Field Guide

Field Guide

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Introduction

Growth

It might surprise you to hear that effective growth isn’t all about the numbers. Focusing exclusively on data and optimization can subtly overtake innovation and leave you with a lackluster product. Besides A/B testing, long-term growth requires outsized bets and an organization designed for innovation. 


This chapter will explore product testing techniques, designing an organization for growth, and creating content communities to help you gain traction with users.

Chapter Authors

Andy Johns
Andy Johns
Andy Johns
Andy Johns
Andy Johns
Andy Johns
Andy Johns
Andy Johns
Andy Johns
Andy Johns

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Balancing Optimization and Innovation
Andy Johns
Start module

A Single-Minded Perspective on Growth

“Our industry does not respect tradition— it only respects innovation.”

That’s what Satya Nadella wrote in his opening email to the company shortly after becoming Microsoft’s new CEO. It was a clear call to arms that Microsoft needed to reignite innovation in order to scale the company after roughly 15 years of stagnation. The price of Microsoft’s stock has increased ~3x since he came back because the market seems pleased with Microsoft’s sharpened focus, progress made in the cloud business, and willingness to change how it used to do things in order to compete in the future. Some of this could be window dressing or marketing speak, but the changes happening at Microsoft seem genuine.

Satya said nothing about doubling down on what’s already working in order to get more juice out of the squeeze. Rather, he ended the email by emphasizing the need for clarity of focus on new innovations and on changing the culture which, for the most part, was focused on preserving the status quo for over a decade. It’s not unheard of that a large company often forgets how to innovate.

I haven’t spent enough time at companies with 1,000+ employees to speak deeply about the dynamics of large company stagnation, but I can speak to it happening at early-stage startups. In particular, I find it interesting that the same two problems Satya outlined for Microsoft often appear within early stage startups as well: i.e. the culture becomes comfortable with the status quo and the company loses its ability to innovate.

How does it happen? When a startup becomes obsessed with and designed around data and optimization. Today, every 50 - 100+ person startup has multiple business intelligence tools, off-the-shelf A/B testing tools, a data science team, and product managers who know much more about writing SQL than they do about interviewing customers.

In fact, I kept score while interviewing PM candidates in 2017. I spoke with 67 product managers. About 50 of them were reasonably proficient in SQL and could write a few queries on the spot. Guess how many knew how to conduct customer development? Three. That’s it. Only three product managers could proficiently describe the purpose, process, and outcomes from customer development. 75% could write SQL, but only 4% knew how to properly interview a customer. It’s a small sample size, but the gap is large.

Here’s why that’s bad: Most startups, just like large companies, need to go through continuous phases of innovation in order to create 2x+ step changes in the potential for their business. The process of going from 0 to 1 with their first product is an innovation. It’s what allows the company to get off the ground. Sometimes, that original innovation is enough to carry them from seed to IPO. But that is incredibly rare. What’s more common is that startups need to innovate several times over in order to create step changes that help them scale from early stage to growth stage and from growth stage to a publicly traded company.

Over the last 10 years, there has been a massive overcorrection in the direction of optimization based on broad availability of data, leading me to find that most PMs are incapable of effectively deriving insights from customer conversations and most startups are incapable of producing new product innovations beyond the initial product that they take to market. They’re great at A/B testing, but not great at creating new features based on customer insights and a leap of faith.

To put it plainly, growing through data analysis and A/B testing isn’t the only path to future growth. While it seems obvious, I see very few startups designed for innovation, which may be the biggest driver to new growth for your business. Do you think Facebook would be at its current scale without innovations like News Feed? Community-driven translations to expand globally? Or the developers platform? The answer is obviously “no”. Take a look at MAU acceleration beginning in 2007 / 2008. That coincides with the launch of the international translations app, which allow Facebook users to crowdsource the translation of the product. It took several months to build and a few years of ongoing maintenance and development to mature the product. That innovation led to a boom in active user growth.

The point I’m making is that today’s startups very quickly fall into the optimization trap where they think future growth will largely come from optimizing their existing product. The better approach is finding the right balance between optimization and innovation since both methods can produce future growth.

