One of my favorite thought exercises to do when I meet with founders who have reached the A- and B-stages (or beyond) is:
“What will his/her company look like if he/she is no longer there?”
The Preface
While the question looks like one that’s designed to replace the founder(s), my intention is everything but that. Rather, I ask myself that because I want to put perspective as to how the founder(s) have empowered their team to do more than they could independently. Where the collective whole is greater than the sum of its parts. Have the founders built something that is greater than themselves? And is each team member self-motivated to pursue the mission and vision?
“Well, Mr. President,” the janitor responded, “I’m helping put a man on the moon.”
From the astronaut who was to go into space to the janitor cleaning the halls of NASAs space center, each and every one had the same fulfilling purpose that they were doing something greater than themselves.
And if the CEO is able to do that, their potential to inspire even more and build a greater company is in sight. Can he/she scale him/herself? And in doing so, scale the company past product-market fit (PMF)?
For the purpose of this post, I’ll take scale from a culture, hiring, operating, and product perspective, though there are much more than just the above when it comes to scale. Answering the questions, as a founder:
How do you expand your audience?
How do you build a team to do so?
And, how do you scale yourself?
And to do so, I’ll borrow the insights of 10 people who have more miles on their odometer than I do.
While many of these lessons are applicable even in the later stages of growth, I want to preface that these insights are largely for founders just starting to scale. When you’ve just gone from zero to one, and are now beginning to look towards infinity.
The TL;DR
Build a (controversial) shocking culture.
Hire intentionally.
Retaining talent requires trust.
Build and follow an operating philosophy.
Create, hold, and share excitement.
Align calendars.
Upgrade adjacent users as your next beachhead.
Capture adoption by changing only 1 variable per user segment.
Over the weekend, I was brewing up some mad lemonade. ‘Cause well, that’s the summer thing to do. Since I’m limited in my expeditions outdoors, it’s just watching the sun skim over the horizon, blossoming its rose petals across the evening sky, in my backyard, sipping on homemade lemonade. If you’re curious about my recipe, I’ll include it at the bottom of this post.
Thomas Keller. An individual probably best known, among many others, for his achievements with The French Laundry. Needless to say, I was enamored by his talk. But the fireworks in my head didn’t start going off until the 12:46 mark.
Product-market fit is fluid. Just because you’ve attained it once doesn’t mean you’ll have it forever. The market is constantly changing. And that means the intersection where supply meets demand will always be changing as well. That said, regardless of how and where you move to, you’ll always have a subset of your customers who aren’t happy. Who might miss the old ways. Who might wish for something else entirely.
To put it into perspective, I’m going to quote Casey Winters (his blog), the current Chief Product Officer at Eventbrite:
“Product-market fit isn’t when your customers stop complaining, it’s when they stop leaving.”
Retention and its Touch Points
If you run a business, you’re going to have a leaky funnel. Your job is to minimize the leaks. Double down on not just adoption, but especially retention. What does that mean? Engagement and the often, overlooked category, for many early-stage teams, re-engaging those that have become inactive over a set period of time. Whether 30 days or 7 days. It depends on what solution your product is providing for the market and how frequently you normally expect them to use the product. For example, for most consumer apps, as investors, we expect a minimum of usage for 3 days out of the 7 calendar days a week. So I characterize inactivity aggressively as after a month of inactivity.
In the past few months, since the health and economic crisis began, the conversation has shifted from ‘growth at all costs’ to profitability. And similarly, from an overemphasis on adoption to a better understanding of retention.
Speaking of retention, 2 days ago, the afore-mentioned Casey Winters and Lenny Rachitsky published their homework on the the dichotomy between good and great retention, which you can find here and here, respectively. Their research provides some useful touch points about “golden” numbers from some of the smartest people in the industry. Of course, as their research suggests, everyone’s “golden” number is different. At different points in time.
So, how are you tracking how lovable your product is?
One of my favorite ways to track what keeps users coming back for more is the Depth vs. Breadth graph. Plotting how long people use certain features and how often they click into it. You can easily substitute length of time (depth) with the number of actions taken for each product feature you have. Or as you grow into having multiple product offerings, this graph works just as well.
