 All right, good morning everybody. This is Dave Vellante of wikibon.org. We're here at Wikibon headquarters, worldwide headquarters in Marlboro, Mass. We're here with Marco Peccelli. Marco, welcome to theCUBE. Thank you. Marco's with ClickFox, everybody. And if you don't know ClickFox, a very hot company, software vendor. We first met down in New York City, right? You were the guys who were ringing the bell with a green plum. Ringing the bell right there in New York. And we learned a little bit about ClickFox. Now, big data, as our listeners know, is a theme that we've really been going after here. We were at Hadoop World, and that's where we met, we, you weren't at Hadoop World, we were, and then we met somewhere downtown. But the big data trend is just exploding. We see with Cyber Monday, everybody's talking about what the web traffic has been doing and comparing growth to last year. And that's all about big data. So, and ClickFox is right at the heart of it, right? So why don't you tell us a little bit about ClickFox and your story? Sure, we've got an amazing solution. Big data is the name of the game now. There's more and more data generated every day. It's with the social networks, with all the customer interactions that are going on. Just the volume of data generated on a daily basis is huge. All these companies store this data and they store it in different silos of information, towers, and they access the data for localized retrieval of value. What we've done is a little bit different. We've taken an approach that says there is value across this data that can be linked together to show the linkage of one data element to the next. And we have a patented technology that's able to take all of this data in, unstructured or structured, and link it all together for a single view of what a customer experiences and how those experiences affect the business positively and negatively. The important thing is is every enterprise out there looks at the data from their own aspect of what doesn't mean to them as a company. No one's ever taken approach of putting themselves inside the customer's feet, inside the customer's shoes, and looking at what is my actual customer's experience? When they walk in my store, what do they feel? What do they experience? And from that moment on, down the path that they interface with us as a company, what are the experiences they have? And at what point in time does that experience become a bad experience and become a bad customer sat and become perhaps a churn where I lose the client? And that's where really we're focused. All that value is in that data. So it's that end, my colleague, by the way, I should have mentioned David Floyer's on the phone. Hello, David. Good morning from California. So when Floyer and I first heard about you guys, the way he described it and David maybe chime in here, you really unique because you do the end-to-end customer experience, is that right? Correct. And David, you had said you hadn't seen anything like that. Is that, am I putting words in your mouth or? Not at all, the degree to which you could combine the data and the speed at which you could develop the applications to look at this and make sense of it, is just revolutionary. To me, it's a game changer. So let's talk a little bit more about ClickFox, who you just closed another round with Morgan Stanley, right? We sure did. A little bank in New York. So it was a C round, correct? It was the C round. And you were talking off-camera, you've raised what, $39 million? $39 million total. All right, and are you done raising money? We're done. We're done. So talk a little bit about some of the company and where you're at and where you're headed. So the money was not raised because we needed it. We're profitable. Nice sign to raise money. Yeah, exactly, right? We're actually profitable, we're doing very well. We've grown at the rates we've wanted to grow in the past six years. We made a decision this year to scale. We believe we're sitting on something that has a tremendous value. And in our customer base, we notice year per year we're moving up the food chain more and more every year in these customers and demonstrating the return on investment is greater than just one division of a customer. So the money was raised for us to really invest into three areas. One is to build a global sales organization that actually focuses on a customer issue and hire people that can actually understand solution selling. Second is to create a viral marketing machine around the concepts of experience analytics, behavior analytics, customer experience analytics, and really help the industry and market understand the value around these kind of analytics. And the third is I plan on probably doubling my research and development organization. There are a lot of really cool things we want to do in the next 12 to 24 months around using artificial intelligence and neural networks to do automated type analytics and predictive analytics. And we do have a lot of patents already and many more in filings. So we believe we can probably triple the volume of patents that we have been doing over the next couple years around some of the concepts. So what's your head count today? Head counts around 120 people. 120, all right, so you've already got a pretty good critical mass. And where do you see that a year from now? I think by the end of 2011, it'll probably be around 190. OK, these guys are hiring folks. We're hiring. What are you looking for? Good salespeople. Yeah, OK. Good salespeople and innovative, young, talented minds. All right, good, good. So can you tell us some of your customers? Yeah, we have all the wireless providers in North America, so the Sprint, Verizon, AT&T, T-Mobile, Rogers, Boost, Virgin Mobile, all clients. We have Chase, B of A's clients. The financial services must love this stuff, right? And we have a handful of electric and power companies, you know, Duke, Florida Power, Sempra, Atmos. And they're becoming even more and more accepting more and more of this concept of experienced analytics in order to give better service to their customers. So a lot of clients, a lot of different verticals. It is expanding. And it's really cross-channel. What I want to make sure you understand is I can take any kind of data. So even in the power electric field, I can take everything from a meter, reader, data, all the way down to an agent interaction and tie it to a phone call. You know, in the cable business, we can take what's done on a cable box every day by a client, all the way down every path that the customer might touch, and tie it all together to determine is there something that customers are trying to do on that cable box that causes them to go to a website, that causes them to make a phone call, that causes them to talk to an agent and repeat and repeat and repeat and never get their answer or get their