19:41,191 Thanks for having it delivered. 427 00:19:41,191 --> 00:19:46,700 Two is, like, people use Uber to get to, like, see people at important times. 428 00:19:46,700 --> 00:19:47,600 Maybe it's romantic. 429 00:19:47,600 --> 00:19:48,860 Maybe it's a business meeting. 430 00:19:48,860 --> 00:19:51,430 When people are late, they don't necessarily say, hey, 431 00:19:51,430 --> 00:19:54,740 it would've been better if I, like, left my house 30 minutes earlier. 432 00:19:54,740 --> 00:19:56,990 It's, like, the Uber got stuck in traffic. 433 00:19:56,990 --> 00:19:58,760 Right? 434 00:19:58,760 --> 00:20:00,150 And I'm guilty of that, as well. 435 00:20:00,150 --> 00:20:03,560 And third is, say it's the sort of thing where 436 00:20:03,560 --> 00:20:05,800 we tell you there's a 95% chance you're going 437 00:20:05,800 --> 00:20:10,700 to arrive at Logan for your flight. 438 00:20:10,700 --> 00:20:12,530 And you're like, OK, I'll take those odds. 439 00:20:12,530 --> 00:20:16,280 But then you arrive, that 5% of the time where you arrive later 440 00:20:16,280 --> 00:20:17,780 than that time, that happens. 441 00:20:17,780 --> 00:20:20,850 And, because we're doing billions of rides, happens all the time. 442 00:20:20,850 --> 00:20:22,490 As a result, you miss your flight. 443 00:20:22,490 --> 00:20:26,390 All of a sudden, that decision to choose a certain sort of Uber product 444 00:20:26,390 --> 00:20:27,290 failed you. 445 00:20:27,290 --> 00:20:31,430 And that's going to color the rest of your Uber experiences. 446 00:20:31,430 --> 00:20:33,980 So the point here is that this is a super-tricky area 447 00:20:33,980 --> 00:20:35,900 to sort of describe experiences. 448 00:20:35,900 --> 00:20:40,820 And, two, when things go wrong it can have these real-world consequences 449 00:20:40,820 --> 00:20:43,900 that are tough for our customers. 450 00:20:43,900 --> 00:20:46,360 So Daniel was talking about this sort of, colored in green, 451 00:20:46,360 --> 00:20:47,570 the Eats timeline. 452 00:20:47,570 --> 00:20:51,670 This is the journey map of what is happening over the course of an Eats 453 00:20:51,670 --> 00:20:54,400 order, from order to delivery. 454 00:20:54,400 --> 00:20:56,902 On the top, you have this as if you are an UberX. 455 00:20:56,902 --> 00:20:59,110 You request the ride, there's some uncertainty, here, 456 00:20:59,110 --> 00:21:01,151 in terms of when the car's going to actually sort 457 00:21:01,151 --> 00:21:03,550 of-- when that actual time of arrival pops up, 458 00:21:03,550 --> 00:21:05,440 but then you get picked up and dropped off. 459 00:21:05,440 --> 00:21:08,440 What I'm going to focus on, for this experiential portion of the talk, 460 00:21:08,440 --> 00:21:10,900 is the middle line, which is Express Pool. 461 00:21:10,900 --> 00:21:15,310 And, just curious, show of hands, people who have used Express Pool? 462 00:21:15,310 --> 00:21:17,550 People who use Lyft Line? 463 00:21:17,550 --> 00:21:18,360 Cool, cool, cool. 464 00:21:18,360 --> 00:21:19,840 Share rides-- it's good. 465 00:21:19,840 --> 00:21:21,700 Share rides is where it's at. 466 00:21:21,700 --> 00:21:24,090 So we actually get, like, $200 worth of credits a month, 467 00:21:24,090 --> 00:21:25,632 as part of being employees at Uber. 468 00:21:25,632 --> 00:21:27,840 And I try to stretch them by only using shared rides. 469 00:21:27,840 --> 00:21:30,420 So Lyft Line's cool. 470 00:21:30,420 --> 00:21:33,362 So, yeah, so in the shared-rides world-- and I'll go into a bit more 471 00:21:33,362 --> 00:21:35,820 about what Express Pool is, for those who haven't tried it. 472 00:21:35,820 --> 00:21:37,530 But, at the start, you request a ride. 473 00:21:37,530 --> 00:21:40,890 You walk to a pickup spot, to create a straighter route 474 00:21:40,890 --> 00:21:42,990 for you and your co-riders. 475 00:21:42,990 --> 00:21:46,380 Over the course of your journey, riders are getting picked up and dropped off. 476 00:21:46,380 --> 00:21:48,720 You get dropped off, and you walk a little bit to your destination. 477 00:21:48,720 --> 00:21:50,428 Sometimes you don't walk at all, but this 478 00:21:50,428 --> 00:21:57,030 is the expectation we set with riders when they enter the Pool products. 479 00:21:57,030 --> 00:22:02,070 And so people who have ridden, sort of, ride-shared in general, 480 00:22:02,070 --> 00:22:04,950 and Express Pool in particular, have a general sense of this. 481 00:22:04,950 --> 00:22:09,210 But, going back, Boston was one of our first [INAUDIBLE] for Express Pool. 482 00:22:09,210 --> 00:22:12,420 Going back a little bit, though, this was 483 00:22:12,420 --> 00:22:15,280 our strategy for why Express Pool was a game-changer. 484 00:22:15,280 --> 00:22:17,460 So this is Pool, step 1. 485 00:22:17,460 --> 00:22:20,040 And it's basically, there are a few pickup spots. 486 00:22:20,040 --> 00:22:23,730 Those are all riders who are all going to get batched together 487 00:22:23,730 --> 00:22:24,850 in the same car. 488 00:22:24,850 --> 00:22:28,680 And so car does some sidetracking, to get to them. 489 00:22:28,680 --> 00:22:31,920 Express Pool, the big idea is putting steps 2 and 3 together. 490 00:22:31,920 --> 00:22:34,860 So, for 2, it's, hey, the driver has to go out 491 00:22:34,860 --> 00:22:36,767 of their way to pick these people up. 492 00:22:36,767 --> 00:22:39,600 It probably means they're looping around a block with extra traffic, 493 00:22:39,600 --> 00:22:42,340 and that hurts everyone-- the driver and the riders, included. 494 00:22:42,340 --> 00:22:46,380 So what if our riders actually walk to a straight route, 495 00:22:46,380 --> 00:22:48,990 so that it was more a sort of express and sort 496 00:22:48,990 --> 00:22:51,510 of an A-to-B route for the driver. 497 00:22:51,510 --> 00:22:54,660 And 3 was, hey, we can have people walk, but if we just 498 00:22:54,660 --> 00:22:58,530 make them wait a little bit up front we can get even smarter about our matches. 499 00:22:58,530 --> 00:23:01,620 Because there are more people populating this graph, 500 00:23:01,620 --> 00:23:04,860 and we can stitch them together into cars in more efficient ways. 501 00:23:04,860 --> 00:23:08,879 So this was the visualization that sold Express Pool, internally. 502 00:23:08,879 --> 00:23:10,670 It's something we had to do and invest in-- 503 00:23:10,670 --> 00:23:13,200 504 00:23:13,200 --> 00:23:14,910 combining 2 and 3's Express Pool. 505 00:23:14,910 --> 00:23:17,050 And this is basically what we launched with. 506 00:23:17,050 --> 00:23:19,656 And so, if you were in Boston a bunch of months ago, 507 00:23:19,656 --> 00:23:21,030 this is what you would have seen. 508 00:23:21,030 --> 00:23:25,184 Basically, we put Express Pool to the left in our product selector. 509 00:23:25,184 --> 00:23:27,600 And you can see that, when you're about to request a ride, 510 00:23:27,600 --> 00:23:29,974 you're doing a trade-off, which is, there are the prices, 511 00:23:29,974 --> 00:23:31,320 and there are also these times. 512 00:23:31,320 --> 00:23:35,220 So riders are making this calculation of value of price in time. 513 00:23:35,220 --> 00:23:38,240 And then, if you hit Request you go into this batching screen, 514 00:23:38,240 --> 00:23:39,990 which is that waiting part I talked about, 515 00:23:39,990 --> 00:23:42,990 where we're seeing all the other potential riders also online 516 00:23:42,990 --> 00:23:44,340 that we could batch you with. 517 00:23:44,340 --> 00:23:47,550 And then you walk, you get in your car, and then you're on your way. 518 00:23:47,550 --> 00:23:51,050 So this is the basic Express Pool experience. 