Below are just a few examples of breadth and depth metrics:
Breadth
Depth
# of logins/week
# actions/session
Session count
Session time length
D1/D2/D7/D30 sessions
# concurrent devices logged in
Platform-specific sessions
DAU/MAU
# paid users/ # total
The above graph should also help you better optimize your features/offerings. For instance, let’s say you’re a startup in your growth stages. Going by Reid Hoffman‘s rule of thumb for budgeting, spend:
70% on your ‘popular‘ product offerings,
20% on your ‘niche‘ product offerings,
And 10% exploring your any hidden gems in your ‘broad‘ quadrant.
In closing
If you have your finger on the pulse about what your customers love about you at all times, you’ll be able to create a more robust product. As a final note, I want to add that while this piece has been dedicated to what your customers love, please always keep in mind what they hate as well. And why they hate what they hate. Who knows? You might discover a larger secret there.
Many founders I meet focus on, and rightly so, optimizing their core metrics – a set of units that surprisingly don’t change after its initial inception. But metrics and the way you measure them should undergo constant iteration. Metrics are a way to measure and test your assumptions. 9/10 assumptions, if not all, are honed through the process of iteration. And by transitive property, the metrics we measure, but more importantly, the way we measure them, is subject to no less.
Though I’m not as heavily involved on the operating side as I used to be, although I try to, the bug that inspires me to build never left. So, let’s take it from the perspective of a project a couple friends and I have been working on – hosting events that stretch people’s parameters of ‘possible’. Given our mission, everything we do is to help actuate that. One such metric that admittedly had 2 degrees of freedom from our mission was our NPS score.
The “NPS”
“How likely would you recommend a friend to come to the last event you joined us in?” Measured on a 1-10 scale, we ended up seeing a vast majority, unsurprisingly in hindsight, pick 7 (>85%). A few 9’s, and a negligible amount of 5s, 6s, and 8s. 7 acted as the happy medium for our attendees, all friends, to tell us: “We don’t know how we feel about your event, but we don’t want to offend you as friends.”
We then made a slight tweak, hoping to push them to take a more binary stance. The question stayed the same, but this time, we didn’t allow them to pick 7. In forcing them to pick 8 (a little better than average) and 6 (a little worse than average), we ended up finding all the answers shift to 6s and 8s and nothing else. Even the ones that previously picked 9s regressed to 8s. And the ones who picked 5s picked 6s. Effectively, we created a yes/no question with just this small tweak.
There’s 3 fallacies with this:
Numbers are arbitrary. An 8 for you, may not be an 8 for me. Unless we create a consolidated rubric that everyone follows when answering this question, we’re always going to variability in semi-random expectations.
It’s a lagging indicator. There’s no predictive value in measuring this. By the time they answer this question, they’d already have made their decision. Though the post-mortem is useful, the feedback cycle between events was too long. So, we had to start looking into iterating the event live, or while it was happening.
Answers weren’t completely honest. All the attendees were our friends. So their answers are in part, a reflection of the event, but also in part, to help us ‘save face’.
In studying essentialism, Stoicism, and Rahul Vohra‘s Superhuman, we found a solution that draws on the emotional spectrum that answered 1 and 3 rather well. Instead of phrasing our questions as “How much do you value this opportunity?”, we instead phrased them as “How much would you sacrifice to obtain this opportunity?” Humans are innately loss-averse. Losing your iPhone will affect you more negatively and for longer, than if you won a $1000 lottery.
So, our question transformed into: “How distraught would you be if we no longer invited you to a future event?”, paired with the answers “Very”, “Somewhat”, and “Not at all”. Although I’m shy to say we got completely honest answers, the answers in which we did give allowed for them to follow-up and supplement why they felt that way, without us prompting them.
The only ‘unaddressed’ fallacy by this question – point #2 – was resolved by putting other methods in place to measure attention spans during the event, like the number of times people checked their phone per half hour or the number of unique people who were left alone for longer than a minute per half hour (excluding bio breaks).
Feedback
“How can we improve our event?” We received mostly logistical answers. Most of which we had already noticed either during the event or in our own post-mortem.
In rephrasing to, “How can we help you fall in love with our events?”, we helped our attendees focus on 2 things: (1) more creative responses and (2) deep frustrations that ‘singlehandedly’ broke their experience at the event.
And to prioritize the different facets of feedback, we based it off the answers from the questions:
“What was your favorite element of the event?”
