 Live from Las Vegas, it's theCUBE. Covering Adobe Summit 2019, brought to you by Adobe. Well everyone, welcome back to theCUBE's live coverage here in Las Vegas for Adobe Summit 2019. I'm John Furrier with Jeff Frick, my co-host this week. Michael Young is the CIO of Asia Miles. Welcome to theCUBE, thanks for joining us. Great to be here. So take a minute before we get into the conversation about machine learning and all the cool tech. What does Asia Miles do? What's your role there? What's the company do? Right. Asia Miles is the loyalty reward program of the Hong Kong's Cafe Pacific Airways. So typical airline, but we have the reward program to support our members of Cafe Pacific Airways. Okay, so we have over about 11 million members and over 700 partners around the world. How many members? 11 million. 11 million. Yeah. That seems like a lot to me. Yeah. Right. We're the leading loyalty program in the region in Asia. In fact, we started the program about 20 years ago. So 1999. So this is our 20th anniversary. Wow, congratulations. So similar to any loyalty program, our members can earn miles by flying, traveling, dining, shopping, even have your mortgage with our banking partners. At the same time, using the miles, you can redeem rewards. Hotel stays, freight tickets, and even for laptop computers or mobile phone. So you can do all sorts of this. So you did the Web 1.0, Web 2.0, Web 3.0, you've seen it all. You've lent the journey. Yes, yes, yes. Paper, paper 1.0. Yeah. And so my job is actually leading the digital product team. As you know, like loyalty program, we don't have production lines. We don't have branches. Everything is digital. So our web, our mobiles, our engines to support the earnings, and engines to support the redemption are all digital. So basically, we are more like a digital marketing company that we link the partners, their products, their offers to our members. So important is obviously the data. Yeah. It's super important. And having connection points, APIs, open systems, is it open APIs? Yes, so all sorts of these technologies in our stack. Yes, and so basically our membership profile or databases, and then with APIs, we can do all sorts of modeling or calculation or segmentation. And then we push through our marketing offers or campaigns to our targeted members. So that's a good architecture. Now what specifically of Adobe product stack are you using for Adobe? We used almost a whole switch of Adobe products. We started our baby step about three years ago with Adobe experience manager. Basically our content management system support our website or mobile. And then we extended to campaign to automate our marketing campaigns. And then it on audience manager, targets, and analytics, so on and so forth. So basically a full stack. So you were a big customer of all the products? Yes, you did. So one of the big things you're talking about is the data, role of data, and machine learning's coming up a lot. Yes. How are you applying machine learning with all those millions of members? Yes. And all the different diverse content you have. Exactly. And the different connection points to partners. You have to kind of have this free flow and operating environment. Platform yourself. So how are you using machine learning to either automate it away, things that you're doing manually, or creating new innovation insights? Yeah, as I mentioned, we have to match the offers from our 700 partners to the other millions members, right? And therefore we build certain technologies like propensity modeling that we can tell from, say, from John, your mouse balance, your life stages, your persona, and your lifetime value. And then we do what we call the partner recommendation engine. So the recommendation engine will push to certain offers to John or to Jeff directly based on all your profiles. And that requires some machine learnings and modeling as well from our data scientists. I'm curious how the expectation has changed over time in terms of what your members kind of expect to get out of the application. Because I assume they want more and more and more. What was special today is common tomorrow. And how you've been able to continue to adapt and change what you offer the experience. First question. First of all, our members really like to go mobile. So our offers have to be location-based. So with your mobile apps, then you can say, okay, what are the popular restaurants around me that I can earn miles easily? Or if it's a Monday, then you can earn, say, double miles if you buy something with retail partners as well. So all these, the partners and the members expect more. And secondly, members are smart enough to tell that, oh, your offers is generated by a machine. It's not personalized enough. For example, if I just fly to San Francisco last week, why you promote San Francisco flight ticket to me? Or hotel, again. I'm not going to visit San Francisco again. The re-targeting thing is brutal. Yeah, brutal, yeah. So you have to really, based on their transition history and their other features or signals, and then define the next offer. And this is really important, yeah. And do you help the customers figure, because you just said, if you eat out on a Monday, maybe you get double miles because the restaurants are slow. Is that something that you guys have discovered in your analytics that you're helping your partners to get more pull on their offers? Or is that being driven from them? Because you have a lot, you've got a lot more data than an individual restaurant or some of your other partners. And I mean, even in Hong Kong, Monday is a slow day for restaurants. So it's good to help out the partners a bit. You earn double miles. Or