60. Algorithm-Based Investing, Part 2 (Andrew Parker)

The Full Ratchet Podcast on iTunesNick Moran Angel List

Today we cover Part 2 of Algorithms as a Competitive Advantage with Andrew Parker of Spark Capital. In this segment we address:

  • Parker data algorithms p2You’ve written about your investments in digital healthcare and education. Where do you see the future of these sectors and what areas are still potentially untapped?
  • Are there particular sectors or types of startups that lend themselves moreso to an algorithmic-based competitive advantage?
  • Do you think investors should use data and/or their own algorithms to better assess startups either pre or post investment?
  • Any final thoughts or tips on data and algorithms whether it be for investors or entrepreneurs?
  • What’s your take on Title III of the JOBS Act allowing unaccredited investors to invest in startups?
  • What are you currently focused on at Spark?
  • If we could cover any topic, what do you think should be addressed and who would you like to hear speak about it?

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Key Takeaways:

1- On evaluating a technical founder’s expertise

Andrew said that he assesses technical founders by treating them like a black box.

One way he does this is to look at the outcome of their work. What is the raw product they’ve produced? If there’s value in the algorithm, that should be transparent in the product execution.

Another method is to assess one’s thought leadership in code. A primary way to evaluate this is by checking Github to see the frequency of their published code and how well it’s reviewed and cited by other coders.

And Andrew’s last point here is that he is largely language and stack agnostic. He said that the best computer scientists are strongly rooted in programming paradigms that transcend all languages. So if they’re really good, they’re going to be able to pick up the language they need for the task at hand.

2- Data as a competitive advantage during early-stage startup assessment
During early-stage startup assessment, Andrew first asks, is the startup creating a new market or entering an existing one. If it’s the creation of a new market, it’s not clear how data is going to help. There must be imagination, faith and a compelling founding vision, a great founder to be able to evangelize that vision and an audience that believes in that vision. And here Andrew said that the audience doesn’t have to be the customer. The audience could be the investors or even the startups employees. I hadn’t thought of this point before and I think it was a great one that talking to the startups employees and not just the founders, prior to an investment, can give some insight on how much others also believe and are passionate about the vision.
3- Public algorithms, private source data
On this point, Andrew encourages companies to publish what they’re doing openly. He’s found that the more you give away, the more you get back. And by publishing an algorithm open source, others will contribute to it and make the idea better faster. And if the source data itself remains proprietary, while the community is helping improve the algorithm, then the startups competitive advantage is compounds. Here he cited examples including Google’s publishing of their machine learning framework TensorFlow, while retaining their source data around searches and other web activity in their data archive.
4- Continuous Feedback Loop of SaaS
In the era of shelf-ware, most software wasn’t internet enabled and didn’t have a method for improving user experience based on the collective experience of other users. This is why SaaS works so well for data and algorithm focused startups because both the value the product delivers and the innovation on the product-side doesn’t stop upon launch. Rather, after launch the product can increase in value at a compounding rate. And this also allows the startup to increase both the top line and bottom of the business over time by increasing the price/month of the SaaS product, if they are in fact increase the value of the product. Recall the interview with Mamoon Hamid on SaaS investing where we talked about Net New MRR. One component of this metric is expansion MRR, more dollars/month generated for existing customers. Whether expansion MRR is being driven by new feature sets, enhanced capabilities or just a better core product offering, it’s clear that the compounding value of data can be a force-multiplier here.

Tip of the Week:   Data as a Network Effect

*Please excuse any errors in the below transcript

Nick Moran: So you’ve written about your investments in the digital healthcare and education markets. Where do you see the future of these sectors and what areas are still potentially untapped?