519 00:23:51,050 --> 00:23:54,050 What we realized, though-- and Express Pool was positioned as a commuter 520 00:23:54,050 --> 00:23:54,839 product-- 521 00:23:54,839 --> 00:23:56,630 it was cheaper, there was walking involved, 522 00:23:56,630 --> 00:23:58,550 people walk generally during their commutes, 523 00:23:58,550 --> 00:24:00,008 and commutes are generally cheaper. 524 00:24:00,008 --> 00:24:02,060 So this was our commuting product. 525 00:24:02,060 --> 00:24:06,770 And what we found was that, among people who chose shared rides, 526 00:24:06,770 --> 00:24:13,040 riders were more likely to choose Pool than Express Pool during AM commutes. 527 00:24:13,040 --> 00:24:16,740 And this was a very striking thing, as this was bad. 528 00:24:16,740 --> 00:24:18,740 Like, we've made this product for commute times, 529 00:24:18,740 --> 00:24:22,550 for commuters who are price-sensitive and could mentally budget, OK, like, 530 00:24:22,550 --> 00:24:27,570 $4 per ride is in the realm of what I can spend-- $4 or $5. 531 00:24:27,570 --> 00:24:31,110 So usually what happens, when we figure out 532 00:24:31,110 --> 00:24:33,810 that there is a problem that exists in our world, 533 00:24:33,810 --> 00:24:36,780 is, we ask our data scientists to, can you 534 00:24:36,780 --> 00:24:39,840 figure out some sort of insights from the data we collect? 535 00:24:39,840 --> 00:24:42,750 And from a user-experience side, can we talk 536 00:24:42,750 --> 00:24:46,060 to a bunch of riders who seem to be engaging with this product 537 00:24:46,060 --> 00:24:49,420 and see why it is that they're making the decisions that they're making? 538 00:24:49,420 --> 00:24:53,050 So the data scientists were the ones you sort of unveiled this trend, that, hey, 539 00:24:53,050 --> 00:24:55,050 we're not actually capturing a lot of people who 540 00:24:55,050 --> 00:24:57,310 we thought we would with this product. 541 00:24:57,310 --> 00:25:01,350 And from the user-research side, these are some of the things that we found. 542 00:25:01,350 --> 00:25:06,670 So these points are the three I'll walk through. 543 00:25:06,670 --> 00:25:12,320 So one was, riders weren't sure if this time range included 544 00:25:12,320 --> 00:25:13,460 that waiting and walking. 545 00:25:13,460 --> 00:25:13,960 Right? 546 00:25:13,960 --> 00:25:16,490 Because, in the past, for X and Pool, you 547 00:25:16,490 --> 00:25:20,570 had these estimated time to destinations, these ETDs, 548 00:25:20,570 --> 00:25:23,720 and those didn't have any waiting and walking, so it wasn't clear to riders 549 00:25:23,720 --> 00:25:25,970 if Express Pool captured that. 550 00:25:25,970 --> 00:25:31,284 It also wasn't clear to riders why the Express Pool time range, when selected, 551 00:25:31,284 --> 00:25:33,200 was the same as the Pool range, when selected. 552 00:25:33,200 --> 00:25:36,152 If I'm waiting and walking, why is this going to be faster for me? 553 00:25:36,152 --> 00:25:38,360 If you had seen that chart up front, where it's, hey, 554 00:25:38,360 --> 00:25:40,370 we're finding a better route, maybe you'd get it. 555 00:25:40,370 --> 00:25:43,495 But there was, like, an intuitive feel of, hey, there are more steps, here. 556 00:25:43,495 --> 00:25:45,710 How is that going to be faster? 557 00:25:45,710 --> 00:25:49,640 So there was this lack of clarity-- were these numbers even real? 558 00:25:49,640 --> 00:25:50,510 And then-- 559 00:25:50,510 --> 00:25:53,900 So FTUX stands for First Time User eXperience. 560 00:25:53,900 --> 00:25:57,120 In the first-time user experience, we basically say, hey, 561 00:25:57,120 --> 00:25:59,030 these are straighter, faster routes-- 562 00:25:59,030 --> 00:26:01,700 like I've hopefully sold you all on. 563 00:26:01,700 --> 00:26:04,220 In reality, though, our riders typically spend 564 00:26:04,220 --> 00:26:06,050 somewhere between four and eight seconds, 565 00:26:06,050 --> 00:26:07,570 like, flipping through those cards. 566 00:26:07,570 --> 00:26:10,430 It's like, you see a cheap price for a product, you're like, hey, 567 00:26:10,430 --> 00:26:11,027 I'll try that. 568 00:26:11,027 --> 00:26:12,110 And you flip through them. 569 00:26:12,110 --> 00:26:14,901 So the time at which we were trying to communicate this information 570 00:26:14,901 --> 00:26:16,151 wasn't going through. 571 00:26:16,151 --> 00:26:17,900 And then the third thing, which I actually 572 00:26:17,900 --> 00:26:20,330 thought was the most important, was that, 573 00:26:20,330 --> 00:26:22,400 in a world where you take out Express Pool 574 00:26:22,400 --> 00:26:25,410 and you have Pool and X, basically from left to right 575 00:26:25,410 --> 00:26:28,395 you have increasing price and things get slower-- 576 00:26:28,395 --> 00:26:30,900 or, I'm sorry, things get faster as you go left to right. 577 00:26:30,900 --> 00:26:34,130 So, if you put something on the left side, that's cheaper. 578 00:26:34,130 --> 00:26:37,820 It's a pretty logical conclusion to draw that it's also going to be slower. 579 00:26:37,820 --> 00:26:40,391 Even if those times look the same, you may just think, hey, 580 00:26:40,391 --> 00:26:41,390 it's slower and cheaper. 581 00:26:41,390 --> 00:26:42,620 Right? 582 00:26:42,620 --> 00:26:45,920 So we talked to riders, and they were all convinced-- 583 00:26:45,920 --> 00:26:49,130 even the ones who would use Express who were set on Express Pool 584 00:26:49,130 --> 00:26:50,180 as their commute option-- 585 00:26:50,180 --> 00:26:53,000 that they should be using Pool if they were in a hurry. 586 00:26:53,000 --> 00:26:53,570 Right? 587 00:26:53,570 --> 00:26:57,560 And so we specifically tuned our systems, our back-end systems, to be, 588 00:26:57,560 --> 00:27:00,850 like, riders' total time should be the same. 589 00:27:00,850 --> 00:27:03,350 But all our riders who we're talking to who are power riders 590 00:27:03,350 --> 00:27:05,180 felt that this was slower. 591 00:27:05,180 --> 00:27:08,120 So the high-level point were, all these paper cuts 592 00:27:08,120 --> 00:27:12,380 were making they experience a lot worse-- in terms of the perception, 593 00:27:12,380 --> 00:27:15,050 despite how we were representing the time information. 594 00:27:15,050 --> 00:27:17,690 And then, quickly, on the second part, after your request you 595 00:27:17,690 --> 00:27:22,370 and you go into your batching window, we have this sort of itinerary, 596 00:27:22,370 --> 00:27:24,420 here, which is, these are all the steps. 597 00:27:24,420 --> 00:27:26,600 And, at the end of it, there's this arrival time. 598 00:27:26,600 --> 00:27:28,742 First point is that we decided-- 599 00:27:28,742 --> 00:27:30,950 Again, this was like, we had to launch this-- we had, 600 00:27:30,950 --> 00:27:32,730 like, two months to launch this thing. 601 00:27:32,730 --> 00:27:35,190 And this was our first best version, right? 602 00:27:35,190 --> 00:27:38,420 We decided to stick in a no-later-than time. 603 00:27:38,420 --> 00:27:42,200 So that's, like, a very-worst-case scenario that you probably are only 604 00:27:42,200 --> 00:27:42,790 hitting-- 605 00:27:42,790 --> 00:27:46,830 you know, like that airport case, that 1%, 2%, 3% of the time. 606 00:27:46,830 --> 00:27:49,160 And we did that because we wanted to avoid 607 00:27:49,160 --> 00:27:51,710 those cases of really bad experiences. 608 00:27:51,710 --> 00:27:57,890 But, for a rider who sees this, it's like, it's a pretty scary time to see, 609 00:27:57,890 --> 00:27:59,480 because the range may be-- 610 00:27:59,480 --> 00:28:03,370 the more reasonable time-to-show would have been 12:15. 611 00:28:03,370 --> 00:28:05,960 So there's a sense, there, we're showing a later time. 612 00:28:05,960 --> 00:28:09,290 The bigger point, though, was that riders weren't actually swiping up. 613 00:28:09,290 --> 00:28:12,740 So we were putting this time information in this batching card, 614 00:28:12,740 --> 00:28:15,180 but riders weren't even getting there in the first place. 