Stealing Signs - Issue 15
Aliens & Jedi, Business Equations & Flywheels, Marketplace Models, Data NFX, & A Major League Cowboy
Worth Reading
The Business Equation
Brett Bivens, TechNexus
“A good business equation meets three straightforward criteria:
It focuses on isolating controllable high-leverage inputs
It is deeply tied to value delivery (user engagement)
It maps directly to a company's accumulating advantage”
Brett deeply explores what makes a business tick — what he, and originally Keith Rabois, call a “business equation.” I was surprised to learn (or maybe realize) that most business equations are relatively simple. What’s actually complex is both identifying and understanding the relationship between the inputs and variables of a business equation, specifically the high-leverage ones. It’s an insightful observation imo. While it seems obvious from the way Brett identifies their simplicity, business equations are not usually obvious from the beginning and must be built one sub-variable at a time. The key variable in Peloton’s business equation, he notes, is “monthly workouts per connected fitness subscriber,” because it’s a reflection of engagement (discussed below) and is a deeply connected to important sub-variables of the equation, like workouts per subscription, brand awareness, and ARPU.
I really like PayPal example about the importance of "the “engagement” metric. It’s far more important to PayPal than take rate, for example, because the more the user thinks about the product and interacts with the product, the more likely they are to stick around for long run and become champions promoters of the product — it’s the purest measure of the value the active users are getting from PayPal. This thinking likely informed Venmo’s activity newsfeed. It allowed users to see what their friends were exchanging money for and to whom, and created a pseudo-social network. This encouraged users to frequently scroll the activity feed and interact with the product more than they likely would have otherwise — regardless of user behavior change in dollars exchanged on Venmo or exchange frequency, it was top of mind for users and captured a piece of their attention — it increased user engagement, which, in the long run, likely benefited the company more than volume or frequency metrics.
Brett includes two business flywheel models in this piece — I think these are extremely helpful models for establishing the relationship between each of the variables in a business equation, especially the sub-variables. It’s also quite fun to create them! Here’s one I created a few months ago for a blue-collar job marketplace business:
Aliens, Jedi, & Cults
Richard Burton, Balance.io
“The short time I spent at Stripe had a profound impact on me. It showed me what was possible when a few brilliant people get together and make something people want… Stripe gave me a mental model for potential. An alien founder assembles a group of Jedi to start a cult and go on a mission together.”
Richard explains his mental model for identifying ‘things’ with potential, which in this case include Stripe, Bitcoin, SpaceX, Plaid, and Monzo long before the masses. This model is typically referred to broadly as ‘pattern recognition,’ but I love both the simplicity and specificity of Richard’s version. At the seed stage, we’re focused on the Alien characters in this model. The group of Jedi that help the Alien start a cult often comes after we’ve invested, but sometimes the Jedi will be the co-founders. I’d love to hear more from Richard on how he identifies ‘Aliens’ — I don’t think I’ve met enough of them yet to be able to describe what an Alien profile might be, but something that has stuck with me is Keith Rabois’ observation that great entrepreneurs often have a legitimately contrarian opinion about the world. Keith might be the most famous contrarian out there, at least in my world, so this is no surprise, but I think this may be a piece of Richard’s ‘Alien’ archetype — these founders are just different.
I also appreciate Richard’s openness at the end of this article, where he states he’s made a ton of mistakes in investing and that this model has failed him many times. What’s that saying? All models are bad, but some are useful? It’s a good reminder that even models that help identify $80B+ worth of potential still miss!
I think Richard’s version of pattern recognition helps craft a compelling story about a unique person rallying a group of uniquely talented people to create something special.
Four Paths to Marketplace Success
D’Arcy Coolican, Andreessen Horowitz
“founders building low-frequency marketplaces—be it the higher-priced Splurges or the Fits and Starts—face a different risk: disintermediation and competition. Here, founders need to focus on building significant value into the platform, beyond discovery and matching. Vetting childcare providers, like Wonderschool, or authenticating merchandise, like StockX and GOAT, are two examples of this.”
Andreessen Horowitz took the internet (at least VC & Tech Twitter :)) by storm last week with a ton of content on marketplace businesses. This piece identifies the four most successful types of marketplace businesses based on their analysis of the Top 100 Marketplaces in the market, and demonstrates the beauty of this business model: there are many, many different types successful marketplace!!
I tend to gravitate towards the relatively low-frequency marketplaces because it enables one my favorite strategies for building a marketplace, the SaaS tool turned marketplace (See: OpenTable), otherwise called a “SaaS Enabled Marketplace.” While somewhat controversial, I like this approach because it’s focused on immediate value for the supply side (usually), which is often the most important user group to capture at the start. This approach is also an effective way to combat a big risk for low-frequency, high transaction value marketplaces: disintermediation. D’Arcy uses Airbnb as an example of successful combating this:
“For example, in 2011 Airbnb began offering all hosts liability insurance on damage caused by Airbnb guests. This gave hosts an incentive to stay ‘on platform,’ while also making it harder for smaller upstarts to capture market share from Airbnb.”
