 from San Francisco. It's theCUBE. Here is your host, Jeff Frick. Hi everybody, Jeff Frick here. We're on the ground at the JW Marriott in San Francisco, California at the location and context world 2014. An interesting show, kind of small, but a bunch of passionate people that are early stages in really bringing a whole lot of new capabilities via mobile phone. It's kind of like GPS for inside, which is really cool using sensors and RFID and a lot of technologies that have been around before Bluetooth are really now optimized around mobile applications. So we're excited to be joined by our next guest, Patrick Leddy, the CEO of Pulse8. Welcome. Thanks very much. It's certainly a very interesting day here today. We're talking about a lot of different topics, as you said, indoor GPS. We think it's more about context, not only understanding where a customer is, but also who they are. The mobile device is a great expression of who we are as people, and we can really use that to our advantage. Location is a context, but it's kind of weak in and of itself. So if we can understand customers, not only where they are, but also their behaviors, their habits, their interests, and we can segment against that, that's when we can get really sensible with our interactions and really get a great return and give value to the customer. Yeah, we were talking before we went on there. It's really a layering of the context, right? So it's a layering of, first off, you need the map of the store so you know not only longitude, latitude, and altitude, but actually where you are relative to the store. But then as you said, what you guys help people do is bring in additional layers of context via external sources, I presume, internal sources if they're part of a rewards program. Talk a little bit about the different sources of contextual data that you can bring to bear. Yeah, absolutely. So poll data is about true context. So it's the hybridization of different data inputs and really taking information from different sources. So yes, it's really important to understand customers outside of the store with cellular triatulation and Wi-Fi. And when they come indoors, beacons are great for getting that micro location and understanding where people are relative to their location. But it's only when we understand the person and the expression of who they are. And that's why we look at CRM data and taking in what defines them as a customer. What have they bought before? What are they interested in? And we infer interests on the device by looking at the categories of apps that you have installed so that when we do go out and engage with you, it's not just based on location data. It's based on what you've done before, what you've purchased before. So we're much more relevant, far more integrated with you and considered with communications. We're even taking in weather data. So if it starts to rain outside, probably more likely to buy an umbrella than at some address. So you're like a sub-app that sits in your customer's app that's pulling that data from their CRM and their rewards program. You're pulling the data, the behavioral data on the way they operate their phone and operate the app. You're pulling some inferences based on the app set that they have in their phone. And you said you're also pulling social data. You're pulling Twitter feeds or those types of things as well. So if you want to identify your most influential customers and treat them as VIP and you might take your fans, we have people that have just come onto your venue and you know they're tweeting about it and they've got a lot of followers. You can turn them into superfans by segmenting them and saying you guys are getting a free VIP upgrade today. So we take in social data as well and that social data helps us infer interests. We can look at the categories of apps that are installed but that's kind of broader by taking in the social data. We can get at exact likes and turn them into interests. So when you're talking to customers in marketers specifically, well, let me ask you another question. Do you integrate back into, back out if I've got a really different kind of marketing application that I'm using or I'm using different analytics packages like Tableau, do you guys integrate back out as well? We do, yeah Tableau. I mean, so we've got a restful API on top of the whole product. So you can actually, and I think you can do as a human user in the interface, you can do over machine. So you can API in and you can set up a campaign for your geofence so you can pull a segment, pull your users and you can also pull all of the location analytics data out, put it into Tableau and visualize it or do whatever it is that you might want to do. And yes, you can synchronize your CRM with our system as well. So we ingest all of the CRM data but it's important to flush it back on into your own system so that through your other channels you're making sense of that data. Keep that as privacy. So you want the users to understand that when they're opting in for things like location services that that location data, that they are aware and opting in for it to be used in other channels. It seems to be very careful with that. Right, right. So give us some good examples, right? There's the easy example. I'm standing in front of the shirts at Macy's in a buy shirt and it says, hey, why don't you go buy a tire and send off a tie? But share with us some surprises, some interesting things that nobody expected that using this type of technology really