615 00:28:15,180 --> 00:28:17,600 So some of this first-principles thinking about, hey, 616 00:28:17,600 --> 00:28:21,052 like, what's the most important information you want to present, 617 00:28:21,052 --> 00:28:23,510 those choices weren't actually making it through to riders. 618 00:28:23,510 --> 00:28:26,600 And so the high-level point here was that the IA or the information 619 00:28:26,600 --> 00:28:29,540 architecture we were trying to serve this information through just 620 00:28:29,540 --> 00:28:34,580 wasn't communicating really anything to our riders. 621 00:28:34,580 --> 00:28:38,600 So the next step was figuring out what were the most important questions 622 00:28:38,600 --> 00:28:39,980 that we wanted to solve for. 623 00:28:39,980 --> 00:28:42,770 But first was, how did we break this mental model, 624 00:28:42,770 --> 00:28:47,120 that Pool is a lot faster than Express when really they're the same? 625 00:28:47,120 --> 00:28:50,180 Two was, how might we better illustrate the estimated time 626 00:28:50,180 --> 00:28:52,880 to destination, the ETD information, to make 627 00:28:52,880 --> 00:28:55,430 them actually actionable for riders? 628 00:28:55,430 --> 00:28:58,250 And the third was, how do we sell people on the fact that 629 00:28:58,250 --> 00:29:02,320 the waiting-and-walking time doesn't make your final destination time-- 630 00:29:02,320 --> 00:29:04,870 like, doesn't push that out? 631 00:29:04,870 --> 00:29:08,100 So-- I just went through that [INAUDIBLE].. 632 00:29:08,100 --> 00:29:10,865 This is basically, let's make this information more prominent. 633 00:29:10,865 --> 00:29:12,240 And this is what we came up with. 634 00:29:12,240 --> 00:29:15,770 And if you're using Express Pool today in Boston, this is what you'll see. 635 00:29:15,770 --> 00:29:18,620 We've just roll this out to all our Express Pool cities, 636 00:29:18,620 --> 00:29:20,630 which is pretty cool. 637 00:29:20,630 --> 00:29:23,270 So, for starters, this was-- 638 00:29:23,270 --> 00:29:27,230 you know, they're all sort of small touches, but again they all add up. 639 00:29:27,230 --> 00:29:28,700 This was the big update-- 640 00:29:28,700 --> 00:29:31,700 was that, instead of emphasizing your latest time, 641 00:29:31,700 --> 00:29:35,375 we emphasized your median time of arrival or something 642 00:29:35,375 --> 00:29:37,665 in that range, that was going to be, hey, 643 00:29:37,665 --> 00:29:40,040 assuming that there isn't crazy traffic, this is probably 644 00:29:40,040 --> 00:29:42,500 when you're going to arrive. 645 00:29:42,500 --> 00:29:44,270 Also, we had your latest time. 646 00:29:44,270 --> 00:29:46,010 So that, if you were in a rush, you still 647 00:29:46,010 --> 00:29:49,640 have kind of an out to say, hey, that latest time is too close. 648 00:29:49,640 --> 00:29:52,550 Let me cancel this route and move on. 649 00:29:52,550 --> 00:29:54,500 Other point, in terms of the scrollability 650 00:29:54,500 --> 00:29:57,680 was that we made this panel just feel more 651 00:29:57,680 --> 00:30:01,490 scrollable for riders, where there was a clear itinerary that was being cut off. 652 00:30:01,490 --> 00:30:04,980 So, sure enough, more riders now are scrolling when they see this. 653 00:30:04,980 --> 00:30:09,170 And the final point I'll make here is that walking and waiting 654 00:30:09,170 --> 00:30:12,320 are both steps that lead up to that estimated time, now. 655 00:30:12,320 --> 00:30:17,070 Which is a more clear, hey, these steps are part of your arrival time. 656 00:30:17,070 --> 00:30:20,610 Final point, I promise, is that, as this countdown is happening, 657 00:30:20,610 --> 00:30:22,350 this arrival time is staying the same. 658 00:30:22,350 --> 00:30:24,480 Which, when we user-tested it with riders, 659 00:30:24,480 --> 00:30:28,444 was a very helpful indication of, oh, I'm not losing time by waiting. 660 00:30:28,444 --> 00:30:30,610 This waiting actually doesn't feel like wasted time. 661 00:30:30,610 --> 00:30:33,450 662 00:30:33,450 --> 00:30:37,230 And then a quick note on, we also just put more information 663 00:30:37,230 --> 00:30:38,850 for those riders who were interested. 664 00:30:38,850 --> 00:30:41,400 This was supposed to be only our sort of most-engaged riders 665 00:30:41,400 --> 00:30:43,200 who are actually going to click around. 666 00:30:43,200 --> 00:30:46,110 But clicking into that estimated arrival time, 667 00:30:46,110 --> 00:30:49,410 or why the wait, on that second screen, gives you more information 668 00:30:49,410 --> 00:30:53,160 about selling our riders on the value prop that we're actually delivering on. 669 00:30:53,160 --> 00:30:55,860 670 00:30:55,860 --> 00:30:59,269 And we didn't know how it was going to go, but it worked out. 671 00:30:59,269 --> 00:31:00,810 I need permission to view this video. 672 00:31:00,810 --> 00:31:01,650 That's too bad. 673 00:31:01,650 --> 00:31:02,640 DANIEL: I do not have permission. 674 00:31:02,640 --> 00:31:03,400 EVAN: That's OK. 675 00:31:03,400 --> 00:31:05,190 Basically, it shows a time lapse of all these people 676 00:31:05,190 --> 00:31:06,660 using Express Pool on a given day. 677 00:31:06,660 --> 00:31:08,070 It's cool. 678 00:31:08,070 --> 00:31:09,930 But, for the results, it was opt-in-- 679 00:31:09,930 --> 00:31:13,200 so, people using Express Pool in mornings 680 00:31:13,200 --> 00:31:15,840 was, like, corrected over the course of a few weeks, 681 00:31:15,840 --> 00:31:17,820 among our most engaged riders. 682 00:31:17,820 --> 00:31:20,550 And cancellations on that waiting screen went down. 683 00:31:20,550 --> 00:31:22,320 So we were telling a more effective story 684 00:31:22,320 --> 00:31:25,790 to our riders when they were waiting, at that point where they're sort of, 685 00:31:25,790 --> 00:31:27,831 they're putting their money where their mouth is, 686 00:31:27,831 --> 00:31:31,200 and they're either going to cancel or go forward with the experience. 687 00:31:31,200 --> 00:31:33,210 All of which is to say, from-- 688 00:31:33,210 --> 00:31:37,900 this was one of my key projects, this past year. 689 00:31:37,900 --> 00:31:40,860 And, for me, coming out of it, the first one I mentioned, 690 00:31:40,860 --> 00:31:44,545 the little paper cuts all build up, it's like a bunch of small things. 691 00:31:44,545 --> 00:31:47,670 And sometimes we talk about it, when we're building a big project, in terms 692 00:31:47,670 --> 00:31:50,820 of tech debt, where, over time, you amass tech debt 693 00:31:50,820 --> 00:31:53,730 and then you want to deploy some new feature 694 00:31:53,730 --> 00:31:59,160 and you just have to fix so much stuff to sort of renew the whole system. 695 00:31:59,160 --> 00:32:01,650 Also is true from an experience perspective. 696 00:32:01,650 --> 00:32:05,190 Over time, all these little products, these, like, where the text is, 697 00:32:05,190 --> 00:32:07,020 that actually matters. 698 00:32:07,020 --> 00:32:11,110 Two is that, over time, frameworks become less and less valuable. 699 00:32:11,110 --> 00:32:13,830 So, as we try to put more and more products into that product 700 00:32:13,830 --> 00:32:17,880 selector, the original intent of explaining our products-- 701 00:32:17,880 --> 00:32:19,920 we were basically failing. 702 00:32:19,920 --> 00:32:23,786 Third, Daniel's models, as much as we need them to get better, to some extent 703 00:32:23,786 --> 00:32:26,910 they're only as good as the experience that actually reveals them to riders 704 00:32:26,910 --> 00:32:28,620 and information that they can use. 705 00:32:28,620 --> 00:32:31,949 So simple language, like "estimated arrival" and "latest time," 706 00:32:31,949 --> 00:32:34,740 that's what tested the best with our riders to actually be helpful. 