And, “How distraught would you be if we no longer invited you to a future event?”
For the attendees who were excited about elements closely aligned with our mission, we put them higher on the list. There were many attendees who enjoyed our event for the food or the venue, though pertinent to the event’s success, fell short of our ultimate mission. That said, once in a while, there’s gold in the feedback from the latter cohort.
On the flip side, it may seem intuitive to prioritize the feedback of those who were “Very distraught” or “Not at all”. But they exist on two extremes of the spectrum. One, stalwart champions of our events. The other, emotionally detached from the success of our events. In my opinion, neither cohort see our product truly for both its pros and cons, but rather over-index on either the pros or the cons, respectively. On a slight tangent, this is very similar to how I prioritize which restaurants to go to or which books to read. So, we find ourselves prioritizing the feedback of the group that lie on the tipping point before they “fall in love” with our events.
Unscalability and Scalability
We did all of our feedback sessions in-person. No Survey Monkey. No Google Forms, Qualtrics, or Typeform. Why?
We could react to nuances in their answers, ask follow-up questions, and dig deeper.
We wanted to make sure our attendees felt that their feedback was valued, inspired by Google’s Project Aristotle.
And, in order to get a 100% response rate.
We got exactly what we expected. After our post-mortem, as well as during the preparation for our next event, I would DM/call/catch up with our previous attendees and tell them which feedback we used and how much we appreciated them helping us grow. For the feedback we didn’t use, I would break down what our rationale was for opting for a different direction, but at the same time, how their feedback helped evolve the discourse around our strategic direction. Though their advice was on the back burner now, I’ll be the first to let them know when we implement some element of it.
The flip side of this is that it looks extremely unscalable. You’re half-right. Our goal isn’t to scale now, as we’re still searching for product-market fit. But as you might notice, there are elements of this strategy that can scale really well.
In closing
Of course, our whole endeavor is on hold during this social distancing time, but the excitement in finding new and better ways to measure my assumptions never ceases. So, in the interim, I’ve personally carried some of these interactions online, in hopes of discovering something about virtual conversations.
Founders take on many different types of risk when creating a business. Subsequently, investors constantly put founders and their businesses under scrutiny using risk as a benchmark. In broad terms, in my experience, they largely fall under two categories: execution risk and market risk.
Where I first introduced the dichotomy of market and execution risk in the frame of idea-market fit.
Some Background
Contrary to popular belief, VCs are some of the most risk-averse people that I know. As an investor, the two goals are to:
Take calculated bets, via an investment thesis and diligence;
And de-risk each investment as much as possible.
From private equity to growth equity to venture capital, more and more investors are writing ‘discovery checks.’ Typically, funds write checks that are 2-4% of their fund size. For example, $100M fund usually write $2-4M initial checks. Yet, more and more investors are writing increasingly smaller check sizes (0.1-0.5% fund size). In the $100M fund example, that’s $100-500K checks. This result is a function of FOMO (fear of missing out), as well as a proving grounds for founders before the fund’s partners put in their core dollar. Admittedly, this upstream effect does lead to:
Less diligence before checks are written (closing within 48-72 hours on the extreme end, and inevitably, more buyer’s remorse);
Less bandwidth allotted per portfolio startup (even less for startups given discovery checks);
The risks for a startup investor are fairly obvious, and so are the rewards. Effectively, an early-stage investor is betting millions of dollars on a stranger’s claim. But not all risks are the same.
In the eyes of a VC, an execution risk is categorically less risky than a market risk. Furthermore, even within the category of execution, a product risk is usually less risky than a team risk.
Execution Risk
Why are more and more early-stage investors defaulting to enterprise over consumer startups?
Two reasons.
Enterprise startups often run on a SaaS (software-as-service) subscription business model. There will always be recurring revenue, assuming the product makes sense. For an investor, that’s foreseeable ROI.
It’s an execution risk, not a market risk. Often times, an enterprise tech startup is the culmination of existing frustrations prevalent in the respective industry already. And therefore, have reasonably stable distribution channels and go-to-market strategies.
Using discovery checks, and playing pre-core business, VCs can evaluate team risk. Between the discovery check and their usual ‘core checks’, VCs can also test their initial hypotheses on their founders.
