Part 1: 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.

Innovation 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.

Source: https://moneyinc.com/differences-between-stocks-and-bonds/

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.

In the next part in this series I’ll discuss how you can design an org chart and product teams that create an explicit balance between optimization and innovation. If you’re ready for that, go ahead and jump right in. And for broader context, here’s a list of all four parts in this series.

Part 4: Product Development for Innovation

Modern software companies follow a variety of common conventions to scale quickly and efficiently. For example, most software companies have a defined and documented approach for engineers when it comes to writing, reviewing, editing, and deploying new code. It’s important to settle on some standards and procedures for software development because it means a company can write code quicker, reduce mistakes that are inherent in writing code, and provide a better working environment for software developers. The end result is more and better products delivered to the customer, which in turn is good for the business.

However, standardization of a product development process is uncommon within startups. Most companies lack a clear procedure for taking an idea and turning it into a high quality, shippable product. What typically happens is product teams form and are left on their own to figure out how they want to drive new product development. For example, who is responsible for conducting customer research, when, and how should it be conducted? How does a team come up with an initial prototype for a new product? How do you iterate on it over time? In what ways can you maintain clear internal communication with key stakeholders as the product is being built? When and how do you come up with the go-to-market plan for the product? A well-designed product development process will have an answer for each of these questions and will help you ship more and better products to your customers. Without such standards, each product team will build products through different methods, leading to inconsistent product delivery timelines and inconsistent product quality. The last thing a startup needs is more unpredictability.

I created the following content to prevent unnecessary churn when trying to create new innovative products. It describes a product development process I’ve refined over the years and use on a day-to-day basis when building compelling products customers love. The process is described in a way that will make it clear and easy to implement within your company. It is specifically designed for building large customer-facing features where “large” is defined as a product that requires 1 month or more of engineering time to complete.

Common Product Development Issues

First, it’s useful to point out the ways in which product development is typically broken or inefficient at young technology companies. Here are the common issues that I tend to see at startups:

  1. The value you want to create for your customer has not been clearly articulated upfront.
  2. Projects get “blown up” late in development due to large communication gaps during development.
  3. Creating the first product prototype takes far too long, leading to a lull in the pace of development.
  4. Customers aren’t being talked to enough, leading to products that don’t adequately reflect customer wants and needs.
  5. The project team building the product doesn’t have a clear escalation path to get unblocked.

The below process has been designed to explicitly solve or greatly mitigate each of the above issues when developing new products.

Guiding Principles

In addition to solving common product development pitfalls, this method of developing products is rooted in a set of guiding principles which further prevents the above issues and gives product teams a common language to use when describing how they build product:

  1. “Work backwards” from the customer: Start with intense focus and clarity on the value the company wants to create for customers as opposed to thinking about the value the company wants to create for itself. The belief is that if a startup makes the customer very satisfied, customers will engage deeper with the product, which leads to an increase in the key business metrics. Amazon is the best example of a company that begins product development with an intense focus on value to the customer.
  2. Collaborative: All key functions (e.g. product, design, engineering, and customer support) are present from beginning to end since each function provides a unique and valuable perspective. That means everyone must own the outcome of the product— e.g. engineering should care just as much about the quality of the user experience as a designer should. I don’t believe in the “PM as the CEO of the product” idea because most PMs don’t have CEO quality judgement. Software development is best conducted as a team sport.
  3. Interactive prototypes: A product development process should aim towards creating interactive prototypes worthy of being tested on actual customers, as quickly as possible. The reason is that startups learn the most when testing an interactive prototype on customers. Interactive can mean working code or a high fidelity visual prototype using something like Framer, which strings together visual designs through clickable hotspots.
  4. Measure and learn: Once a product is shipped, you’ll want to measure the outcome to see if it created the expected impact. If not, you can investigate why that is the case and use those insights to either deprecate the product, improve it, or carry forward those learnings into future products that are built. Shipping products without understanding the impact is unacceptable.