707 00:32:34,740 --> 00:32:37,739 We tried things like putting little percentages up and things like that, 708 00:32:37,739 --> 00:32:40,770 but the "estimated" and "latest" were what riders actually preferred, by 709 00:32:40,770 --> 00:32:42,160 and large. 710 00:32:42,160 --> 00:32:44,490 And finally, it was pretty nerve-wracking, 711 00:32:44,490 --> 00:32:49,290 because we probably invested a couple months of time, working on the UX 712 00:32:49,290 --> 00:32:53,460 and testing and building this feature out, with all of the color variations. 713 00:32:53,460 --> 00:32:55,470 And it took a lot from our eng team. 714 00:32:55,470 --> 00:32:58,080 And we just didn't know if people would care at all. 715 00:32:58,080 --> 00:33:01,100 Like, maybe it just matters with the prices, and that's it. 716 00:33:01,100 --> 00:33:04,350 But it actually turned out that, based on real behavior that we were tracking, 717 00:33:04,350 --> 00:33:07,660 that this experience made a real difference in our metrics. 718 00:33:07,660 --> 00:33:11,190 So, with that, come work with us. 719 00:33:11,190 --> 00:33:13,550 And Allie can tell you more. 720 00:33:13,550 --> 00:33:17,130 ALLIE: All right, thanks, you guys. 721 00:33:17,130 --> 00:33:20,160 Again, my name is Allie, a university recruiter. 722 00:33:20,160 --> 00:33:23,530 Do we have any former interns here? 723 00:33:23,530 --> 00:33:24,230 No? 724 00:33:24,230 --> 00:33:25,841 OK, cool. 725 00:33:25,841 --> 00:33:26,340 Awesome. 726 00:33:26,340 --> 00:33:30,030 I will tell you guys a little bit about our internship 727 00:33:30,030 --> 00:33:33,420 program, our new grad opportunities, and everything like that. 728 00:33:33,420 --> 00:33:36,960 So, just some cool facts for you guys. 729 00:33:36,960 --> 00:33:39,810 We definitely create opportunities at Uber. 730 00:33:39,810 --> 00:33:41,140 This is a fun fact-- 731 00:33:41,140 --> 00:33:45,330 more people earn income from Uber than any other privately held company, 732 00:33:45,330 --> 00:33:49,410 other than McDonald's and Walmart. 733 00:33:49,410 --> 00:33:52,530 Obviously, as you guys know, there are fewer cars on the road. 734 00:33:52,530 --> 00:33:54,690 They decrease congestion. 735 00:33:54,690 --> 00:34:01,620 It also frees up parking garages, residential space, et cetera. 736 00:34:01,620 --> 00:34:07,230 How many of you guys have heard of our Advanced Technologies Group, our ATG? 737 00:34:07,230 --> 00:34:07,770 Awesome. 738 00:34:07,770 --> 00:34:08,699 Very cool. 739 00:34:08,699 --> 00:34:11,040 So we are investing in the future. 740 00:34:11,040 --> 00:34:14,070 If you are interested in any of those opportunities, 741 00:34:14,070 --> 00:34:16,530 you can come and talk to me afterwards. 742 00:34:16,530 --> 00:34:19,950 Happy to take your resume and send it to the recruiters that 743 00:34:19,950 --> 00:34:23,719 sit out in Pittsburgh. 744 00:34:23,719 --> 00:34:25,719 This is an interesting fact, as well. 745 00:34:25,719 --> 00:34:28,760 This hasn't actually been updated in a few months, 746 00:34:28,760 --> 00:34:32,350 but Uber Eats recently expanded to at least 31 747 00:34:32,350 --> 00:34:37,870 or more college campuses, since last August-- 748 00:34:37,870 --> 00:34:41,210 which is pretty crazy. 749 00:34:41,210 --> 00:34:44,830 These are our engineering locations in the US. 750 00:34:44,830 --> 00:34:49,370 Majority of our team sits in San Francisco, which is our headquarters. 751 00:34:49,370 --> 00:34:52,400 We recently-- well, I guess not "recently," anymore-- 752 00:34:52,400 --> 00:34:56,460 but we opened an office in Palo Alto about a year ago, 753 00:34:56,460 --> 00:35:00,080 which sits mostly a lot of our engineers. 754 00:35:00,080 --> 00:35:05,040 We do have an office in Seattle, New York, and Colorado, as well. 755 00:35:05,040 --> 00:35:11,000 So, if you're interested in any of those locations, we are hiring for all. 756 00:35:11,000 --> 00:35:15,650 I would say that Colorado is definitely a super-small office. 757 00:35:15,650 --> 00:35:18,740 New York and Seattle are growing like crazy. 758 00:35:18,740 --> 00:35:23,317 And then San Francisco and Palo Alto are always growing, as well. 759 00:35:23,317 --> 00:35:24,275 DANIEL: And Pittsburgh. 760 00:35:24,275 --> 00:35:24,774 ALLIE: Oh! 761 00:35:24,774 --> 00:35:25,930 And Pittsburgh, yeah. 762 00:35:25,930 --> 00:35:27,269 ATG. 763 00:35:27,269 --> 00:35:28,810 DANIEL: I lived there for six months. 764 00:35:28,810 --> 00:35:29,310 ALLIE: Ah! 765 00:35:29,310 --> 00:35:30,260 There you go. 766 00:35:30,260 --> 00:35:32,700 Yes, we are hiring in Pittsburgh, as well. 767 00:35:32,700 --> 00:35:35,900 Unfortunately, we don't have anybody here from the ATG side. 768 00:35:35,900 --> 00:35:40,040 So, as I said, yeah, come up to me afterwards, and we can chat. 769 00:35:40,040 --> 00:35:43,850 These are just a few of our engineering teams, across the board. 770 00:35:43,850 --> 00:35:47,390 We have Maps, Core Infra, Consumer Products, 771 00:35:47,390 --> 00:35:54,470 Marketplace-- which seems to be a super-popular team for interns 772 00:35:54,470 --> 00:35:55,820 and new grads to join. 773 00:35:55,820 --> 00:36:01,670 Then we have Security, Uber for Business, and plenty more. 774 00:36:01,670 --> 00:36:03,150 Uber is a place to belong. 775 00:36:03,150 --> 00:36:08,330 So we have a plethora of employee resource groups that you can join, 776 00:36:08,330 --> 00:36:10,970 whether you're an intern or a new grad. 777 00:36:10,970 --> 00:36:12,410 We want you to be involved. 778 00:36:12,410 --> 00:36:16,430 We want you to be a part of these programs that we have. 779 00:36:16,430 --> 00:36:22,790 This just lists a few of them, but we have a ton more that you can join 780 00:36:22,790 --> 00:36:24,710 or you can create, yourself. 781 00:36:24,710 --> 00:36:27,800 782 00:36:27,800 --> 00:36:30,450 There's a ton of benefits and perks we have 783 00:36:30,450 --> 00:36:36,350 at Uber, when you become a full-time employee or even an intern. 784 00:36:36,350 --> 00:36:39,590 There is medical benefits. 785 00:36:39,590 --> 00:36:42,510 We do have free breakfast, lunch, and dinner. 786 00:36:42,510 --> 00:36:45,890 So it saves you a ton of money in the grocery department. 787 00:36:45,890 --> 00:36:50,540 We do receive, as Evan was saying, monthly Uber credits, 788 00:36:50,540 --> 00:36:54,830 which is probably one of the best perks I've ever had. 789 00:36:54,830 --> 00:36:58,540 You don't really realize how much money you spend on Uber, you know, per month. 790 00:36:58,540 --> 00:36:59,780 So it's crazy. 791 00:36:59,780 --> 00:37:04,790 Once you do run out of those credits, you still receive a 17% discount. 792 00:37:04,790 --> 00:37:10,040 You do get either a company phone or a monthly stipend that'll 793 00:37:10,040 --> 00:37:12,260 go directly into your paycheck. 794 00:37:12,260 --> 00:37:15,690 We do have unlimited paid time off, which is great, as well. 795 00:37:15,690 --> 00:37:20,090 So, if you wanted to take a vacation, or if you are sick, 796 00:37:20,090 --> 00:37:23,600 wanted to work from home, super-flexible at Uber. 797 00:37:23,600 --> 00:37:27,890 You just have to have manager approval, and you should be good to go. 798 00:37:27,890 --> 00:37:31,160 We do have paid time off for the holidays-- 799 00:37:31,160 --> 00:37:34,040 so, Thanksgiving, if you were going-- 800 00:37:34,040 --> 00:37:38,030 you know, whatever holiday it may be, if you wanted to go visit your family, 801 00:37:38,030 --> 00:37:42,590 friends, usually those are paid time off. 802 00:37:42,590 --> 00:37:46,050 We do have a lot of volunteer events, as well, in the community. 