The Entrepreneur’s Essentials #10: The strength of natural network effects
For this lesson from The Entrepreneur’s Essentials, I’m going with recency bias once again. Yesterday I was having a discussion with one of our executives on the strength of network effects inherent in the data.world business model. This led to a far ranging strategic discussion that got us both really excited about the huge potential of our business. Our mission is “to create the most meaningful, collaborative, and abundant data resource in the world.” There is a lot to unpack there and we have a multi-year roadmap that I look forward to us unveiling over time. data.world is the most exciting and ambitious business I have ever worked on. It is going to make writing this chapter a little more difficult as I can only unveil so much about data.world at this time, but I’ll do my best!
So here we go with this lesson from The Entrepreneur’s Essentials. It was first shared at Lucky7 on January 15, 2015. I made very few edits to the original post, mostly in the area of readability and grammar (not in substance of content). After the original post, I’ll write about how data.world and some of our more recent investments map to this:
Sometimes startups we meet with (I’ve personally seen over 1,000 pitches in the last two years) talk about their network effect in a hopeful way. But most of the time it is just that — hope, and hope is not a strategy. But Bazaarvoice actually has a working network effect that benefits all participants: retailers, brands that sell through those retailers, consumers that shop at those brands and retailers, and Bazaarvoice and some of its partners. In other words, the more participants that are on the Bazaarvoice network, the great the effect of that network for the benefit of all. I wrote about this in detail in my first annual shareholders letter after Bazaarvoice became a public company.
I believe the single best report released from Bazaarvoice to describe this network effect with hard data is The Conversation Index, Vol. 8, which was released earlier this week. I encourage startup founders or anyone interested in social commerce to read it:
When you think about Big Data Applications (a term coined in 2012 by Raj De Datta in this TechCrunch article), Bazaarvoice is one as Raj points out in his article. I’ve also heard BDAs called SaaS 3.0 although the SaaS 3.0 term has evolved to become muddled since I first read what analysts in 2011 meant by it. This, by the way, isn’t very different from how the term social commerce became muddled after Bazaarvoice first started promoting it in 2006 to describe what we do — unfortunately, social commerce morphed to mean solutions like Facebook eCommerce stores offered by numerous small vendors who are now mostly no longer in business. In contrast, our definition of social commerce meant social media, or word of mouth, applied in a commerce environment.
When we consider investing in SaaS startups at Hurt Family Investments (34 SaaS startups and counting in our portfolio), we always look for natural network effects. I use the word natural deliberately because it is far easier to build solutions that will offer network effects if your market is actually wired that way. As Michael Dell said recently in an interview with Walter Isaacson (author of Steve Jobs and the new book The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution) that Josh Baer and I attended at U.T. Austin, the level of difficulty in changing an industry’s behavior depends on how long that industry has been engaging in that behavior. In Bazaarvoice’s case, the market was, fortunately, wired for brands and retailers to collaborate in a kind of grand bargain. The retailers were to provide the audience — and therefore the sales — that the brands needed. The brands were to provide the market-development funds (MDF), or the co-op advertising, to support the retailer in that endeavor (co-op advertising dollars are as high as $50 billion in the US to a staggering $520 billion worldwide according to sources that The Altimeter Group has cited). Brands would get higher margins but lower revenues while retailers would get lower margins but higher revenues (generally speaking, with Apple being a notable exception owning both sides of that for much of their sales). So it was natural for Bazaarvoice to tap into this network effect and provide solutions for not only retailers but also the brands that sold through them. You can see one of the ways how this network effect works for Bazaarvoice and its clients in this Bazaarvoice Connections video.
I believe that SaaS startups we’ve invested in (either as advisors or investors, or both), such as AlertMedia, Clarify, Edgecase, eRelevance, Fashion Metric, NewComLink, OneSpot, Onor, OrderGroove, Pivot Freight, RealMassive, and ShelfBucks, have natural network effects, some inherently stronger in their industries than others. It is one of the key ingredients we look for. I think any SaaS startup would be wise to identify if their B2B market has a natural network effect to tap into. This past Sunday, for example, I engaged in a brainstorm with a SaaS startup that wasn’t sure but by the end of the session we had all convinced ourselves (and not hopefully, I might add) that indeed a natural network effect existed. That made all of us much more excited about the business than ever before.
