>> Live from Las Vegas, it's theCUBE. Covering Adobe Summit 2019. Brought to you by Adobe. >> Hello everyone, welcome back to theCUBE's live coverage, here in Las Vegas for Adobe Summit 2019. I'm John Furrier, Jeff Frick my co-host this week. Michael Yung 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, and stuff they do? >> Asia Miles is the loyalty reward program of the Hong Kong, Cathay Pacific Airways. So, typical airline, but we have the reward program to support our members of Cathay pacific airways. We have over, about 11 million members, and over 700 partners around the world. >> How many members? >> 11 million. >> 11 million? >> Wow. >> That seems like a lot to me. (laughs) >> We are the leading loyalty program in the region, in Asia. In fact we started the program about 20 years ago, so in 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, flight tickets, and even for tablet computers or mobile phones. So you can do all of this. >> So, you did the web 1.0, web 2.0, web 3.0. (laughs) You've lived the journey. >> Paper 1.0. >> So my job is actually leading the digital part of the team. As you know, like loyalty program, you don't have protection lines, you don't have branches, everything is digital. So our web, our mobiles, our engines to support the earnings, and engines to support the reductions are all digital. So basically, we are more like a digital marketing company, we links the partners, their products, their offers, to our members. >> So, important is obviously the data, it's super important. And having connections points, APIs, open systems. Is it open APIs? >> Yes, all of these are technologies in our stack. So, basically our membership profiles are 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. >> That sounds like good architecture. Now what, specifically of Adobe product stack, are you using, for Adobe? >> We used almost the whole suite of Adobe products. We started our baby step about three years ago with Adobe Experience Manager. Basically our contact management systems are website or mobile. And then we extended to campaign to automate our marketing campaigns. And then later on audience manager, target and analytics. So it has evolved. So basically a full stack. >> So you're a big customer of all the products. So one of the big things they're talking about is the data, role of data, and machine learning's coming up a lot. How are you applying machine learning, with all those millions of members, and all the different diverse contact you have, and the different connection points to partners. You have to, kind of have this free flowing operating environment, platform yourself. So how are you using machine learning to either automate away things that you're doing manually, or creating new innovation insights. >> As I mentioned, we have to match the offers from our 700 partners to 11 million members, right. And therefore we build certain technologies, like propensity modeling, that we can tell, say from you miles balance, your life stages, your persona, and your lifetime varial, and then we do, what we call the partner recommendation engine. So the recommendation engine will push certain offers to John, or to Jeff already, based on all your profiles. And that requires some machine learning and modeling as well, from our data scientists. >> I'm curious how the expectation has changed over time in terms of, kind of what your members 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 often experience. >> Right, great 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 see, 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 retails partners as well. So all this, 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'd you promote San Francisco flight ticket to me? Or hotel again? >> Right. >> I'm not going to San Francisco again. >> The re-targeting thing is brutal. >> Brutal, yeah. So you have to really base it on the transaction history, and the other features or signals, and then define the next offer. And this is really important. >> 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. >> But I mean, even in Hong Kong, Monday's a slow day for business. >> Right, right, right, right. >> So it's good to help out the partners a bit, you earn double miles. Or on certain important days, or holidays, you get triple miles by buying something. So it's natural for our partner's, and our member's expectations. >> You have an economy. (laughs) It's like, you've got to have a fiscal policy. >> Well let me tell you all loyalty programs pretty run like this. >> It's really highly data driven, you have reputation, you have influence. >> Exactly. >> It's very important, I'd almost imagine, contextual understanding about what's happening, and having the right data. You mentioned that re-target thing, about San Francisco. I see this all the time on re-target, they don't have the context. I mean that really makes for a really poor personalized experience. Talk about context, having data in context to something. How hard is that? >> Well it's really from data, turned into information, and then actionable insight, it's really hard. So, even we have so many team members doing all this modeling, 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 context. 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 digital, all digital. You have Adobe Suite, cloud computing scale, a lot of data context, a lot of usable data. What's next for your business, what's next for you. >> Well, last year we started to test the water to try out blockchain technology. So we have the marketing campaign rules, and packed that in a 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 has got to be more real time, and you can earn your bonus miles straight away, instead of waiting two, three months until the end of the campaign. So hopefully with the marketing platform, and also newer technology, and better data, we can do better campaigns. >> In terms of skills to deal with the kind of things that you're doing, with future proofing your business with blockchain, love that. Smart contracts going on, peer to peer, immutable, love that value proposition. You get reputation, move that over into currency. >> One of the options. >> Asia coin. (laughs) >> Optimize is one of the options. >> What else is on your mind? KPIs, how do you look at data sets, how do you guys view? >> Measure success. >> How do you measure success? >> Well, I would say first of all, all the stakeholders have got to be happy with the program. I mean, the stakeholders include our members, partners, our shareholders, and our employees. They're important to make sure that the program is successful. And also including the engagement ratio, and our package ratio, where there are a lot of members that usually don't have chances to redeem things, and then they let the miles expire, for example. So helping them maintain a healthy package ratio is also a KPI that we measure carefully. And then, other than that I think all our employees or staff, they let you know, or they need 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 skillset why you have to understand both. >> How are you using technology, especially Adobe, how is Adobe helping you, and then what other things you might be doing, to help internal processes get better? Because one of the things 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 to insights that might not be there. So that's going to cause some internal disruption. People might lose there job, or new jobs emerge, there's always conflict 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 marketing platform, or Adobe days, you may need to take weeks to prepare a campaign, if not months. Because you need to prepare all the contents, all the lead assignments, and then you push out through all the different channels. But now you can be always on campaign, different dates. 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 number crunching, we do reconciliation. So I think, it's not about with less resources, but with the same resources, how to do more things. >> Right. >> And it's almost continuous improvement on the campaign. >> Yes, continuous, all the time. >> Versus just, you know, let's plan a campaign, run a campaign, measure the campaign. It's just constantly going. >> Prepare, run it, and then measure. Just never ending. >> As an Adobe customer do you like the direction that they're going? >> Yes, yes. All exciting products are in the road map. And we are ready to explore more in the future. >> Michael, thank you for coming on and visiting us. >> Okay, my pleasure. >> We appreciate it. Here inside theCUBE we're taking all the action, here at Adobe summit. Getting the data, sharing it with you out in the open internet. Thanks for watching, I'm John, with Jeff Frick. Stay with us for more coverage from day one after this short break. (upbeat music)