Archive of posts with tag 'Business'

For any company, keeping track of your position in the competitive landscape is an important element of making the right decisions. When I think about competitors, I think about them separately as “direct” vs. “indirect”:

• Direct competitor: one with a product offering highly similar and seen by your customer as a direct substitute
• Indirect competitor: one that might look glancingly similar (or very different) on the surface, but addresses the same jobs to be done

Direct vs. indirect doesn’t matter all that much at the end of the day; you’re still trying to occupy the same problem space for your target audience. The important piece is to remember that it should be inclusive of the indirect: those that obliquely address the same concern for a user.

The big question is: how much time and attention should you spend on monitoring and analyzing your position relative to your competitors? Clearly the right answer lies somewhere on the spectrum between none and way too much.

It’s commonplace to skew too far to the left or right on this spectrum — to either be too concerned with the day-to-day movement of what your competitors are doing, or to have too much confidence in your own position, willfully ignoring what your competitors or the market are doing. On one end you can’t stop worrying about it every time a customer mentions a competing product’s features. On the other, you’re saying “we have no competition.”

Clearly the right answer is somewhere in the center, taking behaviors from each side and combining them in creative ways to do something unique, but with enough similarity to the market that you can sell the future you’re building with minimal friction.

What if we could think about “competitive attention” on a spectrum? The spectrum could go from:

• Zero attention on competitors or the market, to
• Spending all of your time worried about and analyzing what competitors are doing

Broadly speaking, moving along each direction of the spectrum trends toward being more of a leader or a follower. Leaders blaze their own path and drive toward a vision of the future they define. Followers can’t stop chasing what someone else is doing. It might sound like I’m making the case for “leader” here — after all, leader has the positive connotation, and follower makes you sound like an also-ran. But there are plenty of areas in all businesses where it’s smart to conform to market norms rather than trail-blazing. You should make sure your unique, innovative edge is concentrated in specific areas. Even world-changing product innovators don’t completely ignore what existing markets look like of course.

The best companies view the world from a demand perspective (What do customers want? What are the jobs to be done?) rather than a supply one (Who is building what in the market?)

Let’s define a spectrum, and some points along it:

Definitions on the spectrum — the left side leans toward independence (with an extreme of brazen ignorance), the right toward paranoia (some is a healthy thing, but in the extreme, it’s obsession):

• Independent mindset:
  • ⟽ Willful Ignorance — You’re irrationally overconfident in your market position. You intentionally don’t care what competitors are doing, you denigrate other options or solutions as irrelevant or obsolete, and you believe you’re more important to your customer than you are. “We could never be replaced” (You can definitely be replaced).
  • ⟻ Blissful Naiveté — You don’t go so far as to think you’re invincible, but you have an ignorance of the market due to naiveté more than overconfidence. You don’t bother to spend the time to understand the landscape you participate in.
  • ⭐️ Creative Confidence — The ideal place to be. Confident in your own ideas, maintaining the ability to design and build solutions independently of what others in the market are doing. You focus on creating from first principles based on customer demand, not on copying what others do.
• Paranoid mindset:
  • ⟾ Dangerous Obsession —  You let competitors dominate your thinking, constantly worried that every new one you discover is already eating your lunch. You call meetings and have big discussions about what competitors are doing, in the worst cases moving your own goalposts continuously when competitors make movements. Instead of worrying about how to solve your current customers’ problems and inventing new solutions for them, you’re spend all your time looking for a customer you don’t have. The grass is always greener somewhere else.
  • ⟼ Frequent Distraction — You do spend time with your own customers looking at what you can build for them, but you let what other people are building distract you from the time required to build truly novel product. Monitoring others’ feature lists and building comparison matrices steals away enough attention to slow you down in your new creative efforts. You pull punches on your innovative work because you’re scared to step too far off a well-trod path. Keeping up with what’s in the rearview steals away your attention from solving your current customer’s problems.
  • ⭐️ Informed Balance — You’re knowledgable about the market and its players, but not in a way that keeps you up at night. you have a confident, informed understanding of what other players can do, and you spend most of your time in “competitive” headspace thinking about articulating your differentiation. Because you’re out in front building your own thing, people copy you more than the other way around. But you do maintain just enough useful paranoia to temper your overconfidence.

It takes willful effort to position your mind in the right spot on the line. And depending on the situation, pushing your view left or right is defensible. In some areas you shouldn’t care what the competition is doing — dare to solve the problem differently. In other cases, not all things you need to build are key differentiators; some things are simply table-stakes things that need doing. Innovation and creativity are expensive. You want to spend those resources where they count.

If you’ve been involved in investing or fundraising activities in the past, you’ve likely heard about “TAMs” (total addressable market), as in “So what’s your TAM look like?” The general idea is to determine a metric that communicates in few words the nature of a given market for a product or service. Investors want to know how a company thinks about its market opportunity (investors generally want large ones), and startup founders need to have a sense for what they can realistically target, build for, sell to, and capture to build a business. You may also have heard of TAM’s cousins, like SAM and SOM (serviceable addressable market and serviceable obtainable market), but let’s stick to the big one here.

TAM is part of the lingua franca of the fundraising process, a term thrown around casually and understood in a variety of ways by investors and operators alike. For those of us on the operator side of the table, calculating your TAM is a necessary part of the company-building and fundraising process. Most founders don’t start with the question of “What’s the biggest TAM I could target?” before starting on their idea. Picking apart the market specifics is typically downstream of prototyping an idea for a job to be done. As a founder you have to think about how to effectively communicate just enough here to be useful to the audience. But how is TAM useful? What is an investor doing with those numbers beyond saying “Yep, this is big enough”?

What TAM analysis is good for

Firstly, a TAM serves to communicate how a company thinks about its target customer. Let’s say you build a team collaboration tool for cross-company projects. Who are the targets? What size companies? Is there a narrower industry focus? Generally the wider your target, the harder it will be to convincingly portray a useful TAM. Simply saying “anyone on Earth could be a customer, our TAM is $500 trillion” won’t persuade anyone, not to mention you’ve got quite the uphill battle to build and sell to such a broad audience. Being narrower isn’t a flaw, it’s a feature.

Which brings me to the second use for TAM: signaling the quality of your thinking about your business. A thoughtful, concise TAM analysis functions not only as a means to convey who your target is, but also as a sign of how thoroughly you’ve thought about the prospects of the business itself, meaning how to tackle a go-to-market. If you can tell a persuasive story about your target market, it lends credibility to why an investor should trust you with their money. A sloppy, or overconfident, or poorly-articulated market description (even if you have a baller product) doesn’t reassure anyone that you’ll be a good steward of the dollars. It’s like some people say about college degrees: they’re useful as signals of wherewithal and commitment as much as they are on their own merits of intelligence. They signal that you see something through to the end, a quality in higher demand by some employers than whether you have a BS in physics or a BA in philosophy.

But enough about the reasons why TAM matters. Let’s cover some reasons it’s not very useful, at least beyond a “qualifying variable” stage.

Then what’s wrong with TAMs?

Well, nothing is “wrong” with them, but they are quite often overvalued. For investors, how you read a TAM analysis and what emphasis to place on it is something to be circumspect about.

For one, markets aren’t static fixtures; they move, expand, and contract all the time. They’re moving targets. Sure, even with an understanding of this factor, a TAM can be useful as a snapshot in time as it stands the moment a deal is getting done. Even well-defined categories move all over the place. It’s more informative to look at it directionally — whether the market is expanding, or if it’s shrinking or being subsumed by an adjacent one.

Some of the best startups are great precisely because they’re targeting a strange combination of submarkets, or they’re in the early stages of creating a category, where there isn’t even an agreed-upon way of describing it yet. Conjuring TAMs for startups like this involves a mixture of alchemy and storytelling that can sometimes be unconvincing in the early days¹.

For example, I’ve been building a low-code platform for field service organizations since 2011, but I’d never heard the term “low-code/no-code” (and we didn’t use it) until the mid-2010s sometime. Had we oriented on the “TAM” of no-code back then, what would we have determined? If you could even define a known boundary around the category at that time, it was probably in the 10s of millions or maybe $100 million total. Now the market is expanding at a CAGR of 30%, headed north of $180 billion by 2030, from around $10 billion in 2019. This market became a known one gradually over time. Initially it was just an undifferentiated, messy mass of people and companies doing things that looked similar to one another with app-builder tools, WYSIWYG editors, and pluggable integrations. Over time the ecosystem became legible and better understood, and now it’s a named category tracked by the likes of G2 and Gartner. But, importantly, some of the best investments in the space happened long before analyst acknowledgement. These are lagging indicators if you’re looking to be the first in, funding the most exciting new ideas.

But an even more important reason why TAMs are dangerous is that even if a market itself is relatively stable over time, your company doesn’t need to be stable. A company has agency. A great product team doesn’t stand still and let their market “happen to” them. Dimitri Dadiomov nails it here — couldn’t have said it better myself:

Part of the calculus with investing in an early product is measuring the future potential of the founders and the company. How a TAM is computed given the current status of a market, or the fitness of a product to said market, is irrelevant to capturing the future potential. A great product team will not stand by and let a better market, or a more focused market pass them by. In the best instances of this, in fact, startups can be singularly responsible for (or close to it) the creation of a market from whole cloth. Or at minimum they can greatly accelerate the realization of latent, obscured demand. Think iPad for tablet devices, Dropbox for personal cloud storage, Salesforce for CRM.

All of this is largely academic if you’re researching your market size to communicate with stakeholders. Take these tips for sizing and refining your definition and run just enough with them to communicate clearly. Just be wary not to internalize too directly the left and right bounds of your market. That’s not to say that defining ideal customer profiles is a bad thing… Far from it! But there’s a difference between “Alex here is the type of persona we’re building for” and “Our market is X big, and defined in this specific way.” Taking to heart what your market looks like without appreciating your own ability to change it can drive fatalistic behavior on the team. If your success in a market is flagging, or you’re making discoveries that the go-to-market angles into a specific customer set aren’t working, you have the steering wheel. You can change course, experiment your way into new markets, and make your TAM simply a snapshot of where you are, not a strict destiny.

Product-led growth has been booming in the B2B software universe, becoming the fashionable way to approach go-to-market in SaaS. I’m a believer in the philosophy, as we’ve seen companies grow to immense scales and valuations off of the economic efficiencies of this approach powered by better and better technology. People point to companies like Atlassian, Slack, or Figma as examples that grew enormously through pure self-service, freemium models. You hear a lot of “they got to $NN million in revenue with no salespeople.”

This binary mental model of either product-led or sales-led leads to a false dichotomy, imagining that these are mutually exclusive models — to grow, you can do it through self-service or you can hire a huge sales team, pick one. Even if it’s not described in such stark terms, claims like “they did it without sales” position sales as a sort of necessary evil we once had to contend with against our wills as technology builders.

But all of the great product-led success stories (including those mentioned above) include sales as a component of the go-to-market approach. Whether they refer to the function being performed by that name or they prefer any number of other modern euphemisms (customer happiness advocate, growth advisor, account manager), at scale customers end up demanding an engagement style most of us would call “sales.”

Product-led, self-service models and sales are not incompatible with one another. In fact, if structured well, they snap together into a synergistic flywheel where each feeds off of the other.

Early-stage customers

Product-led tactics have the most benefit in the early stage of a customer’s lifecycle, when your product is unproven. Free trials and freemium options lower the bar to getting started down to the floor, self-service tools allow early users to learn and deploy a tool in hours on their own timeline, and self-directed purchasing lets the buyer buy rather than be sold to. In 2021, flexibility is table stakes for entry-level software adoption. There are so many options now, the buying process is in the customer’s control.

With the right product design, pricing, and packaging structure, customers can grow on their own with little or no interaction through the early days of their expansion. For small to mid-size users, they may expand to maximum size with no direct engagement. Wins all around.

For larger customers (the ones all of us are really after in SaaS), this process gets them pretty far along, but at some stage other frictions enter the picture that have nothing to do with your product’s value or the customer’s knowledge of it. Financial, political, and organizational dynamics start to rear their heads, and these sorts of human factors are highly unlikely to get resolved on their own.

The Sales Transition

Once the bureaucratic dynamics are too great, for expansion to continue we need to intervene to help customers navigate their growing usage. As I wrote about in Enterprises Don’t Self-Serve, several categories of friction appear that create growth headwinds:

• Too many cats need to be herded to get a deal done — corralling the bureaucracy is a whole separate project unrelated to the effectiveness or utility of the product; no individual decision maker
• The buyer isn’t the user — user can’t purchase product, purchaser has never used product; competing incentives 
• If you have an advocate, they have a day job — And that job isn’t playing politics with accounting, legal, execs, IT, and others

As you start encountering these, you need to proactively intervene through sales. The role of sales is to connect with and navigate the players in the organization, then negotiate the give and take arrangements that create better deals for both parties: e.g. customer commits to X years, customer gets Y discount. Without a sales-driven approach here, every customer is treated as one-size-fits-all. Not the best deal for the vendor or customer. When you insert sales at the right stage, you increase the prospect of revenue growth, and the customer’s ability to sensibly scale into that growth with proper integration throughout their organization.

In SaaS literature you’ll read about the notion of “champions”, internal advocates for your product within your customer that are instrumental in growing usage. Champions serve a function in both methodologies — with product-led, they’re pivotal for adoption to perpetuate itself without your involvement, and when engaging with sales, we need those champions to be intermediaries between vendor and buyer. They act like fixers or translators, helping to mediate the communication between the sides.

A well-built, product-led product mints these champions through empowerment. We give users all the tools they need — documentation, guides, forums, SDKs — to build and roll out their own solution. After a couple phases of expansion, users evolve from beginners to experts to champions. If we’re doing it right and time sales correctly, champions are a key ingredient to maximizing relationships for customers and product-makers. Product-led approach early creates inertia to keep growing, a back pressure that sales can harness to our advantage.

AppCues publishes their product-led growth flywheel, which describes this cycle succinctly:

As they demonstrate, a user becoming a champion isn’t the end state; champions beget future brand new users through advocacy, word-of-mouth, and promotion within their own networks.

It’s dangerously short-sighted to look down the nose at sales as a bad word. Sales isn’t just something you resort to when you “can’t do PLG”, it’s a positive-sum addition to your go-to-market when you execute this flywheel properly.

Morgan Mahlock wrote recently about the promise of Stripe Press, Stripe’s book publishing outfit:

Within the legacy publishing industry, Stripe’s young publishing press is refreshing - it is Sutherland’s electric cover art on a dusty, tired bookshelf. An Authoritative Look at Book Publishing Startups in the United States by Thad McIlroy states, “Book publishing has never been a technology-adept industry; indeed it is historically technology-averse. This is a challenge for the (minority of) startups targeting existing publishing companies.” Stripe Press is different because it was born from a technology company. It is a strategic asset because it allows Stripe to shape and share influential knowledge with its interconnected ecosystem of entrepreneurs, businesses, authors, and technologists.

Her post gives a good summary of why Stripe Press is exciting for the book publishing industry. The catalog only sits today at 10 titles, but I believe 4 those were released this year. The pace has been increasing, but they keep elevating the quality bar.

They’re not only attracting original works like Nadia Eghbal’s excellent Working in Public (2020), but also breathing new life into notable books from the past. Both Martin Gurri’s Revolt of the Public (published in 2014, one of my favorites this year) and Donald Braben’s Scientific Freedom (2007), to name two examples, saw relatively small initial publishing runs. The editorial staff over at Stripe is doing amazing work to bring these books back into wider circulation using a spotless curatorial eye for the noteworthy and influential.