803 00:37:46,050 --> 00:37:49,910 So, if you are an intern at Uber, we do have volunteer events 804 00:37:49,910 --> 00:37:51,180 throughout the year, as well. 805 00:37:51,180 --> 00:37:55,700 So, this past year, I think, we had about three this summer. 806 00:37:55,700 --> 00:37:59,730 And you can pick and choose which event interests you. 807 00:37:59,730 --> 00:38:04,260 So, if you are interested in applying, you can go to this link. 808 00:38:04,260 --> 00:38:06,650 And this link will basically show you all the positions 809 00:38:06,650 --> 00:38:08,900 that we do have available. 810 00:38:08,900 --> 00:38:12,500 So we, this year, are hiring for our software engineer-- 811 00:38:12,500 --> 00:38:14,180 intern and new grad. 812 00:38:14,180 --> 00:38:20,510 The software-engineer class seems to be the biggest of all of our teams. 813 00:38:20,510 --> 00:38:24,410 We are hiring for data science, new grad and intern. 814 00:38:24,410 --> 00:38:27,530 Data analyst, new grad and intern. 815 00:38:27,530 --> 00:38:31,250 Design, and then associate product manager, 816 00:38:31,250 --> 00:38:34,370 which is a program for new grads. 817 00:38:34,370 --> 00:38:38,960 So, if you guys are interested in APM, you guys can talk to them afterwards. 818 00:38:38,960 --> 00:38:42,110 And then, if you are interested in any of the other ones 819 00:38:42,110 --> 00:38:47,680 I just named, feel free and come up to me afterwards, and we can chat. 820 00:38:47,680 --> 00:38:49,060 Any questions? 821 00:38:49,060 --> 00:38:50,400 [INTERPOSING VOICES] 822 00:38:50,400 --> 00:38:54,880 EVAN: --we thought some Q&A. And we got Uber, new grad life, whatever-- 823 00:38:54,880 --> 00:38:55,730 just Q&A. 824 00:38:55,730 --> 00:38:56,820 DANIEL: Yeah, it could be anything. 825 00:38:56,820 --> 00:38:57,320 Yep? 826 00:38:57,320 --> 00:38:58,750 AUDIENCE: How's Pittsburgh? 827 00:38:58,750 --> 00:39:00,370 DANIEL: Pittsburgh was awesome. 828 00:39:00,370 --> 00:39:04,720 So I was out there working on ATG for six months. 829 00:39:04,720 --> 00:39:07,150 I like Pittsburgh a lot. 830 00:39:07,150 --> 00:39:08,270 Pretty cool city [LAUGH]. 831 00:39:08,270 --> 00:39:09,620 The roads kind of suck. 832 00:39:09,620 --> 00:39:12,070 [LAUGHTER] 833 00:39:12,070 --> 00:39:14,230 But it was awesome. 834 00:39:14,230 --> 00:39:16,750 It's an up-and-coming city. 835 00:39:16,750 --> 00:39:20,110 It was way more affordable [LAUGH] than SF, 836 00:39:20,110 --> 00:39:26,470 so I could actually afford a pretty nice apartment super-close to the office. 837 00:39:26,470 --> 00:39:30,190 The ATG office itself is also just recently expanded. 838 00:39:30,190 --> 00:39:34,210 So we had this one building where all the engineers and everyone sat. 839 00:39:34,210 --> 00:39:38,020 We just bought out this other huge building, where all the cars are, 840 00:39:38,020 --> 00:39:39,460 as well. 841 00:39:39,460 --> 00:39:42,040 So it's an exciting place to be, for sure. 842 00:39:42,040 --> 00:39:44,644 EVAN: Daniel and another friend of ours, Justin, 843 00:39:44,644 --> 00:39:46,560 they were super-sold on Pittsburgh by the end. 844 00:39:46,560 --> 00:39:49,226 At first, he was like, hey, I'm interested in self-driving cars, 845 00:39:49,226 --> 00:39:50,862 and so I'm going to do this thing. 846 00:39:50,862 --> 00:39:51,820 We'll see what happens. 847 00:39:51,820 --> 00:39:54,130 But midway through, it was like, they were-- 848 00:39:54,130 --> 00:39:55,860 I felt like you guys were both converts. 849 00:39:55,860 --> 00:39:57,490 Like, Pittsburgh is like the next. 850 00:39:57,490 --> 00:39:58,810 Really, it's not about San Francisco. 851 00:39:58,810 --> 00:40:00,018 You want to be in Pittsburgh. 852 00:40:00,018 --> 00:40:00,662 Yeah, yeah. 853 00:40:00,662 --> 00:40:03,608 854 00:40:03,608 --> 00:40:06,063 AUDIENCE: [INAUDIBLE] 855 00:40:06,063 --> 00:40:07,252 856 00:40:07,252 --> 00:40:07,960 EVAN: Yeah, yeah. 857 00:40:07,960 --> 00:40:12,280 So we're both in the same program, the APM or Associate Product Manager 858 00:40:12,280 --> 00:40:12,880 program. 859 00:40:12,880 --> 00:40:17,250 And basically, in terms of-- 860 00:40:17,250 --> 00:40:20,250 I mean, actually, it might be worth saying, like, Daniel and I actually 861 00:40:20,250 --> 00:40:23,486 met in an Uber, talking about, like, this program. 862 00:40:23,486 --> 00:40:24,630 You know, very romantic. 863 00:40:24,630 --> 00:40:25,980 DANIEL: Yeah. 864 00:40:25,980 --> 00:40:27,450 We shared a Pool together. 865 00:40:27,450 --> 00:40:30,170 It was a manual Pool, because it was-- 866 00:40:30,170 --> 00:40:34,487 it was an X. There was an UberX that we both were standing around for, 867 00:40:34,487 --> 00:40:35,070 that we split. 868 00:40:35,070 --> 00:40:35,900 I remember. 869 00:40:35,900 --> 00:40:37,362 EVAN: OK. 870 00:40:37,362 --> 00:40:39,070 So that's actually how you become an APM. 871 00:40:39,070 --> 00:40:39,456 No, no, no. 872 00:40:39,456 --> 00:40:39,955 Uh-- 873 00:40:39,955 --> 00:40:40,635 [LAUGHTER] 874 00:40:40,635 --> 00:40:45,150 In terms of the process, I'll speak for myself and Daniel for himself-- 875 00:40:45,150 --> 00:40:48,810 had done a few internships, over the summer, one 876 00:40:48,810 --> 00:40:50,820 as sort of an application engineer and the other 877 00:40:50,820 --> 00:40:54,050 as a product-management intern, at a startup. 878 00:40:54,050 --> 00:40:57,600 Definitely it just felt like I gravitated more towards the PM path. 879 00:40:57,600 --> 00:41:02,100 There are a bunch of APM programs, now, at a bunch of the big companies. 880 00:41:02,100 --> 00:41:05,550 For me, why Uber, it was, like, A, like, love to travel. 881 00:41:05,550 --> 00:41:08,850 And I'd wanted to hopefully, at some point in my career, 882 00:41:08,850 --> 00:41:12,060 do work in some of these international places. 883 00:41:12,060 --> 00:41:15,510 So far, that has been the case, which has been very cool. 884 00:41:15,510 --> 00:41:18,420 And then, in terms of last year, too, it was 885 00:41:18,420 --> 00:41:23,070 kind of this period where every single-- we basically accepted our roles 886 00:41:23,070 --> 00:41:26,279 and then, like, week after week, that we all saw, like, headlines 887 00:41:26,279 --> 00:41:27,570 were coming out left and right. 888 00:41:27,570 --> 00:41:28,710 And it was like, wow. 889 00:41:28,710 --> 00:41:31,610 This-- are we signing up for the right thing, here? 890 00:41:31,610 --> 00:41:32,380 --in all honesty. 891 00:41:32,380 --> 00:41:34,620 And we would have conversations about it. 892 00:41:34,620 --> 00:41:37,380 The past year has been a super-cool time to be at the company, 893 00:41:37,380 --> 00:41:40,050 because just so much is developing so quickly, 894 00:41:40,050 --> 00:41:42,480 with the new CEO and leadership. 895 00:41:42,480 --> 00:41:46,350 So, why Uber initially was, like, transportation seemed really cool. 896 00:41:46,350 --> 00:41:48,840 International work seemed very cool. 897 00:41:48,840 --> 00:41:50,730 And that's what got me into it. 898 00:41:50,730 --> 00:41:55,539 And then it's been a really cool place to be, over the past year. 899 00:41:55,539 --> 00:41:56,080 DANIEL: Yeah. 900 00:41:56,080 --> 00:41:58,150 I think I had a similar story. 901 00:41:58,150 --> 00:42:02,020 So I hadn't done any, like, PM internships in the past. 902 00:42:02,020 --> 00:42:05,440 All my previous experiences were in engineering. 903 00:42:05,440 --> 00:42:09,670 But I think, through those internships, started getting a sense that I really 904 00:42:09,670 --> 00:42:15,380 like this sort of PM position that was getting more and more popular, 905 00:42:15,380 --> 00:42:16,972 especially in the Bay Area. 906 00:42:16,972 --> 00:42:19,680 I think it's one where you definitely get to where a lot of hats. 907 00:42:19,680 --> 00:42:20,180 Right? 908 00:42:20,180 --> 00:42:25,030 And I've found that I personally don't sort of sitting and doing 909 00:42:25,030 --> 00:42:27,890 the same thing, day in, day out. 910 00:42:27,890 --> 00:42:31,360 And I think it's very much maybe like a personality thing 911 00:42:31,360 --> 00:42:33,460 and, like, what you want out of your career. 912 00:42:33,460 --> 00:42:35,800 I've found it very fulfilling, in that sense. 913 00:42:35,800 --> 00:42:39,850 What drew me specifically to Uber for PMing was I 914 00:42:39,850 --> 00:42:42,310 was just fascinated by the logistics sort 915 00:42:42,310 --> 00:42:47,740 of real-world, like, physical-world problems that Uber was solving. 916 00:42:47,740 --> 00:42:51,610 I think that what makes us unique, compared to a lot of other companies, 917 00:42:51,610 --> 00:42:54,790 is that we are a software technology company 918 00:42:54,790 --> 00:42:57,830 but we're moving real things in the real world, every day. 919 00:42:57,830 --> 00:42:59,860 And I don't think there's a lot of companies 920 00:42:59,860 --> 00:43:03,070 that operate in that problem space. 921 00:43:03,070 --> 00:43:04,930 And it's a really, really interesting one, 922 00:43:04,930 --> 00:43:08,860 in terms of data-science problems, logistics, dispatch, 923 00:43:08,860 --> 00:43:11,164 and those are things that I really enjoy. 924 00:43:11,164 --> 00:43:13,152 [LAUGH] 925 00:43:13,152 --> 00:43:21,240 AUDIENCE: [INAUDIBLE] about two areas that [INAUDIBLE].. 926 00:43:21,240 --> 00:43:25,245 First is, so the analysis that you guys do, 927 00:43:25,245 --> 00:43:29,370 especially [INAUDIBLE] end, that's a [INAUDIBLE] sensitive data. 928 00:43:29,370 --> 00:43:33,180 So how do you guys think about [INAUDIBLE],, but also [INAUDIBLE] 929 00:43:33,180 --> 00:43:37,930 do analysis and create conclusions that [INAUDIBLE]?? 930 00:43:37,930 --> 00:43:42,008 And then, second, that kind of line to the employees [INAUDIBLE] 931 00:43:42,008 --> 00:43:47,150 contractors, and how directing work starts to make [INAUDIBLE].. 932 00:43:47,150 --> 00:43:49,400 And when you think-- actually, especially about, like, 933 00:43:49,400 --> 00:43:52,850 you need to be at this restaurant at this time, [INAUDIBLE] whether you 934 00:43:52,850 --> 00:43:56,320 ever [INAUDIBLE]. 935 00:43:56,320 --> 00:43:56,830 EVAN: Yeah. 936 00:43:56,830 --> 00:43:57,996 Sure you want to split that? 937 00:43:57,996 --> 00:43:59,160 You want to take part 1? 938 00:43:59,160 --> 00:44:00,580 I can try and take part 2? 939 00:44:00,580 --> 00:44:01,121 DANIEL: Sure. 940 00:44:01,121 --> 00:44:05,011 [LAUGH] The first one was about how, do we do data analysis with anonymization 941 00:44:05,011 --> 00:44:05,510 and-- 942 00:44:05,510 --> 00:44:07,070 AUDIENCE: [INAUDIBLE] 943 00:44:07,070 --> 00:44:09,840 DANIEL: Yeah, yeah, on the policy side. 944 00:44:09,840 --> 00:44:13,760 So, when we do any of this analysis, it's all anonymized. 945 00:44:13,760 --> 00:44:18,770 Like, we have access to it, but it's basically, 946 00:44:18,770 --> 00:44:21,170 like, they're all data points satellite. 947 00:44:21,170 --> 00:44:26,990 We don't have any personal identifying information, when we do the analyses. 948 00:44:26,990 --> 00:44:31,670 I think one thing that Uber has started to do a better job of, 949 00:44:31,670 --> 00:44:34,460 and I'm actually quite proud of, is starting to use 950 00:44:34,460 --> 00:44:37,400 that data in conjunction with cities. 951 00:44:37,400 --> 00:44:39,640 I think, in the past we-- 952 00:44:39,640 --> 00:44:43,970 you know, we have this wealth of data, and it's pretty unique to Uber. 953 00:44:43,970 --> 00:44:46,760 And so there's new groups like-- 954 00:44:46,760 --> 00:44:48,890 I think it's called the Movement Team, right? 955 00:44:48,890 --> 00:44:52,460 --that's basically building out tools for cities and regulators, 956 00:44:52,460 --> 00:44:57,190 to understand how traffic patterns evolve in their cities. 957 00:44:57,190 --> 00:44:58,780 And I think that's unique to us. 958 00:44:58,780 --> 00:45:03,170 So we're partnering with a ton of municipalities, regulators, 959 00:45:03,170 --> 00:45:05,600 giving them access to this tool. 960 00:45:05,600 --> 00:45:09,440 I think we're piloting the technology still with a couple cities, 961 00:45:09,440 --> 00:45:16,590 but we want to eventually use this data to make transportation more fluid. 962 00:45:16,590 --> 00:45:19,582 And so I've personally seen that, in the past year. 963 00:45:19,582 --> 00:45:22,040 We've actually been more proactive with working with cities 964 00:45:22,040 --> 00:45:25,810 and providing them with this data. 965 00:45:25,810 --> 00:45:28,290 EVAN: And then, on the employment piece, it's 966 00:45:28,290 --> 00:45:30,760 super-difficult. I'm actually currently on a team 967 00:45:30,760 --> 00:45:35,050 right now that is the Driver Pricing team, figuring out how we pad bonuses 968 00:45:35,050 --> 00:45:37,090 to our driver partners. 969 00:45:37,090 --> 00:45:41,050 And part of the reason that I wanted to join the team in the first place 970 00:45:41,050 --> 00:45:44,470 was because I can answer all sorts of questions about Uber, 971 00:45:44,470 --> 00:45:48,730 I think, based on my first rotations on the various teams. 972 00:45:48,730 --> 00:45:51,427 I think there is this question that probably a lot of you, 973 00:45:51,427 --> 00:45:54,260 if you've talked to your Uber driver in the car before, you're like, 974 00:45:54,260 --> 00:45:55,343 hey, how are things going? 975 00:45:55,343 --> 00:45:56,320 How's this week? 976 00:45:56,320 --> 00:46:01,520 And there's a sense of, you know, drivers-- 977 00:46:01,520 --> 00:46:05,080 there's a sense of, like, how good are these opportunities? 978 00:46:05,080 --> 00:46:09,040 And part of the plus column is that it has 979 00:46:09,040 --> 00:46:10,990 this sort of essence of flexibility. 980 00:46:10,990 --> 00:46:15,400 You choose your own hours, and, to some extent, you are your own boss. 981 00:46:15,400 --> 00:46:17,950 On the other side, it's, that flexibility 982 00:46:17,950 --> 00:46:21,160 is sort of coming at the cost of, you don't have all these benefits 983 00:46:21,160 --> 00:46:26,210 that a full-time employee at a company like, you know, we at Uber have. 984 00:46:26,210 --> 00:46:29,920 And so, in terms of how it actually works at the company, it's basically, 985 00:46:29,920 --> 00:46:33,040 we have product counsel and legal in all sorts of conversations, 986 00:46:33,040 --> 00:46:35,770 to make sure that there isn't a feature that is, like, pushing 987 00:46:35,770 --> 00:46:39,160 what is the existing legal boundary. 988 00:46:39,160 --> 00:46:44,110 Actually, I think the way-more-interesting and cool answer 989 00:46:44,110 --> 00:46:45,790 is-- 990 00:46:45,790 --> 00:46:50,110 In Europe, where some of the regulation laws are, in some ways, 991 00:46:50,110 --> 00:46:54,790 more sophisticated, where the employment law isn't bifurcated into full-time 992 00:46:54,790 --> 00:46:59,170 and, like, non employees, there are these middle areas, 993 00:46:59,170 --> 00:47:02,350 Uber is currently working on sorts of, like, insurance offerings 994 00:47:02,350 --> 00:47:04,534 for this middle tier. 995 00:47:04,534 --> 00:47:06,200 So I can't really speak for the company. 996 00:47:06,200 --> 00:47:10,180 I think what is happening, though, is that, as the gig-economy jobs are 997 00:47:10,180 --> 00:47:12,610 exploding, as a real opportunity for people, 998 00:47:12,610 --> 00:47:17,230 and you'll start to see more and more workers stitching Uber and food 999 00:47:17,230 --> 00:47:20,970 delivery and all sorts of other sort of gig-economy type work, 1000 00:47:20,970 --> 00:47:23,470 I think we're just going to get more advanced in our policy, 1001 00:47:23,470 --> 00:47:25,510 and the definitions will change. 