As a startup grows, especially after realizing product-market fit, market risk becomes more of a product risk. Best illustrated by market share, product risk is when a product fails to meet the expectations of their (target) customers. It can be evaluated via a permutation of key metrics, like unit economics, NPS, retention and churn rate. There is an element of technological risk early on in the startup lifecycle for deep tech ventures, but admittedly, it’s not a vertical I have my finger on the pulse for and can share insight into.
Given that VCs are either ex-operators or have seen a breadth of startup life-cycles, VCs can best use their experience to mitigate a startup’s execution risk.
Market Risk
Market risk requires a prediction of human/market behavior. And unfortunately, the vast majority of investors can predict about the constant evolution of human behavior as well as a founder can. What does that mean? Founders and VCs are walking hand-in-hand to gain market experience. It, quite excitingly, is an innovator’s Rubrik’s cube to solve.
Market risk is frequently attributed to consumer tech products. In an increasing proliferation of consumer startups, consumers have become more expensive to acquire and harder to retain. Distribution channels change frequently and are determined by political, economic, technological, and social trends.
In Closing
Every VC specializes in tackling a certain kind of risk. But founders must quickly adapt, prioritize, and tackle all the above risks at some point in the founding journey. As Reid Hoffman, co-founder of LinkedIn, famously said:
“An entrepreneur is someone who will jump off a cliff and assemble an airplane on the way down.”
In the first startup I joined, we messed up our initial business model by not providing a reason for small- and medium-sized business (SMB) owners to stay. We created a marketplace between SMBs to transact with each other. But, after the first one to three transactions, they had no need for our platform. The scary thing about marketplaces isn’t that you’re connecting suppliers to their demand network, but not providing any bonuses after onboarding – a reason to stay.
Some of the stickiest companies are marketplaces because they provide that reason to stay. More often than not, providing a lovable product so convenient, it’s much easier to use the marketplace platform than to do the transaction themselves, and an easy, passive way to be discovered by future clients/customers that would be much more difficult on their own.
Why Multiplayer Video Games Work
In his book The Messy Middle, Scott Belsky, Chief Product Officer at Adobe and founder of Behance (acq. by Adobe), a discovery platform where creatives can showcase their portfolios and engage with others’, shares that when crafting the ‘first mile’ experience, you need to optimize for three questions:
Why are your customers here?
What can they accomplish?
What can they do next?
Arguably, I believe that founders should always have these three questions hovering above their product strategies, beyond the ‘first mile’, only embedded more implicitly. Video games do an amazing job in this regard, especially massively multiplayer online role-playing games, or MMORPGs for short.
Why play the game? Find escape and sanctuary to be someone players want to be but can’t in the confines of reality.
What can they accomplish? Achieve that endgame that players see in the trailers and in the tutorial (the onboarding for an MMORPG user). The endgame is self-defined as well. Of course, the game optimizes for the power creep meta endgame. Yet, players can always opt for a ‘destiny’, a story, they find compelling, like becoming a fashionista, a wealthy merchant, a mentor, a content creator, and with faster computing systems and more robust infrastructure, a contributor to the game itself, through user-generated content (UGC). The Steam Workshop is an excellent example of UGC.
What can they do next? Level up their character and gear. Tackle the next quest – main or side – towards something larger than themselves. There’s always a defined goal, as well as actionable steps and additional incentives laid out for the players. This creates high retention value – a reason to stay.
The same is true for many other types of genres of multiplayer games – multiplayer online battle arenas (MOBA), battle royale (BR), first-person shooters (FPS), and more. It’s just the narrative of the endgame may change a little towards leaderboard domination. E-sports, content creation, and live streaming then offers a new tier of recognition and endgame for many veteran players.
Back to Marketplaces
I’ve always argued that as a founder, you want to focus on unscalable wins before thinking of scale pre-product-market fit. Focus on the individual experiences. As Li Jin, partner at the reputable a16z, wrote in a post about the passion economy, “[great founders] view individuality as a feature, not a bug.” The best marketplaces, like Uber, Airbnb, and Medium, started off focusing on the unscalable wins for a small individual subset of their potential users. These products offered their early users a reason to stay:
(Additional) Incentives and tools, to make their stay worthwhile;
Discovery platform to help them grow their brand and customer base, actively and passively;
And, subsequent community and network effects.