Stripe Press is, of course, producing excellent books for us to read, and giving authors writing about technology a channel for getting their work out there. But it’s also a marketing channel for Stripe.

Content marketing, my favorite of the marketings

I have a soft spot for quality content. The best content marketing doesn’t feel anything like marketing. Its value is so deep you don’t even think about what you’re giving in return to its creator.

The tech companies of the last 20 years didn’t invent content marketing, though our scene talks about it more than any other. Even Ben Franklin used a content marketing play when he published Poor Richard’s Almanack as a way to promote his printing business.

The scene is now full of companies that embrace the multichannel returns they can drive through quality, helpful content. A few favorites of mine:

• Zapier’s technology guides
• 1Password’s combo of business and security practices
• Buffer’s podcast on social media
• Basecamp’s Ryan Singer writing Shape Up, on processes for product teams
• SaaStr’s bottomless resources on Quora (if there was a “SaaS MBA,” you can get one from following @jasonlk)
• Stripe’s fantastic guides for startups

Not only does Stripe do a stellar job at the traditional CM channels — blog, help guides, developer documentation, email — they went farther than anyone and became a book publisher¹.

What differentiates Stripe as a publishing house from the HarperCollinses or Hachettes is that it’s not their core business, but a component that drives other parts of the business. Direct sales revenue is only 1 channel of value they’re deriving from putting this catalog in print. Stripe sees their Press group as a content marketing strategy, especially to raise global interest in technology, pushing their mission to “raise the GDP of the internet.” At the most tactical level, the Press catalog increases interest in tech, creates more founders, who then start companies that become Stripe customers.

Stripe flywheels

I linked a while back to Max Olson’s excellent post Advantage Flywheels, which presents a great framework for analyzing the causal loops that power businesses. Irrespective of Press, Stripe’s built a fantastic advantage with feedback loops combining in powerful ways. Using Max’s same architecture of flywheel archetypes, I took a stab at drawing out what Stripe’s machinery looks like, with its products in blue:

At its core, Stripe serves developers who build applications which expand in usage and generate financial transactions.

Spinning off from those central inputs and outputs are several flywheels that create momentum that feeds back into the core business. Radar does fraud detection, which improves with masses of transaction data. Billing and Sigma are tools that improve finance management and reporting. Atlas helps founders incorporate and get started, thereby generating more customers for Payments, Issuing, and more. That’s where I see book publishing fitting into the machine: as a mechanism to expand the TAM for internet businesses.

Press is unique in this regard for a tech content strategy. Normally something like a blog, video channel, or newsletter would be tied more directly to the “more developers” nexus, but for Stripe, book publishing is playing a longer game. Even though this feedback loop has a long time delay (publishing a book won’t make a new founder overnight), I believe it’s a powerful one. The best strategies serve more than one function; Press is a brand builder, a recruiting tool, a direct revenue driver (from book sales), and most importantly, a way to increase the number of people interested in technology over the long term. Founder Patrick Collison himself described this exact strategy in response to a Hacker News thread:

The vast majority of Stripe employees (and there are now more than 1,000) work on our core functionality today. But we see our core business as building tools and infrastructure that help grow the online economy. (“Increase the GDP of the internet.”) When we think about that problem, we see that one of the main limits on Stripe’s growth is the number of successful startups in the world. If we can cheaply help increase that number, it makes a lot of business sense for us to do so. (And, hopefully, doing so will create a ton of spillover value for others as well.)

Stripe’s long been known for it’s writing culture, so I suppose it’s also not surprising that a company of readers and writers would want to make books.

When you pop the hood on a strong business like Stripe, you’re always likely to find interesting systems dynamics — multiple outputs feeding other inputs. It’s fascinating that an old, traditional business like publishing could be done in a novel way like this. They’re positioned to bring in new innovations for authors (and readers) that they haven’t scratched the surface on yet; it’s still just paper books. If there’s room for innovation in writing books, Stripe will find it.

There’s a myth in popular culture that associates “being an entrepreneur” with “making a lot of money.” But do they, if compared to a world where an entrepreneur did the same job in the employ of someone else?

In this post, Jerry Neumann references a chart from Scott Shane’s The Illusion of Entrepreneurship that tells a much more realistic story of what creating your own business means financially:

The vast majority make the same, if not less than, their non-self-employed peers, at least until you’re into the 80th+ percentile where the incomes balloon, those massively successful entrepreneurs who create something much more valuable, well beyond their own level of input. Political economist Joseph Schumpeter described this as one of inputs and outputs. In both cases — where a person is a founder versus a contributor in another company — they’re an input to the system, contributing in the same capacity toward profit regardless of their status:

In a market economy, at equilibrium, Schumpeter says profit gets competed away. By profit he means “surplus” profit: the money a company makes if its inputs are priced correctly. Crucially this includes the cost of money adjusted for the risk the investor is taking. That is, you can’t increase risk and say “look, now there’s a profit.” That profit is the cost of the money used in the business.

Neumann points out an interesting insight around why entrepreneurs start their own businesses. It may be that the popular understanding of what drives a founder is financial, that their primary motivator is to make more money. But here’s a shocking statistic: that 81% of founders have no desire to grow their business:

Shane notes that the median revenue of an owner-managed firm is $90,000 and that 81% of founders have no desire to grow their business. This is because most founders are “just trying to make a living, not trying to be a high-growth business.” And they “start firms in industries where there are a lot of firms already in operation” and “report they have no competitive advantage.”

It turns out that independence is one of the largest drivers for people to work for themselves, not to get rich (though I’m sure most would agree that they’d like that, too, just maybe not enough to push that much harder than working as an employee). Quoting Shane again:

“The real reason most people start businesses…has nothing to do with wanting to make money, to become famous, to better their own communities, to seek adventure, or even to improve the world. Most people start businesses simply because they don’t like working for someone else.”

He goes on to tie this back into how it drives typical business strategy. Not all “startups” (in the loosest definition of the term — “independent company” rather than “SV company building software”) have the same underlying motivations driving their creation, so should not be compared in economic terms on the same dimensions.

If you polled government civil servants, regulators, or funding organizations, everyone would agree that new business formation is something to be encouraged and enabled. But if so much policy is developed with a definition of “startup” that you read in the news (your Ubers and WeWorks and Airbnbs and other hypergrowth compatriots, driven by market expansion and dominance rather than simply personal independence), then they’re only framing the infrastructure around a small (actually, by quantity, tiny) subset of companies. Refining the popular understanding of what “starting a business” actually looks like on the ground would go a long way to helping make it easier to do, and give your average person a better appreciation for what the motivators really are.

Everyone I know who runs a small business would be first in line to tell you: if you want a bigger paycheck, don’t start your own company. If you asked a random person on the street, though, they’ll generally associate “business owner” with “person that makes a lot of money.” I’ve never been one myself (yet), but I have a totally different perspective than this with so many direct relationships with business owners.

If most people had a better understanding of what motivates the founder, how much better could we do at supporting entrepreneurial activity?

💼 Collaborative Enterprise

Elad Gil describes the trend of continuing consumerization of enterprise software.

🤖 Seeing Like an Algorithm

Part 2 in Eugene Wei’s series on TikTok. See part 1.

🏫 Fifth Generation Management

Venkatesh Rao’s Breaking Smart podcast is always a must-listen.

In his article “Strategy Under Uncertainty,” Jerry Neumann contrasts the traditional Porter model of business strategy with one more suited to startups, the former being modeled around mature organizations operating in known competitive spaces, the latter around startups moving in opaque environments with higher uncertainty and more moving parts.

In the piece he defines “strategy” as a framework for “how to make decisions in situations that are not yet known.” To have a purposeful, intentional approach to an objective, whether in war, sports, or business, you have to formulate a model for predicting the future. Without clarifying some future state you think will develop, it’s impossible to take the right series of actions to end in success.

Strategy in some businesses is all anyone wants to talk about. In others it’s taken for granted or not discussed enough explicitly. Each of these paths is incorrect.

In the second case, most anyone would acknowledge that little meaningful progress can happen in a business with no strategy. You’re merely subject to the whims of the environment and other players around you. You rely on getting lucky.

The first case, though, is also surprisingly common. In the modern business universe, being a “strategic thinker” is an accolade. The business literature surrounding us pushes this idea so much that in some environments, everyone wants to strategize. Without the right guidance and guardrails, this can go on far too long and can be actively detrimental to execution.

One of the dangers I see of over-strategizing in startup environments is that it tempts you into searching for information that may not be there in the first place. We think we can “map the competitive landscape”, but what if we can’t lay out an immediately clear picture? We begin to prioritize having a drawable diagram of our competitive landscape over the actual reality on the ground. In pursuit of having a legible map, we stretch definitions or cut corners. The map becomes the goal.

As Neumann points out, the environment in startups is riven with uncertainty and moving targets. You have more unstable dynamics than you’d find in many mature, large companies:

Startups operate as part of a complex system that encompasses not just their internal operations, but their customers, their suppliers, other companies that might compete or cooperate with them, financiers, the media, the government, and society at large. Each of these other entities also makes decisions, and the results of their decisions must factor into the startup’s decision model. The changes most likely to affect a startup are the ones that happen as a result of the decisions the startup itself makes, a complex feedback loop.

One of my core beliefs is that in spaces of high uncertainty, too much analysis and planning builds in more risk rather than less. Partially this is because of the time we spend in planning; more time spent equates to higher expectations and an inflated sense of what we know. I’ve analogized this situation to a high-wire act: the more assumption you make that your Big Prediction is correct, and the more you build toward that up front, the higher you raise that wire over the ground. As your predictions go farther into the future and your bets get bigger, the risk keeps rising.

Building strategy is so tempting. It always sounds like a good thing to spend time on, and it often is. Somewhat perniciously, it can be even more tempting in a startup environment, where the risk is high and the runway short. You perceive little margin for error, so you have to get the plan right.

The trouble is that there’s a limit to how much it can do for you in a startup. And the more novel your idea the higher the uncertainty as to what the future of the market holds. Bias creeps in about what you think you know about the predictability of the space; just because you’ve done n hours of analysis doesn’t guarantee you have any clearer an answer than at hour two. But human biases will tempt you to believe you’ve imbued it with more legibility than there really is. The only worse decision making environment than one with no information is one with actively misleading information.

Like many things, I believe the right way to approach strategy in startups is a nuanced middle.

You should always start with a clear vision of the future. What world is your business or product trying to create? How are you changing life for your customers or users? The answer to this question sets the course, but not the strategic roadmap, go-to-market, or many other things you need to figure out.

I like to think of strategy in medium term chunks. The goal is to build a hypothesis that we can push forward, rollout a strategy for, and test with real feedback on the order of weeks or months. More frequent, lower intensity strategy sessions that allow you to come up for air and work with real information about the world that you’ve learned, rather than speculating on your 5 year strategy at a whiteboard for a month.

Thinking you know more than you do leads to dangerous and risky plans. Usually it’s possible to know enough to take smaller, incremental steps based around much more reliable signal, with less interference from bias. There’s typically room for a couple of riskier moves here and there where you could earn outsized returns if you’re right. But the majority of the time, a tempered approach to just-right “Goldilocks” strategizing is the right way to go for a small team and a new product.

🕰 Timeful Texts

A new piece from Andy Matuschak and Michael Nielsen (beautifully illustrated by Maggie Appleton). Can we make reading a more engaging and interactive learning experience? This builds on previous ideas from the authors on spaced repetition.

🤼‍♂️ Software, Full-Stack, and Sumo Startups

Interesting take from one of Byrne Hobart’s recent newsletters. Contrasting a typical “full-stack” model of company-building and VC funding to a “sumo” model:

The amount of VC funding has been rising steadily, and returns are skewed by a few positive outliers, so any fund that doesn’t have a specific size mandate is actively looking for companies that can absorb a lot of capital as they grow. The best way to get more capital is to move from a capital-efficient business to a capital-inefficient one, so there’s a strong incentive to pivot in this direction.

The incentive is sometimes too strong. Some companies go beyond the “full-stack” model to what I think of as the “sumo” model: raising an intimidating amount of money just to scare off everyone else. The sumo model does prevent one failure mode for startups: the situation where every time Company A raises a round, it validates the model and lets Company B raise more, which forces Company A to burn through their marketing budget faster and raise an even bigger round, and so on until the entire space is over-capitalized and everyone’s assumptions about long-term unit economics are implausibly optimistic. It’s an easier strategy to try when capital is abundant, but it’s a harder strategy to pull off; the bar for “an absurd amount to invest in a company that just does X” keeps going up.

🔗 The Canva Backlink Empire

In the arena of geeky digital marketing, this is a great deconstruction of organic optimization tactics in play at Canva, one of the best out there at enabling discovery through search and backlink traffic. I love how thoughtful and intentional their page architecture is; it enables so much adaptive targeting to sweep up long-tail keyword spaces.

🎖️ Commandos, Infantry, and Police

Jeff Atwood on Robert X. Cringely’s descriptions of three groups of people you need to “attack a market”:

Whether invading countries or markets, the first wave of troops to see battle are the commandos. Woz and Jobs were the commandos of the Apple II. Don Estridge and his twelve disciples were the commandos of the IBM PC. Dan Bricklin and Bob Frankston were the commandos of VisiCalc.

Grouping offshore as the commandos do their work is the second wave of soldiers, the infantry. These are the people who hit the beach en masse and slog out the early victory, building on the start given them by the commandos. The second-wave troops take the prototype, test it, refine it, make it manufacturable, write the manuals, market it, and ideally produce a profit.

What happens then is that the commandos and the infantry head off in the direction of Berlin or Baghdad, advancing into new territories, performing their same jobs again and again, though each time in a slightly different way. But there is still a need for a military presence in the territory they leave behind, which they have liberated. These third-wave troops hate change. They aren’t troops at all but police.

😴 Why Sleep Deprivation Kills

Behind all this is the astonishing, baffling breadth of what sleep does for the body. The fact that learning, metabolism, memory, and myriad other functions and systems are affected makes an alteration as basic as the presence of ROS quite interesting. But even if ROS is behind the lethality of sleep loss, there is no evidence yet that sleep’s cognitive effects, for instance, come from the same source. And even if antioxidants prevent premature death in flies, they may not affect sleep’s other functions, or if they do, it may be for different reasons.

📥 The Observer Effect: Marc Andreessen

A new interview series from Sriram Krishnan:

The Observer Effect studies interesting people and institutions and tries to understand how they work.

He kicks it off big with an interview with Marc Andreessen.

⚗️ COVID and Forced Experiments

Benedict Evans looks at what could return to normal after coronavirus, and what else might have accelerated change that was already happening.

“Every time we get a new kind of tool, we start by making the new thing fit the existing ways that we work, but then, over time, we change the work to fit the new tool. You’re used to making your metrics dashboard in PowerPoint, and then the cloud comes along and you can make it in Google Docs and everyone always has the latest version. But one day, you realise that the dashboard could be generated automatically and be a live webpage, and no-one needs to make those slides at all. Today, sometimes doing the meeting as a video call is a poor substitute for human interaction, but sometimes it’s like putting the slides in the cloud.”

📈 COVID-19: What’s wrong with the models?

One of the things continually aggravating about all of the data, models, projections, and analyses about COVID-19 is how little anyone cares to retroactively analyze prior predictions. Over the last two months the predictions have been all over the map, and as time marches on and many are wrong, some are right, there’s no analysis of what assumptions were made that turned out not to be true causing the wide divergence between projection and reality.