1002 00:47:25,510 --> 00:47:29,200 But today it is, like, there are principles about flexibility 1003 00:47:29,200 --> 00:47:32,950 and about a certain amount of control that drivers have to have, 1004 00:47:32,950 --> 00:47:34,480 that we have to stick by-- 1005 00:47:34,480 --> 00:47:38,450 and that, honestly, like, I personally want to stick by. 1006 00:47:38,450 --> 00:47:40,900 And it's about figuring out where that grey line is. 1007 00:47:40,900 --> 00:47:45,010 Because it's, like, basically, it's the driver, it's Uber, it's regulators, 1008 00:47:45,010 --> 00:47:48,490 and there is some area where we all intersect and people are happy. 1009 00:47:48,490 --> 00:47:52,257 And currently everyone's a little bit all over the place. 1010 00:47:52,257 --> 00:47:54,742 Yeah? 1011 00:47:54,742 --> 00:47:55,736 DANIEL: Yep. 1012 00:47:55,736 --> 00:47:58,970 AUDIENCE: I was wondering how you guys like, localize products 1013 00:47:58,970 --> 00:48:00,934 to all these sort of [INAUDIBLE]. 1014 00:48:00,934 --> 00:48:15,156 1015 00:48:15,156 --> 00:48:17,905 EVAN: You want to talk about [INAUDIBLE] some of the safety stuff? 1016 00:48:17,905 --> 00:48:19,880 DANIEL: Yeah, yeah, sure. 1017 00:48:19,880 --> 00:48:22,620 So, quick plug for the APM program that we're part of. 1018 00:48:22,620 --> 00:48:24,620 For those of you who don't know, the APM program 1019 00:48:24,620 --> 00:48:28,050 is a new grad rotational product management role. 1020 00:48:28,050 --> 00:48:31,360 So it's three rotations, six months each. 1021 00:48:31,360 --> 00:48:35,840 The whole idea is, we sort of throw the APMs into this whole world of product 1022 00:48:35,840 --> 00:48:36,380 management. 1023 00:48:36,380 --> 00:48:40,820 You honestly learn how to learn, by going through three different teams 1024 00:48:40,820 --> 00:48:42,110 really quickly. 1025 00:48:42,110 --> 00:48:43,870 Usually they're vastly different. 1026 00:48:43,870 --> 00:48:46,070 So my first one was on Uber Eats. 1027 00:48:46,070 --> 00:48:49,110 Then I went to Pittsburgh, working on ATG. 1028 00:48:49,110 --> 00:48:51,650 Evan has worked on Express Pool. 1029 00:48:51,650 --> 00:48:55,250 Just really different parts of the company. 1030 00:48:55,250 --> 00:48:58,430 And then part of the APM program is, after the first six months, 1031 00:48:58,430 --> 00:49:00,950 we do this global trip where we-- 1032 00:49:00,950 --> 00:49:03,680 sort of like, to your point, go around the world 1033 00:49:03,680 --> 00:49:08,390 to really understand how our product is different in different countries 1034 00:49:08,390 --> 00:49:11,000 and just how the sentiment of transportation-- 1035 00:49:11,000 --> 00:49:15,410 how do people think about getting from point A to point B? 1036 00:49:15,410 --> 00:49:23,460 So, for our year, we went to DC, Sao Paolo, Bangalore, and Singapore. 1037 00:49:23,460 --> 00:49:28,400 So, some crazy flights, some 28-plus-hour [LAUGH] flights, in there. 1038 00:49:28,400 --> 00:49:30,380 So it was fascinating. 1039 00:49:30,380 --> 00:49:36,140 Like, when we got to Sao Paolo, for instance, you know, I think in the US 1040 00:49:36,140 --> 00:49:40,100 we sort of obsess over the UI and the UX of, 1041 00:49:40,100 --> 00:49:42,290 how do we communicate everything in the app, 1042 00:49:42,290 --> 00:49:44,990 to the, how do we make the best app experience? 1043 00:49:44,990 --> 00:49:46,310 But they were like, no, no no. 1044 00:49:46,310 --> 00:49:48,710 The most important thing for us is safety. 1045 00:49:48,710 --> 00:49:52,250 Like, being a rider or a driver is, like, that's-- 1046 00:49:52,250 --> 00:49:55,790 you know, the country that you're in, the set 1047 00:49:55,790 --> 00:49:59,930 of problems that you think about, are just entirely different. 1048 00:49:59,930 --> 00:50:04,400 So, like, Sao Paolo was just like, safety is the number one priority. 1049 00:50:04,400 --> 00:50:11,540 We need to bake that into every aspect of both the driver and rider apps. 1050 00:50:11,540 --> 00:50:13,380 Singapore was very different. 1051 00:50:13,380 --> 00:50:15,770 It was like, hey, like, we have no crime here. 1052 00:50:15,770 --> 00:50:16,340 [LAUGHTER] 1053 00:50:16,340 --> 00:50:19,820 It's like, you get fined for chewing bubble gum on the streets. 1054 00:50:19,820 --> 00:50:24,080 So their sets of problems were, like, hey, actually, 1055 00:50:24,080 --> 00:50:28,340 we have intense competition from both our competitors 1056 00:50:28,340 --> 00:50:30,580 but also other forms of transportation. 1057 00:50:30,580 --> 00:50:32,930 Like the subway system is fantastic here. 1058 00:50:32,930 --> 00:50:34,070 Taxis are awesome. 1059 00:50:34,070 --> 00:50:35,420 People trust taxis. 1060 00:50:35,420 --> 00:50:38,910 How does Uber fit into this transportation ecosystem? 1061 00:50:38,910 --> 00:50:41,490 And then Bangalore, yet again, was totally different. 1062 00:50:41,490 --> 00:50:44,670 It was about, hey, cars don't work [LAUGH] in Bangalore. 1063 00:50:44,670 --> 00:50:47,360 You can't get around in a four-wheel car. 1064 00:50:47,360 --> 00:50:50,960 So we need to start thinking about apps with just, like, different vehicles, 1065 00:50:50,960 --> 00:50:53,100 different products, that we can offer. 1066 00:50:53,100 --> 00:50:57,330 So I think, when we went, it was the second week that, uh-- 1067 00:50:57,330 --> 00:51:02,149 was it auto rickshaws, or-- yeah, auto rickshaws had just launched on Uber. 1068 00:51:02,149 --> 00:51:04,940 And it was crazy, because we went out onto the streets of Bangalore 1069 00:51:04,940 --> 00:51:07,040 and we were out there with a city apps team. 1070 00:51:07,040 --> 00:51:09,950 And they were driving around this truck with, like, a stage. 1071 00:51:09,950 --> 00:51:12,140 And they were literally just, like, going, like, 1072 00:51:12,140 --> 00:51:16,820 around the city, getting drivers to drive these auto rickshaws for Uber. 1073 00:51:16,820 --> 00:51:23,180 And it was super, like, on the ground, like, getting the work done, sort of-- 1074 00:51:23,180 --> 00:51:25,920 just, that mentality was awesome. 1075 00:51:25,920 --> 00:51:26,420 So, yeah. 1076 00:51:26,420 --> 00:51:31,410 Like, every country we operate in, the sets of product problems we think about 1077 00:51:31,410 --> 00:51:34,080 are just day-and-night different. 1078 00:51:34,080 --> 00:51:36,402 It's like one really exciting part about Uber. 1079 00:51:36,402 --> 00:51:37,610 EVAN: And then I'd just add-- 1080 00:51:37,610 --> 00:51:38,130 DANIEL: I'm sure you have more to add. 1081 00:51:38,130 --> 00:51:39,921 EVAN: Yeah, the only other thing I'd add is 1082 00:51:39,921 --> 00:51:42,660 just that part of what enabled Uber to grow so fast 1083 00:51:42,660 --> 00:51:44,580 was having this ops playbook. 1084 00:51:44,580 --> 00:51:50,460 So there was, like, literally a bunch of sheets that somebody still at Uber 1085 00:51:50,460 --> 00:51:53,490 put together on how you launch a new city for Uber. 1086 00:51:53,490 --> 00:51:55,950 And that basically created this network of-- 1087 00:51:55,950 --> 00:52:00,540 we call them "ops," "operations hubs," all across the globe, really, 1088 00:52:00,540 --> 00:52:01,590 in all our cities. 1089 00:52:01,590 --> 00:52:05,130 And the way that we figure out-- say, that, in Singapore, there 1090 00:52:05,130 --> 00:52:07,650 are a lot of malls, and so the GPS signal is often 1091 00:52:07,650 --> 00:52:11,820 blocked by these buildings-- is, the product-ops teams there communicate 1092 00:52:11,820 --> 00:52:16,500 that up, through various sort of bottoms-up planning processes, 1093 00:52:16,500 --> 00:52:18,450 and say hey, this is what we need to fix. 1094 00:52:18,450 --> 00:52:21,164 And so it's part of what makes Uber so effective, 1095 00:52:21,164 --> 00:52:23,580 is having that on-the-ground presence in all these cities. 