Andrew Parker: I think both of these segments of the IT market are still in early innings because historically both of these markets have been subjected to gatekeepers. So like in the case of healthcare, there is some government regulation in terms of what you can or cannot do with data. And this menace starts itself largely in HIPPA compliance. And then also the IT departments inside a hospital, you know, have some say about alright you’re going to be, you know, using health related data to try and say, you know, improve patient outcomes or something like that. You know you have to pitch the CIO inside a hospital top down. It can’t just be like a random researcher or surgeon or whatever pulling you in from the bottom up. And then too in education, if you want to get massive penetration for a education based technology, you know, you’re at some point going to have to talk to school districts or at least individual decision makers within schools, to help kinda push your product up that way. You can’t do a better job of bottom up innovation in education where you get teachers to be your vector for growth. I just think if you want to acquire users in large bunches, specially if you’re hoping that the school will be a source of monetization for you. So it’s some revenue for you. Then I think that top down sale’s a little bit inevitable. So I think that because both these industries had gatekeepers historically, I think that it’s just starting to get interesting in both of those categories because these gatekeepers have been a little bit, you know, lagging indicators in tech adoption but they’re ready. And I heard some statistic that ten years ago something like if 3% of public schools in the US had really great broadband penetration and now that number is more like 80%. That’s a really big deal. That like the schools are now ready to be using you know high speed internet in order to maybe replace a textbook with a digital learning curriculum or something like that.


Nick Moran: Sure


Andrew Parker: There is some market timing to both of those segments that you identified that are just really starting to warm up right now.


Nick Moran: Yeah, I’ve read a little bit about your investments and different sectors that you invest in. Are there specific sectors that you look at for algorithm based startups and/or other specific business models? Let’s say, SAS for instance, a subscription based software business models that have to be in place in order for you to take a hard look?


Andrew Parker: Yeah, you know, if, if you’re selling B to B, I do think SAS lends itself nicely to an algorithmic based business if you know you really have found some way to compound your value through the data that you acquire. Meaning as you get more and more data, the company becomes increasingly more valuable. And the reason why those two go hand in hand nicely is, you know, the days of like the box offer are a raid, you know walk over to Best Buy and pump down $50 for you know the newest version of turbo tax or something like that. You know, it wasn’t really like an internet enabled product. There wasn’t a way in which, you know the data was improving the experience for all users in a compounding way. Now I do think there is something to like, you know, as more people use turbo tax, I’m sure the product got better. But I think that’s like a natural evolution of just, you know, kind of product design iteration. I’m not sure that millions of tax returns ended up having the same impact that say you know the millions of, or now billions of, of google searches also has improved google. And so the reason why SAS works really well there is because the innovation, the product and the value for the product doesn’t stop when the code is done being written. Instead it continues to get better as the data gets better. So it makes sense that you’d be subscribing to a service that is fed by that data. And it’s just, it’s a very natural way in terms of continuing the, the contract value with customer, you know, continuing to increase that over time through you know a monthly or a yearly subscription revenue. Because you’re constantly increasing the value that you’re providing to the end consumer.


Nick Moran: Yeah, certainly your point earlier about data exhaust, you know without SAS how do you really collect all of that, that exhaust in real time if you have a , if you have a software product. Whereas now with SAS you, you can pluck that from the very beginning.


Andrew Parker: Yeah, that’s right. That’s absolutely right and that’s a big part of the jump as we you move to connected applications and as we move to a lot of the application layer logic into the club


Nick Moran: Any thoughts on if investors should use data and/or their own algorithms to better assess startups, either pre or post investment?


Andrew Parker: Yeah, you know it’s funny, we were talking about this recently at a # Spark off site, amongst our partners here. And I personally came down to this topic because I am definitely a big believer in the disruption of VC. I think it would be really naive of me to assume that I’m going to invest in wildly disruptive interesting businesses and other industries, but no it will never happen in VC. Like that doesn’t make any sense, right. Like of course ultimately VC as we know it today will get disrupted, and I would love to invest in those companies that are doing that disruption. We have made an investment in one company that leverages crowd funding today called # Funder’s Club. It’s specifically for VC backed startups. And then we have another investment in another company that leverages crowd funding through an equity relationship for video games, called Fake. So we are thinking about this, we are putting our money where our mouth is. But I think that those are a little bit more business model innovations than they are algorithmic innovations. And the distinction here is important because I think VC today is a data sparse problem. Meaning that the number of startups that emerge during, you know, any given year , and I think the modern VC equals to some just a couple thousand. And a database of all these companies and all their founders and all the products that they’ve built and the market statistics and they performed something like that. If you took all of that and put it in one big table, I bet that whole table would fit in like probably like a single giga of RAM. So it’s not a big data problem, right