Peter Attia calls out here something rarely acknowledged about why projections are wicked:

“Projections only matter if you can hold conditions constant from the moment of your prediction, and even then, it’s not clear if projections and models matter much at all if they are not based on actual, real-world data. In the case of this pandemic, conditions have changed dramatically (e.g., aggressive social distancing), while our data inputs remain guesswork at best.”

💉 The Pandemic Isn’t a Black Swan but a Portent of a More Fragile Global System

Nassim Taleb, making his way into the New Yorker.

This is a weird time.

The COVID-19 pandemic is the biggest global event that’s happened in my lifetime. It hasn’t impacted me personally that much (yet), but the financial and public health implications are clearly already disastrous, and bound to get worse.

Most concerning, though, is how little we know today about what’s in store for the rest of 2020 and beyond.

I don’t use this outlet to make predictions, and I’m generally not a fan of trying to call shots on uncertainties. But as an experiment, let’s set down some open-ended questions to revisit in 6 months to see what’s different.

What will be different by mid-September?

Restaurants and bars

• Will the restaurant market return to how it was before? If it rebounds, how does the renewed landscape look different?
• Does the expansion of the food delivery market change the kinds of restaurants that open? Not all food types are equally compelling when jammed in a box. Does that influence what’s available?
• We were already Shipt customers before all of this for grocery delivery. Is COVID-19 the stressor that shifts more grocery business from brick-and-mortar to delivery?

Hotels

• Airbnb already impacted the hotel business over the last 10 years. But as we return to normal, what changes? Do people start putting extra priority on personal space?
• Airbnb has been, generally speaking, cheaper than traditional hotels over the years, but does this balance shift?

Airlines

• Seems like a fairly irreplaceable business, but does air travel return to pre-COVID level? Do people reduce non-essential travel?

Cruises

• Already an expendable industry, but not a small one ($45bn annually). After COVID, how does it ever return to normal
• Where would this spending go if it doesn’t? What form of recreation, travel, entertainment picks up that spending?

Businesses

• Businesses have gone dormant, people laid off, reduced hours, high unemployment. When things start to rebuild, what returns?
• For those of us that have gone to remote work with minimal disruption, how many companies return to an office full time?
• If even 20% of these remote-capable companies decide either “we don’t need an office” or “we could downsize to a smaller one,” what impact does it have on commercial real estate?

Schools

• Schools around the world closed pretty quickly, most moving to remote learning. Universities mostly have some infrastructure in place now for online coursework, even though most traditional ones are still in-person heavy. Given that there was already a trend (albeit small) toward distance learning in higher-ed, and assuming at least moderate success in moving to remote over the next several months, are colleges ever the same again?
• At elementary and high school levels, the move to remote Zoom-based classes seems shakier. Our daughter is still in pre-school, so we aren’t that impacted (plus the first week of this quarantine spanned spring break, with no school anyway). But I’ve heard from others mixed experiences with their kids trying to “homeschool” while they work from home. When do the kids return to a normal school life? Will it be back to normal by the fall and start of the 2020 school year?

Entertainment

• The feature film industry could be done-for. With theaters all closed for a while, what happens to them after? Will they re-open? And if so, how long does it take to reconstitute a business in which many will likely have permanently closed and laid off their staff?
• Film studios are now forced to release new movies online, jumping the theatrical release completely and dropping movies directly on iTunes for $20. What will these new “virtual box office” results look like compared to their predicted receipts if they’d been released traditionally? If the earnings are still attractively high, will this new release model be permanent?
• What happens to film and television production over the next 6 months? Do we end up with a lull in new content similar to the writers strike from 2007?

I was looking around for a summary of Clayton Christensen’s Innovator’s Dilemma and ran across this neat YouTube channel that does book summaries in visual form, with drawings representing the concepts in the book.

It’s a cool way of getting a different presentation of subject matter, especially of nonfiction and business books.

🥩 The Internet of Beefs

Venkatesh Rao has assembled a most compelling explanation for how the internet polarization machine works:

The semantic structure of the Internet of Beefs is shaped by high-profile beefs between charismatic celebrity knights loosely affiliated with various citadel-like strongholds peopled by opt-in armies of mooks. The vast majority of the energy of the conflict lies in interchangeable mooks facing off against each other, loosely along lines indicated by the knights they follow, in innumerable battles that play out every minute across the IoB.

Almost none of these battles matter individually. Most mook-on-mook contests are witnessed, for the most part, only by a few friends and algorithms, and merit no overt notice in either Vox or Quillette. Beyond a local uptick in cortisol levels, individual episodes of mook-on-mook violence are of no consequence.

🎭 The Curse of Culture

I have a working draft post on this topic for sometime in the future. This is one of my favorites from the Stratechery archives — on corporate cultures and how they impact company strategy:

As with most such things, culture is one of a company’s most powerful assets right until it isn’t: the same underlying assumptions that permit an organization to scale massively constrain the ability of that same organization to change direction. More distressingly, culture prevents organizations from even knowing they need to do so.

📚 Book Review: Secular Cycles

The Slate Star Codex review of Turchin and Nefedov’s Secular Cycles, which seeks to understand patterns in technological and social development, and underlying causes for expansion and stagnation periods.

⚖️ Tradeoffs: The Currency of Decision Making

Farnam Street:

Time is our most fundamental constraint. If you use an hour for one thing, you can’t use it for anything else. Time passes, whatever we do with it. It seems beneficial then to figure out the means of using it with the lowest possible opportunity costs. One of the simplest ways to do this is to establish how you’d like to be using your time, then track how you’re using it for a week. Many people find a significant discrepancy. Once we see the gulf between the tradeoffs we’re making and the ones we’d rather be making, it’s easier to work on changing that.

The article reminds me of Sowell on economics. Take this and apply to any other life domain:

Economics is the study of the use of scarce resources which have alternative uses.

💡 The Power of the Marginal

A timeless one from Paul Graham, 2006. On the advantages of outsiders:

Even in a field with honest tests, there are still advantages to being an outsider. The most obvious is that outsiders have nothing to lose. They can do risky things, and if they fail, so what? Few will even notice.

The eminent, on the other hand, are weighed down by their eminence. Eminence is like a suit: it impresses the wrong people, and it constrains the wearer.

Outsiders should realize the advantage they have here. Being able to take risks is hugely valuable. Everyone values safety too much, both the obscure and the eminent. No one wants to look like a fool. But it’s very useful to be able to. If most of your ideas aren’t stupid, you’re probably being too conservative. You’re not bracketing the problem.

📝 PR FAQs for Products

This is an extension of the Amazon mantra of forcing your team to “write the press release” for a product or feature before starting on it. The goal is to concretely visualize the end state as clearly as you can, and get on the same page strategically to outline the why of what you’re building. The PR FAQ is another assistive technique for setting and articulating the goal.

🧮 Neutrinos Lead to Unexpected Discovery in Basic Math

As long as you consider linear algebra and eigenvectors “basic math”:

They’d noticed that hard-to-compute terms called “eigenvectors,” describing, in this case, the ways that neutrinos propagate through matter, were equal to combinations of terms called “eigenvalues,” which are far easier to compute. Moreover, they realized that the relationship between eigenvectors and eigenvalues — ubiquitous objects in math, physics and engineering that have been studied since the 18th century — seemed to hold more generally.

🚙 Waymo celebrates first year of self-driving taxi service

Progress here seems positive:

The Google-backed service has delivered more than 100,000 trips to more than 1,500 monthly riders in the Phoenix area, according to a blog post. The number of weekly rides has tripled since its first full month of service in January 2019.

🆓 The Three Rules of Freemium

I’ve been reading more lately about freemium models in SaaS, where they work, where they don’t, risks vs. upsides. This is a good one from Christoph Janz on the basics.

Unlike most other enterprise software, which traditionally used to be chosen by the IT department, Dropbox is typically adopted by individual employees from various departments, who then lobby management into switching. As I noted in my piece, Dropbox was one of the early champions of the ‘consumerization of enterprise software’ movement, which was one of the strongest drivers of SaaS success in the last ten years.

But not every SaaS company can be a Dropbox or a Typeform. Done wrong, freemium can end up cannibalizing your paid user base while also draining your company’s precious engineering and customer support resources. So how do you know if launching a freemium product is the right move for your company?

IT consumerization is one of those secular shifts that’s changing many factors in the software space. The key to getting freemium right (assuming your product and market are conducive to it in the first place) seems to be a willingness to experiment with where the boundaries should be between what’s free and what isn’t.

In SaaS there are dozens of common metrics to measure on performance, like a pulse check on your company. Because of the often-high customer-to-revenue ratio with SaaS products, recurring revenue itself becomes a watermark metric to watch as an indicator. Recurrence leads to investing in and measuring customer success metrics, in order to keep that recursion happening indefinitely — customer lifetime value gets large with satisfied customers!

NPS, time-to-value, net retention, and customer health scores are just a few of those metrics that help give you a leading indicator of future revenue potential.

I love the term “second-order revenue” as a way of emphasizing the value of these customer success tactics. First-order revenue is that which is generated directly by a customer. Second-order revenue is money that is a step removed from, but still derived from, that customer. Some examples:

• Customer implements your product at one company, switches to a new company, then rolls it out there, too. One relationship, two accounts.
• Referrals from one customer to other folks in their network — why NPS is so valuable!
• Word of mouth is a type of referral, but is frequently less direct. Customer hears “good things” about your product from people in their network, an event they attended, or similar, then buys for themself.

For a company with good product-market fit and a high NPS, it’d be amazing to calculate hard numbers around second-order revenue. It can be hard to trace the exact source of a customer — often it’s not a single thing, but a combo. We’ve found single individuals that highly value our product can drive enormous amounts of benefit by getting this exchange right.

As Jason Lemkin says in the SaaStr post linked above: “don’t shortchange second-order revenue.”

🧠 To Pay Attention, the Brain Uses Filters, Not a Spotlight

For a long time, because attention seemed so intricately tied up with consciousness and other complex functions, scientists assumed that it was first and foremost a cortical phenomenon. A major departure from that line of thinking came in 1984, when Francis Crick, known for his work on the structure of DNA, proposed that the attentional searchlight was controlled by a region deep in the brain called the thalamus, parts of which receive input from sensory domains and feed information to the cortex. He developed a theory in which the sensory thalamus acted not just as a relay station, but also as a gatekeeper — not just a bridge, but a sieve — staunching some of the flow of data to establish a certain level of focus.

⛰ Climbing the Wrong Hill

Using the hill climbing problem as an analogy for challenging yourself and achieving long-term goals.

👨🏽‍💼 What Do Executives Do, Anyway?

The key takeaway of High Output Management:

To paraphrase the book, the job of an executive is: to define and enforce culture and values for their whole organization, and to ratify good decisions.

That’s all.

Not to decide. Not to break ties. Not to set strategy. Not to be the expert on every, or any topic. Just to sit in the room while the right people make good decisions in alignment with their values. And if they do, to endorse it. And if they don’t, to send them back to try again.

Any business that makes money from the same customer more than once can be said to have “recurring revenue.” But the term in the SaaS universe has a more specific flavor to it, thanks to the unique nature of the business model, value delivery, and the commitments between vendor and consumer. You may think “so what” when you hear that SaaS revenue is special or somehow better than other ways of making money; after all, the money’s still green, right? But there are a number of benefits that come with the “as-a-service” relationship between vendor and customer. Software companies fit uniquely well with a subscription-based model because of the fixed, up-front heavy nature of the investments required to build software platforms. In a traditional business performing services or building physical products, new customers come with higher marginal costs — the cost incurred to add each new dollar of revenue. With hosted software there are certainly marginal costs (scaling servers with growth, providing support, etc.), but the gross margins are typically much higher. And if done efficiently, that margin can stay very high even at scale.

Let’s review some advantages of SaaS, each of them a mutual advantage to both the vendor and customer¹:

Simpler adoption

Because the customer is buying a product that already exists (not a bespoke one), there’s no need to wait for complex customizations right out of the gate to realize initial value. In order to maximize customer growth and expansion velocity, developers are motivated to create smoother implementation experiences harnessing tools like Intercom, with in-app walkthroughs, on-boarding, guides, and help content to get the customer off the ground. A traditional “old world” software company is less motivated to make on-boarding such a smooth experience, since often they’re already required to do on-premise implementations and trainings for users. There are certainly enterprise SaaS products that start to move into this arena (i.e. non-self-service), but typically that’s due to the specifics of certain business workflows being reliant on custom integrations or customer data imports (think ERP systems). Also, because a customer can start small with “pilot” sized engagements at no additional cost to the vendor, they can ramp up more comfortably.

Low initial costs

Related to adoption, the customer can control their costs transparently as they scale, to see impact before they expand team-wide. Once initial ROI is visible, subsequent expansion is less painful and much easier to justify — after all, you have results to prove that the product is useful before going all-in. The ability to hedge risk in scaling up by monitoring value returned is one that was hard to achieve in the days before service-based products.

Reduced time to benefit

Since the customer can lower the requirements for an initial rollout, they can see benefit quickly. Rather than having to take a salesperson’s word for it that you’ll see an ROI in 6 months, a 30-day pilot can deliver answers much more quickly. Don’t take the vendor at their word; use it for yourself and prove the value. Imagine what it would take to realize any benefit from a completely custom-built piece of software? (Hint: A long time, or maybe never if you don’t ship it. This should cross a customer’s mind when they want to build instead of buy.)

Economies of scale

The SaaS vendor is responsible for hosting, improving, and developing the core systems to the benefit of many at once. The revenue benefit of individual improvements or features are realized across the entire customer base. With good execution, the economy of scale can make the new feature or capability cheaper for each customer, while generating more aggregate revenue for the vendor — everyone wins. Compare this with scaling boxed software or even self-hosted, on-site software where human hours are required for each customer to deliberately receive new things of value. With product maturity, not all new developments provide equal value to every customer, which is where product packaging and positioning becomes critical to align costs and outcomes.

Continuous (versus staggered) upgrade

Any engineer knows that small, frequent updates beat out large, infrequent ones when it comes to efficiency. The overhead involved with testing and shipping each release is minimized, then spread over hundreds of small deployments. With tools like continuous integration, automated testing, and rolling deployment, developers can seamlessly (and with low risk) roll out tiny incremental changes all the time, silently. Every SaaS product of note works this way today, and often only the largest customer-facing features are even announced at all to customers. With many small releases over few large ones, the surface area for potential problems is reduced enormously, making a catastrophic problem far less likely. Also, customers don’t have to arbitrarily wait for the ArcMap 10.1 update or the annual release to receive a minor enhancement or bug fix.

Alignment of incentives

This, to me, is one of the most important factors. Two parties that share incentives make for the most advantageous economic relationships. Both vendor and customer have incentives that benefit both sides baked into the business model, and importantly, these incentives are realized early and often:

• Customer Incentive: Since the customer has a defined problem for which they seek a solution (in the form of software), they’re incentivized to pay proportionally for received value, security, attention, support, and utility. With a subscription pricing model, customers are happy to pay for a subscription that continues to deliver value to them month after month.
• Vendor Incentive: For a vendor, the real money is made not from the first deal with the customer, but from a continued relationship over a (hopefully) long period of time. Known as lifetime value (LTV), the goal is to maximize that number with all customers — a good product-market fit and customer success strategy leads to long LTV and therefore very large revenue numbers. To realize that LTV, however, it’s incumbent upon the vendor to stay focused on delivering the above — value, security, support, et al.

With these incentives in lock-step, everyone wins. After achieving product-market fit and a repeatable solution for customers, you turn attention toward continued engagement in customer success, incremental value-add through enhancements and new features, and a long-term customer relationship based on mutual exchange of value. The best customers not only drive high revenues to the top line, but also become better companies as a result of using your software. We’ve had this happen with Fulcrum customers, and for a product developer, it’s the thing that gets your out of bed in the morning; it’s why we do what we do, not just to make money, but to transform something from good to great.