answer, but could have gotten it a lot quicker. So you're actually putting forth a promise that our cable service is gonna get better? Yeah, I wish I could. So David, you've talked about the analytics business. I wanna talk about what's changed. So, Floyer, talk a little bit about, you call it rear view mirroring, right? And how the business has been just struggling to get to sort of real time or near real time. Can you characterize that a little bit? And then I wanna, Marco, to talk about what's changed in your business and where you guys are at and where we see you coming. Sure. I was talking to Rob Strickland yesterday, when was one of your customers at T-Mobile. And he was talking about two things which are really important to him. First of all, it's just the speed at which you, which he could develop these sort of analytic views of the world, of his world. And with his previous experience with some of the other larger methods of doing it, he, it took him about six months to get things off the ground. But with the new methods of doing it, he could do that within a quarter. And so he characterized it as rear view mirror for one year versus rear view mirror for one quarter, getting too closer to being able to affect things within the quarter, look at trends, look at what is going on and actually make decisions and change things within a quarter. So that doesn't sound that dramatic. I mean, it's dramatic in the sense that it's cut it in half or maybe even more than half, but are we at the point now where we can get to real time or near real time or is that sort of a decade off, Marco? It depends how you want to define a real time. So there is the real time processing of data to analyze it real time, meaning get the data real time, analyze it real time. I believe that we are very close to achieving that in the industry whereby you can ingest the data at the time it happens and analyze it. The problem is making the decision on what to do with it real time, because you want to get to a point eventually where you can change the experience the customer just had the next time they have it in order to make it better. So therefore you gotta look at a window and predict when will that client have that similar experience again? Will it be in 24 hours or will it be within day? Can we ever get to a point where we can change the behavior of a customer real time? That will be very difficult because there are so many systems out there that have been built that you have to touch everything from a retail experience to a web experience to a self service IDR experience to a routing experience to an agent, to an email, to an SMS, to a chat. There are some of those experiences that I believe we can affect real time so that for instance, let's say you did walk in a store and you upgraded your phone and you walked out of that store and the first thing you did was make a phone call because you couldn't figure out how to set up your voicemail. Now, I have a couple of things I can do. If I analyze that experience real time and I was able to compare it to maybe a million other experiences that have happened with similar clients, with similar phones and similar type of behavior patterns of customers. Maybe the same type of age group, the same type of users, the same region. And I could come up with a real time answer says I'm predicting that this customer when they walk out of that store will make a phone call about their mailbox. I could in a real time fashion create a message, SMS, email or some type of communication back to that client at the time they walk out the store explaining to them something that I know they're gonna have to understand and do that other clients similar did even yesterday, right? That's the kind of real time interaction I can get. I'm being proactive and I'm deflecting. I'm deflecting a bad experience. I'm deflecting a bad customer set from happening. Because you're anticipating the recording. I picture myself walking into an Apple store going through a process of buying a new device and it's always real comfortable. Then you walk out and say, okay, how do I do that? And then maybe send me a little message. There you go. I knew you were gonna do it, right? Or better yet, now with the social networks, what I could do eventually is I could put you in touch as soon as you walk out of that store with clients that are very similar to you and link you together as a social network so that you can tag them and say, hey, by the way, I understand you just have bought the same phone. How do you do this? You're not even calling me or asking me as a service provider for the answer. You're asking a social network that has done the same thing as you. Right, so you can actually bring in that type of social, gestural interactive data into your system? I can feed the information so that the right social network links are created for the right purpose, for the enterprises, for the wireless companies. Says there are a thousand customers like you that bought the exact same phone, have similar type of experiences and similar type of segmentation information. Why not link you together? So when you walk out that phone, you already got a list that says, by the way, if you wanna talk to anyone that has the same phone as you and ask them how to do things, it's right there. All right, so you're talking about recognizing patterns of, say, a mob of people who are doing similar behavior. Is your industry actually, I mean, there's no privacy in the internet anymore, right? Is the industry actually getting down to, you know, targeting specific individuals, maybe IP addresses, putting aggregating information about that individual, or is that sort of taboo, or is it just gray area? I see it happen in the next three years, absolutely. So, I mean, that's pretty powerful from a lot of standpoints. I mean, there's a unity yang there, right? There's the opportunity for advertisers, for example, and then there's the whole issue of privacy and that does not track me. There's also the opportunity of deflection, of creating the right atmosphere for the provider to create the right experiences for the clients, which says I'm trying to get you your answers in a way that, so I don't waste your time, right? And the one thing we all hate is we hate getting on the phone and trying over and over and over again to get an answer to a question. I don't have 20 minutes in the day, do you? You're solving a good problem, right? Because everybody hates that, right? Whether you're calling a bank or a cable company or any retailer. Correct, I mean, you'd rather get your answer by asking someone via text message or just having the answer sent to you, right? Yeah, so this privacy thing is interesting to me because, you know, again, on the one hand, people are concerned about their privacy. On the other hand, it sounds like you're offering some benefit to the