1 00:00:15,316 --> 00:00:23,356 Speaker 1: Pushkin. Here are two ways of thinking about technology and 2 00:00:23,476 --> 00:00:28,076 Speaker 1: the pizza business. One, technology creates a wiener take all 3 00:00:28,156 --> 00:00:31,956 Speaker 1: game with economies of scale and network effects. The bigger 4 00:00:31,956 --> 00:00:35,316 Speaker 1: a company gets, the better and cheaper its product gets, 5 00:00:35,436 --> 00:00:37,916 Speaker 1: and so it just runs away with the game. This 6 00:00:37,996 --> 00:00:41,276 Speaker 1: is the story of Domino's Pizza, which, underneath the hood 7 00:00:41,636 --> 00:00:44,596 Speaker 1: is a really smart tech company that has used data 8 00:00:44,676 --> 00:00:49,316 Speaker 1: and algorithms to drive incredible growth. Investors know this. Over 9 00:00:49,356 --> 00:00:52,556 Speaker 1: the past ten years, Domino's stock has risen more than 10 00:00:52,596 --> 00:00:56,916 Speaker 1: the stock of Amazon or Google or Facebook. On the 11 00:00:56,916 --> 00:01:01,356 Speaker 1: other hand, technology can also benefit small businesses by lowering 12 00:01:01,396 --> 00:01:04,956 Speaker 1: barriers to entry and creating powerful new efficiencies. But we 13 00:01:04,996 --> 00:01:08,156 Speaker 1: haven't seen much of this side of technology in the 14 00:01:08,196 --> 00:01:11,596 Speaker 1: pizza world, least not yet. Most mom and pop pizza 15 00:01:11,636 --> 00:01:15,476 Speaker 1: shops haven't changed in decades, and if they don't change soon, 16 00:01:15,876 --> 00:01:22,436 Speaker 1: they may get destroyed by the tech juggernaut of big pizza. 17 00:01:25,116 --> 00:01:27,836 Speaker 1: I'm Jacob Goldstein, and this is what's your problem. My 18 00:01:27,876 --> 00:01:31,116 Speaker 1: guest today is Elier Sella. He's the co founder and 19 00:01:31,236 --> 00:01:34,396 Speaker 1: CEO of Slice, a tech company that works with thousands 20 00:01:34,436 --> 00:01:38,196 Speaker 1: of small pizza shops across the United States. The Elier's 21 00:01:38,196 --> 00:01:41,356 Speaker 1: problem is this, how do you bring the technological revolution 22 00:01:41,476 --> 00:01:44,756 Speaker 1: to thousands of tiny mom and pop pizza shops, the 23 00:01:44,836 --> 00:01:47,236 Speaker 1: places where they don't even have time to answer the 24 00:01:47,276 --> 00:01:50,596 Speaker 1: phone every time it rings to start. I asked Elier 25 00:01:50,676 --> 00:01:52,676 Speaker 1: to talk me through all the things that can go 26 00:01:52,756 --> 00:01:55,276 Speaker 1: wrong at a pizza shop every time somebody calls in 27 00:01:55,316 --> 00:02:00,196 Speaker 1: an order. So when you dial in, you'll either get 28 00:02:00,196 --> 00:02:03,156 Speaker 1: a busy signal. If you don't get a busy signal, 29 00:02:03,436 --> 00:02:05,996 Speaker 1: very likely you'll be put on hold for sure. All right, 30 00:02:06,036 --> 00:02:07,916 Speaker 1: the guy's got to somebody who can hear people screaming 31 00:02:07,956 --> 00:02:11,956 Speaker 1: in the background right exact phone. The guy disappears exactly. 32 00:02:11,996 --> 00:02:14,436 Speaker 1: And so what we know is, you know, twenty plus 33 00:02:14,436 --> 00:02:17,716 Speaker 1: percent of phone calls go unattended. And then they will 34 00:02:18,116 --> 00:02:21,556 Speaker 1: take your order, but they will mishear you because it's 35 00:02:21,636 --> 00:02:24,436 Speaker 1: very loud on the other side. And that means that 36 00:02:24,516 --> 00:02:27,556 Speaker 1: either the quantity of something that you ordered will be wrong, 37 00:02:28,196 --> 00:02:31,196 Speaker 1: maybe a topping will be forgotten, but there will be 38 00:02:31,236 --> 00:02:34,596 Speaker 1: something wrong with your order. Sometimes then you have to 39 00:02:34,676 --> 00:02:38,396 Speaker 1: read out your address. That's a whole other issue, so 40 00:02:38,556 --> 00:02:41,956 Speaker 1: they might get the address wrong exactly exactly. Your neighbor 41 00:02:41,996 --> 00:02:44,676 Speaker 1: gets a free pizza and I get nothing. You're there 42 00:02:44,716 --> 00:02:47,356 Speaker 1: two hours later waiting for your pie to arrive. That 43 00:02:47,436 --> 00:02:50,676 Speaker 1: kind of stuff. The bottleneck isn't the pizza oven, it's 44 00:02:50,716 --> 00:02:54,756 Speaker 1: the telephone. It's the telephone. So what Elier's company, Slice 45 00:02:54,876 --> 00:02:57,356 Speaker 1: is trying to do is to get small pizza shops 46 00:02:57,436 --> 00:02:59,876 Speaker 1: out of the world of telephones and orders scrolled on 47 00:02:59,956 --> 00:03:03,036 Speaker 1: paper and into the world where customers use an app 48 00:03:03,076 --> 00:03:05,316 Speaker 1: to order and where the pizza shop runs on a 49 00:03:05,356 --> 00:03:09,276 Speaker 1: computer system designed by Slice. There is a Slice app, 50 00:03:09,356 --> 00:03:11,676 Speaker 1: and when you open it, it looks like Seamless or 51 00:03:11,716 --> 00:03:14,796 Speaker 1: Grubhub or Uber Eats, but Slice is really different than 52 00:03:14,836 --> 00:03:18,876 Speaker 1: those companies. Those are basically delivery services that charge a 53 00:03:18,916 --> 00:03:22,956 Speaker 1: fee of around twenty five percent. Slice does not provide delivery, 54 00:03:23,076 --> 00:03:26,476 Speaker 1: and its fee is typically around five percent. What Slice 55 00:03:26,516 --> 00:03:29,276 Speaker 1: is really trying to do is to bring Domino's level 56 00:03:29,316 --> 00:03:32,236 Speaker 1: technology to the mom and pop pizza shop on the corner. 57 00:03:32,596 --> 00:03:34,876 Speaker 1: These are pizza shops that are about as far from 58 00:03:34,876 --> 00:03:38,116 Speaker 1: Dominoes as you can get and still be selling pizza. 59 00:03:38,316 --> 00:03:43,436 Speaker 1: These are independently owned and operated. I call them micro businesses. 60 00:03:43,476 --> 00:03:46,556 Speaker 1: They're not even small businesses. They don't have a staff 61 00:03:46,596 --> 00:03:49,156 Speaker 1: of ten or twenty. They are usually a team of 62 00:03:49,196 --> 00:03:53,196 Speaker 1: two or three people, most often a family, and they're 63 00:03:53,756 --> 00:03:58,876 Speaker 1: working alone. They're responsible for making the pizza, answering the phone, 64 00:03:59,716 --> 00:04:03,036 Speaker 1: serving the person who's walking in at the counter, making 65 00:04:03,076 --> 00:04:06,676 Speaker 1: sure perilous processed. Sometimes they run out of inventory and 66 00:04:06,676 --> 00:04:09,316 Speaker 1: they have to run out and pick up inventory. But 67 00:04:09,396 --> 00:04:15,956 Speaker 1: it's a very reactive, unpredictable, lonely job. So that's that's 68 00:04:16,036 --> 00:04:19,116 Speaker 1: your client. That's what it's like, sort of behind the scenes. 69 00:04:19,116 --> 00:04:21,756 Speaker 1: That the pizza shop on the corner, which everybody has 70 00:04:21,876 --> 00:04:25,436 Speaker 1: right wherever you live, there's some neighborhood pizza shop. And 71 00:04:25,476 --> 00:04:30,076 Speaker 1: then on the other hand you have Dominoes. Right, Dominoes, 72 00:04:30,516 --> 00:04:33,116 Speaker 1: which you know you think of as a pizza company, 73 00:04:33,156 --> 00:04:38,396 Speaker 1: Buddy is actually like a really savvy, big tech company essentially, 74 00:04:38,516 --> 00:04:41,276 Speaker 1: right it is. And so I mean it seems like 75 00:04:41,356 --> 00:04:46,316 Speaker 1: in fact Slice is not so much about you know, 76 00:04:46,996 --> 00:04:49,676 Speaker 1: Uber eats or a door dash, but he is really 77 00:04:49,716 --> 00:04:53,916 Speaker 1: more like a Domino's alternative. Is that right? It's fun on. 78 00:04:54,236 --> 00:04:59,116 Speaker 1: So Domino's made the decision about fifteen years ago to 79 00:04:59,356 --> 00:05:04,036 Speaker 1: focus on becoming a technology first company because they realized 80 00:05:04,836 --> 00:05:08,676 Speaker 1: that those inefficiencies that we spoke about earlier exist and 81 00:05:08,756 --> 00:05:12,716 Speaker 1: they all to realize that they can create much stronger 82 00:05:12,756 --> 00:05:19,476 Speaker 1: consumer relationships customer relationships if the customer was online. Remember now, 83 00:05:20,236 --> 00:05:24,676 Speaker 1: you know for a Domino's, seventy five percent of their 84 00:05:24,796 --> 00:05:30,476 Speaker 1: orders are being placed digitally through their own native channels. 85 00:05:30,676 --> 00:05:34,396 Speaker 1: That was a brilliant call by Dominos, right, like incredible. 86 00:05:34,956 --> 00:05:38,596 Speaker 1: They made a really smart call, you know, kind of 87 00:05:38,636 --> 00:05:40,796 Speaker 1: at the beginning of the iPhone era. They saw what 88 00:05:40,916 --> 00:05:43,436 Speaker 1: was coming and they did it exactly. And tell me 89 00:05:43,476 --> 00:05:45,796 Speaker 1: all the ways that's paid off for them. So they 90 00:05:45,836 --> 00:05:51,236 Speaker 1: were the first to first national chain to invest in 91 00:05:51,276 --> 00:05:56,076 Speaker 1: a best in class native mobile ordering experience. They were 92 00:05:56,076 --> 00:06:01,396 Speaker 1: the first company to have a nationwide rewards program that 93 00:06:01,596 --> 00:06:06,036 Speaker 1: is tied to the online mobile ordering experience. So you 94 00:06:06,076 --> 00:06:09,916 Speaker 1: get all these rewards if you ordered online. And in fact, 95 00:06:09,956 --> 00:06:13,636 Speaker 1: they're the first company to stop advertising phone numbers. Interesting, 96 00:06:13,716 --> 00:06:15,596 Speaker 1: Dominos does not want you to call them. They don't 97 00:06:15,596 --> 00:06:17,836 Speaker 1: want you to call that's the user point of view. 98 00:06:18,036 --> 00:06:21,196 Speaker 1: Tell me all the reasons that's good for Dominoes. So 99 00:06:21,356 --> 00:06:23,636 Speaker 1: why is that good for Dominoes. Let's fast forward and 100 00:06:24,116 --> 00:06:26,476 Speaker 1: look at it today. And as I mentioned, seventy five 101 00:06:26,516 --> 00:06:30,236 Speaker 1: percent of the volume is digital. When that happens, you 102 00:06:30,276 --> 00:06:34,276 Speaker 1: tip a scale when it comes to insights. Because most 103 00:06:34,276 --> 00:06:37,756 Speaker 1: of the customer base is now digital, I can now 104 00:06:37,916 --> 00:06:42,276 Speaker 1: predict using data, data science, and insights, I can predict 105 00:06:42,396 --> 00:06:45,676 Speaker 1: what will happen at a Domino's location every single day. 106 00:06:46,476 --> 00:06:49,596 Speaker 1: So Dominos for a Friday, they know how many sales 107 00:06:49,636 --> 00:06:53,836 Speaker 1: they will have. So one, the experience for the consumer 108 00:06:53,876 --> 00:06:56,636 Speaker 1: becomes much better because when you order a pizza for Dominoes, 109 00:06:56,716 --> 00:06:59,596 Speaker 1: most likely that pie is already in the oven. They're 110 00:06:59,636 --> 00:07:03,236 Speaker 1: anticipating its super sides are very powerful. They knew I'm 111 00:07:03,476 --> 00:07:06,036 Speaker 1: going to order that pizza right now, and so they 112 00:07:06,036 --> 00:07:09,636 Speaker 1: started cooking it five minutes before I ordered it, exactly, 113 00:07:09,836 --> 00:07:13,796 Speaker 1: and I would say that's fun. Two, there's no food waste, 114 00:07:13,836 --> 00:07:16,196 Speaker 1: so they don't have to they don't have to remake 115 00:07:16,196 --> 00:07:21,956 Speaker 1: a pizza or throw it away. Three, it's directly connected 116 00:07:21,996 --> 00:07:27,396 Speaker 1: to their entire sort of workflow, and so there isn't 117 00:07:27,756 --> 00:07:31,236 Speaker 1: a ton of inefficiencies in terms of which driver is 118 00:07:31,276 --> 00:07:33,716 Speaker 1: going to take which order and where it's going to 119 00:07:33,756 --> 00:07:37,236 Speaker 1: be delivered. All of this data feeds into their team 120 00:07:37,636 --> 00:07:41,196 Speaker 1: and gives them insights, gives them direction into how they 121 00:07:41,236 --> 00:07:44,516 Speaker 1: need to handle every single customer. So when you talk 122 00:07:44,556 --> 00:07:49,596 Speaker 1: about all those things that Domino's does, and how smart 123 00:07:49,676 --> 00:07:52,516 Speaker 1: they are because of it. It's sort of amazing that 124 00:07:52,556 --> 00:07:56,556 Speaker 1: there's so many mom and pizza shops still in business. Ah. Yes, 125 00:07:56,716 --> 00:07:59,276 Speaker 1: and this is the this is the AHA moment for me. 126 00:08:00,596 --> 00:08:03,916 Speaker 1: Domino's has solved a lot of the business challenges and 127 00:08:04,636 --> 00:08:09,476 Speaker 1: inefficiencies of running a pizza shop. However, it asked for 128 00:08:09,556 --> 00:08:13,596 Speaker 1: one major tradeoff, and that is your creative freedom. You 129 00:08:13,636 --> 00:08:18,116 Speaker 1: cannot have you know, Elier's pizza shop in my own 130 00:08:18,196 --> 00:08:21,796 Speaker 1: family recipe. You know, you can't bring that forward when 131 00:08:21,836 --> 00:08:24,996 Speaker 1: you open up a Dominoes. You have to trade that off. Sure, 132 00:08:25,036 --> 00:08:26,596 Speaker 1: I mean that's true from the point of view of 133 00:08:26,596 --> 00:08:29,116 Speaker 1: the entrepreneur, but from the point of view of the customer, 134 00:08:29,516 --> 00:08:32,836 Speaker 1: I mean I don't prefer Domino's pizza, Like does the 135 00:08:32,836 --> 00:08:35,236 Speaker 1: shop around the corner just stay in business because people 136 00:08:35,356 --> 00:08:38,756 Speaker 1: prefer the pizza there, or they like, like, why hasn't 137 00:08:38,756 --> 00:08:42,476 Speaker 1: Dominoes just wiped everybody out? It's kind of the question. Well, 138 00:08:42,716 --> 00:08:45,276 Speaker 1: they've they've done a great job and they've taken market share, 139 00:08:45,556 --> 00:08:48,756 Speaker 1: but it's clear that the consumer loves the authenticity and 140 00:08:48,796 --> 00:08:52,996 Speaker 1: the variety that comes with local. There's you know, closer connections, 141 00:08:53,036 --> 00:08:57,116 Speaker 1: better connections with that local operator. And in many cases, 142 00:08:57,156 --> 00:09:02,716 Speaker 1: the consumer is forging a more convenient mobile experience for 143 00:09:02,996 --> 00:09:06,916 Speaker 1: better quality product. And you know, I think there's a 144 00:09:07,036 --> 00:09:11,036 Speaker 1: there's an opportunity to solve for both. Okay, remember Domino's 145 00:09:11,076 --> 00:09:15,116 Speaker 1: has six thousand locations. Yeah, Slice works with eighteen thousand locations. 146 00:09:15,276 --> 00:09:17,796 Speaker 1: So team Slice is bigger than team Dominoes in terms 147 00:09:17,836 --> 00:09:20,436 Speaker 1: of revenue, in terms of number of pizzas sold, without 148 00:09:20,476 --> 00:09:23,516 Speaker 1: a doubt. Our goal and my vision is to help 149 00:09:23,596 --> 00:09:27,476 Speaker 1: that independent operator to be in business for themselves, but 150 00:09:27,596 --> 00:09:31,956 Speaker 1: not by themselves. How do we create a community around 151 00:09:31,956 --> 00:09:35,916 Speaker 1: this industry and empower it with the similar sort of 152 00:09:35,956 --> 00:09:40,356 Speaker 1: operating system and tools and technology that Dominoes brings to 153 00:09:40,396 --> 00:09:45,116 Speaker 1: their franchisees. So there's an idea that's interesting to me 154 00:09:45,916 --> 00:09:49,556 Speaker 1: that is bigger than just pizza. Pizza's big. But this 155 00:09:49,956 --> 00:09:54,596 Speaker 1: is not just a pizza idea, and it's about technology 156 00:09:55,036 --> 00:09:59,596 Speaker 1: and how it seems like sometimes technology drives us toward 157 00:09:59,676 --> 00:10:03,716 Speaker 1: a winner. Take all. Right, you know, certainly we've seen that, 158 00:10:03,756 --> 00:10:06,876 Speaker 1: say with a Google or even with social media. Right, 159 00:10:06,916 --> 00:10:10,236 Speaker 1: you have these very powerful network effects, but it also 160 00:10:10,316 --> 00:10:15,836 Speaker 1: seems like there are instances when technology can lower barriers 161 00:10:15,876 --> 00:10:20,156 Speaker 1: to entry and create efficiencies for you know, smaller businesses. 162 00:10:20,436 --> 00:10:22,796 Speaker 1: And I don't know what quite the right analogy is, 163 00:10:22,836 --> 00:10:25,316 Speaker 1: but I was thinking, like it reminds me a little 164 00:10:25,356 --> 00:10:28,076 Speaker 1: like Dominoes, and Slice reminds me a little of like 165 00:10:28,396 --> 00:10:33,276 Speaker 1: Amazon and Shopify. Right. Amazon is this giant, centralized, brilliant 166 00:10:33,276 --> 00:10:35,596 Speaker 1: e commerce company, and then you have Shopify, which is 167 00:10:35,716 --> 00:10:39,316 Speaker 1: very clever and lets anybody who wants to sell stuff 168 00:10:39,316 --> 00:10:44,196 Speaker 1: online with a lot of like powerful smart technology. It's 169 00:10:43,516 --> 00:10:47,716 Speaker 1: as well said, we get a lot of analogies and 170 00:10:47,756 --> 00:10:51,556 Speaker 1: a lot of examples are sort of similar to that, 171 00:10:52,476 --> 00:10:55,876 Speaker 1: you know, specifically to our customers, there's a major difference 172 00:10:55,996 --> 00:11:00,556 Speaker 1: even with Shopify that that is an incredible company that 173 00:11:00,636 --> 00:11:04,116 Speaker 1: has built all of these tools, and then the entrepreneur 174 00:11:04,316 --> 00:11:07,316 Speaker 1: goes and leverages those tools to be to be successful 175 00:11:07,756 --> 00:11:11,116 Speaker 1: and to have an online presence. The challenge in our 176 00:11:11,196 --> 00:11:15,276 Speaker 1: industry is if we just built the tool and then 177 00:11:15,436 --> 00:11:19,036 Speaker 1: made it available to these operators, well, that's just another 178 00:11:19,156 --> 00:11:23,236 Speaker 1: job for them to do. And what the mistake a 179 00:11:23,236 --> 00:11:27,116 Speaker 1: lot of people make is they say, Okay, hey, independent operator, 180 00:11:27,196 --> 00:11:30,796 Speaker 1: small business owner, you can go and compete with Dominoes. 181 00:11:31,236 --> 00:11:34,636 Speaker 1: And they're still gonna lose. They're still gonna lose because 182 00:11:34,636 --> 00:11:37,076 Speaker 1: they're because they're alone and they don't have that experience. 183 00:11:37,916 --> 00:11:41,076 Speaker 1: So the difference between even a Shopify and Slice is 184 00:11:41,116 --> 00:11:45,556 Speaker 1: that Slice takes on the responsibility of partnering closely with 185 00:11:45,596 --> 00:11:51,836 Speaker 1: these operators and managing the online economy, the online experience, 186 00:11:51,916 --> 00:11:55,716 Speaker 1: and the digital tools on their behalf, and then giving 187 00:11:55,716 --> 00:11:58,596 Speaker 1: them all the insights back so that they know exactly 188 00:11:58,596 --> 00:12:04,556 Speaker 1: what's going on in this online world. So why only pizza? Like, 189 00:12:04,636 --> 00:12:07,196 Speaker 1: your app looks just like a you know, a Grubhub, 190 00:12:07,236 --> 00:12:08,996 Speaker 1: happ or whatever, but I open it in its own 191 00:12:09,236 --> 00:12:11,156 Speaker 1: pizza and like, I mean, I like pizza. I know 192 00:12:11,236 --> 00:12:15,036 Speaker 1: people love pizza, but like, why only pizza? Why not? 193 00:12:17,996 --> 00:12:20,876 Speaker 1: It's a big market. It's a forty seven billion dollar 194 00:12:21,436 --> 00:12:24,676 Speaker 1: industry in the US, and that's just revenue that passes 195 00:12:24,716 --> 00:12:28,436 Speaker 1: through seventy seven thousand pizza shops. What share of those 196 00:12:28,436 --> 00:12:35,356 Speaker 1: are independent shops? Seventy fivest most most most? Why does 197 00:12:35,396 --> 00:12:41,836 Speaker 1: everybody love pizza? Why does everybody love pizza? Asking the 198 00:12:41,876 --> 00:12:44,676 Speaker 1: tough questions on what's your problem? No, no, I think it. 199 00:12:44,836 --> 00:12:50,316 Speaker 1: I think it brings us back to happy, celebratory times 200 00:12:50,316 --> 00:12:53,156 Speaker 1: in our life. If you remember growing up, when did 201 00:12:53,156 --> 00:12:57,596 Speaker 1: you have pizza? Soccer games, football game, Yeah, birthday parties, 202 00:12:57,996 --> 00:13:02,276 Speaker 1: moments where you typically came together with other people. So 203 00:13:02,356 --> 00:13:06,476 Speaker 1: pizza is the ultimate social food. It has eight slices. 204 00:13:07,076 --> 00:13:09,116 Speaker 1: I mean, I'm sure some people will eat all eight. 205 00:13:09,436 --> 00:13:12,316 Speaker 1: I've done it before and it's it's great. But it's 206 00:13:12,316 --> 00:13:16,876 Speaker 1: meant to be shared. And so there's this incredible positive 207 00:13:18,236 --> 00:13:21,276 Speaker 1: feeling that you get when you order a pizza. I 208 00:13:21,316 --> 00:13:26,836 Speaker 1: would say two, it is um delicious, It travels well, 209 00:13:27,796 --> 00:13:30,476 Speaker 1: and it just tastes really good. I mean, it's a 210 00:13:30,556 --> 00:13:34,356 Speaker 1: nice combination of you know, a Doughey based sort of protein. 211 00:13:34,636 --> 00:13:37,956 Speaker 1: You know, obviously that's sauce, and it's it's a really 212 00:13:37,956 --> 00:13:40,916 Speaker 1: bad balanced problem. Fat is so good. You didn't say fat. 213 00:13:41,156 --> 00:13:44,156 Speaker 1: That's really what's doing the lifting. Let's be honest, right, 214 00:13:44,316 --> 00:13:46,916 Speaker 1: And I would say the last part is this matters. 215 00:13:47,516 --> 00:13:52,676 Speaker 1: It's incredibly affordable. Yeah. For many families and many households, 216 00:13:52,796 --> 00:13:56,836 Speaker 1: pizza is the night out in Yeah. And so I 217 00:13:56,876 --> 00:14:00,316 Speaker 1: would say for those reasons, um, it has become a staple. 218 00:14:00,356 --> 00:14:04,236 Speaker 1: It's an American institution. American is an interesting word there, 219 00:14:04,676 --> 00:14:07,316 Speaker 1: not Italian, like the thing we eat is not even 220 00:14:07,396 --> 00:14:09,636 Speaker 1: it used to be, but not not the pizza we 221 00:14:09,716 --> 00:14:13,036 Speaker 1: consume is very American, very much in an American product. 222 00:14:17,356 --> 00:14:20,556 Speaker 1: After the break, how Slice may soon be solving problems 223 00:14:20,596 --> 00:14:24,836 Speaker 1: in the physical world, including how to deliver cardboard boxes 224 00:14:24,996 --> 00:14:34,836 Speaker 1: to thousands of pizza shops. Now back to the show. 225 00:14:35,876 --> 00:14:38,956 Speaker 1: Elier was born in Macedonia, moved to Staten Island when 226 00:14:38,996 --> 00:14:40,996 Speaker 1: he was ten, and kind of grew up in the 227 00:14:41,036 --> 00:14:44,196 Speaker 1: pizza business. I have a lot of family members that 228 00:14:44,236 --> 00:14:47,196 Speaker 1: owned pizza shops, so hung out at my uncle's pizza shop, 229 00:14:47,236 --> 00:14:51,116 Speaker 1: it was called John Anthony's in Brooklyn. You know, would 230 00:14:51,116 --> 00:14:53,476 Speaker 1: make pizza's if we needed to. We did a bunch 231 00:14:53,516 --> 00:14:59,876 Speaker 1: of deliveries. But most of my teenage years, whether it 232 00:14:59,916 --> 00:15:03,556 Speaker 1: was the weekend or some weeknights, we would spend at 233 00:15:03,556 --> 00:15:06,276 Speaker 1: a pizza shop. Elia went to college in Staten Island 234 00:15:06,316 --> 00:15:08,996 Speaker 1: and started a tech support business that he later wound up. 235 00:15:09,796 --> 00:15:12,076 Speaker 1: So I had that business. Because of that, family members 236 00:15:12,076 --> 00:15:14,436 Speaker 1: saw me as the tech guy, and they wanted me 237 00:15:14,476 --> 00:15:17,156 Speaker 1: to build them websites and online ordering for their shops, 238 00:15:17,396 --> 00:15:20,036 Speaker 1: for their pizza realized for their pizza shops. And then, 239 00:15:20,236 --> 00:15:21,876 Speaker 1: you know, I kind of realized that all these pizza 240 00:15:21,916 --> 00:15:24,796 Speaker 1: shops were very similar, they just had a different recipe. 241 00:15:25,636 --> 00:15:28,276 Speaker 1: The first version of Slice was called my Pizza dot Com. 242 00:15:28,756 --> 00:15:31,916 Speaker 1: I bootstrapped the company for six years. We didn't raise 243 00:15:31,956 --> 00:15:35,316 Speaker 1: any any money. Um So in the beginning, it was 244 00:15:35,436 --> 00:15:40,236 Speaker 1: mostly the consumer was placing an order online. The order 245 00:15:40,276 --> 00:15:43,036 Speaker 1: would make it to my telephone and then I would 246 00:15:43,076 --> 00:15:46,156 Speaker 1: phone the pizzeria. I would call the pizzeria with your order. 247 00:15:46,396 --> 00:15:47,836 Speaker 1: What year was this, by the way, it was it 248 00:15:47,876 --> 00:15:51,796 Speaker 1: was twenty twenty ten, so the people had there was 249 00:15:51,836 --> 00:15:54,236 Speaker 1: there were iPhones, it was an app store. I'd go online. 250 00:15:54,236 --> 00:15:56,116 Speaker 1: I'd think I'm doing this high tech thing ordering on 251 00:15:56,196 --> 00:15:59,076 Speaker 1: my phone. It turns out there's just a guy namely 252 00:15:59,156 --> 00:16:01,836 Speaker 1: You just you read it and then you called the 253 00:16:01,836 --> 00:16:03,876 Speaker 1: pizza shop. That's right, presumable you had to call it 254 00:16:03,916 --> 00:16:06,596 Speaker 1: in because it was twenty ten, Like, they probably didn't 255 00:16:06,596 --> 00:16:09,276 Speaker 1: even have iPhones, a lot of them, right, Like all 256 00:16:09,316 --> 00:16:11,196 Speaker 1: they had was a telephone and a pizza oven and 257 00:16:11,276 --> 00:16:13,036 Speaker 1: cheese at a cash register. Right. There would be no 258 00:16:13,076 --> 00:16:15,236 Speaker 1: way with a lot of these shops in twenty ten 259 00:16:15,516 --> 00:16:19,436 Speaker 1: to send them a digital order. Absolutely, And even if 260 00:16:19,476 --> 00:16:21,636 Speaker 1: you had a way, they were not going to adopt 261 00:16:21,676 --> 00:16:24,596 Speaker 1: that just because they were old fashioned, basically old fashioned. 