Alignment in vendor-customer goals used to be harder to achieve in the pre-SaaS era. A vendor needed only to be “good enough” to secure the single-point initial purchase, and could largely shirk their end of the bargain in successive months².

Subscription models for physical products

Subscription business are no longer limited to software. We now see companies operating in the physical realm moving into subscription models — Lyft Pass for transit, Blue Apron for food delivery, or even Apple’s movement in this direction with its Upgrade Program for iPhones³. Once the economics make this possible (more expensive in up-front capital for physical versus software), the subscription model turns into, often, a better deal for both sides.

The market is moving toward services for everything, which is a good thing for the industry all around. Okta’s Businesses at Work report for 2019 reports that their customers are using commonly over 100 apps with Okta in the first year of use. In fact, all of the trends they report on show strong motions up and to the right. Given what I said previously about incentive alignment, I’m a believer that these trends are great for the software economy as a whole, with all parties benefiting from a healthier marketplace.

One of the big events in tech last week was that Verizon offloaded Tumblr to Automattic, Matt Mullenweg’s company most known for Wordpress.

I had my main blog/website on Tumblr back when it first launched in 2007, which I used for a number of years before migrating it over to this current self-managed iteration on GitHub back around 2011¹. At the time I loved Tumblr’s middleground between the long-form-friendly full Wordpress blog and the short-form nature of Twitter. Tumblr’s “tumblelog” concept easily supported either mode depending on what you wanted to post. Their post editor was fantastic (and still is, in my opinion), especially good back in the days before Medium when WYSIWIG editors we’re all pretty terrible. It was the place I learned to use Markdown in everyday writing, which I still use everywhere today, even in my own personal note text files.

Though I haven’t been a user of Tumblr in years, I have some negative and positive feelings about it. The negative is, of course, that Verizon is treating it like a fire sale “write down”, with the previously $1.1bn acquisition in 2013 degrading down to now selling it off to Automattic for a rumored price of “less than $3m”. It’s astonishing that something could lose that much value in the marketplace in such a short period of time.

The upside here is that there’s no better owner and future shepherd of the product than its new one. Automattic has been one of the best community-oriented companies for 15 years, with a publishing platform that powers a quarter of the internet. It’s sad to see it lose so much of it’s former self, but maybe it’ll see a revitalization under new ownership.

This talk from a16z’s Martin Casado covers how the market for B2B SaaS go-to-market is changing from sales-driven to a marketing-driven. We’ve been thinking a lot about this lately in the context of Fulcrum — how the “consumerization of IT” plays into how business users today are finding, evaluating, purchasing, and expanding their usage of software.

As he describes in the talk, consumer business tend toward a marketing-led GTM, and enterprise ones toward a sales-led GTM. A combined sales-plus-marketing approach to customer enablement and growth is super hard to execute on, and under the hood requires an excellent “adoptable” product at the center. You’ve got to enable the customer to try and implement your technical solution through a self-service and self-adoption model.

We’ve had this kind of land-and-expand phenomenon with Fulcrum since 2011 — wherein we attract early adopter types from within a company, get traction with smaller use cases, then watch as the company spreads the usage of Fulcrum horizontally to different teams and use cases. In the beginning we structured our GTM this way by necessity (a tiny team couldn’t do full stack marketing and enterprise sales), but have come to enjoy the fruits of this decision as we’ve scaled. I can sympathize with the challenges described here, though; building the right interplays and feedback loops between sales, marketing, and customer success is unnatural for a lot of people, and hard to execute on. The silver lining is that while you might have growing pains with process, at least you’ve got interest, usage, and revenue happening regardless. The magic is in the optimization of the cycle.

Andy Grove is widely respected as an authority figure on business management. Best known for his work at Intel during the 1980s, his book High Output Management is regularly cited as one of the best in the genre of business books. After having it on my list for years and finally reading it earlier this year, I’d wholeheartedly agree. It’s the best book out there about business planning, management, and efficiency, still just as pertinent today as it was when it was first published in 1983.

Its relevance more than 30 years later attests to the universality of its value. I’ve mentioned before here my personal interest in understanding first principles approaches to thinking over derivative systems typically touted by the self-help and business publishing community. The book’s extreme practicality and information density falls in line with what you’d expect from an engineer like Grove — light on the fluff and “case study”-type stuff that permeates and inflates page counts of other business books.

I’ve written here before about a couple of specific topics from the book — about Grove’s perspective on meetings, and on the concept of “modes of control” — but I wanted to give some more space to the book overall, as I believe it’s one of those rare pieces of core reading material on which hundred of other works are based.

The thesis he lays out is simple in principle: a business is a machine, the people and processes are its parts, with inputs (human effort, ideas, work) and outputs (its products and services). To develop a high output system, you have to peel apart its internal components, inspect how they interface with one another, and create a management infrastructure throughout that enables high leverage. Throughout the book’s chapters he touches on the stables of a manager’s workload: planning, meetings, making decisions, reporting, oversight, training, and more. What’s truly important isn’t any one of these particular components, though, it’s in the efficiency of the connections between them. In the analogy of the business to a machine, effective management is the design of the parts, the connections between them, and the lubrication to avoid slippage.

I’ll point out here that the book’s value is not limited to those that manage people. If you manage any system or procedure at all you’ll get value out of it. In fact, it’s useful to anyone that wants to understand what makes their organization tick and where they might fit into the machinery.

Creating Clarity from Abstraction

One of the driving factors that’s created a cottage industry around business processes, teamwork, and strategy (an industry that’s generated thousands of how-to books on the theme) is that the modern era of “knowledge work” requires working in so many abstractions. In the good old days of industrial production, the inputs, outputs, and stages in between were manifest in physical systems you could watch working together. Grove recognizes this point early¹ (emphasis mine):

Of course, the principle of work simplification is hardly new in the widget manufacturing arts. In fact, this is one of the things industrial engineers have been doing for a hundred years. But the application of the principle to improve the productivity of the “soft professions” — the administrative, professional, and managerial workplace — is new and slow to take hold. The major problem to be overcome is defining what the output of such work is or should be. As we will see, in the work of the soft professions, it becomes very difficult to distinguish between output and activity. And as noted, stressing output is the key to improving productivity, while looking to increase activity can result in just the opposite.

Too many businesses sit down and “strategize” by developing high-altitude mission statements, corporate principles, and annual goals. There’s nothing wrong with these things, but they ultimately aren’t granular enough to become actionable by individual team members. Aligning around a well-articulated output at each employee’s level is critical to avoiding the “busyness” syndrome that plagues so much of the modern workplace. What’s missing is a tool to bridge this gap between high-minded mission statements and employees, one that arms them with actionable targets they can point at and measure progress on — enter OKRs.

Objectives, Results, and Measurement

A key concept articulated in High Output Management, one that’s been adopted widely today, is the Objectives and Key Results (OKRs) framework. It’s clear from the way Grove articulates it that he didn’t see OKRs as some kind of brand-building opportunity with an intent to sell this idea to the business community; he merely saw it as a way to give the company a circulatory system throughout to keep its teams in alignment on output. Like many of the ideas in the book, he has a succinct style of communicating these ideas that make them seem patently obvious, with a clarity that’s easy for anyone to comprehend.

Anyone in the knowledge work space (which is most of us) has seen this all over our organizations. Without the focus around the outcome — What exactly are we making? What do we want that to do for us? Why? — any organization can dissipate much of its energy in simply performing activities, the way an inefficient machine gives off much of its energy as heat from friction. The mission then is to make sure any activity performed at any level is clearly tied to output that stacks up with the organization’s top-level expectations.

The venture investor John Doerr, most known for his work with Kleiner Perkins and investments in Amazon, Google, Netscape, and other early internet companies, was an employee and colleague of Grove in the Intel days. I recently read his Measure What Matters, a book on the concept of OKRs and how they’re employed in various modern businesses. My problem with that book was that it’s simply a retelling of the core principles laid out in High Output Management, with most of the pages devoted to the “see how it works for organization X?” type of commercial trying to sell you on the idea of OKRs. That might be a good communication style for a certain type of reader, but I’d rather have the core building blocks and let me do the imagining of how it might impact my own work and organization. There are tons of other books and blog posts out there about OKRs, but I’d point anyone looking into them to High Output Management as a resource.

At the core OKRs are a great system because of how little “system” there really is. They’re intended to get a bunch of diverse people in a hierarchy working as a well-oiled machine, with the strongest emphasis on keeping the machine and it’s components focus on shared, agreed-upon outcomes. It’s about having the diligence to create a stacked set of priorities and goals, mutually agreed on, that cascade from the top down into the ranks. A well-designed OKR process should create a universe where everyone in the organization can point directly to their objectives, and any colleague can see the wiring up and down from there to the OKRs of others.

Writing as Reporting (and Thinking)

It’s partially my personal style, but I’m a huge believer in the idea of writing as a tool for status reporting, intra-office communication, and teamwork. Not only does writing things down create a log of someone’s idea or design concept, it’s a fantastic medium for forcing critical thinking. Jeff Bezos has famously required agendas for meetings at Amazon to be written up as long form proposals. This forces rigor in having focused meetings with thought out discussion topics. No one will spend time writing up a document if they don’t truly believe in it or haven’t thought it through, which saves everyone the wasted time of discussing poorly-considered ideas. The act of writing something down also forces you as the generator of an idea to ruminate on its implications, think about how to articulate it, and to create an element of knowledge to leave as an institutional guidepost for future coworkers thinking about related ideas.

Grove approaches managerial reporting and planning from a similar angle. While he highly values passing informal conversation in maintaining time-sensitive communication, he respects writing as a tool for clear thinking²:

I have to confess that the information most useful to me, and I suspect most useful to all managers, comes from quick, often casual verbal exchanges. This usually reaches a manager much faster than anything written down. And usually the more timely the information, the more valuable it is.

So why are written reports necessary at all? They obviously can’t provide timely information. What they do is constitute an archive of data, help to validate ad hoc inputs, and catch, in safety-net fashion, anything you may have missed. But reports also have another totally different function. As they are formulated and written, the author is forced to be more precise than he might be verbally. Hence their value stems from the discipline and the thinking the writer is forced to impose upon himself as he identifies and deals with the trouble spots in his presentation. Reports are more a medium of self-discipline than a way to communicate information. Writing the report is important; reading it often is not.

This same logic applies to so many things in business — the final version of a report, design spec, marketing strategy, or budget isn’t where all the value lies; the final output document is what enforces the discipline of that business process. The requirement to come away with the “Budget 2020.xlsx” file forces us to run through the planning process thoroughly. If done well, we only need to look at the document as a quarterly gut check. The planning process itself makes us think through priorities, objectives, and where we want to focus.

Add It to the Library

There’s a lot more excellent material in the pages of High Output Management than I can cover in a single blog post. My paperback copy sits on my shelf in my office and is scribbled all over. I pull it out regularly to cite paragraphs or reference things for my own communication within the company. It’s one of my first recommendations to anyone looking for a book on business or productivity.

Earlier this year at SaaStr Annual, we spent 3 days with 20,000 people in the SaaS market, hearing about best practices from the best in the business, from all over the world.

If I had to take away a single overarching theme this year (not by any means “new” this time around, but louder and present in more of the sessions), it’s the value of customer success and retention of core, high-value customers. It’s always been one of SaaStr founder Jason Lemkin’s core focus areas in his literature about how to “get to $10M, $50M, $100M” in revenue, and interwoven in many sessions were topics and questions relevant to things in this area — onboarding, “aha moments,” retention, growth, community development, and continued incremental product value increases through enhancement and new features.

Mark Roberge (former CRO of Hubspot) had an interesting talk that covered this topic. In it he focused on the power of retention and how to think about it tactically at different stages in the revenue growth cycle.

If you look at growth (adding new revenue) and retention (keeping and/or growing existing revenue) as two axes on a chart of overall growth, a couple of broad options present themselves to get the curve arrow up and to the right:

If you have awesome retention, you have to figure out adding new business. If you’re adding new customers like crazy but have trouble with customer churn, you have to figure out how to keep them. Roberge summed up his position after years of working with companies:

It’s easier to accelerate growth with world class retention than fix retention while maintaining rapid growth.

The literature across industries is also in agreement on this. There’s an adage in business that it’s “cheaper to keep a customer than to acquire a new one.” But to me there’s more to this notion than the avoidance of the acquisition cost for a new customer, though that’s certainly beneficial. Rather it’s the maximization of the magic SaaS metric: LTV (lifetime value). If a subscription customer never leaves, their revenue keeps growing ad infinitum. This is the sort of efficiency ever SaaS company is striving for — to maximize fixed investments over the long term. It’s why investors are valuing SaaS businesses at 10x revenue these days. But you can’t get there without unlocking the right product-market fit to switch on this kind of retention and growth.

So Roberge recommends keying in on this factor. One of the key first steps in establishing a strong position with any customer is to have a clear definition of when they cross a product fit threshold — when they reach the “aha” moment and see the value for themselves. He calls this the “customer success leading indicator”, and explains that all companies should develop a metric or set of metrics that indicates when customers cross this mark. Some examples from around the SaaS universe of how companies are measuring this:

• Slack — 2000 team messages sent
• Dropbox — 1 file added to 1 folder on 1 device
• Hubspot — Using 5 of 20 features within 60 days

Each of these companies has correlated these figures with strong customer fits. When these targets are hit, there’s a high likelihood that a customer will convert, stick around, and even expand. It’s important that the selected indicator be clear and consistent between customers and meet some core criteria:

• Observable in weeks or months, not quarters or years — need to see rapid feedback on performance.
• Measurement can be automated — again, need to see this performance on a rolling basis.
• Ideally correlated to the product core value proposition — don’t pick things that are “measurable” but don’t line up with our expectations of “proper use.” For example, in Fulcrum, whether the customer creates an offline map layer wouldn’t correlate strongly with the core value proposition (in isolation).
• Repeat purchase, referral, setup, usage, ROI are all common (revenue usually a mistake — it’s a lagging rather than a leading indicator)
• Okay to combine multiple metrics — derived “aggregate” numbers would work, as long as they aren’t overcomplicated.

The next step is to understand what portion of new customers reach this target (ideally all customers reach it) and when, then measure by cohort group. Putting together cohort analyses allows you to chart the data over time, and make iterative changes to early onboarding, product features, training, and overall customer success strategy to turn the cohorts from “red” to “green”.

We do cohort tracking already, but it’d be hugely beneficial to analyze and articulate this through a filter of a key customer success metric is and track it as closely as MRR. I think a hybrid reporting mechanism that tracks MRR, customer success metric achievement, and NPS by cohort would show strong correlation between each. The customer success metric can serve as an early signal of customer “activation” and, therefore, future growth potential.

