Here in Seattle, when you think of companies leading the conversation around delivering products on demand, one company comes to mind: Amazon. Drones and IoT aside, there are actually a lot of other great startups who are contributing to this trend of “Now.”
Recently, I sat down with Angus Fan, our Director of Product here at PitchBook, as well as Chad Wittman, Co-Founder and VP of Product at Dolly, and Jason Carlson, Co-Founder and CTO at Liquid Planner to talk shop about building great products for the Now Economy. Here’s a short video of our conversation (audio transcript pasted below).
JG: Hello, I’m John Gabbert, the founder and CEO of PitchBook and I’m pleased to be joined today by Angus Fan, our Director of Product; Jason Carlson, the co-founder and CTO of LiquidPlanner; as well as Chad Wittman, co-founder and VP of Product at Dolly.
To frame this conversation, a little bit about the Now Economy — it’s interesting as it represents a convergence of four relatively new technologies. We have mobile, social, big data and cloud computing infrastructure.
In the Now Economy, also interesting is that things that used to be stored in inventory can now be produced and distributed in real time — or just in time — as well as things that used to be owned really can now be rented, and a lot of events that you used to have to plan for in the past can now be much more spontaneous.
I think this has a lot of implications for a lot of different industries and really presents a great opportunity to find a better way to get products to customers.
On doing business in the Now Economy
CW: I think one thing that’s interesting about Dolly as it relates to that concept is saying like, now there’s a guy actually using his pickup truck, driving to work, maybe doing construction work or whatever it may be. He’s able to leverage that truck in a much more realistic, more on-demand-esque platform in the way that he’s able to get that utility and leverage it right away at the time that we need it.
JC: Getting in front of customers early and often, doing user research, ethnographic studies, finding out what it is that they’re demanding and having quick-turnaround cycles, building, iterating, doing follow-up research, evaluating, and then iterating on the product.
AF: Doing customer research obviously is key, but actually what we found has been even more imperative is spending time with customers in person to understand not just their workflow and how they accomplish a certain task using the PitchBook software, but really how does it fit into the entire workflow of their day. There’s other things that they’re doing that are not PitchBook-related that PitchBook somehow fits into — how do we better smooth those kind of transitions out and find a way for PitchBook to help?
On building products in the Now Economy
JG: So when it comes to developing products for the Now Economy, what are some trends that you see evolving, whether it be on the methodology side, technology or the iterative process of working with customers?
CW: We use data and try to collect as much data as possible to help us answer questions, but I like to use our intuition to help say “Hey, what would be best for our customer, let’s talk to some customers, let’s try it and then measure it and use the data to help guide us after that intuition has been applied.”
JG: Nice. And Jason, how about at LiquidPlanner?
JC: There’s been an amazing set of tools that have come out recently to help in user research and data gathering. We can track every single click, every single thing that people are doing in the app and aggregate that — again, based on user personas or customer type or industry — and that informs the decisions that we’re going to make. If we decide to kill a feature for instance, we can go in and we can say 1.2% of this type of people are using it. It’s that data — all in the cloud, all hyper-connected — and the analysis tools on top of it that really have up-leveled our game quite a bit.
Angus Fan: We said, okay, well we have all these tools, but they don’t actually like using some of the tools that we have because it doesn’t quite fit into their workflow. They want these kind of custom one-offs that would be good for that client maybe, but not good for other people — so maybe we can find a different solution. And so we came up with a couple of solutions: One is a Datafeed and one is an API — straight into our database, so that we just give them the data and turn the keys over to them. They can build, using a car analogy, whatever kind of car they want. We give them all the core components of the car and allow them to design the rest of it however they want and how fast they want and how they want it to look.
On the future of the Now Economy
CW: Again, going back to this idea of saying which businesses are going to happen on-demand — meaning the definition that I think we all kind of picture in our mind, the sub-five-minute — versus how many of these businesses will exist when the customer demands it — so on-demand in that fashion — and then which businesses can supply that in a way that’s cost effective and is driving really low pricing for the customer and is convenient and has awesome customer service. I think the millennial generation is a forcing function on this and demanding really great service at a pretty good price and at the time they want it. I think this next phase in the next few years will be sorting out which of these business models really work and which ones aren’t cost effective.
JG: Yeah. That’s great. Jason, how about for you guys?
JC: I think underpinning all of this is data, and you know we’ve been moving this direction for a very long time, but it’s all in the cloud, it’s all interconnected, and it’s going to accelerate. But more than just data, it’s the predictive nature of what you can do with that data — the things you can connect together to either make an interesting new business or a service that didn’t exist before…
AF: If you can start removing some of those decisions and thoughts from people’s heads and try to do that for them I think there’s tremendous value there — and so the part about AI, or data, and tying all those elements together to somehow predict and infer what would be interesting to this person, what should they be thinking about next that’s in line with how they think or what they’re working on, and triangulating all these different data points that are spread out in kind of a big data environment into something that’s actionable.
JG: Well with that, I wanted to say thank you very much to all of you for being here today. I appreciate the insights and your thoughts. So again, thank you very much for being with us today.