In this case, Airbnb built lock-in value after the marketplace, but starting with a SaaS tool can create similar lock-in value from the start. As mentioned, OpenTable is the darling of this approach. They started with software to help restaurants manage reservations more efficiently — the SaaS tool solving a real problem for the supply side. Once they had a bunch of restaurants using the software, they enabled customers to book reservations and search/discover those restaurants on OpenTable — opening the platform to the demand side and creating a marketplace.
I’m a fan of SaaS-enabled marketplaces because I love solving problems with software and see so many opportunity areas in massive markets (healthcare & gaming to name a few) to leverage this model. If you’re building a SaaS-enabled marketplace, or any marketplace at all, I’d love to learn more!
The Truth About Data Network Effects
James Currier, NfX
“Data is not inherently valuable. Most data doesn’t produce a real data network effect, and most data network effects aren’t that powerful even once established. Consider the idea that your data strategy, in terms of value creation, is overrated. It can be powerful, but it’s typically not.”
This is the best explanation of data network effects I’ve found and includes many great examples of how to build them.
I particularly like James’s Netflix example, where the company initially made a big deal about their viewer data and algorithms to match viewers with content, but in reality this data was not central to their value. Instead, their core value lays in their ever-expanding content library. It’s a great example of a data network effect that isn’t particularly powerful and why the streaming space has been so competitive in recent years.
The asymptotic value of data is also a valuable insight from James — the notion that a bigger collection of data has diminishing returns for the value being provided to the customer. As he notes, it’s an extremely important phenomenon to be aware of when evaluating defensibility — sometimes small volumes of data can solve enough of a problem, which means the startup boasting vast quantities of data may not actually have a meaningful advantage.
I haven’t come across many businesses built on data network effects recently, but the most common data advantage we see is the what James calls “data embedding” — the idea that defensibility can be built by holding client/user data. This makes it harder for users to remove a product from their workflow because it is embedded in their operations and the data it holds is central to their business. This is a powerful strategy for SaaS companies. While it’s common to attempt to create stickiness — the ability to keep customers using a product — with a slick and easy to use product, I think holding valuable data central to a business can replicate the stickiness of even the very best user experience. I think a lot about what data is the most impactful and, if the company is not yet collecting this, how collecting it would change the user experience. It’s a very interesting exercise.
<stuff> Weekly!
LOL Weekly - A Major League Cowboy??
“[Madison] Bumgarner has been competing in rodeo events under the alias "Mason Saunders" and won $26,560 in a team-roping competition in December”
lol this is just so awesome. What a guy. Plus he ACTUALLY WON a competition. lol.
Funding Weekly - Stonly: Thoughtful Customer Service
“The service lets you create scripted guides with multiple questions to make this process less intimidating. Some big companies have built question-based help centers, but Stonly wants to give tools to small companies so they can build their own scenarios.”
Stonly raised $3.5M from Accel, Eventbrite CTO Renaud Visage, and the founders of PeopleDoc. Customer support is commonly cited as the number one thing startups can differentiate on in the early days, and it works! I love Stonly’s mission to empower small companies elegantly handle custom service needs and I especially love the data they’ll capture on customer interaction with their support guides. Excited to keep an eye on this one.
Baseball Weekly - Astros’ Brand Suffers
Alex Sliverman, Morning Consult
“In the two weeks following the release of MLB’s report on Jan. 13, the Astros’ net favorability — the share of adults who view them favorably minus the share who view them unfavorably — dropped 12 percentage points. It made them the least-liked club in MLB and the only team with a negative net rating, with more adults having an unfavorable than favorable view”
Interesting data here on the Houston Astros’ favorability after the sign stealing scandal and… *shocker* it’s abysmal. Not only that, it’s the worst in the league by far. It’s no surprise, really, but I would have guessed the drop would’ve been much larger that 12 percentage points. I’d be interested to see what their rating was prior to any mention of the scandal. If spring training is any indication of how their favorability is trending… it’s not good.
Side note — one of my Government Professors at Dartmouth, Kyle Dropp, is a Co-founder of Morning Consult, the firm who captures and analyzes all of this data. They’re doing some super interesting things with survey research data and have a few awesome daily newsletters on relevant news and research data across industries. Alex Sliverman, author of the article referenced here, writes my favorite one, Morning Consult Sports!
Art Weekly - Composition (1988)
Pierre Soulages
The blue in this piece is so striking. I love the broad and seemingly deliberate brush strokes.
Thanks for reading! I’d love to hear your thoughts and feedback on Twitter: