1 00:00:05,360 --> 00:00:09,119 Speaker 1: So I mentioned earlier, alter X rising big time in 2 00:00:09,200 --> 00:00:12,039 Speaker 1: today's session, a big mover if you will. This coming 3 00:00:12,080 --> 00:00:15,640 Speaker 1: after the company's latest quarterly released. We want to talk 4 00:00:15,640 --> 00:00:18,560 Speaker 1: a little bit about the company, the earnings, the interesting 5 00:00:18,560 --> 00:00:20,960 Speaker 1: I p O path at this data storage company took 6 00:00:21,000 --> 00:00:24,000 Speaker 1: this year. Dean Stoker is chief executive officer at alter X, 7 00:00:24,440 --> 00:00:28,640 Speaker 1: on the phone from Irving, California. Dean, nice to have 8 00:00:28,800 --> 00:00:31,360 Speaker 1: you here stock. Uh. First of all, I got to 9 00:00:31,400 --> 00:00:34,360 Speaker 1: talk about earnings because stock did rally up about four 10 00:00:35,440 --> 00:00:38,199 Speaker 1: in today's session. Walk us through the quarter and the 11 00:00:38,240 --> 00:00:42,120 Speaker 1: outlook for the company, if you may. Yes, it was 12 00:00:42,159 --> 00:00:45,599 Speaker 1: a great quarter for for all tricks. We had revenue 13 00:00:46,200 --> 00:00:50,640 Speaker 1: at thirty point three million, uh seven percent growth in 14 00:00:50,640 --> 00:00:56,800 Speaker 1: our international business, growth in in our total customer base 15 00:00:57,360 --> 00:01:00,880 Speaker 1: period over period. There's almost enough not to like about 16 00:01:00,920 --> 00:01:04,840 Speaker 1: what we what we've posted, and thirty four net retention. 17 00:01:04,959 --> 00:01:08,320 Speaker 1: So things are good, and uh, you know, we're we're 18 00:01:08,959 --> 00:01:11,440 Speaker 1: bullish on the future. So I'm anticipating that the kind 19 00:01:11,480 --> 00:01:14,800 Speaker 1: of metrics that we saw in this most recent quarter 20 00:01:14,840 --> 00:01:19,240 Speaker 1: they'll continue for the rest of the year. Looking into well, 21 00:01:19,440 --> 00:01:21,640 Speaker 1: I think I would have advised everyone to look at 22 00:01:21,640 --> 00:01:24,520 Speaker 1: the analyst reports and take a look at consensus. That's 23 00:01:24,560 --> 00:01:28,560 Speaker 1: probably the best way to view it. H check our 24 00:01:28,560 --> 00:01:32,400 Speaker 1: earnings report. We did give some guidance for for Q 25 00:01:32,600 --> 00:01:34,520 Speaker 1: three and four. But you feel good about the business 26 00:01:34,560 --> 00:01:36,440 Speaker 1: in terms of what you're seeing from your customers and 27 00:01:36,480 --> 00:01:40,039 Speaker 1: in terms of lining up new customers. Oh. Of course, 28 00:01:40,120 --> 00:01:43,040 Speaker 1: the the market for self service data analytics is UH 29 00:01:43,520 --> 00:01:46,120 Speaker 1: opening up more and more every quarter. The emergence of 30 00:01:46,240 --> 00:01:50,120 Speaker 1: chief data officers arriving on the scene, especially in global 31 00:01:50,120 --> 00:01:53,880 Speaker 1: two thousand companies UH is refreshing. UM. Nobody wants to 32 00:01:54,000 --> 00:01:55,960 Speaker 1: limp their way to greatness in the analytics. They want 33 00:01:55,960 --> 00:01:57,960 Speaker 1: to They want to be all in and get the 34 00:01:58,080 --> 00:02:01,800 Speaker 1: get the networking, effective bull data in technology to drive 35 00:02:01,840 --> 00:02:04,640 Speaker 1: business forward through analytics. You guys have an interesting story, 36 00:02:04,680 --> 00:02:07,800 Speaker 1: did I p O. You're up about fifty since that 37 00:02:07,880 --> 00:02:10,320 Speaker 1: I p O earlier this year. UM, tell us a 38 00:02:10,320 --> 00:02:12,959 Speaker 1: little bit about how you guys went about going public, 39 00:02:12,960 --> 00:02:16,399 Speaker 1: because you did stay private for some time. We did. 40 00:02:16,480 --> 00:02:19,800 Speaker 1: It was a twenty year old overnight success. I guess 41 00:02:19,840 --> 00:02:24,840 Speaker 1: you could say we were being an entrepreneur in Orange County, 42 00:02:24,840 --> 00:02:28,880 Speaker 1: California's maybe a little bit different than entrepreneurialship in in 43 00:02:28,960 --> 00:02:32,520 Speaker 1: the Silicon Valley. We We were very disciplined in our approach. 44 00:02:32,960 --> 00:02:38,519 Speaker 1: We were self funded for fourteen years. We after fourteen years, 45 00:02:38,520 --> 00:02:40,959 Speaker 1: we knew the market for self service was opening up 46 00:02:41,160 --> 00:02:45,000 Speaker 1: and that it would be a land grab. We raised 47 00:02:45,040 --> 00:02:47,520 Speaker 1: a hundred and sixty three million through three year rounds, 48 00:02:48,120 --> 00:02:52,120 Speaker 1: have an amazing group of vcs that have helped us 49 00:02:52,320 --> 00:02:56,800 Speaker 1: get to the public sector. And uh, now we're on 50 00:02:56,800 --> 00:02:59,320 Speaker 1: a new journey. It's it's kind of like the same 51 00:02:59,360 --> 00:03:02,320 Speaker 1: all over again, but we now have some strategic capital 52 00:03:02,400 --> 00:03:06,240 Speaker 1: for acquisitions too, of which we've made this year one 53 00:03:06,320 --> 00:03:09,440 Speaker 1: pre I, p O and one Post. And the team 54 00:03:09,480 --> 00:03:12,320 Speaker 1: is excited and we see lots of opportunities. Had Dean 55 00:03:12,400 --> 00:03:14,760 Speaker 1: by staying private and doing things kind of on your 56 00:03:14,800 --> 00:03:17,200 Speaker 1: own and staying private for two decades, what did that 57 00:03:17,240 --> 00:03:21,519 Speaker 1: allow you to do? Well? It allowed us, I think, 58 00:03:21,560 --> 00:03:26,440 Speaker 1: to have the luxury of of adjusting when we needed 59 00:03:26,480 --> 00:03:30,440 Speaker 1: to adjust, and doubling down on spend when we saw 60 00:03:30,480 --> 00:03:33,840 Speaker 1: the opportunities, and holding back on spend when we weren't 61 00:03:33,840 --> 00:03:37,720 Speaker 1: certain of where the market was was going. The whole 62 00:03:37,760 --> 00:03:41,480 Speaker 1: generational shift in enterprise computing has been an interesting one 63 00:03:41,520 --> 00:03:44,440 Speaker 1: and and in some sectors it's moved very very quickly. 64 00:03:44,480 --> 00:03:48,120 Speaker 1: In other areas, it's moved more slowly. We actually, I 65 00:03:48,160 --> 00:03:51,040 Speaker 1: think we're waiting for the big shift in the in 66 00:03:51,080 --> 00:03:55,080 Speaker 1: the analytics space for a long time, and h it 67 00:03:55,160 --> 00:03:58,640 Speaker 1: opened up in in and you know, pedal to the medal. 68 00:03:58,880 --> 00:04:00,800 Speaker 1: What kind of customers do you eyes have? Because I think, 69 00:04:00,840 --> 00:04:03,040 Speaker 1: you know, we're in such an environment where we talk 70 00:04:03,120 --> 00:04:05,680 Speaker 1: so much about the information the data points that are 71 00:04:05,680 --> 00:04:07,880 Speaker 1: out there, and everybody's trying to figure out kind of 72 00:04:07,880 --> 00:04:10,400 Speaker 1: how to sift through it, make it useful, make it 73 00:04:10,400 --> 00:04:16,680 Speaker 1: productive for their companies. Your customers run the gamut well 74 00:04:16,680 --> 00:04:18,920 Speaker 1: they do, and we're you know, I think, I think 75 00:04:18,920 --> 00:04:20,920 Speaker 1: at the end of the day, we're really about putting 76 00:04:20,920 --> 00:04:23,240 Speaker 1: the thrill back into problem solving. And we do it 77 00:04:23,240 --> 00:04:27,760 Speaker 1: in almost every industry, from financial services where we see 78 00:04:27,880 --> 00:04:31,880 Speaker 1: banks doing derivatives modeling, to retailers who are who are 79 00:04:31,920 --> 00:04:36,200 Speaker 1: doing replenishment modeling to improve same store sales. We're seeing 80 00:04:36,200 --> 00:04:41,200 Speaker 1: it in risk and fraud, insurance manufacturing, and so that 81 00:04:41,240 --> 00:04:44,840 Speaker 1: the customers are pretty much everywhere. I would suggest that 82 00:04:45,800 --> 00:04:49,440 Speaker 1: anyone who's not developing a data and analytics culture in 83 00:04:49,480 --> 00:04:54,039 Speaker 1: their company needs to worry, because, uh, data and analytics 84 00:04:54,040 --> 00:04:56,080 Speaker 1: are the things that actually will make the difference between 85 00:04:56,120 --> 00:04:58,599 Speaker 1: winners and losers. Do you see from your experience any 86 00:04:58,640 --> 00:05:00,520 Speaker 1: industry that is kind of behind the times with this 87 00:05:00,600 --> 00:05:04,400 Speaker 1: just got about fifteen seconds here. No, I I think 88 00:05:04,440 --> 00:05:06,720 Speaker 1: everyone's stepping up. People. You know a lot of companies 89 00:05:06,760 --> 00:05:09,240 Speaker 1: went to sleep industrial giants and they woke up data 90 00:05:09,240 --> 00:05:12,400 Speaker 1: and analytics companies, and and they're they're rushing to catch 91 00:05:12,480 --> 00:05:15,440 Speaker 1: up in many cases, and we are there to help them, 92 00:05:15,440 --> 00:05:17,600 Speaker 1: whether they like it or not. Right, they've got to 93 00:05:17,640 --> 00:05:20,520 Speaker 1: embrace all the data that's out there. Jean Stoker, thank you, 94 00:05:20,600 --> 00:05:24,200 Speaker 1: chief executive officer at alter X, on the phone from Irvine, California, 95 00:05:24,720 --> 00:05:27,680 Speaker 1: with customers like Audi, Coca Cola, and hyatte Uh. This 96 00:05:27,760 --> 00:05:28,600 Speaker 1: is Bloomberg Radio.