in a certain important days or holidays, you get a triple miles by buying something. So it's a natural for our partners and our members' expectations. You have an economy. It's like, you've got to have a fiscal policy, you know. Well, let me tell you, all loyalty programs are pretty much like this, yeah. It's really highly data-driven. You have reputation, you have influence. You have... It's very important. I almost imagine contextual understanding of what's happening and having the right data. You mentioned that retargeting thing about San Francisco. I see this all the time on retargeting. They don't have the context. I mean, whether it's a... That really makes it for a really poor, personalized experience. So talk about context. Having data in context to something. How hard is that? Well, it's really hard from data turning to information and the actionable insight. It's really hard. So even we have so many team members with doing all these small things. There are times that we need powerful tools to do proper segmentation and targeting. And that tool's got to be really flexible and fast responsive to certain contacts. And with that, Adobe products help us a lot. What's the biggest to do for you going next step? As you continue to grow, you're a digital, all digital. Yes. You have Adobe suite, cloud computing scale, a lot of data context, a lot of user data. What's next for your business? What's next for you? Well, last year we started to test the water to try our blockchain technology. So we have the marketing campaign rules and back that in the blockchain smart contract. And this is one of the things that we invested a lot of time and resources into it. We believe in the future marketing campaign got to be more real time. And you can earn your bonus amount straight away instead of a way for two, three months until the end of the campaign. So hopefully we have the marketing platform and also new technology and better data. We can do better campaigns. In terms of skills, to deal with the kind of things you're doing with, you get the future, future-proofing your business. Blockchain, love that. Smart contracts going on, peer-to-peer, immutable, love that value proposition, get reputation, move that over to the currency. Yeah, one of the options, yeah. Asia coin. Yeah, tokenize it, yeah, one of the options, yeah. What else is on your mind? KPIs, how do you look at data sets? How do you guys view, how do you measure success? How do you measure success? Well, I would say, first of all, all the stakeholders got to be happy with the program. I mean, the stakeholders include our members, partner, our shareholders, and our employees. They're important to make sure that the program is successful. And it also includes the engagement ratio and our breakage ratio, whether, you know, there are a lot of members that, because they don't have chances to redeem things, and then they let the mouse expire, for example. So how we can maintain a healthy breakage ratio is also a KPI that we measure carefully. And then, other than that, I think all our employees or staff, they lead to know, or they lead to understand how technology and business mix together. If you're good in business, but not knowing marketing technology, for example, or if you only understand technology, but not the business, for example, it's just not good enough for the future. So the skill set why you have to understand both, yeah. How are you using technology and especially Adobe? How is Adobe helping you? And then what are the things you might be doing to help internal processes get better? Because one of the things that I'm seeing here at this show is with the platform, as you start to thread the data together and let the data kind of naturally flow with machine learning and the different data points, you can start to get some visibility into insights that might not be there. So that's going to cause some internal disruption. People might lose their job or new jobs emerge. There's always conflict when you're moving, when you're progressing. How do you use technology and this technology to keep getting higher functionality, better economics? What's the internal struggles and gains look like? Well, for example, before the days of a marketing platform or Adobe days, you may need to take weeks to prepare a campaign, if not months, okay? Because you need to prepare all the contents of the data segments and then you push out through all the different channels. But now it can be always going on campaign within days. And for the blockchain example, we can actually eliminate the reconciliation and settlement effort. So the back office operation team, they can move along to do something else, to do more campaigns or to talk to the partners more to understand their needs. Instead of just keeping just number crunching, we do reconciliation. So I think it's all about with less resources, but with the same resources, how to do more things. Yeah, that's our goal. And is it almost continuous improvement on the campaigns versus just, you know, let's plan a campaign, run a campaign, measure the campaigns. It's just constantly going. Yeah, prepare, run there and then measure. Just never ending, yeah. As an Adobe customer, do you like their direction that they're going? Yes, yes. All exciting products are in the roadmap and now we are ready to explore more in the future. Yeah. Michael, thank you for coming on and visiting with you, appreciate it. Here inside the queue, we're taking all the action here at Adobe Summit. Getting the data, sharing it with you out in the open and the internets. Thanks for watching. I'm General Jeff Frick. Stay with us for more coverage from day one after this short break.