Why Founders Should Blog

Blogging is one of the highest ROI tactics that founders can employ to accelerate growth across the company: revenue or users, employees, and investors.

It’s often times not obvious how blogging can create value until you’ve been doing it for a while though. I’ve published over 250 blog posts over the last 40 months, and I’ve probably written and discarded an additional 25–50 posts. I could have never recognized the value of what’s below until I had done it. Hopefully you’ll take up blogging more actively.

1. I first started blogging January 1, 2013. Going into 2013, I knew that I wanted to start a health IT company, though at the time I didn’t know what that company would be. I was 22 at the time, and knew that I would never be able to raise capital, recruit employees, or sell customers in the health IT space unless the world could feel comfortable I that I understood health IT. So I made a new year’s resolution for 2013 to blog 3x / week for the year. I wrote 156 blog posts in 2013. I can directly attribute my blog posts to $300K of the $1M of seed financing I raised, including the first $100K check investment from an angel. I can directly attribute my blog posts to landing what would become Pristine’s first customer, UC Irvine. And within 4 months of blogging, I landed a role at the most respected blog in the health IT ecosystem — histalk.com. That indirectly drove hundreds of thousands in additional revenue because it made both Pristine and I credible.
2. One of the key tenets of leadership is defining and communicating the company’s vision to the team. Clearer, more concise writing obviously lends itself towards communicating the company’s vision more clearly. Moreover…
3. Satya Nadella, CEO of Microsoft, ranks the ability to clearly communicate as the single most important skill he looks for in executives. Executives judge everyone they work with based on their communication skills. It’s nearly impossible to sell executives expensive solutions if you can’t write. Before an executive writes you a $100K+ check, she will likely read many of your emails and hear you speak and present. She will judge you based on all your communications. If your business model doesn’t involve selling $100K+ solutions, think again. VCs write checks that are 1–2 orders of magnitude larger. VCs are also generally 1–2 orders of magnitude more sophisticated than your customers. They will scrutinize your ability to communicate because they know that as CEO, as you scale, communication becomes the only thing you’ll do.
4. Writing will expedite the interviewing process for non-developers. Use your content as a filter in the interview process. Has the candidate read your last 3–5 blog posts? Can she discuss them? Does she have any opinions of her own? If not, you can reject the candidate in seconds or minutes. At Pristine, I was famous (infamous?) for dozens of 5-minute sales interviews. If they hadn’t Googled me, stumbled into my blog, and read a few posts, I knew they would never pursue customers with enough rigor to be any good. The top quintile of sales reps never go into job interviews without looking up who they’re going to be interviewing with. This filter can be used for all non-developer candidates.
5. When speaking off the cuff, people generally use 2–3x the number of words necessary to describe an idea. Writing forces you to be crisp and clear. As your writing improves through practice, your ability to communicate concisely will naturally translate into your real-time speech, helping you speak more succinctly. It’s hard to believe at first, but you’ll recognize the difference after 6 months.
6. Combining the two prior points, the stronger your writing ability, the more quickly and effectively you’ll be able to judge others. If simplicity is the ultimate sophistication, and if writing helps you think and communicate more clearly and simply, then communicating clearly makes you more sophisticated, and more able to judge others’ sophistication.
7. Define and control your online identity. You never know when a publication will name drop you or your company in a less-than-flattering capacity. When you’re Tony Fadell, you can handle some negative press. But when you’re not a celebrity, it’s far more challenging to recover from bad press. You can’t compete with TechCrunch on SEO, but you can clearly define your voice so that when TechCrunch writes something you don’t like, your stakeholders can judge you for who you really are, and not what a TechCrunch reporter threw together in a few hours of research. You don’t want your first blog post to be a response to a TechCrunch article. Furthermore…
8. Build your brand. Everyone likes doing business and hanging out with known entities. So make yourself a known entity. Ryan Hoover of Product Hunt and Danielle Morrill of Mattermark built themselves into recognizable brands as they launched their respective companies. As a known entity, you’ll be able to get almost any meeting you want. Furthermore, by establishing your brand, candidates and employees will learn how to interact with you, what you like, and how you think. This will reap dividends in perpetuity.

