Michelle Chambers: I would jump in and say that we are positioned as an enterprise analytics platform for a new generation. That means we have a smarter, faster, bigger and easier platform to use. That merely ties back to the convergence that Dave was talking about, which the original founders saw. There was this convergence of open source, along with high-performance computing and analytics. The high-performance computing talks about scalable analytics. Right now our platform is aimed at enterprises and is focused on this being a new generation. Just like business intelligence is a term mostly used for backward-looking kind of analytics, predictive analytics is about discovering patterns and predicting future outcomes. There is a new generation of tools, methodologies, and resources that are being trained at universities today. That is all being done around [the programming language] R.
Sramana Mitra: Let’s talk about your target customers. What segments are you going after?
Dave Rich: The data intensive industry is what typically would come to mind – banking, insurance, capital markets, pharmaceuticals, and life sciences. There is a lot of analytics there. Those are the ones that are pulling us in because R is something they learn off campus, and then they come into the workplace. There they see it is easier and cheaper to use. We are being pulled in by those kinds of companies. Within those there are financial services, risks, scoring, or marketing analytics. But now there is also an explosion of interest around fraud and fraud detection. This is not just in the financial services industry; it is an interesting horizontal that is emerging in a lot of industries. Fraud is obviously a big play [in terms of] customer intelligence. The industries that are intensive around data analytics are where we find ourselves drifting into first. Communications, service providers, or digital media are other categories that I would describe.
MC: Financial services, digital media – which would include online advertisers, marketing service providers and gaming companies, content providers or advertisers in that space. You will also find that our customer base is pharmaceuticals. There it tends to be around customer intelligence, marketing analytics and research, where they are looking at all kinds of interesting models around their research activities. Telecommunications is another area that is picking up. Those are the telco providers, mobile providers and service providers, and also the cable and communications piece of it. They are looking at recommendations, propensity models, and marketing analytics. Most of that tends to deal with the customer, although telecommunications is also exploring more around network analytics as well.
SM: What about size? Are you going after large, medium, or small enterprises?
DR: The large ones. We find that we do not need to sell ourselves in some of the large enterprises. They understand analytics. They already have a lot of people who are trained: their data scientists. They want to put the best tools in the hands of these people. That is why they are large capital market companies, banks or telcos that are names. We also get into industries you wouldn’t think of. We just had a conversation among our internal staff about a sudden and interesting approval from the oil & gas exploration industry. There are other industries that one might consider to be less sophisticated from a data analytics perspective, but not if you know a lot about them. They may be specialized but not less sophisticated – quite the contrary. The main industries and therefore the household names in those industries are the ones that are most interested in us, because they have lots of people joining their companies who understand R.