consumer. Correct. You know, so there's got to be ways for the industry to provide sort of an opt-in model. So, okay, I'll allow you to track my behavior as long as there's something at the other end that I get benefit from of the night. Correct, correct. So over the next few years, that has to come together. And, you know, normally software companies aren't in the middle of that, but you are. Yeah. So, all right, that's cool. So, and the other thing is when we were talking on the phone, David, you and I, you took us through an example of one of your customers that actually, they didn't know what they didn't know in the customer experience. Can you take us through that? You know which one I'm talking about? Sure. Yeah, take us through that because I thought it was fascinating for us. So we have quite a few clients that were doing cross-channel analytics, everything from retail down to agent. And when we start the projects, we typically ask a lot of questions around, what do you believe your self-service rates are? What do you believe your customer's sat reasons are for having low C sat or high C sat? And, you know, even questions around, what do you believe is a reason for churn? And most of the time, actually all the times, you know, they come out with self-service rates that are in the double digits. You know, 30% of our clients self-serve, 45% of our clients self-serve. And they also give us answers such as churn is attributed to the fact our competition has better pricing or our competition releases fancier and cooler products. And C sat, they always end up telling us it's because the customers don't get the service and the products that they want at the time they want. When we start implementing this technology and we uncover things, we quickly can show them that a lot of those things they thought were incorrect. So for instance, we had a client that believed they were doing about 38% self-serve. That means customers would go into their voice response unit and self-serve at a rate of 38%, the rest would push zero and go to an agent or go to the website and self-serve. Well, when we ran the analytics, the reality was about 9% were self-serve. 9%. And they thought it was 38%? They thought it 38%. And if you look at the cost of an agent interaction, it's about $2 to $5 a call. When it becomes a technical call, it goes up to 15 to 25. So if you look at clients that are doing 50 million interactions a month, right? At those kind of numbers, it's a big number. A serious cabbage. And the same thing that you look at customer sat, why are you frustrated with your service provider? I mean, you're only frustrated if you have to call them and you don't get an answer. Right? I mean, you buy a phone, you pretty much use it and you're happy with it. And you'll buy a new one in six months when they have a new product. But if you never call and never have to ask a question, you're pretty happy with everything. As long as the bill's not gonna go over what it should and your phone does what it says. But as soon as you make a phone call and ask for help or ask for a question about a bill, you start getting frustrated, right? So when you look at customer sat and you plug it into a product like this and you're able to go backwards upstream and say, where did this customer start with our relationship? And what have their experiences been since? And tie that to which experiences have caused this customer to get frustrated with us. You find the needle in the haystack. Same concept. Right, right. Now we met you guys through the good folks at Green Plum. Yes. It was serendipitous. But so what do you guys do with Green Plum? What's the relationship there? Green Plum is a really cool technology. We're really happy to have them not only as a partner but also be a client of theirs. Prior to Green Plum, this type of analytics would take hours and days to run because of the efficiencies of the databases. So if we ran Oracle and to load a typical client of ours that does 11 million interactions a day across multiple touches into our system would take a day to load. So you'd always be a day behind. Green Plum has brought to the industry a technology that allows data to be loaded and retrieved so quickly and get to the data you need to so quickly that those kind of loads today, 11 million interactions a day can be done within an hour. And the analytics behind it can be done almost real time. So we love it. It's a great technology and we looked in the future and doing much more with them. Yeah, so they, and they, go ahead, David. Again, going back to talking to Rob, he was saying that the difference between doing it with Green Plum and on an appliance was four to five times. He said that if he'd done it with Teradata it would have been about 18 million but with Green Plum about three to four million to get to the same point. You talking dollars? It was just dollars, yeah. Yeah, okay. Yeah, yeah, very true, very true. Yeah, it's game changing. Look at the volumes of data out there. And what we're analyzing is a type of data that no one ever thought had value, interactions, okay? I mean, most of the data that's stored in these big data warehouses and the EMCs and Teradata's and Oracle's and IBM's of the world has been transactional data. You bought a phone. You paid a bill. You activated an account, that kind of data. No one's ever thought of there'd be value around interactions means you walked into a store and you did something, okay? There's a transaction and an interaction. Now you bring those two worlds together and I have the entire picture of a client. Yeah, so you take that transaction, tie it to the interaction, tie it to a client and then look at that customer experience end to end and from that extract valuable information on how to make that experience better. And now we were talking about the Hadoop movement before. I think we did an intro to Mike Olson. I hope you get a chance to meet him. Fabulous, this is Cloudera, who is sort of the red hat, if you will, of the Hadoop movement. And I think, and now Green Plum has a relationship with Cloudera and it's sort of unclear where they're going. I mean, I think they're both in the big data business, you're in the big data business and Hadoop is all about that massive amounts of unstructured data on the web, sort of very decentralized and so now you can start bringing in all kinds of other information from social networks and user gestural data, cell phone data. Yes, everything. That's just tremendous opportunities there. So that's something that we've been following at Wikibon, our friends at Silicon Angle and we're all over that. We're watching ClickFox, Marco Pacelli, Hot Company. Thank you. Fabulous having you here inside the Cube. Thanks for coming by. Thank you, it's a pleasure. Great to have you. Good to have you. Thanks for watching, everybody. Thank you, David. Bye-bye.