Hopefully I’ve convinced you that you should write more. This naturally begs the question “How do I improve my writing ability?” The answer is simple: practice! My early blog posts were garbage. The only way to get better is to practice. Writing three blog posts per week accelerated the process for me. You don’t need to commit to three per week, but try committing to one or two weekly.

It will be hard. You may be too embarrassed to publish your work. As Mark Suster of UpFront Ventures says, “publish or perish.” Get it out there. Iterate. The best form accountability is public accountability, even if no one reads your blog (fun fact: I had about 20 unique readers in my first 3 months). In 2–3 months, no one will remember the bad content because they’ll see how good you’ve become. If you need help with motivation and enforcement, usestickk.com. I used it successfully to enforce my three posts per week rule in 2013.

Practicing writing is hard. I didn’t do it alone. There were quite a few people who helped me along the way. You may not have someone who you trust to help with your writing. So I’ll make a promise to the world: if you commit to writing at least one blog post per week, I’ll commit to helping you refine and improve each and every post, privately. Just email me if you’re interested.

Computers Commiditize Knowledge

30 years ago, the highest paid person in the room typically made most of the operational decisions. A CMO or Creative Director would determine the final language in an ad. A VP Sales would determine the exact language sales development reps (SDRs) would use when calling prospects. HR directors would set employee policies without much employee input.

Today, things couldn’t be more different. CMOs and Creative Directors may set high-level themes for a marketing campaign, but copywriters and junior marketers generate many unique sets of copy and split-test those messages on a small scale first. The best received messages will then be reproduced on a larger scale. Similarly, a VP Sales no longer needs to claim to know any best-practices about cold calling. She can split-test every variable using a tool like SalesLoft.

30 years ago, a VP Sales would never claim to offer a useful opinion regarding visual direction and ad-copy. And a CMO would never claim to guide SDRs through cold-call scripts. The processes outlined 2 paragraphs above couldn’t be more different. But the process outlined in the paragraph immediately preceding this one are identical. In both cases outlined above, the VP doesn’t need to claim expertise in much of anything beyond managing the experiment. The VP can and should defer to what the data says.

CEOs used to rely on expert VPs to make these kinds of decisions. The VPs had decades of experience. The VPs would synthesize all of their learnings and experiences and offer an answer. This process was not unlike the split-testing process outlined above. The only difference was that humans were responsible for processing the results of the experiments rather than computers. Computers are by definition orders of magnitude better at understanding, normalizing, and comparing data than humans.

Now that we have the tools to split-test and measure almost every action we take through computers, VPs no longer need to do this. In fact, they explicitly shouldn’t because they are inferior to computers.

I’m not suggesting that the experience that many VPs bring to the table is worthless. There will always be questions that computers cannot answer: will this person jump ship if they receive an offer for $5K more elsewhere? how can we structure our pricing proposal and presentation to maximize revenue on this $500K deal?

Rather, VPs are moving up to higher levels of thinking and operation. Without the need to worry about as many tiny operational details, VPs can focus on higher level issues to accelerate firm performance.

Each functional area still requires plenty of nuance that is not easily computable. But a lot of the basic questions can be answered by computers. Thus, VPs should be increasingly valued not on their willingness to dictate what should be done, but how they structure and manage experiments to make the best decisions from data. This isn’t to say that data commoditizes all knowledge, but that many facets of what were once knowledge have indeed by commoditized by data. I suspect that in 20 years, the most efficient management teams will operate in ways that we can’t fathom today. As computers offload more of the busy work that today is in the realm of, management, managers will focus on ever higher-order issues and accelerate firm performance even more.

The real question is: when will computers move further up the chain and commoditize wisdom? Is this possible before we achieve general-purpose artificial intelligence?

The Deception of Positive and Negative Growth Percentages

Growth and decline percentages are deceiving in the startup universe. Many don’t recognize all of the implications of exponential growth and the associated hiring challenges. This problem manifests most commonly when a startup begins to achieve product/market fit and Initial Traction (~$1M ARR). 

When a startup is growing 10-15% m/m on the path from $500K - $1M ARR, the startup is adding $50-$100K in ARR each month, or $4-8K MRR. 10% m/m growth isn’t bad, but it’s not great. Great startups are growing > 15% m/m as they pass $1M ARR, and the Select Few are growing >25% m/m.