I also sat in on a session with Tom Tunguz, VC from RedPoint Ventures, who presented on a survey they had conducted with almost 600 different business SaaS companies across a diverse base of categories. The data demonstrated a number of interesting points, particularly on the topic of retention. Two of the categories touched on were logo retention and net dollar retention (NDR). More than a third of the companies surveyed retain 90+% of their logos year over year. My favorite piece of data showed that larger customers churn less — the higher products go up market, the better the retention gets. This might sound counterintuitive on the surface, but as Tunguz pointed out in his talk, it makes sense when you think about the buying process in large vs. small organizations. Larger customers are more likely to have more rigid, careful buying processes (as anyone doing enterprise sales is well aware) than small ones, which are more likely to buy things “on the fly” and also invest less time and energy in their vendors’ products. The investment poured in by an enterprise customer makes them averse to switching products once on board¹:

On the subject of NDR, Tunguz reports that the tendency toward expansion scales with company size, as well. In the body of customers surveyed, those that focus on the mid-market and enterprise tiers report higher average NDR than SMB. This aligns with the logic above on logo retention, but there’s also the added factor that enterprises have more room to go higher than those on the SMB end of the continuum. The higher overall headcount in an enterprise leaves a higher ceiling for a vendor to capture:

Overall, there are two big takeaways to worth bringing home and incorporating:

1. Create (and subsequently monitor) a universal “customer success indicator” that gives a barometer for measuring the “time to value” for new customers, and segment accordingly by size, industry, and other variables.
2. Focus on large Enterprise organizations — particularly their use cases, friction points to expansion, and customer success attention.

We’ve made good headway a lot of these findings with our Enterprise product tier for Fulcrum, along with the sales and marketing processes to get it out there. What’s encouraging about these presentations is that we already see numbers leaning in this direction, aligning with the “best practices” each of these guys presented — strong logo retention and north of 100% NDR. We’ve got some other tactics in the pipeline, as well as product capabilities, that we’re hoping bring even greater efficiency, along with the requisite additional value to our customers.

📑 Opening Up the Atlassian Term Sheet

This is great to see from a company like Atlassian with “openness” as one of their core values. Their take is that the standard M&A process affords too few protections for the company doing the selling and too many for the big buyer. Most importantly, to me, these M&A engagements are one-sided by nature: the buyer has likely done it before (often many times) and the seller it’s likely their first time around.

M&A is a key part of our strategy – over our history, we’ve acquired more than 20 companies for approximately $1 billion, including Trello, Opsgenie, and AgileCraft. And one thing has become very clear to us about the M&A process – it’s outdated, inefficient, and unnecessarily combative, with too much time and energy spent negotiating deal terms and not enough on what matters most: building great products together and delivering more customer value.

👨🏽‍💼 Why Customer-First Companies Ultimately Win

An interesting way to look at customer service along the dimensions of scale and loyalty.

Customer loyalty is the holy grail of business and the ultimate moat at scale. Brand deposits are made with every single positive customer interaction but the only way to scale these positive interactions is to build a culture that self-enforces a high standard of excellence and customer service.

⚖️ Discourses of Epictetus: Summary & Lessons

I got a copy of Discourses recently and looking forward to reading. This post gives a nice overview of the high level themes of his discourses and lectures.

You hear the criticism all the time around the business world about meetings being useless, a waste of time, and filling up schedules unnecessarily.

A different point of view on this topic comes from Andy Grove in his book High Output Management. It’s 35 years old, but much of it is just as relevant today as back then, with timeless principles on work.

Grove is adamant that for the manager, the “meeting” is an essential piece in the managerial leverage toolkit. From page 53:

Meetings provide an occasion for managerial activities. Getting together with others is not, of course, an activity—it is a medium. You as a manager can do your work in a meeting, in a memo, or through a loudspeaker for that matter. But you must choose the most effective medium for what you want to accomplish, and that is the one that gives you the greatest leverage.

This is an interesting distinction from the way you hear meetings described often. That they should be thought of as a medium rather than an activity is an important difference in approach. When many people talk about the uselessness of meetings, I would strongly suspect that the medium is perhaps mismatched to the work that needs doing. Though today we have many media through which to conduct managerial work — meetings, Slack channels, emails, phone calls, Zoom video chats — the point is you shouldn’t ban the medium entirely if your problem is really something else. I know when I find myself in a useless meeting, its “meetingness” isn’t the issue; it’s that we could’ve accomplished the goal with a well-written document with inline comments, an internal blog post, an open-ended Slack chat, or a point-to-point phone call between two people. Or, alternately, it could be that a meeting is the optimal medium, but the problem lies elsewhere in planning, preparation, action-orientation, or the who’s who in attendance¹.

We should focus our energies on maximizing the impact of meetings by fitting them in when they’re the right medium for the work. As Grove notes on page 71:

Earlier we said that a big part of a middle manager’s work is to supply information and know-how, and to impart a sense of the preferred method of handling things to the groups under his control and influence. A manager also makes and helps to make decisions. Both kinds of basic managerial tasks can only occur during face-to-face encounters, and therefore only during meetings². Thus I will assert again that a meeting is nothing less than the medium through which managerial work is performed. That means we should not be fighting their very existence, but rather using the time spent in them as efficiently as possible.

Geoffrey Moore’s Crossing the Chasm is part of the tech company canon. It’s been sitting on my shelf for years unread, but I’ve known the general nature of the problem it illuminates for years. We’ve even experienced some of its highlighted phenomena first hand in our own product development efforts in bringing Geodexy, allinspections, and Fulcrum to market.

Moore’s “Technology Adoption Life Cycle” is the axis of the book:

In principle, the advice laid out rings very logical, nothing out of left field that goes against any conventional wisdom. It helps to create a concrete framework for thinking about the “psychographic” profile of each customer type, in order from left to right on the curve:

1. Innovators
2. Visionaries
3. Pragmatists
4. Conservatives
5. Laggards

It’s primarily addressed to high-tech companies, most of which in the “startup” camp are somewhere left of the chasm. The challenge, as demonstrated in the book, is to figure out what parts of your strategy, product, company org chart, and go-to-market need to change to make the jump across the chasm to expansion into the mainstream on the other side.

There are important differences between each stage in the market cycle. As a product transitions between stages, there are evolutions that need to take place for a company to successfully mature through the lifecycle to capture further depths of the addressable market. Moore’s model, however, distinguishes the gap between steps 2 and 3 as dramatically wider in terms of the driving motivations of customers, and ultimately the disconnect of what a product maker is selling from what the customer believes they are buying.

The danger of the chasm is made more extreme by the fact that many companies, after early traction and successes with innovators and visionaries, are still young and small. A company like that moving into a marketplace of pragmatists will encounter much larger, mature organizations with different motivations.

The primary trait displayed by the visionary as compared to the pragmatist is a willingness to take risk. Where a visionary is willing to make a bet on a new, unproven product, staking some of their own social and political capital on the success of high tech new solutions, the pragmatist wants a solution to be proven before they invest. Things like social proof, case studies, and other forms of evidence that demonstrate ROI in organizations that look like their own. Not only other companies of their rough size, but ones also in their specific industry vertical, doing the same kind of work. In other words, only a narrow field of successes work well as demonstrable examples of value for them.

Knowing about this difference between market phases, how would a creator prepare themselves to capture the pragmatist customer? One is left with a dilemma: how can I demonstrate proof within other pragmatic, peer organizations when they all want said proof before buying in? We have our own product that’s in (from my optic) the early stages of traction right of the chasm, so many of the psychographics the book provides to define the majority market ring very true in interactions with these customers.

Presented with this kind of conundrum in how to proceed, Moore’s strategy for what to do here is, in short, all about beachheads. He uses the example of D-Day and the successful Allied landings on the Normandy beachhead as an analogy for how you can approach this sort of strategy. Even if you have a broadly-applicable product, relevant to dozens of different industries, you have to spend so much time and energy on a hyper-targeted marketing campaign to connect with the pragmatist on the other end that you won’t have enough resources to do this for every market. The beachhead will be successfully taken and held only if you go deep enough into a single vertical example to hold onto that early traction until you can secure additional adjacent customers. Only then can you worry about moving inland and taking more territory.

All in all it was a worthwhile, quick read. Nothing revelatory was uncovered for me that I wasn’t already aware of in broad strokes. However, it is one of those books that’s foundational to anyone building a B2B software product. Understanding the dynamics and motivations of customers and how they evolve with your product’s growth is essential to building the right marketing approach.

⚾️ The Rays are a Surrealist’s Delight

Love to see the Rays getting some deserved attention in the mainstream sports media. They’ve put together a great, diverse lineup of consistent hitters that have performed well all season:

The Rays emphasize power now, but in a different way: Through Monday, their hitters had the highest exit velocity in the majors, at 90.1 miles per hour, and their pitchers — who specialize in curveballs and high fastballs — allowed the lowest, at 86.3. Hard-contact rates enticed them to trade for Pham from St. Louis last July, and to land Yandy Diaz in an off-season deal with Cleveland. Pham was hitting .248 for the Cardinals, but the Rays assured him he had simply been unlucky; he hit .343 the rest of the season.

And I get to post this on the back of their 11th inning win over the Yankees this afternoon.

📱 The Pivot

Great quick read from Horace Dediu on Apple’s Services business. As he points out in the piece, Apple’s business model is continually oversimplified and/or misunderstood by many:

This disconnect between what people think Apple sells and what Apple builds is as perplexing as the cognitive disconnect between what companies sell and what customers buy.

Companies sell objects or activities that they can make or engage in but customers buy solutions to problems. It’s easy to be fooled that these are interchangeable.

Conversely Apple offers solutions to problems that are viewed, classified, weighed and measured as objects or activities by external observers. Again, it’s easy to be fooled that these are the same.

🧭 Mapping Gridded Data with a Voronoi Diagram

This post goes into how the author put together a visualization of tornado trend data for Axios. Observable notebooks are so great. The interactivity lets you not only see the code and data to create it all, but can be forked and edited right there.

🦸🏽‍♂️ How Superhuman Built an Engine to Find Product/Market Fit

As pointed out in this piece from Rahul Vohra, founder of Superhuman, most indicators around product-market fit are lagging indicators. With his company he was looking for leading indicators so they could more accurately predict adoption and retention after launch. His approach is simple: polling your early users with a single question — “How would you feel if you could no longer use Superhuman?”

Too many example methods in the literature on product development orient around asking for user feedback in a positive direction — things like “how much do you like the product?”, “would you recommend to a friend?” Coming at it from the counterpoint of “what if you couldn’t use it” reverses this. It makes the user think about their own experience with the product, versus a disembodied imaginary user that might use it. It brought to mind a piece of the Paul Graham essay “Startup Ideas”, if you go with the wrong measures of product-market fit:

The danger of an idea like this is that when you run it by your friends with pets, they don’t say “I would never use this.” They say “Yeah, maybe I could see using something like that.” Even when the startup launches, it will sound plausible to a lot of people. They don’t want to use it themselves, at least not right now, but they could imagine other people wanting it. Sum that reaction across the entire population, and you have zero users.

🛤 Stripe’s Fifth Engineering Hub is Remote

Remote work is creeping up in adoption as companies become more culturally okay with the model, and as enabling technology makes it more effective. In the tech scene it’s common for companies to hire remote, to a point (as Benedict Evans joked: “we’re hiring to build a communications platform that makes distance irrelevant. Must be willing to relocate to San Francisco.”) It’s important for the movement for large and influential companies like Stripe to take this on as a core component of their operation. Companies like Zapier and Buffer are famously “100% remote” — a new concept that, if executed well, gives companies an advantage against to compete in markets they might never be able to otherwise.

🛰 Downlink

A neat Mac app that puts real-time satellite imagery on your desktop background. Every 20 minutes you can have the latest picture of the Earth.

One of my favorite tech figures, a16z’s Steven Sinofsky, gives a history of “Clippy”, the helpful anthropomorphic office supply from Microsoft Office. As the product leader of the Office group in the 90s, he gives some interesting background to how Clippy came to be. I found most fascinating the time machine look back at what personal computing was like back then — how different it was to develop a software product in a world of boxed software.

Everyone makes fun of it today, but Clippy did presage the world of AI-powered “assistant” technology that everyone is getting familiar with today.

See also this Twitter thread. Love this stuff.

📖 Gene Wolfe Turned Science Fiction Into High Art

Wolfe’s work, particularly his Book of the New Sun “tetralogy”, is some of my favorite fiction. He just passed away a couple weeks ago, and this is a great piece on his life leading up to becoming one of the most influential American writers. I recommend it to everyone I know interested in sci-fi. Even reading this made me want to dig up The Shadow of the Torturer and start reading it for a third time:

The language of the book is rich, strange, beautiful, and often literally incomprehensible. New Sun is presented as “posthistory”—a historical document from the future. It’s been translated, from a language that does not yet exist, by a scholar with the initials G.W., who writes a brief appendix at the end of each volume. Because so many of the concepts Severian writes about have no modern equivalents, G.W. says, he’s substituted “their closest twentieth-century equivalents” in English words. The book is thus full of fabulously esoteric and obscure words that few readers will recognize as English—fuligin, peltast, oubliette, chatelaine, cenobite. But these words are only approximations of other far-future words that even G.W. claims not to fully understand. “Metal,” he says, “is usually, but not always, employed to designate a substance of the sort the word suggests to contemporary minds.” Time travel, extreme ambiguity, and a kind of poststructuralist conception of language are thus all implied by the book’s very existence.

📺 Zoom, Zoom, Zoom! The Exclusive Inside Story Of The New Billionaire Behind Tech’s Hottest IPO

Zoom was in the news a lot lately, not only for its IPO, but also the impressive business they’ve put together since founding in 2011. It’s a great example of how you can build an extremely viable and healthy business in a crowded space with a focus on solid product execution and customer satisfaction. This profile of founder Eric Yuan goes into the core culture of the business and the grit that made the success possible.

🗺 A Look Inside The GIS World With Anthony Quartararo, CEO Of Spatial Networks

The folks over at FullStackTalent just published this Q&A with Tony in a series on business leaders of the Tampa Bay area. It gives some good insight into how we work, where we’ve come from, and what we do every day. There’s even a piece about our internal “GeoTrivia”, where my brain full of useless geographical information can actually get used:

Matt: What’s your favorite geography fun fact?

Tony: Our VP of Product, Coleman McCormick, is the longest-reigning champion of GeoTrivia, a competition we do every Friday. We just all give up because he [laughter], you find some obscure thing, like what country has the longest coastline in Africa, and within seconds, he’s got the answer. He’s not cheating, he just knows his stuff! We made a trophy, and we called it the McCormick Cup.

All that time staring at maps is finally useful!

🏃🏻‍♂️ On the Go Map

Found via Tom MacWright, a slick and simple tool for doing run route planning built on modern web tech. It uses basic routing APIs and distance calculation to help plan out runs, which is especially cool in new places. I used it in San Diego this past week to estimate a couple distances I did. It also has a cool sharing feature to save and link to routes.

🔮 As We May Think

I mentioned scientist Vannevar Bush here a few days back. This is a piece he wrote for The Atlantic in 1945, looking forward at how machines and technology could become enhancers of human thinking. So many prescient segments foreshadowing current computer technology:

One can now picture a future investigator in his laboratory. His hands are free, and he is not anchored. As he moves about and observes, he photographs and comments. Time is automatically recorded to tie the two records together. If he goes into the field, he may be connected by radio to his recorder. As he ponders over his notes in the evening, he again talks his comments into the record. His typed record, as well as his photographs, may both be in miniature, so that he projects them for examination.

👨🏽‍💻 Best Practices for Managing Remote Teams

I thought this was an excellent rundown of remote work, who is suited for it, how to manage it, and the psychology of this new method of teamwork.

Let’s first cover values. Remote work is founded on specific core principles that govern this distinct way of operating which tend to be organization agnostic. They are the underlying foundation which enables us to believe that this approach is indeed better, more optimal, and thus the way we should live:

• Output > Input
• Autonomy > Administration
• Flexibility > Rigidity

These values do not just govern individuals, but also the way that companies operate and how processes are formed. And like almost anything in life, although they sound resoundingly positive, they have potential pitfalls if not administered with care.