Early adopters jump on a new product, as fast as they jump off one. They’re finicky. They’re window shoppers, but at the same time, the most willing and likely to try out your product. Luckily and unluckily, the San Francisco Bay Area has no shortage of these folks, and being a tech startup, with its initial user base here, often inflates your early metrics. In short, the goal of your product is to make these technological butterflies fall madly in love with you and your product. That’s the tough part, but it’ll also mean you’ve found product-market fit (PMF).
Where do we find ‘love’?
Instead of a minimum viable product, or MVP, Jiaona Zhang, Senior Director of Product at WeWork, in her First Round Review piece, chases the “pixie dust”, or what I like to call the secret sauce – a truly unique, money-making insight. This magic is found through diligent iteration on consumer feedback, especially in the beta stages of a product. During the beta, users have the serendipity to discover “that magical moment in the user journey where the user realizes that this product is different from anything else they’ve ever experienced”. Her framework, designed from the perspective of the consumer:
Wouldn’t it be cool if users could [a process/action that would 10X their lives]?
What We Learned
The same was true for us at Localwise. Of course, we were motivated by poor retention metrics. But, we learned what businesses truly needed by asking each of them in person, as well as flyering (and getting rejected, or worse, ignored) to college students and to shops. So, still deeply in love with the community we built, we found that need when connecting local talent to SMBs. For businesses with high churn rate with temporary employees and a need to build a brand, that was their reason to stay.
I was recently inspired by a fascinating conversation between Mike Maples Jr., co-founder and partner at Floodgate, and Andy Rachleff, co-founder of Benchmark Capital and Wealthfront, but more interestingly, the founder of the term, product-market fit, or PMF – a term that signifies when a product is recognized by a strong demand in the market. Over the years, there have been various ways entrepreneurs, go-to-market strategists, and investors have defined when an idea reaches product-market fit. But before I dive into the PMF, let’s take a look at market definitions first, which admittedly is a step off the beaten path.
The Markets
How I Like to Think about Market Sizes. *Not drawn to scale
Traditionally, the total addressable market (TAM), serviceable addressable market (SAM), and the serviceable obtainable market (SOM) are defined according to the geographic location of your market. It makes sense – your market is as big as where you can offer the service. But now, in an increasingly connected world, technologies are less and less inhibited by the geographical boundaries that plagued the decades before. That said, there are still cultural, social and economic differences when accessing new demographics, which is why I like to characterize the TAM, SAM, and SOM by psychological resistances to new ideas. The TAM is still defined by the total upside potential of a product, where it still excludes laggards, or folks who would most likely never (seek to) use your product. The SAM is construed of people who would use the product after three to five friends in their network recommend and are using the product themselves. And finally, the SOM consists of customers who are desperate, as Andy Rachleff called it, for your product. They have spent sweat, blood, and tears finding or building their own solution. They have already traversed the idea maze themselves and put the dollar (or the euro, peso, krone, pound, yen, RMB, BTC, ETH… you get my point) here their mouth is at. And here, in the SOM, is where you find your product-market fit.
Product-Market Fit
PMF is most noticeable on the hockey stick curve. Before PMF, traction is slow and looks very much like the blade of a hockey stick. And after PMF, traction skyrockets and exemplifies exponential growth.
The Hockey Stick Curve
While there are many heuristics to assess PMF across different verticals, I’m the most fluent in consumer tech where I’ve spent most of my time in. And in consumer tech, I’d like to underscore the notion of ‘exponential organic growth’, and subsequently, a short analysis on each word of that phrase.
Exponential is probably the most straight-forward, where at the early stages of a business, we’re looking for rapidly compounding growth.
Organic growth, as opposed to paid growth, is a measurement for word-of-mouth. Investors tend to measure the effectiveness of a product by its virality from its initial customers to its nth customer – growth that is achieved without directly spending (ad) dollars on acquiring the new customers.
Growth is something I break down into – retention and adoption. Increasing adoption is great as measured by the growth of total users on consumer platform or for a consumer product, but focusing only on adoption leads to a leaky funnel, or in my case, trying to hold too many groceries in my hand without a shopping cart. Every time I grab another item on the shopping list, I drop some other item I was already trying to balance and hold. Of course, focusing only on retention means there’s no growth, which for keeping your best friend circle is fine (unless you want a thousand BFFs), but not for growing a startup.