A SaaS startup without a natural network effect can still be successful. For example, there are replacement-market SaaS businesses like Salesforce.com, who started out by disrupting a massive market (Siebel and CRM) with a better, cheaper, faster, and ultimately more function-rich solution (because they were building on a single platform versus spreading their R&D over a multiple of client computing platforms and armies of installation consultants). Workday is a more recent example with them running the same play as Salesforce.com did against Siebel, but in this case it is Workday against PeopleSoft/Oracle. In fact, replacement-market SaaS businesses have grown much quicker than nascent-market SaaS businesses because the market already existed and can therefore be easily sized and disrupted (assuming great execution). Their success is about execution in sales, services, and feature parity … and then eventually superiority (again, due to that single platform to rapidly evolve on advantage). But now that the SaaS as a superior-business model as compared to enterprise software secret is out of the bag (Workday had a monster IPO and Salesforce.com is the most valuable SaaS business in the world), these replacement SaaS startup opportunities are few and far between. Meaning that most new SaaS startups we see are, in fact, nascent and therefore are trying to create their own demand, which requires a lot of evangelism and education. I’ve written quite a bit about how to evangelize in a nascent market — for example, read #16: Selling to the “Cool Kids”, and I suggest hiring some great evangelists as full-time and part-time employees and also as Advisory Board members — read #13: How to leverage advisors and investors as your extended team on that front). The bottom line is that when we see a SaaS startup pitch us, natural network effects in their industry (or the lack thereof) is one of the first things on my mind. It doesn’t mean they will be successful if their industry has the embedded advantage of a natural network effect, but it will certainly help. Reed’s law on networks is a good resource to really nail this home.
In investing in B2C, we also look for network effects but usually those are created rather than natural. For example, Apple created a network effect by the integration of iPhones and iPads to the App Store to iTunes. Apple then tried to extend this network effect into social with the launch of iTunes Ping, which was one of their biggest failures in recent years. Amazon created a network effect by the launch of Prime and the subsequent launches of Prime Instant Video, Prime Photos, Kindle Owners’ Lending Library, etc. Facebook created a network effect by the launch of Facebook Login, allowing it to proliferate everywhere, and they also tapped into a natural network effect by launching at select universities (where students were naturally connected, the way it has always been) and expanding out from there. LinkedIn tapped into the natural network effect of professionals working together at companies (both as peers and as partners). And so on. B2C startups we’ve invested in like ROIKOI and Thread are tapping into network effects — and they are both natural, like Facebook’s or LinkedIn’s.
For some of the other ingredients we look for when startups pitch, refer to #19: The five critical ingredients to build a big company.
So that is the end of the lesson as I wrote it over four years ago on Lucky7, with some edits and updates (like the number of SaaS deals we are involved in). You can see the original post here, and the long comments thread below it.
How does this lesson resonate with me over four years later, after having more experience in startup investing (#22) and also co-founding and leading data.world?
Well, natural network effects are present throughout the data.world business model. For example, the network effects between the Associated Press and their many newsroom customers have existed for a long time prior to us coming along. We’ve been proud to work with them to accelerate the democratization of data across these newsrooms, leading to very effective reporting and journalism awards. Our case study outlines that work in detail. And we have a natural network effect that exists between open data and the corporations that would love to use it, such as US Census data (businesses like Ancestry and Zillow, for example, are highly dependent on US Census data). And a natural network effect that exists between some of our customers, such as several Fortune-size consulting firms and agencies, and their clients (as they deliver data and analysis to them via our platform). And natural network effects present in many of the organizations that are members of our community (the world’s largest collaborative data community) and their various stakeholders. And a technological network effect underlying in the data itself, as my co-founder and our CTO, Bryon Jacob, outlined in this blog post (the most read blog post we have ever written at data.world). There are other networking effects that we will be unleashing, both natural and technological, as we progress through our multi-year strategic plan. I really look forward to seeing the impact they have in the world.
As angel investors, we’re always thinking about this when we evaluate new startups but it doesn’t mean the business will be successful. Some on the list above in my original post did not turn out to be successful, unfortunately. It does mean that their chances of success are higher. Great execution is still required — and a good dose of luck too (timing luck, especially).
I would love to hear your thoughts below. What are the network effects in your industry? Are they natural? Did this chapter inspire you to go look for them in your startup or investments?