I found nearly all of this very accurate to my perception of remote work, at least from the standpoint of someone who is not remote, but manages and works with many that are. I’m highly supportive of hiring remote. With our team, we’ve gotten better in many ways by becoming more remote. And another (perhaps counterintuitive) observation: the more remote people you hire, the better the whole company gets and managing it.

In his new book Loonshots, author Safi Bahcall uses the concept of phase transitions to analyze how companies work. When a substance changes phase, like water going from solid to liquid, the same exact substance is forced to take on a new structural form when the surrounding environment changes.

As Bahcall points out in the book, companies exhibit a similar behavior in their inventions and strategy. He contrasts two different types of innovations that companies tend to be built to produce: “P” type innovations, where a company is great at producing new products, and “S” type innovations, where they can stay ahead of the pack by developing new business strategies for the same products. There are many examples presented in the book of both types of innovation done right — Juan Trippe and Pan Am, Steve Jobs, Edwin Land and Polaroid, Bob Crandall and American Airlines — each of them was (or has been) a pillar innovator with a specialty in P or S types.

Being great at a single type works great for a time, until the environment changes too much around you.

In the history of business, there are few examples of organizations able to straddle both phases simultaneously. Early on in the book there’s the example of Vannevar Bush, the engineer that led the historic Office of Scientific Research and Development during World War II. The OSRD was legendary for the systems and inventions developed during the war, many of which helped to tip the war in favor of the Allies. From the OSRD wiki page:

The research was widely varied, and included projects devoted to new and more accurate bombs, reliable detonators, work on the proximity fuze, guided missiles, radar and early-warning systems, lighter and more accurate hand weapons, more effective medical treatments, more versatile vehicles, and, most secret of all, the S-1 Section, which later became the Manhattan Project and developed the first atomic weapons.

What makes companies so focused on short term innovation, either in product or strategy? Humans (and organizations) are certainly known to be bad at having a long view of planning and decision making.

It’s a fascinating idea — that a successful, hard-to-kill organization becomes one by having a particular structure, one that can be water and ice at the same time. What Bush figured out 70 years ago was that the organization is what’s important. He focused on making organizations that could make great things, a focus on the process rather than its products:

This bit from a 1990 piece after his death sums it up:

He was an academic entrepreneur who co-founded Raytheon and was a vice president at the Massachusetts Institute of Technology who consolidated the school’s reputation as having the nation’s finest engineering program. It’s not just that Bush was a brilliant engineer; it’s that Bush knew how to map, build and manage the relationships and organizations necessary to get things done. He knew how to craft the human networks that could build the technological networks.

One of my favorite topics on Ben Thompson’s Stratechery, and one that underpins much of his Aggregation Theory, is the role friction plays in economies and cultural forces. Most of the pros (and cons) of internet companies can be tied back to the fact that they took existing businesses or customer demands and removed the friction. Whether it was shipping goods to your door, streaming movies, or communicating with friends, the internet stripped the friction from these interactions for good, but with some downsides that are only recently being realized and understood.

In 2013 he published one of my favorite pieces of his on this subject. One of the reasons the internet stacks up next to the industrial revolution in terms of economic enablement was that it removed friction of many stripes:

With the loss of friction, there is necessarily the loss of everything built on friction, including value, privacy, and livelihoods. And that’s only three examples! The Internet is pulling out the foundations of nearly every institution and social more that our society is built upon.

Count me with those who believe the Internet is on par with the industrial revolution, the full impact of which stretched over centuries. And it wasn’t all good. Like today, the industrial revolution included a period of time that saw many lose their jobs and a massive surge in inequality. It also lifted millions of others out of sustenance farming. Then again, it also propagated slavery, particularly in North America. The industrial revolution led to new monetary systems, and it created robber barons. Modern democracies sprouted from the industrial revolution, and so did fascism and communism. The quality of life of millions and millions was unimaginably improved, and millions and millions died in two unimaginably terrible wars.

On the latest episode of Exponent, Ben and James dive in on this topic as it relates to the recent news of YouTube and its issues with toxic content on its platform, and their response (or lack thereof). We’re all well aware of the benefits of infinite information, reduction in cost, and increase in scale made possible by the internet (and YouTube, specifically), but this is a perfect example of the downsides when you remove friction.

Listen to the episode. It’s an excellent conversation that digs into the costs and benefits both of platforms like YouTube, and kicks around some ideas on how the negatives can be controlled.

I’ve nearly finished reading Andy Grove’s High Output Management. Grove was the one of the founders and CEO of Intel, especially famous for his leadership of the company’s shift from design and fabrication of memory to microprocessors in the 80s.

The book is mostly well known for documenting Grove’s management style, which was later formalized into the OKR framework now widely used by Google and others.

But one of my favorite bits from the book (and there are several) is his concept of “modes of control.”

The fundamental idea is that there are different models in which actions can be controlled or influenced on two dimensions: where the motivations lie and the complexity of the environment (which he terms complexity, uncertainty, and ambiguity, the “CUA factor”).

The axes run across these two spectra:

• Group Interest → Self Interest
• Low CUA → High CUA

Then there are the “modes” themselves. Grove emphasizes the importance of selecting the appropriate mode of control for the position of the relationship or environment, with three fundamental modes:

• Free market forces — Purchasing new tires, you select the lowest price and highest quality for your personal need. High self interest, low ambiguity.
• Contractual obligations — Traffic signals and stop signs. There’s a high group interest in all of us obeying the rules, and low ambiguity about how to do so.
• Cultural values — Getting promoted or hired into a leadership role in a company. Group interest must be satisfied for the company success, but ambiguity can be very high as to how to know what to focus on. Strong cultural values in an organization help guide leaders to the right decisions.

Each combination of motivation and ambiguity creates a unique environment with an optimum mode. In thinking about this in context of my own work, I can easily map past hardships and bad business relationships to a mismatch in environment/mode. In the workplace as managers, what we most often don’t respect enough is the nature of the CUA factor with a given job, project, or task. This mental framework for thinking about relationships is helpful for selecting the appropriate communication or management mode.

When self interest and ambiguity both spike up, you get chaos. The “everyone for themselves” panic on a sinking ship.

💻 Open Sourcing Windows Calculator

Seems silly, but this kind of thing is great for the open source movement. There’s still an enormous amount of tech out there built at big companies that creates little competitive or legal risk by being open. Non-core tools and libraries (meaning not core to the business differentiation) are perfect candidates to be open to the community. Check it on GitHub.

📊 The Metrics Every SaaS Company Should Be Tracking

An Inside Intercom interview with investor David Skok, the king of SaaS metric measurement. His blog has some of the best reference material for measuring your SaaS performance on the things that matter. This deck goes through many of the most important figures and techniques like CAC:LTV, negative churn, and cohort analysis.

🎮 Shockolate — System Shock Open Source

A cross-platform port of one of the all-time great PC games, System Shock¹. I don’t play many games anymore, but when I get the itch, I only seem to be attracted to the classics.

I’ve been thinking and reading more about OKRs and how I might be able to implement them effectively — both professionally and personally. The idea of having clearly defined goals over bounded timelines is something we could all use to better manage time, especially in abstract “knowledge work” where it’s hard to see the actual work product of a day or a week’s activity.

This is an old workshop put on by GV’s Rick Klau. He does a good job giving a bird’s eye view of how to set OKRs and the importance of linking them through the organizational hierarchy:

He also has a good post on the subject from a few years later.

A good overview from YC’s Kevin Hale on how to break down startup ideas:

The “solution looking for a problem” trap is all too easy to fall into, and to justify your way out of even if you fall prey to it. I love the approach here of starting with the end goal ($100M ARR) and backing into what the market size and price point would need to be to hit that target. So simple, but most of us don’t approach the thought process from that end.

🖥 When to Build and When to Buy: The Lure of Building Software

This was one of my favorite reads this week, on the topic of “build vs. buy” in IT organizations. In SaaS, this is one of the most common conversations you run into, particularly with medium to large sized companies. With large enterprises the lure of building their “own IP” is so attractive so frequently (because they have some resources), yet most of the time they have no real clue what they’re convincing themselves to do. Building something great that truly solves a problem and gets better over time is enormously expensive and tiring. If you’re a services or consulting company focused on that type of revenue, trying to maintain the investment over the long term in your own software platforms is almost always a mistake. Even most companies who do nothing but make software fail.

Running the things you built is even more expensive than building them. You may understand the cost to build a product, but you almost certainly haven’t budgeted enough to support it in the future. Software products don’t keep running on their own and will need to be supported, improved, patched and ported to new technology in the future. If you really want to build, plan your ‘run’ costs carefully. Expect it to be at least twice the figure in your head, and add n (where n is a very large integer indeed).

👩🏽‍💻 7 Step Onboarding Process for New Employees

I thought this was a good list with helpful reminders on how to plan for new employee onboarding. Recruiting and hiring new people is so much work and so stressful that it’s easy to fall into the trap of considering it “done” once the offer letter is out and signed. The reality is you haven’t even started yet. I really liked the idea of helping your new hire get excited about the new gig through rallying the rest of the team:

I always ask my current team to each send a personal email and tell the new hire how excited each of them is to work with the new employee. Getting a rush of emails from your new team gives you a huge confidence and motivational boost. It’s also a quick way to build bonds between the team and the new employee. The stronger the bonds are and the more excitement there is about the new role, the more likely it is that the new hire joins your team.\

📈 re:Work OKRs

A nice overview guide on how to use OKRs (objectives and key results) for goal-setting.

I saw this tweet a couple of days back that I thought was interesting:

The topic of “how we got to 1000 users” is an interesting one I thought I could take a stab at…

Fulcrum’s first lines of code were written in the summer of 2011. Initially we put together a basic drag-and-drop form builder interface, the simplest possible authentication system, and a simple iPhone app that let you collect records. There was no concept of multiuser membership within accounts, and we only had a free version. The idea (with little to no planning at all) was to cut loose a free app for basic data collection and see what the traction looked like. We did have in our heads the idea that when we had “Group” account capability, that would be the time to monetize. “Fulcrum Pro”, as we called it then. That launched in around March of 2012.

I don’t recall exactly when we hit the 1,000 user mark, but from some brief investigation of data, early 2013 seems like where we crossed that milestone. About a year and a half from 0 to 1,000.

So what techniques did we use to get there?

At the beginning, the team working on Fulcrum was tiny — maybe 2 doing all the dev work, and 3 (including me) doing part-time effort on all other fronts like customer support, product planning, design, marketing, etc. There wasn’t much there in terms of resources to go around, so we had to do the bare minimum to make something customers could self-serve on their own, that was of some minimum utility.

The only driver for all of our users in those early days, probably the first entire year and a half, was inbound marketing, and really only of two types. Since each of us had a decent sized footprint in the geo Twitterverse back then, we had at least a captive audience of like-minded folks that would kick the tires, help promote, and give us feedback. I’d count that user-base in the dozens, though, so not a huge solo contributor to the first 1,000.

I would attribute reaching the first 1,000 to a hybrid of content marketing through a blog, word of mouth, and (often forgotten) an actual useful product that was filling a void left by the other more mature “competitors” in the space. With a high volume of blog posts, some passable SEO-friendly web content, and a consistent feed of useful material, we attracted early adopters in engineering firms, GIS shops, humanitarian organizations, and some electric utilities.

Fast forward to 2019 and things have changed quite a bit! Not only have we eclipsed well over 100,000 individual users, more importantly we’re approaching the 2,000 paid customer mark. Spanning anywhere from 1 to over 1,000 individual users per customer, it’s safe to call it a repeatable, successful thing at this point. Back where we crossed 1,000 users, we were only hitting the very beginning of true product-market fit.

Building something that catches on and keeping after it are hard. A key learning of mine over the course of this process is to never think you’ve got it all figured out, that you’ve cracked the code. There’s always more to be done to break past inflection points and reach the next level on the step function of successful SaaS business.

When I first heard about his company Opendoor (a real estate startup with the goal of creating faster liquidity for home sellers), I started following Keith Rabois. His Twitter account is a good follow.

This discussion covered topics as diverse as his political views, his original ideas for his companies, and investing principles.

👨🏽‍💻 Okta Businesses @ Work 2019

Interesting data here in Okta’s annual report. It’s clear that the way customer’s buy SaaS is very different than the “single-vendor” purchasing preferences from years past. SaaS allows businesses to buy and integrate the best-fit tools for any jobs:

We also looked at whether companies who invest in the Office 365 suite — the top app in our network — end up committing to a Microsoft-only environment, and the answer was clearly “no.” We found that 76% of Okta’s Office 365 customers have one or more apps that are duplicative of apps offered by Microsoft. Over 28% are chatting on Slack. Nearly 24% are connecting with their colleagues on Zoom. And over 28% of Okta’s Office 365 customers are “double bundling” themselves, subscribing to G Suite as well.

28% of customers have both Office 365 and G Suite. That’s a high number for an area that many consider zero-sum competition.

⚾️ The Most Unhittable Arm in the Minors

The Rays picked up Colin Poche in the Steven Souza, Jr. trade with the Diamondbacks last season. Sounds like he’s making some waves in the farm system:

The most unhittable arm in the minors is Colin Poche. Last year, he led the minor leagues in strikeout rate. This year, he again leads the minor leagues in strikeout rate, having increased his own strikeout rate by a dozen points despite going up against much stiffer competition. When Poche pitched in High-A last year, he struck out 37% of the hitters. In Double-A this year, he struck out 60% of the hitters. In Triple-A this year, he’s struck out 50% of the hitters. All year long, over 41.1 innings, he’s allowed just three runs. He’s allowed an OBP of .185, and he’s allowed a slugging percentage of .184. Colin Poche is turning in one of the most unbelievable performances you might ever see.

🌊 Florida State Parks After Hurricane Michael

The St. Joseph’s Peninsula is special to our family, having gone camping, sailing, and fishing their growing up. The hurricane storm surge cut right through the island north of the boat launch area. I remember walking from the campground down to the marina to go fishing. Now you’d have to swim to get between them.

This is a brief series for those interested in getting into product management, in four parts. This first post is about how I got into this line of work and the beliefs I’ve formed over the years on the discipline. Enjoy!

I never set out of college to get into product development. I was a geography guy with a penchant for maps and wanted to learn how to make them. I bounced from an engineering major over to geography early in school because I was passionate about it, with no clue what the ultimate career destination might look like. After some time with the university IT department, managing servers, computers, and AV equipment, I joined up with a civil engineering firm and worked there for a few years.

I started out with Spatial Networks to work on building a GIS data platform. We had terabytes of GIS datasets, geotagged photos, and video content that needed organization, enrichment, hosting, and staging for our analysis work. At the beginning it was 75% GIS / data management, and 25% systems engineering / devops. Not long after getting integrated with the team, though, it became clear that our engineering group needed domain knowledge to steer the development of our software platform in the right direction. I took my technical background, mapping experience, and past work in civil engineering and other GIS projects and started to help educate our team on end-user expectations. I didn’t realize it at the time, but this was my entrance into product management.

Product management requires a unique skillset to do it well; It touches many areas of a business and requires a diverse range of knowledge to do it right (and a lot of trial and error to calibrate this balance over time). A great product manager lives at the intersection of several disciplines:

• Project management
• Problem identification
• Problem solving
• Engineering
• Communications (maybe the most critical of all)
• Human engagement
• Writing
• Empathy

And I’m sure there are more you could include there. I didn’t get into this discipline intentionally, and certainly have no formal education in it. Everything I know about product I’ve learned by doing and sometimes failing. But it’s one of the most rewarding professions around. It’s excellent for someone with “build stuff” tendencies like me, but also diverse interests in the business-running aspects, marketing, and team collaboration to work in a space where flexing all of those muscles is an advantage.