Nick Moran: Right, right


Andrew Parker: You know, big data is when you’re talking about like tera bytes of information. You’re like drinking from the fire hose or whatever, and it doesn’t have to be big data in order for an algorithm to innovate here. But when it’s not big data, you know, humans can still kind of parse their way through it. So there are definitely some other firms doing some really cool algorithmic innovations. I know # Google Ventures in particular has built something more crazy over there, where like they have acquired a bunch of different data sets and feed that into some model that, you know, helps them figure out which startups they should be focussing on or when to talk to certain companies in their fundraising life cycle. But I think the value, the advantage to getting there is maybe it’s helping them on the filtering front. You know, it’s taking the universe , that universe of a thousand companies and reducing it down to like a hundred. So there’s value in there. I’m not trying to say that there isn’t. It’s just not as impactful as you know say Google helping you parse between the billions of websites up there.


Nick Moran: Right. We had sort of an odd experience recently. We were leading an investment syndicating it on # Angel list for a startup. And of course they had to create an # Angel list profile. So this is the first time they’d created a profile on # Angel list and the very next day they got contacted by two of the biggest name VCs in the Bay area for a meeting. And at first I was shocked, like about the timing of it. But then I realized that after they created that profile, their data must have gotten picked up by some of the data outlets in the VC space, and set off a flag or set off a signal to some folks that wanted to connect with them. So,


Andrew Parker: That’s amazing , wow. Well, you know, I think # Angel list is very much a part of that kind of disruption of the VC ecosystem. And I think what they’re building there is super interesting. So I’m glad that you’re playing at that edge and participating in that ecosystem. But it sounds like other people haven’t seen that as a leading signal too.


Nick Moran: Yeah, right. Fortunately we still got the round, thank God. So # Andrew any final thoughts or tips on data and indoor algorithms, whether before investors or entrepreneurs?


Andrew Parker: No, I think that your questions are great. And I think you did a really good job of kinda you know teasing out this idea and now I have had a lot of fun with it.


Nick Moran: Can you talk about some of the things you’re currently up to and most focussed on over at  #Spark Capital?


Andrew Parker: Yeah, I think it’s just three categories- VR, Drones, run Slack . You know, you’ve brought up also,  digital healthcare and education. Those are the two sectors where we’re intensely focussed and really want to find more opportunities.


Nick Moran: If we could address any topic in venture, what topic do you think should be addressed and who would you like to hear speak about it?


Andrew Parker: I like that there’s kind of two different types of VCs out there. Or you can split VCs into two buckets. There is a bunch of folks where I would count myself as a member of the camp, where they think that the hardest thing a startup is going to do during their life is just matter in the minds of the end consumers. And so getting to scale with users and getting users to love you because that’s so difficult you might as well just do it first. And so people going first scale early in ways that aren’t yet economically viable, like the business model isn’t baked. But there’s just faith that it will get there, because that’s an easier problem to solve. That’s one set of VCs . And I think there’s another set of VCs out there that really want to be abe to analyze a company on a spreadsheet , convince themselves that you know that’s gross margin positive early in the life of the company and really want to focus on business models that are going to work. And it might be interesting if you just bring in or try and foster conversation between these two sides. If you could find VCs that really raise their hand as saying yes I’m a part of this camp or yes I’m a part of that camp, and seeing how they interact or trying to figure out what the core of that debate really is. That’s a contrast that I see a bunch in VC and it’s one that I don’t think there is a right answer. You know, you might just get two people  arguing over exactly what is the right shade to fuschia. But if you’re ready to endure that, I think it actually could be a pretty interesting conversation.


