1 00:00:03,200 --> 00:00:06,600 Speaker 1: Global business news twenty four hours a day. It's Bloomberg 2 00:00:06,640 --> 00:00:09,719 Speaker 1: dot Com the radio plus mobile lap and on your radio. 3 00:00:09,960 --> 00:00:14,040 Speaker 1: This is a Bloomberg business flag from Bloomberg World Handquarters. 4 00:00:14,080 --> 00:00:18,280 Speaker 1: I'm Charlie Palette. The SMP NESTAC both advancing Dow industrials 5 00:00:18,360 --> 00:00:22,680 Speaker 1: declining SMP five hundred index heading for a fifth monthly 6 00:00:22,760 --> 00:00:26,280 Speaker 1: gain on this final trading day of July. This morning, 7 00:00:26,280 --> 00:00:28,800 Speaker 1: we got data showing the US economy grew slower than 8 00:00:28,840 --> 00:00:31,760 Speaker 1: forecast last quarter. That is, giving the Federal Reserve no 9 00:00:31,840 --> 00:00:35,839 Speaker 1: reason to accelerate its timetable for higher rates. The tenure 10 00:00:35,960 --> 00:00:39,200 Speaker 1: up ten thirty seconds, that yield one point four six percent. 11 00:00:39,560 --> 00:00:42,720 Speaker 1: Gold now up eighteen dollars an ounce the thirteen fifty, 12 00:00:42,760 --> 00:00:45,600 Speaker 1: a gain there of one point four percent. Crude West 13 00:00:45,640 --> 00:00:48,480 Speaker 1: Texas Intermediate up nine tenths of one percent, up thirty 14 00:00:48,520 --> 00:00:51,920 Speaker 1: eight cents of barrel fifty five right now on the 15 00:00:52,240 --> 00:00:56,160 Speaker 1: West of Texas Intermediate SMP up four seventy four, a 16 00:00:56,200 --> 00:00:58,680 Speaker 1: gain of two tenths of one percent. The down down 17 00:00:58,800 --> 00:01:02,360 Speaker 1: seventeen to drop there point one percent. I'm Charlie Pellette. 18 00:01:02,440 --> 00:01:06,880 Speaker 1: That's a Bloomberg business splash. You're listening to taking Stock 19 00:01:07,000 --> 00:01:13,120 Speaker 1: with Kathleen pim Fox on Bloomberg Radio. Welcome back to 20 00:01:13,240 --> 00:01:15,920 Speaker 1: Taking Stock. Matt Miller here with pim Fox. We are 21 00:01:16,000 --> 00:01:18,720 Speaker 1: joined by Mike took In, the CEO of Talent, uh 22 00:01:18,840 --> 00:01:23,360 Speaker 1: technology company that helps clients compile and analyze data from 23 00:01:23,360 --> 00:01:25,920 Speaker 1: different sources. That I'm going to get an explanation on 24 00:01:26,040 --> 00:01:28,640 Speaker 1: the most interesting thing about the company right now in 25 00:01:28,720 --> 00:01:31,880 Speaker 1: this moment is that they just price an ip O. Mike, 26 00:01:31,920 --> 00:01:33,880 Speaker 1: you just price an IPO a dollar above the range. 27 00:01:33,880 --> 00:01:36,720 Speaker 1: Congratulations on that eighteen dollars a share and it's trading 28 00:01:36,760 --> 00:01:39,000 Speaker 1: up today on the first day of trading. Uh. So, 29 00:01:39,080 --> 00:01:41,000 Speaker 1: everything went well with this I P O and it's 30 00:01:41,560 --> 00:01:44,680 Speaker 1: right now. It's kind of spent. Procedure is a difficult time. 31 00:01:45,160 --> 00:01:47,760 Speaker 1: How did they go for you? Matt? It was beyond 32 00:01:47,760 --> 00:01:51,120 Speaker 1: the numbers? Obviously, it was a terrific day for us. 33 00:01:51,240 --> 00:01:53,360 Speaker 1: We felt like the market was very receptive to the 34 00:01:53,400 --> 00:01:56,040 Speaker 1: offer that we had, and our bankers told us it 35 00:01:56,080 --> 00:01:58,200 Speaker 1: was one of the most oversubscribed offerings they've seen in 36 00:01:58,200 --> 00:02:01,000 Speaker 1: four and a half years. So what ex act leaded? 37 00:02:01,080 --> 00:02:03,720 Speaker 1: When I say that you help clients compile and analyze 38 00:02:03,800 --> 00:02:06,680 Speaker 1: data from different sources, I mean we do that to 39 00:02:07,000 --> 00:02:09,120 Speaker 1: a lot A lot of companies do that. What exactly 40 00:02:09,120 --> 00:02:12,280 Speaker 1: do you do a talent? We help companies become data 41 00:02:12,320 --> 00:02:15,360 Speaker 1: driven because we're in a world where the better companies 42 00:02:15,440 --> 00:02:17,960 Speaker 1: are able to use their data, the more effectively they 43 00:02:17,960 --> 00:02:21,160 Speaker 1: can compete. In every industry, there's companies that are completely 44 00:02:21,240 --> 00:02:24,400 Speaker 1: changing the game based on using data, and as a result, 45 00:02:24,960 --> 00:02:28,160 Speaker 1: our customers can use their data more effectively to drive 46 00:02:28,200 --> 00:02:32,639 Speaker 1: their An example would be so, for example, every company 47 00:02:32,639 --> 00:02:35,400 Speaker 1: in the world needs to make better decisions on sales 48 00:02:35,400 --> 00:02:41,080 Speaker 1: and marketing, which sales, which marketing um uh messages, in 49 00:02:41,080 --> 00:02:45,000 Speaker 1: which marketing campaigns are driving, which sales opportunities to drive, 50 00:02:45,000 --> 00:02:48,359 Speaker 1: which customers, which customers are therefore more profitable, and so 51 00:02:48,639 --> 00:02:51,280 Speaker 1: all that kind of sales and marketing analytics are things 52 00:02:51,360 --> 00:02:53,440 Speaker 1: that we can help them with. Hey, Mike, I wonder 53 00:02:53,440 --> 00:02:55,000 Speaker 1: if you could just help people a little bit about 54 00:02:55,080 --> 00:02:58,359 Speaker 1: your background, because this is number two for you to 55 00:02:58,480 --> 00:03:00,320 Speaker 1: a certain extent. I'm sure he's done a lot of 56 00:03:00,360 --> 00:03:02,720 Speaker 1: other things, but I remember rapp at seven and I 57 00:03:02,800 --> 00:03:04,720 Speaker 1: wondering if you could tell us about that company and 58 00:03:04,760 --> 00:03:08,200 Speaker 1: how you got connected to this world of technology. You bad. So. 59 00:03:08,240 --> 00:03:11,840 Speaker 1: I actually started out in my career as an engineer. 60 00:03:12,200 --> 00:03:16,080 Speaker 1: I was a computer designer UM with a master's electrical engineering, 61 00:03:16,080 --> 00:03:19,360 Speaker 1: designing computer chips. So then I got my lobotomy, went 62 00:03:19,400 --> 00:03:23,000 Speaker 1: to b school and UH worked for then um eight 63 00:03:23,040 --> 00:03:26,560 Speaker 1: half years at Microsoft in two stints. UM my first 64 00:03:26,720 --> 00:03:29,160 Speaker 1: CEO opportunity, as he said, was rappid seven. Had the 65 00:03:29,200 --> 00:03:31,079 Speaker 1: privilege to work with a terrific group of folks in 66 00:03:31,120 --> 00:03:33,840 Speaker 1: Boston and took them from five million to fifty million 67 00:03:33,880 --> 00:03:36,040 Speaker 1: and four and a half years. And then three years 68 00:03:36,040 --> 00:03:38,880 Speaker 1: ago had the opportunity here to join Talent. I jumped 69 00:03:38,880 --> 00:03:42,400 Speaker 1: at that and excited to be here. Today. Customers include 70 00:03:42,560 --> 00:03:46,680 Speaker 1: Air France, City Bank as well as General Electric. And 71 00:03:46,720 --> 00:03:49,320 Speaker 1: this is all about real time analytics or is it 72 00:03:49,400 --> 00:03:54,080 Speaker 1: about research? Um, it's about real time analytics as well 73 00:03:54,120 --> 00:03:57,000 Speaker 1: as really any way that companies want to leverage their data. 74 00:03:57,040 --> 00:03:58,440 Speaker 1: It might be in real time or it might not, 75 00:03:58,840 --> 00:04:01,280 Speaker 1: but every company has day it all over the place. 76 00:04:01,320 --> 00:04:03,360 Speaker 1: It might be in the cloud, it might be in 77 00:04:03,360 --> 00:04:06,240 Speaker 1: different applications, and they need to bring it together, clean 78 00:04:06,320 --> 00:04:08,839 Speaker 1: it up, make sense of it before they can start 79 00:04:08,960 --> 00:04:11,360 Speaker 1: driving their business from it. That that's the part of 80 00:04:11,360 --> 00:04:12,840 Speaker 1: the business that we help them with. So I'm just 81 00:04:12,880 --> 00:04:15,680 Speaker 1: trying to understand what kind of solution you have. Do 82 00:04:15,800 --> 00:04:19,200 Speaker 1: you come with a platform that Talent already has. Do 83 00:04:19,240 --> 00:04:22,120 Speaker 1: you have Taylor ready solutions for the companies with whom 84 00:04:22,120 --> 00:04:26,640 Speaker 1: you work, Um, how exactly do you get the data? 85 00:04:26,720 --> 00:04:28,600 Speaker 1: Do you only use data that they have? I mean, 86 00:04:28,600 --> 00:04:31,320 Speaker 1: I'm trying to get a little more into what Talent doest. 87 00:04:31,360 --> 00:04:34,360 Speaker 1: So we use all of their companies existing data. We 88 00:04:34,400 --> 00:04:37,760 Speaker 1: don't bring our own data. And it's a platform that 89 00:04:37,800 --> 00:04:41,560 Speaker 1: they can use to develop solutions. We don't have customized solutions. 90 00:04:41,560 --> 00:04:43,880 Speaker 1: So what it is is a platform that allows you 91 00:04:43,920 --> 00:04:46,080 Speaker 1: to pull data from all the different places or take 92 00:04:46,080 --> 00:04:47,960 Speaker 1: a real time for you just pin was just mentioning 93 00:04:48,480 --> 00:04:53,599 Speaker 1: and clean it up. Data is always has mistakes and inconsistencies, 94 00:04:53,680 --> 00:04:55,440 Speaker 1: and you need to be able to blend data from 95 00:04:55,440 --> 00:04:57,359 Speaker 1: a multiple different places. I can give a couple of 96 00:04:57,400 --> 00:05:00,360 Speaker 1: examples if that will help. But so some g genius 97 00:05:00,480 --> 00:05:03,080 Speaker 1: came up with this awesome platform and then you guys 98 00:05:03,080 --> 00:05:06,080 Speaker 1: have helped him bring that to market. Basically, we had 99 00:05:06,120 --> 00:05:08,960 Speaker 1: a founding team that had a unique insight into how 100 00:05:09,000 --> 00:05:12,280 Speaker 1: the market was evolving, and um, they thought this the 101 00:05:12,320 --> 00:05:15,120 Speaker 1: approach that we had technically was significantly better than the 102 00:05:15,160 --> 00:05:17,960 Speaker 1: market and they've proven to be right. Go ahead and 103 00:05:18,000 --> 00:05:20,320 Speaker 1: give examples, but the context, I'm just going to bring 104 00:05:20,400 --> 00:05:23,320 Speaker 1: up his Business Objects because the team that started Talent 105 00:05:23,640 --> 00:05:27,719 Speaker 1: are behind Business Objects, which was sold then to S 106 00:05:27,720 --> 00:05:30,039 Speaker 1: S A P. Yes, we have a couple of our 107 00:05:30,200 --> 00:05:33,400 Speaker 1: exact teams from Business Objects and we had our chairman 108 00:05:33,880 --> 00:05:35,880 Speaker 1: UM was the founder and CEO of Business Objects for 109 00:05:35,880 --> 00:05:38,440 Speaker 1: many years, Bernhard the Toad, and so yes that he's 110 00:05:38,440 --> 00:05:41,880 Speaker 1: been super super helpful along the way with the lessons 111 00:05:41,880 --> 00:05:44,599 Speaker 1: that he's learned from building Business Objects, and so you know, 112 00:05:44,680 --> 00:05:48,240 Speaker 1: I'll take give an example UM. Right now, every one 113 00:05:48,279 --> 00:05:51,919 Speaker 1: of you has had alerts UM phone calls from a 114 00:05:51,960 --> 00:05:56,200 Speaker 1: credit card company saying this transaction might be fraudulent. Well, 115 00:05:56,360 --> 00:05:58,719 Speaker 1: how does that happen? What they need to do is 116 00:05:58,839 --> 00:06:01,760 Speaker 1: look at that transaction and all the pull up all 117 00:06:01,800 --> 00:06:04,560 Speaker 1: the data from that and compare that to other transactions 118 00:06:04,560 --> 00:06:08,200 Speaker 1: they know are fraudulent. That process of pulling data together 119 00:06:08,720 --> 00:06:12,279 Speaker 1: and UM screening it for potential fraud is something that 120 00:06:12,320 --> 00:06:14,359 Speaker 1: we can help them with. I get these phone calls, 121 00:06:14,360 --> 00:06:16,719 Speaker 1: by the way, constantly, I feel like I must just 122 00:06:16,839 --> 00:06:21,159 Speaker 1: make typically fraudulent purchases a lot, literally once a week. 123 00:06:21,240 --> 00:06:23,760 Speaker 1: I'm wow, my credit card isn't working because they think 124 00:06:23,760 --> 00:06:27,000 Speaker 1: I'm someone who's stolen my credit card. Uh all right, 125 00:06:27,080 --> 00:06:29,440 Speaker 1: let me ask about the growth prospects because obviously, if 126 00:06:29,440 --> 00:06:32,040 Speaker 1: people are thinking about an I p O the market, 127 00:06:32,040 --> 00:06:34,560 Speaker 1: the stock, they might want to buy it. Um why 128 00:06:34,560 --> 00:06:37,000 Speaker 1: should they put what's the argument for somebody getting in 129 00:06:37,160 --> 00:06:40,040 Speaker 1: after the I p O, after the pop? What kind 130 00:06:40,080 --> 00:06:42,680 Speaker 1: of growth prospects are you looking at? Well, what what 131 00:06:42,920 --> 00:06:47,360 Speaker 1: talent offers is a combination of strong and accelerating growth 132 00:06:47,880 --> 00:06:51,160 Speaker 1: UM with at the same time, we're already cash flow positive, 133 00:06:51,400 --> 00:06:54,200 Speaker 1: and that's really unique relative to the companies that we've 134 00:06:54,200 --> 00:06:56,520 Speaker 1: seen over the last four or five years. We've been 135 00:06:56,520 --> 00:06:59,880 Speaker 1: growing our subscription business around per year UM for the 136 00:07:00,200 --> 00:07:04,720 Speaker 1: five quarters. And uh as far as the proceeds, I 137 00:07:04,720 --> 00:07:06,880 Speaker 1: always like to read that lion the Bloomberg story. You 138 00:07:06,960 --> 00:07:09,040 Speaker 1: raised almost a hundred million dollars. What are you gonna 139 00:07:09,080 --> 00:07:10,880 Speaker 1: do with it? You invest? This is part of an 140 00:07:10,880 --> 00:07:13,840 Speaker 1: exit strategy? What's what's what's the money for? Well, since 141 00:07:13,880 --> 00:07:16,239 Speaker 1: we're already cash flow positive, we don't need the money 142 00:07:16,280 --> 00:07:18,560 Speaker 1: to run the company, and so that gives us an 143 00:07:18,600 --> 00:07:21,480 Speaker 1: opportunity to look for acquisitions. We can look for small 144 00:07:21,560 --> 00:07:24,880 Speaker 1: tuck ins that allow us to accelerate into the markets 145 00:07:24,880 --> 00:07:27,000 Speaker 1: that are close to what we're doing, or speed up 146 00:07:27,040 --> 00:07:30,040 Speaker 1: our ability to UM build deeper solutions. I should have 147 00:07:30,080 --> 00:07:32,480 Speaker 1: started with that question. Great answer, all right, Mike, thanks 148 00:07:32,520 --> 00:07:34,840 Speaker 1: so much for joining us. Real pleasure to talking to Mike. 149 00:07:35,040 --> 00:07:39,080 Speaker 1: Talent CEO Mike Tookin, sorry, CEO of Talent, on the 150 00:07:39,120 --> 00:07:43,320 Speaker 1: company's I p O and the outlook for acquisitions. Taking stock. 151 00:07:46,840 --> 00:07:49,640 Speaker 1: Coming up on taking stock will take stock of the 152 00:07:49,680 --> 00:07:53,160 Speaker 1: Bank of Japan and what it isn't doing in order 153 00:07:53,200 --> 00:07:57,000 Speaker 1: to spur economic growth in the country. The latest monetary 154 00:07:57,040 --> 00:07:59,640 Speaker 1: easing fell short of investor expectations.