In the next post I’ll talk about our first real product, Geodexy — how we approached it, what we built, and how that all shook out.

🇨🇳 195-Gigapixel Photo of Shanghai

Shot from the Oriental Pearl Tower, the picture shows enormous levels of detail composited from 8,700 source photos. Imagine this capability available commercially from microsatellite platforms. Seems like an inevitability.

🏕 How Basecamp Runs its Business

I, like many, have admired Basecamp for a long time in how they run things, particularly Ryan Singer’s work on product design. This talk largely talks about how they build product and work as an organized team.

📄 Docusaurus

This is an open source framework for building documentation sites, built with React. We’re currently looking at this for revamping some of our docs and it looks great. We’ll be able to build the docs locally and deploy with GitHub Pages like always, but it’ll replace the cumbersome stuff we’ve currently got in Jekyll (which is also great, but requires a lot of legwork for documentation sites).

An excellent talk from a16z’s Benedict Evans on what’s next for tech and the internet.

🛒 What is Customer Acquisition Cost?

This is a great overview of the importance of CAC in a SaaS business.

One of the enjoyable things about SaaS is how much you can modify and optimize what you’re doing by measuring various parts of your process, especially in SMB-focused SaaS. Marketing, early-stage sales, late-stage sales, customer success — it’s like a machine with separate stages you can tweak separately to make incremental improvements.

📜 The Legacy of Alexander Hamilton

On the similarities between Hamilton and Edmund Burke:

“There are several significant points of contact between the two thinkers. Both Burke and Hamilton used historical experience as the standard for judging the validity of ideas and policies. They rejected appeals to ahistorical abstraction, disparaging metaphysical and theoretical speculation. Historical circumstances were paramount in their prudential judgment. Consequently, they avoided ideological rigidity in their thinking because they understood that a priori rationalism could not account for the particular circumstances in which statesmen had to navigate the ship of state.”

🚰 Microsoft Flow

I didn’t realize Microsoft had an automation service akin to Zapier or IFTTT. Will have to check this one out and see what it can do with Fulcrum.

🚙 How Self-Driving Cars Could Communicate with You

Interesting work by Ford’s self-driving team on how robotic vehicles could signal intent to pedestrians. You normally think Waymo, Tesla, and Uber with AV tech. But Ford’s investment in Argo and GM with Cruise demonstrates they’re serious.

📲 The iPad Pro is a Computer

Jason Snell’s thoughts on the new iPad Pro release last week:

I love the new design of the iPad Pro models. The flat back with the flat sides, which remind me of the original iPad design and the iPhone 4/5/SE, is a delight. But when you pick one up, the first thing you notice is that the bezels are even all the way around—and they’re almost, but not quite, gone entirely

An improved keyboard case, new revision to the Pencil, reduced bezel width, and Face ID support are all the right updates to make to get me closer to the goal of iPad Pro over laptop. The Folio idea for the case sounds fantastic, and with the Pencil, it’s amazing how innovative it can seem to add a small flat segment to keep it from rolling off the table.

💵 We Spent $3.3M Buying Out Investors

Buffer’s Joel Gascoigne with an in-depth overview of how they bought out their Series A investors to reset. Their Open blog series is worth a follow. They openly publish all sorts of insider details on running and growing a startup that are insightful for comparison.

In product teams you’re continually faced with the challenge of scoping. When you build something directly for a customer, for a fee (consulting), the scoping process is explicit and has built-in constraints — customer expectations, funding, timelines, deliverables. Even in that scenario, agreeing on a firm scope for an effort isn’t simple, but it’s even harder when working in a product company. The same constraints still exist, but in a more nebulous, undefined form. Constraints aren’t imposed and enforced externally by a single client dangling the paycheck. The demands are dispersed amongst thousands of users with sometimes-competing desires, paying varying amounts for your product. So it’s on you to define hard edges based on your own goals and objectives.

When you set down the idea for a feature’s implementation, you always want your new feature to be cooler or more powerful than you’re able to commit to. With even a touch of investigation, you discover that it’ll take many cycles to get to get all the way there. So you have to forgo achieving your complete vision and put up some boundaries at an interim milestone along the path. This is one of the hardest parts of product design — you have to knowingly ship something “incomplete” according to your vision, but that’s directionally aligned and a solid waypoint on the journey.

If you’re setting your own objectives, you have no one telling you exactly what to ship, how much to spend, and when to be done. Not to downplay the complexities involved in consulting, but the incentives and constraints are generally much clearer; you’re unlikely to commit massive resources to a hazy and overzealous goal, and the customer isn’t likely to pay you what it’d take to commit — so you compromise.

When it’s your own product, you still have end customers, but their individual wishes and needs are distributed enough that it’s on you to synthesize what customers want into a clear vision you can break down into parts, and to enforce your own boundaries. No one is forcing any particular constraint; you could spend months crafting and polishing the most perfect version of the feature until the final day you get it out there. But good products come about as a consequence of savvy incorporation of market feedback into the development process. The best model uses increments that are each valuable enough to earn committed investment and experimentation from customers, which gives you the feedback you need to validate what you’ve made and make the right course-corrections along the path to the vision. As clear as your vision might be, it’s still not proven until it intersects with reality. Like the old adage says: “No plan survives first contact with the enemy”. Or the customer, as it were. Real-world feedback is what separates a vision from a hallucination.

Yoy need to create a trail toward your vision with release points along the way that each constitute something “complete” enough to use and validate. And that’s where I think the core problem lives in product development: the balance of between shipping enough to consider a feature useful but soon enough that you can start measuring the impacts that will inform your next waypoint. Once you’ve established your vision for what you want to achieve, you then carve away at it until you have essential stages that work as milestones. In the best cases, each milestone can be a defensible stopping point for a feature. You could ship v1, never revisit the idea, and still have something whole and useful. The scoping process is a painful, but critical step to getting a useful enough product feature soon enough to close the feedback loop and begin iterating. I find the Shape Up idea of “appetites” a useful metaphor for selecting milestones:

Whether we’re chomping at the bit or reluctant to dive in, it helps to explicitly define how much of our time and attention the subject deserves. Is this something worth a quick fix if we can manage? Is it a big idea worth an entire cycle? Would we redesign what we already have to accommodate it? Will we only consider it if we can implement it as a minor tweak?

It’s tempting to make the scoping and appetite-setting process a group exercise, to fold in product management, design, engineering, ops, sometimes even marketing. A wider aperture of company buy-in makes sense at a macro vision level, but once you get down to the ground defining specifics, you need fewer people involved to be able to drill in on what’s most important. If possible, it’s best to involve only those directly responsible for defining and building the thing in question. Too many heads involved generates too much free-form “ideation” and not enough of the brass-tacks horse trading required to make hard tradeoffs necessary.

I have a two-part theory on scoping:

1. Projects scoped by large committees tend to get larger — the average contributor knows it’s unlikely the next idea tossed into the hat will fall in their lap to execute; lots of people just get to be the “idea person”; incentives to both signal “big thinking” and define concrete goals are competing with each other
2. Projects scoped by individuals or small groups tend to get smaller — because you’re on the hook to build everything, all of the skin in the game is yours; incentives are aligned on making things tangible and reachable

Landing somewhere in between is ideal for product scoping. Removing things from the vision (or deferring) requires honesty, commitment, and focus, which are all much easier to guarantee in small groups.

We’re often too idealistic with scoping and think we can “squeeze in that One More Thing”. Stretch goals are fine and can be motivating, but too far-fetched of an objective point can end up setting the wrong expectations across the company. If you’re not more aggressive with the hatchet than you think you need to be in the early stages of feature planning, the whole team (and your customer) will be worse off.

Spatial Networks is past 50 employees now, with a sizable remote group scattered all over the country. Even though we’ve grown substantially in 2018, we’ve been able to scale our processes, tools, and org chart to maintain pretty effective team dynamics and productivity. When we first started hiring remote folks back in 2010, we had nowhere near the foundation in place to have an effective distributed team.

This week is our 2nd “All Hands” of the year, where our entire remote team comes to St. Petersburg HQ for a week of teamwork, group projects, and fun camaraderie. A total of 18 people representing 11 states will be in town. These weeks are at once energizing, exciting, and exhausting — but also always a positive exercise. I’m glad to work at a place where we’ve consistently valued this investment and made the effort to keep it going as we’ve scaled.

🎛 Geocomputation with R

I’ve had R on my list for a long time to dig deeper with. A while back I set myself up with RStudio and went through some DataCamp stuff. This online book seems like excellent material in how to apply R to geostatistics.

☎️ Listening to Customers At Scale

Given where we are with Fulcrum in the product lifecycle, this rang very familiar on the struggles with how to listen to customers effectively, who to listen to, and how to absorb or deflect ideas. Once you get past product-market fit, the same tight connection between your customers and product team becomes impossible. Glad to hear we aren’t alone in our struggles here.

📈 Linear Growth Companies

This piece from David Heinemeier Hansson is a good reminder that steady, linear growth is still great performance for a business. Every business puts itself in a different situation, and certainly many are in debt or investment positions that linear growth isn’t good enough for. Even so, consistent growth in the positive direction should always be commended.

During this TED talk from 2003, Jeff Bezos compares the Internet revolution to the early years of electrification. Even 15 years ago he was already describing the core philosophy behind his future products, like Amazon Web Services. AWS is like electricity for technology companies: paying the AWS bill is like paying your utility bill.

Slack grew huge on the idea that it would “replace email” and become the digital hub for your whole company. In some organizations (like ours), it certainly has, or has at least subsumed most all internal-only communication. Email still rules for long form official stuff. It’s booming into a multi-billion dollar valuation on its way to an IPO on this adoption wave.

But over the last couple of years there’s been something of a backlash to “live chat” systems. Of course any new tool can be abused to the point of counter-productivity. As tools like Slack and Intercom (a live chat support software) have become widespread, people and companies need to find normal patterns of use that are comfortable for everyone. In our company, Slack is where nearly everything happens — including quite a bit that, on the surface, looks like noise and random chatter (our #random is something to behold). One common argument is that people now spend more time keeping up with Slack conversation than they ever did with email. Maybe so, maybe not. But regardless, isn’t analysis of the time spent on one versus the other missing the point?

My general argument “pro-chat” is that a world with Slack adds the layer of communication that should have been happening all along and wasn’t. For me, I know that I’m better informed about the general activity of the business with Slack than without. It takes some care and attention to keep it from becoming a distraction when it’s unnecessary, but I’m willing to make the effort.

Anyone that compares the world of Corporate Slack to the prior one would notice a striking similarity in work patterns. Workplaces are social, people are people, and will talk, joke, commiserate, and enjoy each others’ company. I try to picture a world where we could effectively work as a distributed team with 50+ people dispersed over 11 states without tools like Slack. Looking at it that way, it’s easy see the downsides as manageable things we’ll figure out.

Effectively using new systems for collaboration is just as much about adapting our own behavior as it is about the feature set of the new tool. Each tool is not perfect for everything (as much as their marketing might say so). I think much of the kickback is from those that don’t want to change. They want all the benefits of a system that conforms around their comfort zone.

On a recent episode of the Knowledge Project podcast, Dr. Atul Gawande compared the relative importance “breakthrough” versus “follow through” innovation:

”We’ve been fantastic at breakthrough innovation, with no real understanding of follow-through innovation… Follow through can seem like it’s about nuts and bolts, and not about new ideas.”

What follows is a discussion about the importance of follow-through and rigor with advances in medicine. A redirection of attention away from the shininess of “breakthroughs” is an interesting idea. It’s not that follow-through doesn’t happen, it’s that it gets backgrounded or treated as less important. It’s not as “cool” as being on the cutting edge.

I was reminded of Clay Christensen’s Innovator’s Dilemma and its notions of “disruptive” versus “sustaining” innovation — really two different names for the same concepts above. It’s become fashionable in business to focus a disproportionate amount of attention on being “disruptive”, perhaps to the exclusion of continued improvement on things you’re already expert at. Staying in front of shifts in the climate of your business sector is certainly important, but so is sharpening the saw and staying competitive in your current space.

As Gawande discusses in the episode, the aggregate good done by simple, sustaining developments like basic sanitation procedures and process improvements far outstrip what most breakthroughs provide.

We’re about to head to SaaStr Annual again this year, an annual gathering of companies all focused on the same challenges of how to build and grow SaaS businesses. I’ve had some thoughts on SaaS business models that I wanted write down as they’ve matured over the years of building a SaaS product.

I wrote a post a while back on subscription models, but in the context of consumer applications. My favorite thing about the subscription structure is how well it aligns incentives for both buyers and sellers. While this alignment applies to app developers and buyers in consumer software, I think the incentives are even more substantial with business applications, and they’re more important long term. The issue of ongoing support and maintenance with a high-investment business application is more pronounced — if Salesforce is down, my sales team’s time is wasted and I’m losing money. Whereas if I can’t get support for my personal text editor application that’s $5/mo, the same criticality isn’t there. Support and updates are just a small (and obvious) reason why the ongoing subscription model is better for product makers, and in turn, buyers. But let’s dig in some more. What’s better about the SaaS model?

First: subscription pricing significantly reduces the “getting started” barrier for buyers and sellers. If I go from charging you $1,000 up front for a powerful CAD application to a monthly subscription model for $79/mo, you and I both win. You like it because you’re comfortable paying that first $79 with no touch to get started, just subscribing online; no friction there. I like it because I don’t have to front-load investment in convincing you of the value. This potentially expands my customer count and gets past the initial transaction quickly.

Second: there’s predictability on the spending and earning side. If you’re a buyer of fixed cost products, you have to predict ahead of time what next year’s cost might be for the 2019 version, decide whether or not you need to include it in your budget, and have to forecast possible expansion use far in advance. With SaaS you can limit all three of those problems¹. As the seller, I get to enjoy the magic of recurring revenue (or in the lingo, MRR – monthly recurring revenue).

Third: pricing is easier². In an older “box software” model, I would have to figure out the appropriate “lifetime value” my product has on the day I sell it, and balance this with what price the market will bear. Once it’s sold, there’s no space for experimentation to map price to value, the deal’s done. SaaS can be fluid here, giving me space to fit the ongoing delivery of value to the price. Of course I don’t want to be changing pricing every month, but it’s within my control to keep the pricing at an effective and sustainable level. When setting pricing, I can break it down to a smaller unit of time, as in “what value does this have to my customer over a month or quarter?”, without trying to predict how long they’ll be my customer. That’s called CLTV (customer lifetime value) and it’s a key metric to track after signing on customers. After a year or so, I have CLTV data I can use to inform pricing. Managing CLTV versus CAC (customer acquisition cost) relationship is part of the SaaS pathfinding to a repeatable business³.

So what are the downsides? I don’t think there are any true negatives for anyone. For the seller, the major downside is that you have to keep earning the money for your product month over month, year over year. And I’d say buyers would actually call that a major upside! There’s no opportunity to sell a lemon to the customer and take home the reward — if your product doesn’t live up to the promise, you might only collect 1/12th of what you spent to get the customer in the first place (see CAC v LTV!). That’s no good. In SaaS you have to keep delivering and growing value if you want to keep that middle R in “MRR” alive. I call this a “downside” for sellers insofar as it creates a new business challenge to overcome. Selling your product this way actually has huge long-term benefits to your product and company health. It prevents you from taking shortcuts for easy money.

This is the greatest thing about the SaaS model: keeping everyone honest. It allows the best products to float to the top of the market. To compete and grow as a SaaS product, you have to keep up with the competition, track ever-growing customer expectations, release new capabilities, maintain stability, and continually harden your security. Buyers are kept honest by their spend; they have to keep buying if they want the backup of ongoing support, updates, new features, solid security, and more.

One thing I’ve seen with a SaaS business is the perception from buyers that the recurring costs will incur a higher total lifetime cost for a solution. So in the case above, if my CAD software is a subscription seat for $79/mo per license, customers will immediately compare it to the old model — “it was $1,000 one time fee, now it’s $79 each month. After a year I’ll be paying more than the one-time cost. The product is core to my business, so I’ll definitely be using it longer than a year.”

While this is true when strictly comparing costs, it doesn’t tell the whole story. In the early days testing a new product, it’s hard to see where the invisible costs will be. How much support will I need? Are there going to be bugs that need patching? What if I need to call someone to troubleshoot major issues? What’ll my internal IT costs be to roll out updates? The SaaS advantage is that (in general) there are good answers to these questions; ongoing support and improvements are part of the monthly tab. Another thing you run into, though less and less these days, is the compulsion to build the capability internally. The perception of high lifetime cost compels technical buyers to spend that money on their internal IT department rolling their own software to solve the problem. Not that this solution never makes sense, but most buyers are not software companies at the core. They’ll never build a great solution to their problem and be willing to commit to the maintenance investment to keep pace with what SaaS providers are doing. Over time as the SaaS model spreads, buyers will get more comfortable with the process and better understand where their SaaS spend is going.

These two posts from Ben Thompson give a great rundown of companies switching to SaaS, and why subscription business models are better for incentives.

Since Apple changed their subscription pricing options for App Store developers back in 2016, several high-profile apps that have made the switch from fixed pricing to the subscription model. TextExpander, Day One, and Ulysses are just three that I know of and use.

I may be biased as I’ve been building and selling subscription software for years, but I love that the Apple ecosystem is supporting this now. Ulysses provides a great example: their fixed model had the price at $45 for the Mac app and $25 for the iOS app. Their new subscription is for universal access on both platforms for either $5 per month or $40 per year, plus a 14-day free trial for new users. I’d long heard that Ulysses was a great editor for writing, but held out forever on really using it because, for one, there are a ton of great text editors, but also I didn’t know how much I’d really use it once I dropped the coin. At a $5/month subscription, I don’t have to feel bad, I can just cancel if I’m not using it enough and be out a $10 or $15.

There’s been some backlash from the community about this shift from fixed to subscription models. The AppStories podcast did an episode recently on the topic with some interesting discussion. To me the reasons for backlash are threefold:

1. Users don’t like change — We’ve experienced this time and again with our product. Even when we release new features that seem universally fantastic, we’ll still get naysayers wanting a checkbox to “make it work like it used to.” Change that makes the price higher, even if it’s only perceived to be higher, or even when the alternative is the developer is no longer able to support the app, there are those that still can’t accept it.
2. Users don’t get continued (or enough) value from the product — Even if the recurring price is super low, like $1.99 per month, some users will feel like they don’t use the app enough to warrant that price forever. A flat $10 might be okay. A fair enough reason. It comes down to who the developer wants as a customer. Are they building something for the few, higher-value niche customers, or a mass market?
3. Most people are cheap — There are a shocking number of people who are willing to have subpar experiences to save some money. The frustrating part for developers is when users want the savings part, but don’t want to make that sacrifice in quality. The glut of free replacements out there makes it challenging for developers to charge anything at all for many users.

Of course it’s possible for a developer to misprice their app, to overpredict the value delivered to a user. I’ve seen it happen with SaaS products: something I use a little changes their pricing model a bit, it becomes not worth it to me anymore so I cancel. But I’m a believer that developers will tend to get this right more often than not (at least eventually). With subscription pricing, small pricing adjustments are easier decisions for a developer to make. Going from $5 to $2.50 a month is less momentous a choice than going from $50 to $25 in fixed price model. It’s better for the user, too; there’s less feeling of being ripped off, and no need for promo codes and refunds.

But hands-down the best feature of subscription models is that the apps you love get to stick around for the long haul. At this point we’ve all been burned by services we rely on disappearing on us. I’m happy to pay to keep things around that I use regularly.

⚗️ Amazon Announces AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata

Interesting new service from AWS (is there a need in computing they don’t cover at this point?), providing serverless ETL transformations on datasets hosted anywhere. The automatic discovery is particularly interesting for applications dealing in highly variable data structures.

🏢 The Strategies and Tactics of Big

A conversation between Benedict Evans and Steven Sinofsky on big companies, their org charts, and what makes each (and their products) different.

💵 Inside Patreon

Patreon is still tiny compared to Kickstarter, where 13 million backers have funded 128,000 successful campaigns, but it’s rapidly growing. Half its patrons and creators joined in the past year, and it’s set to process $150 million in 2017, compared to $100 million total over the past three years.

This is a fascinating company, creating a funding mechanism for independent creators with a different model than the Kickstarter structure.

💻 The Disappearing Computer

For his final weekly column of his long career, Walt Mossberg talks about what he calls “ambient computing”, the penetration of IoT, AR, VR, and computers throughout our lives:

I expect that one end result of all this work will be that the technology, the computer inside all these things, will fade into the background. In some cases, it may entirely disappear, waiting to be activated by a voice command, a person entering the room, a change in blood chemistry, a shift in temperature, a motion. Maybe even just a thought. Your whole home, office and car will be packed with these waiting computers and sensors. But they won’t be in your way, or perhaps even distinguishable as tech devices. This is ambient computing, the transformation of the environment all around us with intelligence and capabilities that don’t seem to be there at all.

🚁 Drones Go to Work

Great piece from Chris Anderson on the prospects of the commercial drone space. He makes great points about the true success of the technology being its penetration into business applications:

Although it might surprise you, I hope the future of drones is boring. As the CEO of a drone company, I obviously stand to gain from the rise of drones, but I don’t see that happening if we are focused on the excitement of drones. The sign of a successful technology is not that it thrills but that it becomes essential and accepted, fading into the wallpaper of modernity. Electricity was once a magic trick, but now it is assumed. The internet is going the same way. My end goal is for drones to be thought of as just another unsexy industrial tool, like agricultural machinery or generators on construction sites — as obviously useful as they are unremarkable.

✅ Can Do vs. Must Do

Another good reminder from Fred Wilson on the importance of focus. He suggests setting no more than 3 “big efforts” in a year, the “must dos”. More than that is lying to yourself and losing steam on the ones you really care about:

But regardless of whether you have two, three, or four big efforts this year, you should test all of your initiatives agains the “must do” vs “can do” test. Just because you can do something doesn’t mean you should. I’ve written about the importance of strategy and saying no. Strategy isn’t saying no. It is figuring out what is the most important thing for your company and deciding to focus on it and say no to everything else.

🚙 Cartography in the Age of Autonomous Vehicles

An excellent, extremely detailed analysis from Justin O’Bierne on how maps and cartography might evolve if autonomous vehicles negate our need for turn-by-turn navigation.

We can’t apply today’s maps to tomorrow’s cars – but this is exactly what those who think cartography is dying are doing. (It’s not that we’ll no longer be navigating, it’s that we’ll be navigating different things – and we’ll need new kinds of maps to help us.)

🌎 Few Interact With Our Interactive Maps–What Can We Do About It?

Brian Timoney’s done some great writing on this topic over the last few years. In the GIS world, enormous amounts of money are spent by governments to build and host map portals. The goals are typically noble (transparency, openness, providing access to citizens), but the results are mixed. Much of the spend is in making the information interactive. The dirty secret is that people don’t actually interact with these maps. He proposes a number of ideas of how to get the best of both: lower costs to create with the same (or higher) consumer engagement. For example, static maps cost much less to create and could even do better at directing a reader to the right information:

Just because you’re publishing a map to the web, doesn’t mean it has to be a web map. If a user is only going to spend 10-15 seconds with your map without interacting, why spend two weeks wrestling with your Javascript? And the great thing is the focus a static map brings–a single view, a single story: don’t bury the lede.

💡 The New Moats

Jerry Chen from Greylock thinks “systems of intelligence” will be the next business model for software companies to create defensible value. He differentiates “systems of record” and “systems of engagement” as two layers in a stack of software applications that have existed since the dawn of the IT revolution in the 1990s.

These AI-driven systems of intelligence present a huge opportunity for new startups. Successful companies here can build a virtuous cycle of data because the more data you generate and train on with your product, the better your models become and the better your product becomes. Ultimately the product becomes tailored for each customer which creates another moat, high switching costs.

Trying out a new thing here to document 3 links that caught my interest over the past week. Sometimes they might be related, sometimes not. It’ll be an experiment to journal the things I was reading at the time, for posterity.

The Arrival of Artificial Intelligence 🔮

Good piece from Ben Thompson comparing the current developmental stage of machine learning and AI with the formative years of Claude Shannon and Alan Turing’s initial discoveries of information theory. They figured out how to take mathematical logic concepts (Boolean logic) and merge them with physical circuits — the birth of the modern computer. With AI we’re on the brink of similar breakthroughs. Thompson does well here to make clear the distinctions between Artificial General Intelligence (what most people think of when they hear the term, things like Skynet) and Narrow Intelligence (which is all we have currently, AIs that can replicate human thinking in a narrow problem set).

The New APFS Filesystem 📱

Apple announced their new APFS file system at last year’s WWDC, and this week launched it as part of the iOS 10.3 update. Their HFS+ file system is now 20 years old, but file systems aren’t something that you change lightly. They’re the core data storage and retrieval engine for computers, and massively complex. APFS is engineered with encryption as a first-class feature and also includes enhancements for SSD-based storage. The most amazing thing to me about this story is the guts it takes to make a seismic change like this to millions of devices in one swoop. It’s the sort of change that is 100% invisible to the average iPhone owner if it works, and could brick millions of phones if it doesn’t. Working in a software company building mission-critical software, it takes serious planning, testing, and skills to deploy risky changes like this to move your platform forward. Kudos to Apple for pulling off such a monumental and thankless change.

Fred Wilson’s MBA Mondays 💼

I’ve read Fred Wilson’s AVC blog for some time, but only through post links that make the rounds. Recently I discovered his archive of “MBA Mondays” articles covering tons of business topics. He’s got pieces on budgeting, cash flow, equity, M&A, unit economics — tons of great stuff from someone learning and practicing all of this in reality. Much more digestible than textbook business school material. I’m gradually making my way through the archive from the beginning and really enjoying it.

This entire post comes with a caveat: I am not a software engineer. I do build a software product, and work with a bunch of people way smarter than me, though. I’m experienced enough to have an opinion on the topic.

I talk to lots of young people looking to get into the software world. Sometimes they want to build mobile apps or create simple tools, and sometimes looking to create entire products. There are a lot of possible places to start. The world is full of blog posts, podcasts, books, and videos that purport to “teach you to code”. Don’t get me wrong, it’s an awesome world we live in where this stuff is accessible, but I think people get priorities twisted during that early impressionable stage by thinking they can make a successful iPhone app from scratch in a few months. Even if it’s possible, is that really a life goal? Or do you want to actually become an engineer?

Young people interested in learning how to code could learn a lot by starting with the smaller steps. Instead of diving immediately into learning node.js, or beginning with “Build Your Own Rails App in 15 Minutes” blog posts, focus your energy on some foundations that will be 100% useful in building your skills as an engineer.

In no particular order:

Learn how to use the Linux command line

It almost doesn’t even matter what exactly you do with Linux to get started on this. Install some variant of the OS on a computer or virtual machine, and start trying to do stuff. Install packages, set up PHP, get Postgres running. Most importantly: learn the basic command line tools that you’ll use for the rest of your working life — things like grep, sed, cat, ack, curl, find. Think of these as tools of the trade; once you know how to work them, you’ll use them every day. Compare your craft to cooking. It’s possible to create good food without a razor sharp chef’s knife, a large rigid cutting board, and fresh ingredients, but it’s a lot easier when you have them.

Work on tools instead of systems

Starting out by building entire products is a bad idea. The most readily available ideas are ones that require a lot of moving parts, typically. These are the ones that sound fun. Starting to assemble some knowledge by building your own blog engine or social sharing site or photo database system won’t teach you nothing, but it puts the cart before the horse. A few hours into building your photo sharing site (with an objective of making something to share photos) you’ll be working on a login system and a way to reset passwords, instead addressing the problem you identified to solve in the first place. The easier place to start is to identify small pain points in your technology life, and build utilities to fill these voids. A script for uploading files to Google Drive. Wrappers to simplify other utilities. A command line tool to strip whitespace from files. You’ll be biting off something you can actually build as a novice, and you might be able to ship and open source something useful to others (one of the bar-none best resume builders around). Scratching small itches is your friend when you’re learning.

Prime yourself on “devops” knowledge

The “cloud” sounds like a huge loaded buzzword, and it is. But nearly every useful technology stack, even if it’s not a publicly facing consumer product is now built using these core architectures. If your mission is to build iOS games you’ll think this stuff isn’t valuable, but learning how to stand up instances on AWS, install database servers, and understanding the network security stack will guaranteed add indispensable chunks of knowledge that you will need in the near future. This stuff is free now to get a place to hack around, so there are no excuses to not plunging in.

Spend hours on GitHub

Dig for open source projects you find interesting. Pick apart their code. Follow the developers. Read the issue threads. You’ll find something you can contribute back to, without a doubt, even if in tiny ways at first. This is not only hugely satisfying to an engineer’s brain, but you’ll slowly build valuable trust and presence within the community. Don’t be afraid to dig in and have conversations on GitHub projects (trust me, no one else is scared to make comments or offer opinions). Being thoughtful, considerate, positive, and thinking like you’re working as a team are excellent ways to get invited into the fold on open source efforts.

See also, traditional resources

Code schools and crash courses are awesome new resources, without a doubt. I don’t mean to discount traditional educational structures for foundation-building and creating a regimented path for walking through the process. The good ones will teach you plenty of my previously-mentioned core pieces without getting you ahead of yourself. But the bad ones get new students thinking about picking libraries and frameworks immediately. So little of the initial hill to climb has to do with your choice of Javascript vs. Python vs. Ruby, or whether to use Angular or Backbone in your first app. None of that matters because you don’t really know anything yet, and you haven’t even climbed the initial three or four rungs of the ladder. You shouldn’t attempt leaping to the sixth or seventh rung without having some scars from the lower levels. Jobs that have you mucking around with data in VBscript or maintaining old SQL Server databases are (unfortunately) excellent seasoning for your career. This is usually where you’ll determine whether you really like this career choice that much. If you come out of the trenches still interested in being a programmer, you’ll love it when you get to work on something satisfying, and you’ll appreciate what you have.

I’m a huge fan of starting by getting your hands dirty. This post was intended to help you find the best mud pits to put those hands into.

Jeff Lawson of Twilio gave this talk on SaaS pricing at the Business of Software conference last year:

Everyone in the SaaS product business should watch this. Great approach to thinking through putting prices on your SaaS service.

The key is to understand all the facets of your product and what things cost you as the creator, in addition to slicing and dicing options for your customers to buy what they need. Facets like:

• Quantities (How many gigabytes? What kind of bandwidth is allowed?)
• Features (Can I do push notifications and alerts? What about management dashboards?)
• Support (24 x 7 phone support?)

These things on their own are even sometimes difficult to figure out. What are all the variables you can tweak and change about your offerings? And those are thing that you can control, properties of your own product? I still maintain that the hardest part, bar none, of building pricing models (though it seems obvious) is understanding what your potential customers are willing to pay for your service, and then making sure you can serve them a product cheaper than that.