By the time you’re done with this series of blog posts, you’ll have the knowledge and tools you need to do the following:

  1. Design a company-wide org chart that creates an explicit balance between optimization efforts and innovation efforts
  2. Wisely select the “right” types of experiments to run to increase your chances of improving growth through optimization
  3. Implement a repeatable product development process for creating new, innovative features

Optimization Versus Innovation

We should first start with a more detailed explanation of the difference between optimization and innovation. Optimization is when a startup iterates on its existing products or services to squeeze more juice out of the orange. Typically, the results of optimization are incremental in nature.

If they are incremental in nature, then why do them? Well, because many small optimizations can accrue into large long-term results when you allow those optimizations to compound.

Here’s a simple example. In the below graph, I compare the 12 month growth in monthly active users (MAUs) in 4 hypothetical cases. The blue line is the base case where the monthly growth rate is slowly declining, leading to flattening growth. The red line is for sustained 10% month-over-month growth (MoM), yellow is sustained 12% MoM, and green is sustained 14% MoM. If a startup can optimize its way towards a slightly higher and sustained rate of growth, the compounded outcome is very different relative to the base case. In fact, this is what we did in 2009 at Facebook. Our growth team focused on optimizing our way towards a sustained 2% week-over-week growth rate because we knew that we would grow from ~100 million MAUs to ~300 million MAUs in 12 months if we did so. This happened to be the company-wide goal for that year.

nnovation is when a company embarks on building entirely new products or services for existing customers or for a new segment of customers. Innovation can also involve expanding into an entirely new business line. However, this happens so rarely (hello, Amazon!) that I won’t focus on this definition for the time being. Additionally, innovation can create step change improvements in the trajectory of the company, although they are much more difficult to discover and successfully execute on.

I’ve taken the same scenario above, but added in a 5th option which is labeled as “with innovation” in the below graph. What this does is take the base growth rate scenario and applies a 2x multiplier to growth midway through the year (e.g. you build a new feature, such as Facebook’s News Feed and it leads to a step change in monthly active usage). This assumes no optimizations along the way.

The point isn’t that you should pick one approach to growth over the other. Rather, the ideal outcome (and most realistic) is a healthy combination of both optimization and innovation. In the below scenario, I assumed that a segment of the company is working on optimizing the existing products and services to sustain 10% MoM growth and another segment is working on new product innovation that leads to a 50% bump in MAUs midway through the year. This scenario is plotted as a black dashed line on the graph.

Picking a Path

The appropriate question to ask is, “For my company, should I be innovating or optimizing?”

For Seed and Series A startups the practical reality is that you are headcount constrained into picking one over the other because you’ll have less than 20 employees. Prior to establishing product market fit, you’ll be entirely focused on innovation because you’ve yet to figure out the new technology that delivers something better, faster, cheaper, and more convenient relative to the alternatives in the market. Consequently, you’ll have very little growth or customers to optimize on top of, so don’t waste your time optimizing if you don’t already have exponential organic growth.

As a company matures to the point of Series B and beyond (sometimes with a large Series A) it can hire enough people that it can contemplate doing more than one thing at a time. From my experience that’s at the point in which a consumer software company has 30 or more employees. On average, about half of the employees will be engineers, so that means you’ll have 15 people that can do the building. With 15 people doing the building you can divide them amongst 3-4 teams— e.g. 2 product teams, an infrastructure team, and a floating pool of engineers needed for miscellaneous tasks and on-call work.

When a company reaches 100 employees it can certainly multi-task. Its 50 engineers can be subdivided amongst 2-3 well-staffed product teams, 2-3 infrastructure teams, and still be able to manage on-call support and miscellaneous tasks.

Stocks and Bonds

Assuming a company is able to reach the scale of 30+ employees and is now capable of walking and chewing gum at the same time, the question becomes, “How do you allocate those people in terms of optimization versus innovation?” I like to use investing analogies when thinking through this decision.

Most investors should have an investment portfolio that maximizes their returns given the amount of risk that is appropriate for them to take (this concept is known as Modern Portfolio Theory). Put in simple terms, it stipulates that you’ll want a diversified portfolio comprised of a mix of higher risk, higher return investments (e.g. stocks) and lower risk, lower return investments (e.g. bonds). Depending on the level of risk you can afford to take, you’ll want to shift the allocation towards certain investments and away from others. For example, if I’m 70 and ready to retire, I should be taking very little risk and will want a portfolio weighted heavily towards low risk, low return investments (bonds). If I’m 30 and putting money into a retirement account that I’ll use  30 to 40 years from now, then I should be taking on more risk to generate more returns during that long time horizon (i.e. more stocks).

I hope you are starting to see how this investing analogy applies to your startup thinking. Innovation is your stocks and optimization is your bonds. The question to ask is, “What proportion of my company’s focus should be on optimization versus innovation?”

If you’re building a seed stage startup, then you’ll solely be focused on innovation (all stocks and no bonds) because you’re trying to build something new and innovative that finds product market fit. If you’re working on a series A or series B startup with clear indicators of product market fit (i.e. exponential organic growth), then you should be considering the trade-off between optimization and innovation.

Facebook is a good example of optimization and innovation at play. While I was at the company (2008-2010), we did a bit of both. The Growth Team was focused predominantly on optimization by improving sign up conversion rates, new user onboarding, reactivated user onboarding, getting people to add more friends, and a vast library of miscellaneous A/B tests for the sake of getting more users. Meanwhile, several of the core product teams were pushing out big innovations like the first smartphone app, various News Feed innovations, large enhancements to photos, and the developer’s platform.

Creating Content Communities
Andy Johns
Start module

Creating Content Communities

Flywheel Fundamentals

Each year a batch of entrepreneurs set out to build the next great online community. Some attempt to build large horizontal platforms where users engage on topics ranging from immunotherapy to the Boston Celtics. Reddit would fit that description. Others seek to create vertical communities tailored to a particular subject and audience, such as Wheelwell for car enthusiasts. 


There are many reasons to be on the prowl for the next great online community, either as an investor or operator. A leading reason is that the winners tend to be massive. Another reason is that successful online communities seem to grow perpetually through organic growth, which is the holy grail in startup land.


The unstoppable momentum of organic growth, driven by users creating new content on the platform, is what is often referred to as the “flywheel”. In technical terms, a flywheel is a device that stores energy. The more it’s revved up, the more energy it stores and the longer it can spin unaided. It sounds like magic, but there’s a simple explanation for it—at least as simple as physics goes.


What a flywheel does is it converts kinetic energy into potential energy. Kinetic energy is the energy that an object possesses due to its movement. Potential energy is the energy stored by the object due to its position. Archery provides a basic example. When you pull the bowstring back, you can say that the arrow has potential energy. And when it is released the arrow has kinetic energy. 


Another key point about flywheels is that the bigger it is and the faster it spins, the more energy it stores and the longer it takes to slow down. Online communities that have established a “content flywheel” behave similarly. 


Let’s take Reddit as an example. The stockpile of registered users is Reddit’s version of potential energy. When those users create content and the content is discovered in Google, shared via social media, or distributed online through other means, then the “arrow has been shot” so-to-speak. In this analogy, the user-generated content is kinetic energy. When new content is created it fetches new traffic and users into the platform, increasing the size of the flywheel and accelerating its rotational energy. It becomes self-propagating. And once that kicks in, good luck stopping it.


To put the power of a content flywheel in perspective, Reddit recently claimed 430M monthly active users. It’s 15 years old and still spreading its wings. 


But how does one create such a platform driven by perpetual organic growth? Clearly, it can’t all be distilled down into a simple formula and bottled up in cans to be sold next to ketchup and mustard. It’s no commodity. There is no “secret sauce” that only Italian grandmothers and a few exceptional founders figure out. However, I do believe some of the ingredients are knowable and repeatable. This playbook will describe what those ingredients are, how they work, and what you can do about them in pursuit of building your own startup fueled by a flywheel. 

The Ingredients

I believe there are seven primary ingredients when it comes to building flywheels in software. Six of them are knowable and I’ll go into detail on each below. One ingredient is something only you, the founder, can figure out. It’s the “secret sauce” that makes your community stand out relative to the rest and is your unique innovation. 


Here’s the full set of seven ingredients: 


  1. Basic flywheel design (flywheel 1.0): a high-level description of how your community acquires users, gets them to consume content, converts some users to creators of content, and how that new content leads to new traffic and users.
  2. Consumption flywheel design (flywheel 2.0): a secondary flywheel that the product uses to accelerate the rate of content consumed within the community.  
  3. Creation flywheel design (flywheel 3.0): a tertiary flywheel that the product uses to accelerate the rate of content created within the community. 
  4. A “cold start” solution: a strategy for putting the initial momentum in the flywheel by identifying early adopters and coaxing them into becoming the first creators within the community.
  5. Moderation and quality control: human-based and software-based solutions that maintain a bar of quality content creation and quality user interaction. 
  6. Beachheads and vertical expansion: a strategy for establishing your initial user and content beachhead and a method for expanding into user adjacencies and new content verticals.
  7. “Secret Sauce”: the unique “hook” that makes your community attractive, fun, and worthy of engaging with and that would entice users to ditch other communities in favor of yours. 


Let’s jump into each, how they work, and what you can do about them. 

Designing the Content Flywheel

Let’s assume at this point that you, the founder, have already decided on the type of content community you want to build. It could be for scientists, sports enthusiasts, Chief Information Officers, or be the world’s next horizontal platform to compete with Reddit, Youtube, and so on. It doesn’t matter which option you’ve selected. What matters is you’ve vowed to create a dent in the content universe.


You begin toiling away in your preferred design tool with product prototypes, first starting with low-resolution concepts. After a bit of user testing, you’ve identified a variety of UX snafus, shuffled around the deck chairs a bit, and arrive at a prototype that’s ready for development.


The designs turn into an alpha. You test it with more users. The alpha becomes a beta. You test it with more users. Finally, you’re ready to launch it. You turn on the TV and play the iconic scene from Field of Dreams where the spirit of Shoeless Joe Jackson whispers, “If you build it, he will come.” And like ghosts emerging from a cornfield, users show up and engage with each other like long lost friends. Hours and hours of lively conversations are created and your community is flush with chatter. 


Except, that’s not what happens. Conversations don’t spontaneously ignite and engagement is at a whisper. You’ve built it, but no one has come. 


This is where your journey to creating the flywheel begins.Kevin Costner’s journey began with designing a field, but yours begins with designing your flywheel and hand-picking your early adopters.

Flywheel 1.0 - The Fundamentals

Don’t get fancy. Start with 2,000-year-old technology and 500-year-old technology; paper and pencil. You don’t need modern software to design your first flywheel, so shut your laptop.


I believe there are four atomic units of a 1.0 flywheel:

  1. Acquire: how users are acquired into the community (e.g. they sign up)
  2. Consume: the mechanisms that drive content consumption (e.g. a newsfeed)
  3. Create: the mechanisms that entice users to create content (e.g. social status)
  4. Harvest: how new content created leads to more inbound growth (e.g. SEO)


Those elements represent the common building blocks of a content flywheel. 


It begins with a visitor signing up to use the product. After a user has signed up, the user will gain access to the stockpile of content that exists in the application, which they may start consuming. Note that the stockpile of content won’t exist at first. I’ll get to that in the section about solving the cold start problem. 


After consuming enough content, some users evolve into creators of content. The content the user creates leads to new traffic headed your way. A common example would be content indexed in a search engine or shared on social media, which fetches more traffic back to your community. Lastly, some of the newly harvested traffic will convert into newly acquired users that sign up to be a part of the community.

With a sketch similar to this, you can begin with a simple conceptual understanding of the content flywheel for your application. Assuming your product has launched and has at least a few hundred users, you can then measure the baseline conversion rates (CVR) at each step in the flywheel. 

In the above example, the conversion rate (CVR) for the initial signup is 1.5%. Of the users that sign up, 20% of them go on to consume content in the application and 5% of consumers go on to become creators of content. The new content that is created then leads to new traffic generated for the application.


In this example, I chose the metric of visits per piece of content per month. A practical example would be a question answered on Quora or a thread posted on Reddit. In this case, each question on Quora or thread on Reddit would receive an average of 2 visits per month. Finally, the new traffic generated from the content created by new users leads to brand new users signing up for the product at a rate of 0.2%, which is a fairly common conversion rate for long-tail traffic coming from SEO.


The conversion rate to signup from this traffic is typically lower than traffic that goes directly to an application’s homepage, such as someone opting to go directly to Reddit.com and sign up. Visitors navigating directly to an app’s homepage have relatively high intent, likely because someone told them about the product, which is why the conversion rate is highest for direct homepage traffic.


And just like that, you have your first content flywheel designed and instrumented with empirical metrics. But your homework isn’t done yet. You’ve only completed the first of three assignments. And this one was the easiest since most online communities have a nearly identical 1.0 flywheel. In fact, you can just copy this flywheel and you’re off to a good start.

Continue reading Part 2: Accelerating Your Content Flywheel

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Look beyond the numbers to balance optimization and innovation in your first steps towards growth.