Sramana Mitra: What are you able to do? What strategies do you follow to make that an instant fit in some sense on the mobile phone?
Darren Hill: If you think about products, products have attributes and tags. The consumer uses those tags to find products on the website. The biggest thing we’re doing is tagging the customer. We look at searches that the customer makes based on past purchases, the last place the customer has been, or even the way they shop. Some shoppers are shopping for something very specific. Some shoppers are shopping to fill their closets. Some shoppers are buying gifts. We’re doing a lot of work to identify what type of shoppers are here. Those shoppers actually respond differently to different page layouts. Someone who’s buying just a pair of jeans is much interested in seeing those jeans >>>
During today’s roundtable, we celebrated the launch of my new book, Billion Dollar Unicorns, with a special guest, Warren “Bunny” Weiss, General Partner, Foundation Capital. I have known Bunny for many years, and he has a long track record in the Silicon Valley venture capital industry. Bunny shared areas he and Foundation are excited about as opportunities for building future Unicorns.
As for the pitches, first up, Prasad Kothe from Hyderabad, India, pitched Snapptrix, an integration software business catering to SMBs. The company has a handful of paying customers, and is looking to broaden its footprint.
Sramana Mitra: Given that it is such a high-touch customer service model, the question that I’m trying to ask you is. Right now you’re heavily venture-funded and you can finance your cost. At scale, does this company have the unit economics to become profitable?
Ambarish Gupta: Well, that’s true. The unit economics of this product is very good. It’s incoming calls and they are free in India. Our growth margins are quite high, probably around 70%. Then we spend money on acquiring customers, which in the first year was around 75% of our revenue. After a year and two or three months, we were able to break even. With a renewal, everything you make after the first year is profit. >>>
Today’s 245th FREE online 1M/1M roundtable for entrepreneurs is starting NOW, on Thursday, January 22, at 8:00 a.m. PST/11:00 a.m. EST/9:30 p.m. India IST. Click here to join.
Continuing with our coverage on Billion Dollar Unicorns, here is an EdTech company Pluralsight, which has successfully bootstrapped their business and followed a roll-up strategy with additional funding to move closer to becoming a proud member of the club.
Sramana Mitra: One question on Nasty Gal and the scalability point, what are the issues? What are we trying to solve?
Darren Hill: The issue is making sure the site is up. It works for all of your customers and they’re getting the environment that they’re expecting. For us, it’s really a technology play. Our systems are built on Ruby on Rails with a MongoDB database on the back-end. That technology allows us to rapidly scale in a virtual environment so that we can add new servers very easily. We can handle insane amounts of traffic almost seamlessly.
Sramana Mitra: Talk to me about pricing. I know the pricing of some of these other players that are serving much larger number of customers. You seem to be serving 100 customers that are much larger. I imagine your pricing is very different from theirs. Talk to me a bit about how you charge.
Sramana Mitra: What’s his background? What did he do after IIT?
Ambarish Gupta: Like most IIT guys, he went to Silicon Valley and worked for startups for eight to nine years. I got him back. He was also my colleague in McKinsey in the Pittsburgh office. He has a PhD in Highway Engineering from the University of Illinois. Then, he worked in McKinsey for four years. A lot of people in the product team working on this platform are people who have returned from the Valley. >>>
Sramana Mitra: How do you do the recommendation on what goes with what? Is that a manual recommendation system or is there technology that enables scaling of something like that?
Darren Hill: It’s a mix. There are several data points that go into it such as what people have bought and what they have looked at. That’s the easy one and something that everybody’s been doing for years. The shoppers can put products together in their own category. They’re being a stylist, if you will, on the site. We use that data as well. We are able to see which customer is the best stylist based on how much sales is generated through what they’re suggesting. Then we have professional stylists who work there. They’re also putting this information in. We essentially take those data points and make the suggestion based on all of that. >>>