uncovered. Sure, I'll give you an example. So let's take Macy's. Let's jump outside the store for a second. We could create a geofence around the store and we could use that to try and retarget and recapture disengage customers that are passing on by. So we'd start off by going into pulsate and creating a segment. So we'd say show me customers who have been at a beacon in more than 60 days or entrance beacons. So you haven't had any impressions on your beacons. So you just know they haven't come on into the store. So now we've got our disengaged cohort that we've built. What we could define that as well with some loyalty data. So we bring in the loyalty card for Macy's and we say who's disengaged with the physical store but also used to be loyal. So this is a customer base that once was loyal and starting to really move away. So now we can create that geofence around the store, connect the segment with the geofence so that when you walk through outside the store, we process that event on our cloud platform and it's not enough to just know that you're present. We know there's content attached to that location and if you exist in the disengage segment, we can say, hey, we really missed you. Great to see you come back. And then based on the loyalty tier, you could get a free glass of champagne or coffee for coming into the store that day. So looking down the road, I mean, eventually do I even have to do anything or are these applications just gonna start shipping stuff to my house knowing that I'm ready, I like it and this is my size. Yeah, well I think that's definitely where it's heading and I think the measurement for how we improve ourselves as a civilization is the measure of tasks that we can complete without having to be able to even think about them. So the stuff that's just automated for us and I think true contextual awareness and we're debating this here today is when you don't even really know about it, you should just make decisions for you and only interrupt you or only push something to you when it can't really figure out what you want to do. Contextual awareness is about really defining the customer in that moment and preserving the value of their moment and the next moment which is now and then the next moment and preserving that value. And then I think as customers when we understand them and can make decisions for them, they get great value out of that. How are you finding or how a market is finding? Cause I'm sure they're learning on the fly too. To be able to create promotions opportunities that are actually good for me and don't become kind of nagful, just like persistent, like your little brother poking at you all the time. Cause I would imagine there's a learning curve there and I would imagine at some point you're like, all right, I'm just going to my car. I don't need any more Macy's offers. Just drop 300 bucks in there. That's an interesting question. I think it's really important to not fatigue your customer with this kind of information. That is the key word. It's like, oh, I'm getting so much of this stuff and it's like, delete the app, opt out. So you need to be really careful with what you send to customers and that's where the segmentation comes into it. So if there's only one thing that you could send that customer walking into the store that day, you might have a thousand beacons, you might have 10,000 campaigns, but if you really narrow it down with great segmentation, you can send the most relevant content to that person when they walk in the right and really delivering on that message at the right place, the right time. So I think that's important. That's kind of like passively pinging users as they walk around the store while the device might be in their pocket, but I think there is a new way of looking at it. So if they actually actively engage with the app and have the app open, you could build like a context feed of showing them products that are around the location that are near them, that are proximal to them. Can't they see them? But also they can see them with their eyes, but you might want to highlight certain features. You might want to spotlight things that you know they're interested in that could be over there actually heading this way. So on a radar, show that product. It's of interest to them. Show them where it is and what makes it unique and maybe use that to influence their, at the point of purchase. You can bring in weather data and all sorts of emergent data and bring in their actions so you know whether someone is running or walking or cycling or driving and use that to base your decision on whether you message them or not. Are they on a phone call? Do they have low battery? Is their internet connection good or bad? Because if it is, you're at the wrong. You don't want to be going in with a low battery per internet connection and someone is driving. Don't fatigue them if that is their context. Everyone else are competitors. It's focused on we put a beak in there. Someone walks past through an unknown. We don't know about their state. We don't know their somatic data and the emergent data. It's like that's ping them a coupon. So we need to kind of raise the awareness for this technology and what's really possible once we look at the hybridization of data inputs. Wow, so still early days, still a lot of opportunity. So Patrick, thanks for stopping by. Patrick Leddy from Pulse 8, CEO. I'm Jeff Frick. We're on the ground at Location and Context World 2014 in San Francisco at the JW Marriott. You're watching The Cube.