Bootstrapping a Billion Dollar Unicorn with Services from Utah: Dave Elkington, CEO of InsideSales.com (Part 2)

Dave Elkington: I decided to quit and come back to school to get a master’s in Computer Science. The rationale was two-fold. One, I believed that coding was not that hard. It’s not as challenging as the programmers try to make it. We were paying, at that time, exuberant amounts for people who weren’t that qualified. At the same time, they weren’t delivering. Additionally, if we take a trip back to my undergraduate, what really enamored me about Philosophy was a subset around Set Theory, Logic, and Epistemology. I was really interested in the way people learn and categorize data. Specifically, I was interested in the Aristotelian view of the world—categorization of things. I had a philosophy around the way I believed people consume and aggregate data. I believe that, ultimately, it was a system versus something that can be organically built within the human brain.

As I decided to go back to college to get a master’s in Computer Science, I wanted to see if I could demystify code and technology. Even more importantly, I wanted to see if I could systematically and more importantly, algorithmically represent my philosophical theories that I’ve established. I came back to BYU to get a master’s in Computer Science. As you can imagine, I’m sitting in the office of the Department Chair and >>>

Thought Leaders in Big Data: Keith Anderson, VP Strategy of Profitero (Part 5)

Sramana Mitra: Where are the opportunities to do new and innovative companies in this whole space?

Keith Anderson: There’s at least a couple of ideas that spring to my mind. There’s always a diffusion of technology over time from the very high-end to the broader mainstream market. Think of technology that originated from the Department of Defense, for example, and then eventually making it to the more mainstream. There’s the lower end disruption that starts as more affordable and reaches the mainstream by meeting the basic need and getting more sophisticated over time. In this area, I think it’s more of the former. >>>

Thought Leaders in Big Data: Keith Anderson, VP Strategy of Profitero (Part 4)

Keith Anderson: Prices are really a key driver of that ultimate decision to buy from one retailer versus another. The manufacturers of course are wary of deflation. They don’t want a race to the bottom. They introduce policies like minimum advertised price (MAP). MAP policy is a policy that retailers and manufacturers enter into voluntarily. It often has implications or willingness to partner on promotions or programs. >>>

Thought Leaders in Big Data: Keith Anderson, VP Strategy of Profitero (Part 3)

Keith Anderson: Besides exactly matching products, one of the fundamental, but not obvious ways we add value is through the matching of products that are not exactly identical. There is just one example—private label products, which are the products that the retailer sells under their own label or a private brand are often positioned as national brand equivalent, but they’re not exactly the same product. From a data perspective, matching those products is possible with a unique identifier like UPC. You have to have fairly sophisticated matching algorithms that either use product characteristics or visual matching that are associated with the products. >>>

Thought Leaders in Big Data: Keith Anderson, VP Strategy of Profitero (Part 2)

Sramana Mitra: Let me see if I’ve got this. You help retailers with pricing information and you help manufacturers optimize their channels through these retailers. Is that a good summary?

Keith Anderson: That’s a great summary. The retailers also care about assortment. We can also help them, very efficiently, identify products that they don’t carry and that their competitors carry that may be selling well and or that shoppers have reviewed positively. The retailers are getting more sophisticated in how they leverage our capability but it’s primarily about price and assortment for the retailers. For the brands, it’s an even broader set of analytics that they care about.

Sramana Mitra: Let’s double-click down a bit and give us the kinds of insights that you are able to extract for pricing optimization.
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Thought Leaders in Big Data: Keith Anderson, VP Strategy of Profitero (Part 1)

Price personalization has been touted as the holy grail of e-commerce. This conversation brings to light the state of the union in the domain of price optimization, price intelligence, and price personalization.

Sramana Mitra: Let’s start by setting some context for our audience of what Profitero does and what your background is.

Keith Anderson: Profitero was founded in 2010 in Dublin, Ireland by former IBM and Google software engineers. Sometimes people ask why the company was founded in Ireland and not in Silicon Valley. Dublin has established itself as a European technology hub. Our founders live there. We’re backed by Polaris Partners, which has offices both in Boston as well as in Dublin where we’re headquartered. We now have offices in Dublin, London, Boston, San Diego, Belarus, and Minsk. We’ll shortly open offices in Asia and other parts of Latin America this year. >>>

Self-Financing an Artificial Intelligence Software Company: John Rauscher, CEO of Yseop (Part 4)

Sramana Mitra: You got your first customer in 2007. How much money did you put into this?

John Rauscher: A few millions.

Sramana Mitra: Right at the beginning, you put a few millions in the company, right away?

John Rauscher: No, not right away. We stopped looking for customers when we understood that people didn’t want to buy the product without development tools. I was not working there. I was at Oracle. We stepped back and built what people want—development configuration environment. We didn’t have a salesperson. We got sales by chance or through contacts without really looking. We really started looking for customers in 2009. >>>

Bootstrap First, Raise Money from Kleiner Perkins Later: William King, CEO of Zephyr Health (Part 4)

Sramana Mitra: What was the customer segment that you were going after and what was the pain point that you were solving for them?

William King: We’re a technology company first that is focused foremost on life sciences. Simply put, we wrangle this universe of Big Data and put it to use for pharmaceutical companies. What does that mean in practical terms? It means there’s a lot of weakly connected and even disconnected data that exists in the health ecosystem. A great example would be information about physicians, patients, and hospitals. You’d think that there would be strong correlations. What we found is that data is weakly connected or not connected at all. >>>