Nick Moran: It’s a really good question. I just read an article from # Matt Maloney , CEO of # GrubHub about why # Door Dash and # Postmates are going to fail. Because # GrubHub was sort of built with this poor economic model that was viable from the start. Whereas he sees all these other on demand delivery startups spring up that are growth hacking and don’t really have the economics down.


Andrew Parker: I remember that. You know, I thought that was a really good counter point from # Sarah Lacy on that, on # Pando. It’s worth a read if you found # Matt’s take interesting. And I  think even using that as the core of the debate could foster an interesting conversation. I would say though that, you know, when you’re talking about # Door Dash and # Postmates you’re talking about companies that have figured out their —(11:34)— , right,. You know, their services are not inexpensive and so because of that, you know, they are commercially viable. I think the real interesting contrasts in my mind is when you look at a startup like you know Twitter or Facebook, you know, these amazing end consumer experiments that for years went mostly unmonetized. I mean there might have been revenue experiments or something like that. But the revenue growth didn’t really come in large bunches until they, you know, hired a CRO and really intensely focussed on it, knowing that they had already solved the hardest problem which is just being a part of the zike (12:07) guys, being relevant in consumers minds in a big way. There’s a bunch of VCs that are willing to take that bet, and you know fund something without a business model for a long time. But then there’s a bunch that really are uncomfortable with that. And I think that could make an interesting conversation.


Nick Moran: Any quick thoughts on Title III and what it might mean for the startup fundraising landscape?


Andrew Parker: Yeah. Well, you know, I’m generally very liberal. And so what I’m going to say I think is a little bit out of character for myself. But I think on this one particular issue I’m fairly libertarian, meaning like I don’t think that we should necessarily be controlling people’s access to invest in startups they want to based solely on their, you know, net worth or whatever. So I think it’s really interesting that folks that didn’t previously need these qualifications around accreditation can now invest in startups. And it’s not my job to protect them. I’m sure it is someone’s job to protect them and I hope they do a good job of protecting them. But like, you know, it’s not mine, so good luck to them. I think what the impacts for the startup ecosystem will be that, you know, direct investing from a larger swath of retail investors will lead to more seed stage companies getting funded. They could also be investing in later stage rounds or whatever. But I just think on a dollar impact basis, I think that’s where we’re going to see the biggest boost come from , is at the seed stage. Because , you know, the dentist in Demoit and Iowa can actually get access to some material ownership in a company if they’re willing to play at that stage. Whereas they’re investing a couple thousand dollars in Uber or something like that. I just don’t think it makes a difference in how Uber is going to execute or not, right.


Nick Moran: Right


Andrew Parker: It’s not going to like suddenly unlock millions of dollars of liquidity that they didn’t have otherwise have access to, like they have no shortage of libelative fundraise. So not much going to change there. But at seeds, I think you’ll see some move. And so that means that the top of the funnel will then become larger for later stage investors, because there will be more seeds kind of earlier in the funnel. This isn’t necessarily a bad thing. I think it will be hard to argue that there is such a thing as, so too much seed capital, because thinking about like how innovation works, it’s often through quick iterations and like constantly trying new ideas. And if there was only a fixed amount of, you know, seed capital available to all companies, you know, you’ll be limiting the degree to which you could iterate. And so you know, I’m glad to see that this is going to happen. I think it’s a very interesting phenomenon and I’m glad.


Nick Moran: Right, and finally just to wrap up, # Andrew what’s the best way for listeners to connect with you?


Andrew Parker: Yeah, so I have a blog thegongshow.tumblr.com , you can certainly follow me there. You should follow me on Twitter, which is just @andrewparker , pretty easy to find. And then if you want to correspond with me privately I publish my email address on my website, both on the # Spark website and also on my personal website, so I just try and make myself as easy to reach as possible, and feel free to contact me there.


Nick Moran: Well, # Andrew , been a big fan of your blog for a long time and really appreciate you carving out the time and joining us here today.


Andrew Parker: Oh hey, thank you so much for taking the time. This is a real pleasure and I appreciate the opportunity.