Scaling a Cloud Telephony Company in India: Knowlarity CEO Ambarish Gupta (Part 5)

Sramana Mitra: What kind of companies do you recruit as channel partners?

Ambarish Gupta: These are like a tiny version of SIs in India. SMBs in India for their technical support requirements depend on large local companies who for example distribute laptops, computers, and computer accessories.

Sramana Mitra: How do you find them? >>>

Achieving Product-Market Fit That Allows You to Build Billion Dollar Unicorns: Gaurav Dhillon, Founder of Informatica and SnapLogic (Part 7)

Sramana Mitra: Help me rationalize what’s happening right now in the market regarding late-stage valuation bubbles. We have two kinds of bubbles in the startup venture world. One is in the seed capital. In 2013, 70,000 companies were angel-financed. That’s too much actually. It’s great that they got angel-funded, but then if you look at the next level, it’s 1,000 venture funding or 70,000 paired down to 1,000. Technically, those companies probably need to be bootstrapped and they’re not going to be scalable venture-scale companies, which should not have been funded in the first place. A lot of people are going to take tax right off. That’s one part of the bubble.

I actually don’t think the early-stage venture capital Series A and Series B is in that much of a bubble. It’s more in the Series C, Series D, and in some cases, Series E, that is completely out of control valuation, right? Part of the issue that we’re going to have to resolve somehow is that the public market is not in a bubble. What is your analysis of this market? You recently raised money. What was your experience in navigating this market?
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Achieving Product-Market Fit That Allows You to Build Billion Dollar Unicorns: Gaurav Dhillon, Founder of Informatica and SnapLogic (Part 6)

Sramana Mitra: This time around, are there any surprises in the product market fit process? You repeated the same process, I imagine.

Gaurav Dhillon: The same thing but with a twist. With Informatica, the timing was spot on. The change was happening in a big way. Year 2000 was coming. From the time we raised venture capital in 1995 to the time we got to $50 to $60 million in revenue in 1999, our timing was spot on. With SnapLogic, our timing was a little bit early. We had built out this technology. We had SnapLogic running on AWS in 2008. That’s like seven years ago. Enterprises weren’t there yet.

If you look at SaaS at that time, SaaS was a mid-market phenomenon. Among people using SaaS today, the majority of revenues and by far, the vast majority of profits for SaaS companies are coming from the enterprise. In fact, Workday is an enterprise-only SaaS company. They don’t even sell to mid-market. Their average deal size is probably $2 million. All these things were just coming out at that time. I think we were slightly early. Of course, we had the technology. >>>

Thought Leaders in Big Data: Damon Ragusa, CEO of ThinkVine (Part 5)

Sramana Mitra: Pushing that thread forward, let me then ask you an industry level question. Where are the technology gaps? It seems like the analytics infrastructure is lacking in a lot of the inventory that is sitting there. For example, online videos is very big right now. At the same time, for a lot of the online video platforms, while it’s engaging content and video advertising, what exactly is the analytics infrastructure lacking in terms of effectiveness of the advertising.

Damon Ragusa: Obviously, I’m biased. If you pull the right data out of those platforms, which you should be able to, there are plenty of good technologies. Part of the problem is some of the emerging media platforms can be protective of that information so there’s a lack of transparency in terms of data coming out of that. It all comes down to how you fill a bigger gap between analytics platform and data to support smarter decisions. One of the biggest buzzwords right now is programmatic buying—the ability to automate the acquisition of digital media. The real advantage of programmatic buying is the ability to take super-targeted information to play and make those buying decisions. >>>

Achieving Product-Market Fit That Allows You to Build Billion Dollar Unicorns: Gaurav Dhillon, Founder of Informatica and SnapLogic (Part 5)

Sramana Mitra: The story is very different for a Dave Duffield starting Workday or a Gaurav Dhillon starting SnapLogic, because most of the people who are starting companies without that kind of track record do not have the options that you have. They have to figure out some way to navigate that early cash issue.

Gaurav Dhillon: You’re very kind to include me in the same breath as Dave. Dave is on his third company. I’m only on my second. You’ll recall he did this in the mainframe with Tesseract. Every two years, there’s a change in the compute platform.

But you’re right. I do have the advantage of having had a phenomenal outcome for my employees and investors at Informatica. Conversely, there’s also the install base of integration, some of which are products that were built by yours truly in 1992 that are still up and running. For example, at SnapLogic, we have to do a much better job in terms of product attributes for the cloud to win over the very same customers. In a sense, to upgrade them to the new logo. >>>

Thought Leaders in Big Data: Damon Ragusa, CEO of ThinkVine (Part 4)

Sramana Mitra: One of my observations which pertains primarily to electronic commerce is that the more we can attribute, the more we can tie the marketing and advertising and customer acquisition to actual purchases. As you know, one of the most effective ways of marketing e-commerce is through affiliate programs. It’s 100% performance-based. There are lots of sites and apps that have accumulated huge numbers of eyeballs that they’re not able to monetize today. If more and more of this gap that exists today between traffic, lead generation, and conversion into actual transactions could be bridged, that would really relieve a lot of the friction that exists today between advertising and commerce. Can you comment on that?

Damon Ragusa: You’re absolutely correct on that but there are a few challenges. Part of it is based on the realities of digital marketing. You hit on one point. You talked about affiliate marketing as a good example that can be 100% performance-based, but because it is 100% >>>

Achieving Product-Market Fit That Allows You to Build Billion Dollar Unicorns: Gaurav Dhillon, Founder of Informatica and SnapLogic (Part 4)

Sramana Mitra: 156 customer calls that you made gave you real conversations about real customer problems. Even though you didn’t sell what you went out to sell, this was a great opportunity to immerse yourself in customers and learn what they were really looking for.

Gaurav Dhillon: This is AB testing to the power of a million.

Sramana Mitra: By the way, this is a process that we use for all our customer validation work. Many of our entrepreneurs who have been part of the Entrepreneur Journeys case study series have shared this process of immersion.

Gaurav Dhillon: In the complex software, hardware, and networking business, this is a tried and tested method. I was a graduate student in Computer Science. If I’d done an MBA, I would have learned this. It’s a great lesson because that is what we then refined and built. We said, “Everybody’s going to need to move the data. Everybody’s going to need to connect the mainframe to the client server. Then they’re going to want to >>>

Thought Leaders in Big Data: Damon Ragusa, CEO of ThinkVine (Part 3)

Damon Ragusa: What we do is consolidate data from a lot of different sources. There are three V’s of Big Data—Volume, Velocity, and Variety. Variety is my favorite V. I think you get more value by integrating a larger variety of data to explain the thing you’re trying to understand than just more of the same. Fortunately in this world, we get a lot of both. We get a wide variety of data and we use some nifty algorithms that we’re able to connect all these data—demographic, behavioral, sales, digital click stream—to a modeling framework that allows us to understand and start to simulate how individual people carry out their interactions with the brand and media, and how they buy within a category.

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