But what many fail to recognize is how the math scales. At $1M ARR, growing 15% m/m requires adding $150K in ARR, or $12.5K in MRR each month. Thus, from the time a startup achieves $1M ARR, they should hit $1.5M ARR in 3 months if they want to maintain 15% m/m growth.

At $1.5M ARR, 15% m/m growth means the startup needs to add $225K in MRR each month. So the startup should grow from $1.5M to $2M in just over 2 months, or 33% faster than the prior $500K in ARR.

This math isn’t particularly difficult to understand. But many underestimate just how hard it is to accelerate growth as the numbers get bigger. There are tons of startups that get to a point of adding $100K in ARR each month. 10x-ing that - adding $1M in ARR each month - requires real thought and and planning. Jason Cohen of WPEngine describes this problem very well. At a rate of adding $1M ARR each month, it will take 100 months, or more than 8 years, to achieve $100M ARR. Growth has to accelerate.

How does all of this tie to hiring? As startups add multiple customer acquisition channels, hire more than 2 sales people, and generally scale, startups need to invest in infrastructure. All kinds of it. This means marketing automation, sales automation, and customer success automation - collectively customer lifecycle systems and processes - to accelerate growth. There should be minimal innovation around these disciplines: innovate myopically. But startups need to hire the right people so that they don’t have to innovate in these disciplines. Tech/product focused founders rarely have expertise setting up customer lifecycle systems and processes. They should bring in the people who have implemented those processes before, and empower them to do it.

Those people are going to be more expensive than many founders naturally think. I remember the first time I looked at what an amazing VP Customer Success costs after raising a Series A. I couldn’t believe it: $150-$200K and .5%-1.5%. Great VP Sales can go much higher. But the great ones are worth their salary 10x over. Because they will make the startups’ customers happy in a way the founders otherwise never could, and those customers will fuel the best kind of growth: word-of-mouth (WOM). Not only does WOM scale incredibly well, it scales well inexpensively, driving down CAC and ensuring that startups don’t compromise the fundamental law of startup growth (fantastic post). That amazing VP Customer Success will drive down CAC and drive up LTV, ensuring the startup stays venture-fundable as it scales to $100M ARR. The amazing VPs are worth any amount of cash and equity to acquire.

The situation is particularly amplified for companies that are doing $1-2M ARR and are 4+ years old. If the product/market fit is tight and the market opportunity is real, then that means the startup needs serious customer acquisition help across all fronts. If there were any top notch marketing or sales people around, they probably left because they didn’t have the help around them they needed to drive revenue growth. And so now the company is left with B players without strong customer acquisition and customer success leadership. Those companies are the ones that most need to find a spark  to rethink the entire customer acquisition process and ignite growth. Those people will be expensive in cash and equity terms, but if they are any good, they will be worth their cost 100x over. As Jason Lemkin of SaaStr loves to say, salary and equity vest (and equity has a cliff), so the cost of being wrong is relatively low given the slow growth rate anyways.

Hiring becomes even more important when thinking about growth given the time cycles involved. Hiring great executives takes months, and onboarding takes 30-60 days, so founders need to have incredible foresight to time VP-level hires right to ensure they maintain an exponential growth curve. That means in many cases it will take 6 months to hire and onboard great VPs. So that means that startups should start looking for their first great VP hires between $500K - $1M ARR. If the startup is growing is 15% m/m and it takes 6 months to find and onboard the VP, then the startup will be at $1M-$2M ARR - or double the revenue of when the search started - before the VP is delivering material value. This is not intuitive to founders who, up until this point, have been able to tweak almost anything overnight other than the product fuel growth. It’s not naturally intuitive to think that a single thing - hiring an executive - will take 6 months. Exponential growth can be deceiving.

The flip side of thinking about growth - which is theoretically unlimited - is thinking about a fixed pie. And this is where percentages are really deceiving. When discussing negative percentages - revenue declines, accelerating burn, etc - it feels like the pie is being eaten… because it is. These are fixed pie scenarios. All of a sudden, 15% of the pie is gone. It feels like crap because psychology dictates that humans strongly prefer loss aversion to gains.

The problem that many entrepreneurs make is applying the same fixed-pie mindset and applying it to growth percentages and thinking about equity value. Positive and negative percentages need to be thought through opposing lenses: negative percentages should be thought of as a fixed pie that’s being eaten, while positive percentages need to be thought through the lens of infinite growth. Companies can become infinitely large - see Apple and Google - so the real dilemma isn’t how to minimize losses, but how to maximize growth. All decisions should be made through the lens of maximizing growth, even if material costs are incurred. Startups can always grow more than they can lose (unless gross margins are negative, in which case the business shouldn’t exist). Invest in that growth, even if it feels expensive through a fixed-pie lens. 6 months later, it will feel like a bargain when looking backwards through an infinite growth lens.

Should Your Startup Go Full Stack?

I’ve written about the problems associated with selling into value chains where there are disparate P&Ls. In this post, I’ll dive into the solution to this problem: going full stack. I’ll also cover when going full stack makes sense.

Chris Dixon of a16z has noted the rise of a number of high-profile, full stack startups across many industry verticals. What is a full stack startup? Full stack startups are those that, rather than selling a novel piece of technology to incumbents, use the technology they’ve built to serve the incumbent’s customers directly, bypassing the incumbent in the process. There are many of examples of modern full stack startups and their respective antitheses. A few examples:

Uber vs Hailo - Everyone knows that Uber bypassed taxis and instead when straight to the end consumer. On the other hand, Hailo responded to Uber by selling an Uber-like platform to local taxi companies. Hailo has struggled while Uber has run away with the market.

Buzzfeed vs ThoughtLeadr - BuzzFeed is changing the media business because their entire strategy is based on sourcing and producing content that’s designed to be easily consumed through the best discovery channels: social media. Everything they do stems from a deep understanding of how users share content. As a result of that, they have devised an incredible formula that seamlessly intertwines native advertising with socially shared digital content. ThoughtLeadr and many companies like it are trying to empower media organizations to rethink content strategy and distribution around native advertising and social sharing. BuzzFeed does the tech and content production in house. To be clear, I am close with the team at ThoughtLeadr, and I actually love what they’re doing. BuzzFeed can’t be the only media property on the planet. The world needs companies that can help old-school media properties thrive in the digital age. ThoughtLeadr is doing just that.

Oscar vs traditional healthcare insurers - Oscar recognized that health insurance should be completely rethought around smartphones. Smartphones enable new models where insurers consume vastly more consumer data (think movement, etc), and present a potent communication medium. Rather than trying to help traditional health insurers transform their entire businesses - customer acquisition, service, preventative care, etc - Oscar decided to build a brand new health insurance company that didn’t have any legacy baggage.

HealthSpot vs Teladoc - Teladoc owns and operates its own technology to enable virtual physician consults, whereas HealthSpot built/assembled technology and sold it to provider organizations. Healthspot recently ceased operations, while Teladoc recently IPO’d and is growing hand-over-fist as healthcare continues to consumerize.

One Medical Group (OMG) and Kaiser Permanente (KP) vs traditional healthcare providers - KP and OMG are two prominent examples of “full stack” healthcare providers. KP is enormous and has been around for a while and operates as an health plan and fully-featured provider organization. OMG is the leading pioneer of the Direct Primary Care model, which borrows many health-plan like features and functions. These organizations both invest heavily in their own technology development, and use that to offer differentiated healthcare services to their customers. KP and OMG stand in stark contrast to more traditional healthcare providers, who simply make money for doing stuff to patients.

I don’t mean for these examples to paint the picture that being full stack is always the right answer, and that being partial stack is the wrong answer. There are many successful companies that are explicitly not full stack: Workday, SalesForce, Slack, Zenefits, etc. These startups have been successful partial stack companies because they do not break the daily workflow of their users.

For example, let's look at SalesForce. Although sales professionals like to complain that SalesForce is clunky, slow and a not helpful in their daily workflow, SalesForce is just a tool that’s designed to reinforce a process that the sales people should have been adhering to in a paper world: contact and pipeline management. Zenefits users are still buying health insurance, and still filling out forms. The process itself really hasn’t changed; all Zenefits has done is unemploy health insurance brokers. Slack users are still designing, coding, etc; with Slack, they can collaborate with their colleagues more effectively. The same can be said of the other examples above.

On the other hand, tech companies should consider going full stack if they break the operational processes of the primary users of their solution. Uber could never sell into the taxi industry because Uber commoditized taxis, and taxi company owners didn’t want to subject themselves to that. Oscar could never sell its novel technology to traditional health insurers because it would have required rethinking every function in the insurance company. HealthSpot couldn’t usher traditional healthcare providers into a retail care delivery model.

Why can’t incumbents adopt technology solutions that completely break the daily workflow of their primary revenue-generating employees? In short, it’s too operationally disruptive. The organizations simply cannot absorb the change. When an employee’s daily workflow is completely rethought, the vast majority of her existing operational knowledge is not only worthless, but actually destructive in the new paradigm. Processes that made sense in the old paradigm no longer make sense in the new paradigm. Convincing thousands of employees and managers across disparate geographies and changing incentive structures to unlearn bad habits (to be fair, they were good habits in the old paradigm) is nearly impossible. In the case of Oscar, this is even more problematic as the changes would have spanned employees across every function in the company. Old and new paradigms simply cannot coexist in the same organization. Note: this is the fundamental problem that Clayton Christensen details in The Innovator's Dilemma.

As we see more verticals SaaS-ify, I suspect most startups will rightfully decide not to go full stack. For example, TalkDesk, ServiceMax, and Veeva rightfully didn't go full stack.  Although the new wave of vertical-SaaS vendors strive to be the all important system of record, they are generally seeking to re-inforce processes that should have already existed. In most cases, SaaS solutions are not radically changing the cost structure associated with delivering the services in question and are not re-inventing the business.

In summary, startups should go full stack if they are going to genuinely break the workflow of their target user. Otherwise, they should go partial stack.

PS, Given my background in electronic medical records (EMR), I'll add some commentary to that industry. EMRs break the daily workflow of the primary revenue generating employees of healthcare provider organizations. So why haven't EMR vendors gone full stack? Not a single traditional EMR vendor has even tried to offer healthcare services. There are a few reasons: during the golden era of EMRs, telemedicine wasn't reimbursable, so it wasn't practical to consolidate providers centrally given the intrinsically local nature of healthcare delivery. On the other hand, every novel tech-enabled healthcare service builds and manages its own technology in house: One Medical Group, Doctor on Demand, Teladoc, etc. The providers that are pioneering new delivery models recognize the importance of technology in their new models and are rightfully insourcing it to go full stack. This tech investment also serves as their barrier to entry and differentiator.

First Time Entrepreneurs Should Avoid Hot Sectors

I like shiny new objects. Pristine was birthed out of my draw to the shiniest of new objects at the time, Google Glass. Most first-time founders are drawn to shiny new objects too. As such, it’s easy for first time founders to be drawn into hot sectors that receive media attention.

First time founders should avoid shiny new objects and sectors that receive too much media attention. This seems counterintuitive, but it’s purely rational.

As investors begin to recognize a hot sector, they’ll talk with one another and with seasoned entrepreneurs who have tangential experience. The problem that first time entrepreneurs face is that seasoned entrepreneurs with 9-figure exits under their belts recognize the same patterns and have connections to the investor community. The seasoned guys will always raise more money than first timers, attract better teams, know how to achieve product/market faster, and scale faster while making fewer mistakes.

In simple terms, if it seems obvious to you, it’s obvious to someone else who can out-execute you.

So if you’re a first timer, you should avoid spaces that are getting lots of attention. As of Q4 2015, that would include spaces including drones, bitcoin, and the on-demand economy.

There are exceptions to this rule. You should ignore this rule if you have deep domain expertise in a field. For example, if you and 4 MIT buddies have been studying aerial propulsion for 5 years and have built a drone that gets 3x the length of flight of DJI, it’s worth raising VC money for.

As a first time entrepreneur, you’re better off focusing your efforts in spaces that aren’t generating too much attention. Examples in Austin include WPEngine, Aceable, and TrendKite.