Below are some growth signs to pay attention to signify that your product is near/at PMF:
Retention
Adoption
> 25% DAU/MAU
100s of organic signups/day
40% are active day after signup
> 30% MoM growth
Usage 3 days out of every week
“Idea-Market Fit”
As a founder with an ambitious idea, reaching product-market fit is a great goal to have, but the truth is PMF is a mystical beast – a chimera – in and of itself. Market demands change; what satisfied the definition of PMF a decade ago may not satisfy it now and will most likely not satisfy it ten years from now. Many studies have shown that most startups don’t fail from technological risk, but rather the inability to reach PMF, which ends up leading to lack of investor interest, demotivation, and the founding team falling apart. And quite obviously, before you reach PMF, the hardest part about starting a business is reaching PMF, or what Peter Thiel and many call the Zero to One. I’ll dive into the lessons I learned about the journey to “1” in future posts, but for the purpose of this post, I’m going to focus on the “0” – or what I like to call, “idea-market fit“, or IMF.
What differentiates a good idea from a great money-making idea? I’m going to borrow Andy’s thought calculus exercise. In a 2×2 matrix with right/wrong on one axis and consensus and non-consensus on the other, “you want to be right on the non-consensus.”
Andy Rachleff’s 2×2 Startup Idea Matrix
Why? Discounting the situations where you’re wrong (because you don’t make much, if any money), if you’re right on consensus, it means the market’s already mature, and perfect competition in a capitalistic market squeezes you out of your profit margins. If you do pursue this option as a founder, you’re more or less tackling an execution risk. On the other hand, if you’re right on the non-consensus, the market is still nascent, and you have the potential for monopolistic control of the market. In other words, you’re taking a market risk.
It definitely isn’t intuitive. At the very least, it wasn’t to me when I was on the operating side of the table. I wanted validation. When I was at Localwise helping build a community of local talent, I wanted people to say “I totally agree” or “You’re onto something.” But often times, I just received friction and resistance, with the toughest to receive from some of my friends.
“No one would ever buy that.”
“You’re wasting your time.”
“When are you going to get a real job?”
And at some points in time, I did think, “Maybe they’re right.” Until I started meeting a few people who thought a hiring destination for local mom-and-pop shops wasn’t a bad idea, and especially when small business owners started opening up about their frustrations. Hiring platforms, at that time, focused on the sexier brands and companies to get more demand side traction – the Googles, the Big Four’s, or the Bains, but had seemingly completely underrepresented the population of local businesses. Even if these SMBs were on these other platforms, they were overshadowed by the presence of bigger brands.
When validating startup ideas, you don’t want consensus. If your idea is truly revolutionary, people have yet to be conditioned to accept the idea. Take Uber or Airbnb, for example. If you asked the average person if they would use such a product, most would have thought that you’d be crazy to have a stranger sharing a car ride or home with them. These days, take e-sports or streaming. If someone told me in my pre-teen days that I could make a living off of playing video games, I’d most likely think I was dreaming. After all, I grew up playing Snake on my dad’s Motorola Razr, which admittedly seems to have made a return to the markets.
IMF is about challenging convention and the status quo. That’s what makes an idea revolutionary, or as people in Silicon Valley like to call it, disruptive. A crazy good idea challenges the explicit and implicit biases we have about society and ourselves. In other words, we have to detect the deception we bestow onto ourselves to find the gems in the rough, which Josh Wolfe of Lux Capital explains in his 2019 Lux Annual Dinner Talk – one of the best VC thesis-driven thought pieces I’ve ever seen.
In closing
As a geeky quote collector, I’d like to close this piece not in my own words, but in the words of three brilliant investors who have a few more patches of scar tissue on their back than I do now.
“Most of the big breakthrough technologies/companies seem crazy at first: PCs, the internet, Bitcoin, Airbnb, Uber, 140 characters…you are investing in things that look like they are just nuts… it has to be something where, when people look at it, at first they say, ‘I don’t get it, I don’t understand it. I think it’s too weird, I think it’s too unusual. “
“Breakthrough ideas have the traditionally been difficult to manage for two reasons: 1) innovative ideas fail far more than they succeed, and 2) innovative ideas are always controversial before they succeed. If everyone could instantly understand them, they wouldn’t be innovative.”