1096 00:52:23,580 --> 00:52:27,532 1097 00:52:27,532 --> 00:52:28,520 DANIEL: Yep. 1098 00:52:28,520 --> 00:52:33,460 AUDIENCE: [INAUDIBLE] 1099 00:52:33,460 --> 00:52:38,894 So I've been thinking about riding in cars with complete strangers 1100 00:52:38,894 --> 00:52:41,364 and having conversations [INAUDIBLE]. 1101 00:52:41,364 --> 00:52:45,810 1102 00:52:45,810 --> 00:52:50,256 So do you have a group that's working on those types of aspects? 1103 00:52:50,256 --> 00:52:52,726 And what are the changes [INAUDIBLE]? 1104 00:52:52,726 --> 00:52:56,184 1105 00:52:56,184 --> 00:52:56,700 EVAN: Hmm. 1106 00:52:56,700 --> 00:52:58,330 That's a super question. 1107 00:52:58,330 --> 00:53:03,320 In the shared-rides world, where it's still asking people to get in the car 1108 00:53:03,320 --> 00:53:08,560 with the strangers, we're still figuring out how to get-- 1109 00:53:08,560 --> 00:53:10,645 I mean, my parents, for instance are very-- 1110 00:53:10,645 --> 00:53:12,590 like, they'll take an UberX. 1111 00:53:12,590 --> 00:53:14,840 My dad is a big fan of Uber Pool. 1112 00:53:14,840 --> 00:53:17,960 I think he just likes talking to more people-- my mom, less so. 1113 00:53:17,960 --> 00:53:22,700 There's also, we've seen issues around, generally speaking, 1114 00:53:22,700 --> 00:53:25,400 in these sorts of-- like, the vehicle pickup environments, 1115 00:53:25,400 --> 00:53:29,750 men feel safer at night than a woman does, on the street. 1116 00:53:29,750 --> 00:53:33,110 And so it's-- like, we are starting to see more and more, 1117 00:53:33,110 --> 00:53:35,510 based on some of these data trends, how people, 1118 00:53:35,510 --> 00:53:39,150 where they are in the sort of uptake of the various offerings. 1119 00:53:39,150 --> 00:53:42,860 And I think part of a lot of user research 1120 00:53:42,860 --> 00:53:44,644 is constantly happening at the company. 1121 00:53:44,644 --> 00:53:47,810 And part of that is, like, trying to figure out exactly what you're saying-- 1122 00:53:47,810 --> 00:53:52,310 is, how are these technologies impacting folks and how they think about 1123 00:53:52,310 --> 00:53:56,796 basically things that we can't detect in our office-- and, you know-- 1124 00:53:56,796 --> 00:53:57,296 uh-- 1125 00:53:57,296 --> 00:53:59,686 AUDIENCE: So I guess what I was asking is [INAUDIBLE].. 1126 00:53:59,686 --> 00:54:04,986 1127 00:54:04,986 --> 00:54:06,610 EVAN: Oh, totally, totally, yeah, yeah. 1128 00:54:06,610 --> 00:54:08,350 AUDIENCE: So could you talk a little bit about that part? 1129 00:54:08,350 --> 00:54:09,460 EVAN: Yeah, yeah, definitely. 1130 00:54:09,460 --> 00:54:11,376 And so I had only mentioned it really briefly, 1131 00:54:11,376 --> 00:54:14,720 but, for the Express Pool product improvements that we made, 1132 00:54:14,720 --> 00:54:18,560 we spent I think it was, like, three days, back to back to back, 1133 00:54:18,560 --> 00:54:23,880 with all sorts of different cohorts of these riders, ranging-- 1134 00:54:23,880 --> 00:54:26,090 and so, over the course of several days. 1135 00:54:26,090 --> 00:54:29,780 Usually, it's led by kind of a user-research manager and professional. 1136 00:54:29,780 --> 00:54:32,090 And the product managers will sit in. 1137 00:54:32,090 --> 00:54:35,360 But it's, like, asking people-- it's not just, like, how to use this product, 1138 00:54:35,360 --> 00:54:40,040 it's things like, hey, you know, tell me about your commute. 1139 00:54:40,040 --> 00:54:44,090 Tell me about the things that you feel safe about and not so safe. 1140 00:54:44,090 --> 00:54:46,640 The point being is that a lot of the user research 1141 00:54:46,640 --> 00:54:48,920 isn't just the very surface-level, what do you 1142 00:54:48,920 --> 00:54:50,960 think about this feature, this button. 1143 00:54:50,960 --> 00:54:53,300 It's truly trying to get at the needs of, 1144 00:54:53,300 --> 00:54:55,800 how are these riders trying to move around their city? 1145 00:54:55,800 --> 00:54:57,464 I don't know if that's clear. 1146 00:54:57,464 --> 00:54:57,866 AUDIENCE: A little bit. 1147 00:54:57,866 --> 00:54:58,365 I mean-- 1148 00:54:58,365 --> 00:54:58,985 EVAN: Yeah. 1149 00:54:58,985 --> 00:55:00,910 AUDIENCE: I'm part of a public library. 1150 00:55:00,910 --> 00:55:03,444 We spend a lot of time on user research. 1151 00:55:03,444 --> 00:55:07,428 And we feel like we can learn a lot from the different aspects. 1152 00:55:07,428 --> 00:55:11,920 So I'm always curious about how corporations are able to figure it out 1153 00:55:11,920 --> 00:55:14,410 and how can the library learn that part of things. 1154 00:55:14,410 --> 00:55:17,890 EVAN: Yeah, I mean, like, every given day at headquarters, 1155 00:55:17,890 --> 00:55:22,570 in a bunch of our offices, there are user research groups happening, 1156 00:55:22,570 --> 00:55:25,610 every single day, for multiple teams. 1157 00:55:25,610 --> 00:55:29,109 So it's become pretty embedded, in terms of just the product life cycle. 1158 00:55:29,109 --> 00:55:30,900 DANIEL: Maybe I can add just a quick thing. 1159 00:55:30,900 --> 00:55:34,950 So, on the Uber Eats side, I think, in terms of user research, 1160 00:55:34,950 --> 00:55:37,830 I think there's sort of like two different buckets. 1161 00:55:37,830 --> 00:55:42,260 One is more foundational research that Evan was mentioning. 1162 00:55:42,260 --> 00:55:47,660 So, I may actually be going to, like, Mexico or London, in the next month 1163 00:55:47,660 --> 00:55:50,300 or so, to just go out there and understand 1164 00:55:50,300 --> 00:55:53,510 how people think about meal preparation, how 1165 00:55:53,510 --> 00:55:58,610 do people think about eating, eating out, deliveries-- like, sort 1166 00:55:58,610 --> 00:56:01,790 of a foundational body of work of just culture 1167 00:56:01,790 --> 00:56:06,230 and how that is specific to a country. 1168 00:56:06,230 --> 00:56:08,940 And then we also do invite people to the office. 1169 00:56:08,940 --> 00:56:11,030 We also go out in the field, with specific, 1170 00:56:11,030 --> 00:56:14,262 like, hey, here's new concepts or directions 1171 00:56:14,262 --> 00:56:15,470 we want to take a product in. 1172 00:56:15,470 --> 00:56:17,990 And then we'll have them sort of-- 1173 00:56:17,990 --> 00:56:21,390 usually they'll be at pretty low-fidelity mock-ups of some new idea 1174 00:56:21,390 --> 00:56:27,410 we're trying to test, and we'll just gauge feedback from those, as well. 1175 00:56:27,410 --> 00:56:30,420 EVAN: Well, maybe one more question? 1176 00:56:30,420 --> 00:56:32,725 If we have it? 1177 00:56:32,725 --> 00:56:35,044 If not, I think Daniel and Allie and I are just 1178 00:56:35,044 --> 00:56:36,960 going to be lingering around for a little bit. 1179 00:56:36,960 --> 00:56:37,460 ALLIE: Yeah. 1180 00:56:37,460 --> 00:56:41,020 We will hang around for about 10, 15 minutes, if you guys have questions. 1181 00:56:41,020 --> 00:56:42,937 There is some swag in the back. 1182 00:56:42,937 --> 00:56:44,520 But thank you guys so much for coming. 1183 00:56:44,520 --> 00:56:45,560 We really appreciate it. 1184 00:56:45,560 --> 00:56:47,780 And thanks to everybody online. 1185 00:56:47,780 --> 00:56:48,280 Yeah! 1186 00:56:48,280 --> 00:56:49,613 That concludes our presentation. 1187 00:56:49,613 --> 00:56:50,255 DANIEL: Sweet. 1188 00:56:50,255 --> 00:56:51,630 Thanks so much, guys, for coming. 1189 00:56:51,630 --> 00:56:54,109 [APPLAUSE] 1190 00:56:54,109 --> 00:56:54,609