A Repeatable Process for Innovation

First, I’ll describe the process. Following the description is a visual concept. The product development process follows these steps:

  1. Begin with conducting Customer Research as part of “working backwards from the customer”. It’s through this research that you will refine the product hypotheses— i.e. what the product should do and why it should do it, what specific problems you’ll be solving for the customer, and what forms of delight you can provide. Customer Research can either be conducted by a PM or a designer, if your company doesn’t have a full-time research lead. Each conversation is 30 – 60 minutes and follows an open-ended format that allows for spontaneous discovery of rich customer insights. These insights should eventually make its way into product requirements.  
  2. In parallel, the lead Product Manager begins drafting product requirements (which also includes an Amazon-style press release). A draft of the product requirements and press release must be finished before starting the design sprint, which is how a product team develops its first testable prototype. The initial draft should be reviewed by the design and engineering leads, so they are familiar with it and can provide useful feedback. You want all key team members to be versed in what value you intend to create for the customer.
  3. Once Customer Research is complete, and a first draft of product requirements and the press release have been drafted, the team will then run a design sprint to quickly design the first testable prototype of the product. I selected the Google Design sprint method since it was created with the time constraints of a technology company in mind. The issue with most traditional design processes is that they can take weeks to months to get to a testable prototype. That timeframe simply doesn’t work within a startup. The Google Design Sprint method is the most effective that I’ve seen when going from 0 to 1 within a software company. The design sprint takes 1 week, at most.
  4. Once the design sprint is complete, the team can finalize the product requirements and Amazon-style press release so that the requirements and customer value are crystal clear before full development begins.
  5. The results from customer research and the design sprint are brought into a kickoff meeting to get everyone on the same page prior to the development process ramping up to 100%. A kickoff meeting should be no longer than 45 minutes and should be conducted shortly after the design sprint is completed (e.g. within 1 week). You’ll want all primary decision-makers involved so that there are no surprises, which could lead to the project being derailed later in development. Feedback from primary stakeholders should then be taken into account and incorporated into the product plans.
  6. Once development begins, the project team will present the latest prototype(s) (across all platforms— e.g. web, iOS, Android) and overall status of the project during weekly or bi-weekly product reviews until the product is finished and launched to the public. Product reviews are also 45 minutes max and should take significantly less time (e.g. 20 – 30 minutes), if run efficiently. The purpose of product reviews is to maintain coordination throughout the project, give the project team a regular interface with the leadership so that they can ask for help or support when needed, and to incorporate feedback on the prototypes iteratively.

This is a conceptual diagram for the product development process from start to finish. It’s very useful for project leads (especially the product manager) to have this process memorized, so that they always know what should be coming next in the development process. If run well, it should only take 2-3 weeks to finish customer research, the design sprint, and have a kickoff meeting session. Keep in mind that this is for new, innovative products/features, so getting to the point of alignment on a medium fidelity prototype is impressive in such a short timeframe. From there, development starts to move quickly until the product is ready to launch.

Templates

Here’s the full list of templates that you can used in conjunction with the process laid out above. This will allow you to incorporate some or all aspects of this process into your own team or company.

Wrap Up

Thanks to an abundance of data storage, analysis, and visualization tools, startups today have the ability to make rapid improvements to nearly every aspect of their business. However, this overabundance has led to a significant bias in that startups now lean on structured data too much. So much so, in fact, that some of the fundamentals of building innovative products, such as rigorous customer development, have fallen by the wayside. One of the byproducts of this data obsession is that many startups try to optimize their way towards success through relentless A/B testing. This typically pulls them further away from essential insights and truths that they might discover, if they spent less time analyzing structured data from a database and more time collating the unstructured data that can be discovered when talking to customers.

The good news is that data over-reliance can be easily corrected with a shift in mindset and some of the tools and guides I provided in this four part series. In terms of next steps, I hope you take a few key steps from here. First, move forward with designing a company-wide org chart that creates an explicit balance between optimization efforts and innovation efforts. It’s also critical to make wise decisions with the types of experiments to run and avoid running tests that will never meaningfully improve your business. And finally, that you adopt some version of the repeatable product development process I shared, so that you can innovate much more effectively for the betterment of your customers and your business.

As reference, here are all posts in the series in case you’d like to read them again: