1 00:00:00,080 --> 00:00:03,560 Speaker 1: He could have been a contender in baseball, but he 2 00:00:03,680 --> 00:00:05,360 Speaker 1: decided he was going to be a success in the 3 00:00:05,400 --> 00:00:09,240 Speaker 1: world of technology. Tom Lunabus is the chief executive of 4 00:00:09,360 --> 00:00:12,200 Speaker 1: SOSTA and he joins us now in studio. Tom, thanks 5 00:00:12,240 --> 00:00:14,160 Speaker 1: for coming in. Great to be here, Thanks for having me. 6 00:00:14,200 --> 00:00:16,240 Speaker 1: All right, do the quick setup, because I bet if 7 00:00:16,239 --> 00:00:18,800 Speaker 1: I went to the University of San Francisco, I think 8 00:00:18,840 --> 00:00:21,200 Speaker 1: I'd find something that would be a remnant of your 9 00:00:21,800 --> 00:00:27,600 Speaker 1: infielder status. Way too much insider baseball knowledge there. Um. Yeah, 10 00:00:27,640 --> 00:00:29,880 Speaker 1: I'm a little unique in the Silicon Valley where we're 11 00:00:30,000 --> 00:00:34,440 Speaker 1: a tech community made up of engineers. And I an 12 00:00:34,440 --> 00:00:37,320 Speaker 1: odd career in sports where I played baseball for four 13 00:00:37,400 --> 00:00:40,840 Speaker 1: years under a kind of a legend in USF and 14 00:00:40,880 --> 00:00:44,760 Speaker 1: what started off as being talked into going to play, 15 00:00:44,840 --> 00:00:47,000 Speaker 1: I ended up being in the Hall of Fame at 16 00:00:47,080 --> 00:00:50,479 Speaker 1: us F, bait for Baseball and drafted by the Minnesota Twins. 17 00:00:50,560 --> 00:00:55,400 Speaker 1: And but you know, life always has different crossing points 18 00:00:55,400 --> 00:00:58,360 Speaker 1: than I ended up going into tech and not. There's 19 00:00:58,360 --> 00:01:00,440 Speaker 1: not a lot of us that actually gotten that direction. 20 00:01:00,480 --> 00:01:02,480 Speaker 1: So tell us about SOLS Tess tells how you got 21 00:01:02,520 --> 00:01:05,120 Speaker 1: connected in what the company does. Well, So does a 22 00:01:05,160 --> 00:01:07,760 Speaker 1: really kind of cool company in terms of the customers 23 00:01:07,800 --> 00:01:10,320 Speaker 1: we use. You know, we we we work for UH 24 00:01:10,480 --> 00:01:13,840 Speaker 1: five largest digital brands in the world, and as a company, 25 00:01:13,920 --> 00:01:15,640 Speaker 1: it's it's a little bit of a story of getting 26 00:01:15,640 --> 00:01:18,600 Speaker 1: the band back together again. Ken Gardner and I started 27 00:01:18,600 --> 00:01:23,560 Speaker 1: a company back in the nineties around analytics Advanced Analytics UM, 28 00:01:23,720 --> 00:01:26,640 Speaker 1: and we started seeing as more brands we're going to 29 00:01:26,720 --> 00:01:31,120 Speaker 1: go direct to consumers with marketing campaigns selling a million 30 00:01:31,160 --> 00:01:34,600 Speaker 1: tickets for the Olympic Games, or a new iPhone seven 31 00:01:34,840 --> 00:01:36,840 Speaker 1: gets launched, that they're going to drive a lot of 32 00:01:36,880 --> 00:01:40,440 Speaker 1: traffic to web and mobile applications. And the thing that 33 00:01:40,560 --> 00:01:43,600 Speaker 1: we all understand, if that site is down or if 34 00:01:43,640 --> 00:01:46,679 Speaker 1: it's slow, all of us consumers are three clicks away 35 00:01:46,760 --> 00:01:49,680 Speaker 1: from buying the same thing from somebody else. So we 36 00:01:49,800 --> 00:01:53,320 Speaker 1: built a platform UM that in some ways mimics what 37 00:01:53,440 --> 00:01:58,200 Speaker 1: Amazon does in terms of understanding customer experience and a 38 00:01:58,240 --> 00:02:01,480 Speaker 1: platform that allows our brand ends are five digital brands 39 00:02:01,520 --> 00:02:06,919 Speaker 1: to simulate Cyber Monday and Black Friday and Halloween and 40 00:02:06,920 --> 00:02:11,080 Speaker 1: and in big marketing events or or UM you know, 41 00:02:11,120 --> 00:02:14,839 Speaker 1: for hallmarket's UH their super bowl is Valentine's Day where 42 00:02:14,840 --> 00:02:17,880 Speaker 1: they get so many folks coming through so we're helping 43 00:02:17,919 --> 00:02:21,200 Speaker 1: them understand their customers online and we're improving their perform. Okay, 44 00:02:21,240 --> 00:02:24,800 Speaker 1: so you also have the c p I and index 45 00:02:24,800 --> 00:02:28,160 Speaker 1: that measures how users respond to the digital experience and 46 00:02:28,200 --> 00:02:30,800 Speaker 1: how speed and response is nous of the side influences 47 00:02:30,880 --> 00:02:36,200 Speaker 1: engagement and conversion rates. Digital speed. You know, we've all 48 00:02:36,280 --> 00:02:38,360 Speaker 1: kind of become addicted to it, and I think at 49 00:02:38,400 --> 00:02:41,320 Speaker 1: the same time also we kind of fight against that addiction. 50 00:02:41,680 --> 00:02:44,640 Speaker 1: It's neural. I mean, you know, speed is kind of 51 00:02:44,720 --> 00:02:47,560 Speaker 1: inbred into us in all ways. And what we've been 52 00:02:47,560 --> 00:02:50,840 Speaker 1: able to analyze is that we go on these websites 53 00:02:50,880 --> 00:02:54,560 Speaker 1: and if they're slow, um, we're not very happy. And 54 00:02:54,639 --> 00:02:57,800 Speaker 1: so the engagement with the customer is usually cut short. 55 00:02:57,880 --> 00:03:00,320 Speaker 1: They may not watch the full video if they're fat Austin, 56 00:03:00,360 --> 00:03:03,799 Speaker 1: you're liking what the experiences, You're more engaged, and that's 57 00:03:03,840 --> 00:03:05,280 Speaker 1: the key to the game. So what is the number 58 00:03:05,320 --> 00:03:07,919 Speaker 1: one thing people do wrong? It makes a slow experience. 59 00:03:07,960 --> 00:03:10,079 Speaker 1: And I'm the first one I really want to watch 60 00:03:10,080 --> 00:03:12,520 Speaker 1: a video online. I just want the information I want 61 00:03:12,520 --> 00:03:14,320 Speaker 1: to read. It would be really enticing for me to 62 00:03:14,320 --> 00:03:17,679 Speaker 1: watch the video. Well, sometimes it's the video itself, it's 63 00:03:17,720 --> 00:03:20,120 Speaker 1: the content and how long they play it. So you know, 64 00:03:20,160 --> 00:03:23,720 Speaker 1: if you play some videos at fourteen seconds versus ten seconds, 65 00:03:24,000 --> 00:03:26,959 Speaker 1: you'll see a drop off of lots of different consumers. 66 00:03:27,480 --> 00:03:30,640 Speaker 1: Sometimes it's just creating a marketing event where everybody goes 67 00:03:30,680 --> 00:03:32,440 Speaker 1: to the website at one time, so it could be load. 68 00:03:32,880 --> 00:03:35,680 Speaker 1: Sometimes it hasn't been optimized to you know, the device 69 00:03:35,760 --> 00:03:39,160 Speaker 1: that you're particular to or the browser you're not particular too. 70 00:03:39,720 --> 00:03:43,920 Speaker 1: So it's a very you know, delivering um brand and 71 00:03:44,560 --> 00:03:48,280 Speaker 1: consumerism to to the marketplace is very complex and sophisticated 72 00:03:48,320 --> 00:03:51,480 Speaker 1: these days, and what these brands are getting is very 73 00:03:51,480 --> 00:03:54,760 Speaker 1: sophisticated on how they understand the consumer experience. But it's 74 00:03:54,800 --> 00:03:57,080 Speaker 1: a lot of things that create something that could break. 75 00:03:57,720 --> 00:03:59,880 Speaker 1: Tell us about the public cloud, how do you do 76 00:04:00,000 --> 00:04:03,680 Speaker 1: subscribe it to people? And describe also the growth that 77 00:04:03,720 --> 00:04:06,640 Speaker 1: you're seeing there and why it's relevant. And we're particularly 78 00:04:06,680 --> 00:04:09,000 Speaker 1: interesting in that area because we built the company around 79 00:04:09,000 --> 00:04:12,360 Speaker 1: cloud computing when there was very little cloud computing. In fact, 80 00:04:12,400 --> 00:04:16,400 Speaker 1: I was in when we introduced Sosta. The gentleman that 81 00:04:16,480 --> 00:04:18,800 Speaker 1: introduced us was the founder of cloud computing. It's Andy 82 00:04:18,880 --> 00:04:22,800 Speaker 1: Jassey who runs Amazon z Aws and Andy h This 83 00:04:22,880 --> 00:04:24,880 Speaker 1: was at the first cloud event back in two thousand 84 00:04:24,880 --> 00:04:27,880 Speaker 1: and eight. What made it interesting for our use case, 85 00:04:27,920 --> 00:04:31,160 Speaker 1: and our use case became particularly interested in cloud computing 86 00:04:31,560 --> 00:04:35,800 Speaker 1: computing industry was I needed a thousand servers for a 87 00:04:35,880 --> 00:04:37,960 Speaker 1: very short period time. In other words, the way that 88 00:04:38,000 --> 00:04:41,440 Speaker 1: we test these applications is we have to simulate millions 89 00:04:41,440 --> 00:04:43,680 Speaker 1: of consumers going to the sites, and you may need 90 00:04:43,720 --> 00:04:46,080 Speaker 1: a thousand servers to do. No one had a thousand 91 00:04:46,160 --> 00:04:49,760 Speaker 1: servers in their test lab. And so what cloud computing 92 00:04:49,839 --> 00:04:53,280 Speaker 1: does is it gives you, especially public clouds, gives you 93 00:04:53,400 --> 00:04:58,719 Speaker 1: access and availability and affordability to capacities server capacities which 94 00:04:58,760 --> 00:05:02,200 Speaker 1: I needed and I consume in large ways, and I 95 00:05:02,240 --> 00:05:04,200 Speaker 1: can only pay. I only pay for what I use, 96 00:05:04,440 --> 00:05:06,920 Speaker 1: so I will only I may pay forty cents an 97 00:05:06,920 --> 00:05:09,760 Speaker 1: hour for for a server, but if I'm using a 98 00:05:09,800 --> 00:05:12,520 Speaker 1: thousand servers for six hours, it's a very small amount 99 00:05:12,640 --> 00:05:16,200 Speaker 1: comparatively buying him for the the whole quick final question, Uh, 100 00:05:16,440 --> 00:05:19,479 Speaker 1: how do you make money on this? Well, all of 101 00:05:19,600 --> 00:05:23,160 Speaker 1: you know there's over two trillion dollars that are transacted 102 00:05:23,240 --> 00:05:26,599 Speaker 1: online every every year, and so our ability to save 103 00:05:26,839 --> 00:05:30,560 Speaker 1: and um to make sure that those two trillion dollars 104 00:05:30,600 --> 00:05:32,360 Speaker 1: actually get done is the key to the game. So 105 00:05:32,520 --> 00:05:34,840 Speaker 1: where the insurance policy for the industry. I like the 106 00:05:34,880 --> 00:05:37,560 Speaker 1: insurance is a very lucrative business. Tom Lunabus, thank you 107 00:05:37,600 --> 00:05:41,360 Speaker 1: so very much. He's chief executive officer at SOSTA. He's 108 00:05:41,360 --> 00:05:43,880 Speaker 1: based in San Francisco, joining us in New York. This 109 00:05:44,040 --> 00:05:44,680 Speaker 1: is Bloomberg