262 00:16:24,596 --> 00:16:26,356 Speaker 1: They didn't want to change the way that they operated, 263 00:16:26,396 --> 00:16:29,276 Speaker 1: that they worked every day. Great, Remember it's it's an 264 00:16:29,276 --> 00:16:34,996 Speaker 1: individual operator. Any changes to the workflow is a huge 265 00:16:35,036 --> 00:16:37,156 Speaker 1: disruption to your day. You don't have the time, right, 266 00:16:37,156 --> 00:16:41,916 Speaker 1: it's a big disruption. So tell me sort of where 267 00:16:41,996 --> 00:16:47,036 Speaker 1: you are in the arc of figuring all that technology 268 00:16:47,076 --> 00:16:51,356 Speaker 1: stuff out. Like clearly your app lets me order online 269 00:16:51,676 --> 00:16:54,716 Speaker 1: from the corner pizza store, right, so it it at 270 00:16:54,796 --> 00:16:59,356 Speaker 1: least provides the opportunity to move customers off of the 271 00:16:59,436 --> 00:17:03,796 Speaker 1: phone and you know, off of to move customers away 272 00:17:03,796 --> 00:17:07,396 Speaker 1: from calling in their order and toward placing the order digitally, 273 00:17:07,436 --> 00:17:10,636 Speaker 1: which is clearly better and more efficient for everybody. Where 274 00:17:10,676 --> 00:17:13,116 Speaker 1: are you on on all of the other like the 275 00:17:13,156 --> 00:17:15,636 Speaker 1: pizzas in the oven before I even order it, because 276 00:17:15,636 --> 00:17:16,916 Speaker 1: you knew I was going to order it? Like, are 277 00:17:16,956 --> 00:17:19,676 Speaker 1: you doing that? Do you want to do that? Absolutely? 278 00:17:19,956 --> 00:17:22,716 Speaker 1: Now we're on this journey to make sure that all 279 00:17:22,756 --> 00:17:26,396 Speaker 1: of these locations have very similar, a very similar tech 280 00:17:26,476 --> 00:17:31,756 Speaker 1: stack across the board. Once we have this consistent tech 281 00:17:31,796 --> 00:17:37,156 Speaker 1: stack across all these independent shops, we now have community level, 282 00:17:37,316 --> 00:17:41,356 Speaker 1: network level data and insights, and we can use that 283 00:17:41,596 --> 00:17:47,076 Speaker 1: information those insights to empower the individual shops, right, so 284 00:17:47,116 --> 00:17:50,836 Speaker 1: they can they can collectively now know what to charge 285 00:17:50,836 --> 00:17:54,556 Speaker 1: for a large pizza, what is the expected delivery time 286 00:17:54,596 --> 00:17:58,716 Speaker 1: from the consumer in that city or town? How do 287 00:17:58,836 --> 00:18:01,156 Speaker 1: menuse you know, how are they evolving? What are the 288 00:18:01,196 --> 00:18:04,756 Speaker 1: most popular items? So, in essence, they are not they 289 00:18:04,796 --> 00:18:08,116 Speaker 1: are no longer just m you know, alone and looking 290 00:18:08,116 --> 00:18:11,076 Speaker 1: at the data just from your own customer base. But 291 00:18:11,116 --> 00:18:16,076 Speaker 1: they kind of know what's happening industrywide and what kind 292 00:18:16,116 --> 00:18:17,916 Speaker 1: of data, if any, are you sending back to the 293 00:18:17,916 --> 00:18:20,436 Speaker 1: shops now, Like what can you tell them now based 294 00:18:20,476 --> 00:18:23,636 Speaker 1: on what you already know today? It's super I would 295 00:18:23,676 --> 00:18:28,436 Speaker 1: say high, high level basic data about consumer insights. So 296 00:18:28,476 --> 00:18:32,556 Speaker 1: we know tipping point or the threshold for the consumer 297 00:18:32,716 --> 00:18:36,236 Speaker 1: delivery fee, so we know what the consumer expects for 298 00:18:36,356 --> 00:18:38,756 Speaker 1: a delivery fee in a town. Is there a secret 299 00:18:38,876 --> 00:18:41,196 Speaker 1: number that's the most people will pay for delivery? I 300 00:18:41,316 --> 00:18:43,556 Speaker 1: know it varies from town to town. So if you're 301 00:18:43,556 --> 00:18:45,876 Speaker 1: in New York, you have to have free delivery that 302 00:18:46,036 --> 00:18:49,036 Speaker 1: is the expected delivery fee from the consumer. If you're 303 00:18:49,076 --> 00:18:52,916 Speaker 1: in LA you can probably get by with three or 304 00:18:52,916 --> 00:18:56,796 Speaker 1: four dollars delivery fees. It literally varies town to town 305 00:18:56,876 --> 00:18:59,196 Speaker 1: or cities a city. What else do you know already 306 00:18:59,556 --> 00:19:03,676 Speaker 1: we know what is the threshold for delivery time, so 307 00:19:03,756 --> 00:19:08,516 Speaker 1: what is the optimal ETA estimate time of arrival? And 308 00:19:08,636 --> 00:19:11,796 Speaker 1: that is all so geography based, but it's also time 309 00:19:11,836 --> 00:19:14,036 Speaker 1: of day, day of week based. So we know that 310 00:19:14,116 --> 00:19:15,916 Speaker 1: on a Friday night the consumer is a little bit 311 00:19:15,916 --> 00:19:20,116 Speaker 1: more tolerant for you know, a wider delivery time, but 312 00:19:20,156 --> 00:19:23,956 Speaker 1: if it's lunchtime on a Monday, they expected really really fast, 313 00:19:24,316 --> 00:19:27,036 Speaker 1: and that also varies by geography. What else you know? 314 00:19:27,236 --> 00:19:30,076 Speaker 1: We know what is the average price of a pizza, 315 00:19:30,236 --> 00:19:32,716 Speaker 1: of a large cheese pizza by town. We know when 316 00:19:32,716 --> 00:19:35,356 Speaker 1: the price is super high. We know when the price 317 00:19:35,516 --> 00:19:39,556 Speaker 1: is probably on point or when it's too low, especially 318 00:19:39,596 --> 00:19:43,276 Speaker 1: relative to the quality score of the shop. So what 319 00:19:43,436 --> 00:19:45,756 Speaker 1: do you what do you not know yet that you 320 00:19:45,756 --> 00:19:47,196 Speaker 1: hope to know in the future. What are some of 321 00:19:47,196 --> 00:19:50,436 Speaker 1: the data sort of problems you haven't solved yet. We 322 00:19:50,516 --> 00:19:54,196 Speaker 1: don't have a ton of visibility from the shop side. 323 00:19:54,316 --> 00:19:58,036 Speaker 1: So what are they paying for different supplies? What are 324 00:19:58,036 --> 00:20:00,796 Speaker 1: they paying for cheese? What are they paying for you know, 325 00:20:00,956 --> 00:20:03,916 Speaker 1: dough and all these things? And so how does that 326 00:20:03,996 --> 00:20:08,076 Speaker 1: differ from from location to location. So we really want 327 00:20:08,116 --> 00:20:10,876 Speaker 1: to do better is connect the insights and the data 328 00:20:10,996 --> 00:20:14,836 Speaker 1: from the from the seller, from the shop side, so 329 00:20:14,876 --> 00:20:17,556 Speaker 1: that we can sort of create a closed loop of 330 00:20:17,916 --> 00:20:21,116 Speaker 1: insights and information. You give any pushback from shops who 331 00:20:21,156 --> 00:20:24,356 Speaker 1: are like, wait, I got my secret cheese, guy, give 332 00:20:24,436 --> 00:20:26,236 Speaker 1: me a great deal on cheese. I don't want my 333 00:20:26,356 --> 00:20:28,956 Speaker 1: rival across town to know about it. I'm not going 334 00:20:29,036 --> 00:20:32,596 Speaker 1: to tell you that, not really, because every single independent 335 00:20:32,836 --> 00:20:36,396 Speaker 1: is being price gouged because they don't have the leverage. 336 00:20:37,636 --> 00:20:40,396 Speaker 1: You mean, they're paying more than Dominoes anyway. So if 337 00:20:40,396 --> 00:20:43,356 Speaker 1: I got, if I got a corner shop, my competition, 338 00:20:43,396 --> 00:20:45,676 Speaker 1: who I care about is not the other corner shop 339 00:20:45,716 --> 00:20:49,156 Speaker 1: across town. It's Dominoes. Is that what you're saying? Exactly? 340 00:20:49,236 --> 00:20:51,116 Speaker 1: Or to be quite honest, if I can, if I 341 00:20:51,156 --> 00:20:53,956 Speaker 1: can band together with the other shops in town and 342 00:20:54,036 --> 00:20:56,556 Speaker 1: lower the price, I'm willing to do that. Mom and 343 00:20:56,596 --> 00:20:59,036 Speaker 1: pop pizza shops of the world unite. You have nothing 344 00:20:59,196 --> 00:21:03,956 Speaker 1: united but your expensive mazzarella. Exactly exactly. And so I'll 345 00:21:03,996 --> 00:21:08,796 Speaker 1: give you an example. We launched a pilot to bring 346 00:21:09,116 --> 00:21:14,116 Speaker 1: packaging supplies two pizza shops. We filled a warehouse, a 347 00:21:14,156 --> 00:21:17,116 Speaker 1: ten thousand Squareford warehouse with a bunch of pizza boxes 348 00:21:17,196 --> 00:21:19,956 Speaker 1: to an order of magnitude. How many pizza boxes is that? 349 00:21:20,156 --> 00:21:25,596 Speaker 1: It's a good question. Is it a million? Not quite 350 00:21:25,596 --> 00:21:27,956 Speaker 1: a million, but some are in the tens of thousands 351 00:21:28,036 --> 00:21:31,036 Speaker 1: or hundreds of thousands, okay, And there's more orders coming 352 00:21:31,076 --> 00:21:34,036 Speaker 1: because it's it's like the first first order that we placed. 353 00:21:34,036 --> 00:21:36,596 Speaker 1: But what we did was we went to the largest 354 00:21:36,636 --> 00:21:41,276 Speaker 1: pizza box manufacturer in the US and we negotiated a 355 00:21:41,436 --> 00:21:45,276 Speaker 1: rate that is similar to what Dominoes negotiated for their franchises. 356 00:21:45,676 --> 00:21:47,396 Speaker 1: And do you think, I mean, are you going to 357 00:21:47,476 --> 00:21:49,956 Speaker 1: get into the cardboard box business? Like? Is that going 358 00:21:49,996 --> 00:21:52,476 Speaker 1: to be part of what Slice does? It's looking really 359 00:21:52,476 --> 00:21:56,836 Speaker 1: really promising that there's an incredible demand in an appetite 360 00:21:56,916 --> 00:22:00,036 Speaker 1: from pizza shops to pay twenty dollars for a bundle 361 00:22:00,116 --> 00:22:02,756 Speaker 1: instead of thirty five. The reason why I love it 362 00:22:02,796 --> 00:22:05,316 Speaker 1: is because then we can pass that saving back to 363 00:22:05,356 --> 00:22:08,116 Speaker 1: the consumer. By the way, that's that's the secret of 364 00:22:08,156 --> 00:22:13,316 Speaker 1: Dominoes is because of their efficiencies, they didn't raise prices 365 00:22:13,356 --> 00:22:17,516 Speaker 1: for fifteen years, dominos has kept the same prices. So well, 366 00:22:17,516 --> 00:22:20,836 Speaker 1: that's I mean, technological change, driving efficiencies and making things 367 00:22:20,916 --> 00:22:23,436 Speaker 1: cheaper is good, right. That is the long term story 368 00:22:23,436 --> 00:22:26,436 Speaker 1: of how the world gets richer. Exactly so will we 369 00:22:26,476 --> 00:22:29,916 Speaker 1: get into it at scale to be determined, but it's 370 00:22:29,956 --> 00:22:33,276 Speaker 1: certainly looking promising, and we will do anything that is 371 00:22:33,396 --> 00:22:39,956 Speaker 1: necessary to help independent operator small businesses succeed. I gotta 372 00:22:40,036 --> 00:22:43,876 Speaker 1: say I love the story of the startup tech Unicorn 373 00:22:44,236 --> 00:22:47,996 Speaker 1: getting into the cardboard box business, partly because it's just 374 00:22:48,036 --> 00:22:52,196 Speaker 1: so physical and real, but also because it's a reminder 375 00:22:52,236 --> 00:22:55,996 Speaker 1: that this story is really all about slice trying to 376 00:22:55,996 --> 00:22:59,876 Speaker 1: bring the benefits of scale in every dimension. In order 377 00:22:59,876 --> 00:23:02,356 Speaker 1: to compete with dominoes, the mom and pop pizza shops 378 00:23:02,796 --> 00:23:05,796 Speaker 1: need to use the same tech stack and aggregate their 379 00:23:05,876 --> 00:23:08,996 Speaker 1: data so they can use analytics to accurately predict custom 380 00:23:09,196 --> 00:23:12,836 Speaker 1: or behavior. But also they should bend together and buy 381 00:23:12,876 --> 00:23:19,196 Speaker 1: cardboard boxes in bulk because they're cheaper that way. In 382 00:23:19,236 --> 00:23:23,276 Speaker 1: a minute, the lightning round with lots of questions about pizza. 383 00:23:30,996 --> 00:23:33,116 Speaker 1: That's the end of the ads. Now we're going back 384 00:23:33,156 --> 00:23:36,636 Speaker 1: to the show. Okay, let's do the lightning round. Great 385 00:23:37,156 --> 00:23:43,636 Speaker 1: anchovy or pineapple neither neither very good. What's one piece 386 00:23:43,636 --> 00:23:45,516 Speaker 1: of advice you'd give to somebody trying to solve a 387 00:23:45,556 --> 00:23:50,436 Speaker 1: hard problem. Hard problems take a long time to solve. 388 00:23:52,956 --> 00:23:57,756 Speaker 1: Be really passionate about solving the problem. But don't just 389 00:23:57,836 --> 00:23:59,636 Speaker 1: do it because you want to make money or for 390 00:23:59,676 --> 00:24:03,676 Speaker 1: some other reason, because then you will not have the 391 00:24:03,796 --> 00:24:08,356 Speaker 1: patience to stay with it long enough to actually solve it. 392 00:24:09,516 --> 00:24:14,636 Speaker 1: What's the most overrated pizza topping? Mushrooms? What's the most 393 00:24:14,716 --> 00:24:21,836 Speaker 1: underrated pizza topping? Hot honey, hot honey. That's right. I 394 00:24:21,956 --> 00:24:24,036 Speaker 1: never even heard of it, that's how underrated it is. 395 00:24:24,436 --> 00:24:27,756 Speaker 1: There you go, Chicago Pizza or New York Pizza. Just kidding, 396 00:24:28,036 --> 00:24:30,796 Speaker 1: obviously New York Chicago pizza is a pie. It's not 397 00:24:30,836 --> 00:24:38,716 Speaker 1: even pizza. It's not even pizza. Thank you. What do 398 00:24:38,756 --> 00:24:41,356 Speaker 1: you understand about the world Because you grew up in 399 00:24:41,396 --> 00:24:45,436 Speaker 1: Staten Island, people have to work really, really hard for 400 00:24:45,476 --> 00:24:49,996 Speaker 1: a long time to earn more than fifty or sixty 401 00:24:49,996 --> 00:24:56,716 Speaker 1: thousand dollars a year, and for most people, ordering takeout 402 00:24:56,796 --> 00:25:00,836 Speaker 1: or delivery is a luxury and not, you know, not 403 00:25:00,996 --> 00:25:02,916 Speaker 1: something that they can do every single day of the week. 404 00:25:03,836 --> 00:25:08,636 Speaker 1: Do you think Slice will always be a pizza company? No? 405 00:25:09,396 --> 00:25:13,916 Speaker 1: What's next after pizza? We're looking at bakeries, We're looking 406 00:25:13,956 --> 00:25:17,636 Speaker 1: at like taco shops, Mexican takeout and delivery is a 407 00:25:17,756 --> 00:25:20,716 Speaker 1: really big deal. So those are the areas we're kind 408 00:25:20,716 --> 00:25:23,036 Speaker 1: of trying to learn more about. What's the best pizza 409 00:25:23,076 --> 00:25:26,636 Speaker 1: You've ever had? Best pizza I've ever had? On a 410 00:25:27,076 --> 00:25:30,676 Speaker 1: Midsummer day, let's call it around four or five pm. 411 00:25:31,316 --> 00:25:34,676 Speaker 1: You're in Brooklyn. You go to LMB Simoni Gardens. Ah, 412 00:25:34,996 --> 00:25:38,316 Speaker 1: I know it. You get the LMB you know, square pie, 413 00:25:39,516 --> 00:25:41,356 Speaker 1: You get a you know, three or four cans of 414 00:25:41,396 --> 00:25:44,076 Speaker 1: pepsi with a couple of friends, and you sit outside 415 00:25:44,356 --> 00:25:47,116 Speaker 1: and the sun is setting and it's kind of hot, 416 00:25:47,836 --> 00:25:50,316 Speaker 1: and you're having a Slice with a with a cold 417 00:25:50,316 --> 00:25:55,676 Speaker 1: can of of pepsi or cooke. Very nice, Thank you 418 00:25:55,716 --> 00:25:58,716 Speaker 1: for your time. Thank you. This has been one of 419 00:25:58,756 --> 00:26:01,996 Speaker 1: my favorites because it's been a combination of fun and 420 00:26:02,076 --> 00:26:05,356 Speaker 1: challenging at the same time. Oh great, fun and fun 421 00:26:05,396 --> 00:26:08,556 Speaker 1: and fun and challenge, fun and interesting, fun and thoughtful. Yeah, 422 00:26:08,636 --> 00:26:15,996 Speaker 1: we're going I like the thoughtfulness exactly. Elier Sella is 423 00:26:16,036 --> 00:26:19,796 Speaker 1: the founder and CEO of Slice. Over the next few weeks, 424 00:26:19,796 --> 00:26:21,676 Speaker 1: we're going to be doing some more shows about the 425 00:26:21,676 --> 00:26:24,756 Speaker 1: future of food. Coming next week is an interview with 426 00:26:24,836 --> 00:26:29,076 Speaker 1: Pat Brown, the founder of Impossible Foods. His problem, how 427 00:26:29,116 --> 00:26:33,516 Speaker 1: do you make meat without animals. Today's show was edited 428 00:26:33,556 --> 00:26:36,756 Speaker 1: by Robert Smith, produced by Edith Russolo, and engineered by 429 00:26:36,756 --> 00:26:40,836 Speaker 1: Amanda ka Wong. I'm Jacob Goldstein. You can find me 430 00:26:40,876 --> 00:26:43,796 Speaker 1: on Twitter at Jacob Goldstein, or you can email us 431 00:26:43,916 --> 00:26:47,916 Speaker 1: at problem at Pushkin dot fm. We'll be back next 432 00:26:47,916 --> 00:26:51,836 Speaker 1: week with another episode of What's Your Problem