Tag Archives: startups

When and How to Build Second Products

This is part three in a series of posts related to some presentations I did for the TCV Engage Summit. The Summit gathered ~40 CPOs and product leaders to chat through topics centered around product development and product-led growth. This year, topics ranged broadly from incorporating AI to deliver world-class consumer experiences to defining and measuring different forms of community-powered growth. You can read parts 1 and 2 here and here.

In a previous post, I talked about how product work post-product/market fit shifts from zero to one innovation to features, growth, and scaling work. But a question founders and teams often ask is when do we start layering in innovation work again that creates new value props. In Reforge terms, we call this new product expansion. I recently did a talk for TCV’s Engage Summit where I explained the different types of product expansion, when to start building that second product with a new value prop, and how to know if it’s successful.

Why Second Products Matter So Much

Why do we even care about second products? Don’t some of the best companies in the world win with one dominant product? Well, increasingly that’s not the case. Companies can rarely ride one product into the IPO sunset anymore. Yes, the headlines are filled with many of these examples, such as Google in the 2000s or Zoom in the 2010s, but these examples reflect an environment that is becoming increasingly rare. The tech IPO narrative used to reflect stories that would include much of the below:

  • Large markets
  • Low or stagnant competition
  • Rapidly growing markets
  • Strong network effects or economies of scale
  • Scarce talent pools

A lot of those bullets can be explained by just the growth of the internet, and there being no entrenched internet-first competition. The maturity of the internet means most of these are no longer the case. Almost every recent tech IPO is multi-product at time of IPO, and the dynamics of their markets appear much different:

  • International competition
  • Multiple startups in the same space
  • Incumbents are tech native, no longer asleep, and copy what works from startups quickly
  • There is talent across a wide range of companies and skills
  • Network effects are no longer impenetrable

Uber, Instacart, Doordash, Unity, Klaviyo, Nubank, Toast and many other recent IPOs all reflect this new reality.

The Types of New Product Expansion

There are many ways for a company to expand its product offering, with different levels of difficulty. The main vectors on which product expansion should be evaluated is whether the expansion changes the product, changes the target market, or changes the core competencies required to deliver the product’s value. I highlight six different types of product expansion, in increasing levels of difficulty based on these vectors.

Expansion Type Product Market Core Competency Examples
Geography Existing New Existing Grubhub LA, Pinterest Brazil
Category Existing New Existing Whatnot Sneakers, Thumbtack Home
Format Existing Existing New Netflix Streaming, Snap Spectacles
New Value Prop New Existing Existing Uber Eats, Hubspot Sales
Platform Existing Mixed New Shopify App Store, Salesforce App Exchange
Strategic Diversification New New Existing AWS, Cash App

Geographic and category expansion skills are fairly well developed in software businesses. Companies build a deep understanding of how they achieved product/market fit in the first market, and make as few tweaks as possible to adapt the product/market fit to these adjacent audiences. Most marketplaces and social networks have executed these playbooks fairly well.

Format changes are usually only required around platform shifts already occurring or platform shifts a larger company is trying to drive. The last large one was mobile, and most internet companies were able to replicate their success in mobile.  Netflix and Snap have worked on more interesting format shifts, building on entirely new technologies to deliver their value props on new forms of media.

New value propositions are what we traditionally think of for second products, and will be the focus of the rest of this post. This is creating a new value proposition for your existing audience so that you can acquire, retain, and/or monetize them better. In extremely horizontal products, it may be wiser to launch a platform than build a lot of this new product value yourself, but this requires massive scale to attract external developers, and is very difficult to execute. I have written more about platforms here. Strategic diversification is a much rarer phenomenon where a core competency you have built internally is marketable for an entirely new value prop and audience, like Amazon leveraging its core ecommerce infrastructure to sell to other developers, or Square leveraging its financial expertise in SMBs to launch a consumer fintech product with Cash App.

New Product Value and S-Curve Sequencing

In my previous post, I talked about S-Curves. In that post, I mentioned that sequencing from an original S-Curve to a next S-Curve is the key to long term sustainable growth. That sequence can come from finding a new growth loop, but eventually that next S-Curve will require new product value to be created. This is what I realized when I joined Eventbrite. Eventbrite initially found this success with a content loop around event creation.

To continue its growth, instead of investing in new product value, Eventbrite kept grafting new growth loops onto this core loop to acquire more event creators and drive more ticket sales per event, creating a much more complicated growth model that looks like the below.


What became clear after building this model of how Eventbrite grows is that all of this effort would no longer drive the kind of growth Eventbrite needed to be successful on the public markets. We could no longer acquire event creators and ticket buyers fast enough, and we didn’t make enough money from them when we did. If we wanted to grow sustainably, we needed new products. And we probably needed them yesterday. It’s not that the company hadn’t ever tried to invest in new value props, but those that could create significant new growth had eluded them.

When To Invest in New Products

If you want to be proactive in thinking about when you should invest in building new products vs. diagnosing a growth problem and determining new product development requiring years of effort to be the solution, how do you do that? Well, the first step is tracking what impacts the need for a second product inside your company. Besides building a growth model and forecasting your growth from it, which I absolutely recommend you should do, what are the factors that contribute to how quickly you need to be investing in that second product after the first product finds product/market fit?

Historically, the factor that most people use as a heuristic is the business model. Traditionally consumer businesses have longer S-curves, so there is less of a need for a new product to drive growth. B2B requires suite expansion. Why does B2B require suite expansion? Well, they usually do not have network effects which makes marginal growth harder, of course, but the main reason is competition from bundled competitors. Acquisition, retention, and monetization potential of your first product is another reason B2B tends to expand earlier. Second products influence sales efficiency and profitability dramatically. Next is the size and growth of the market. If the market is large, your product can grow inside it for a long time. And if the market is growing fast, market growth can frequently drive enough company growth on its own, like, say, Shopify with ecommerce. The smaller the market is, the faster you need to expand the addressable market to grow. The last factor is how natural product adjacencies are for your first product. Generally, in B2B, product adjacencies are more obvious and less of a gamble to invest in. Launching successful consumer products is very hard with a very high failure rate.

But I’m going to show you why if you pay close attention to these other factors, the business model can be a red herring.

New Product Expansion by Business Model

Let’s break some examples down by business model and start with pure consumer businesses. Pinterest and Snapchat were compared a lot because we started scaling at around the same time. And even though they are both the same business model, you can see some of their attributes look a lot different. 

First, no one actively competed with Pinterest during its rise to be the primary way people discovered new content related to their interests. Snap, meanwhile faced an aggressive competitive response from Instagram as they grew to be a place where friends interacted around pictures. From a customer acquisition perspective, the companies grew in very different ways too. Pinterest grew by capturing users searching for things related to their interests on Google while Snapchat grew virally. Their retention strategies were also different. Pinterest primarily increased engagement by learning more about what you liked and recommending content better and better matched to your interests over time. This is usually a strong retention loop. Snapchat built out your friend graph, but didn’t really get much stronger after that. In fact, too many friends might be off putting. The most important difference was the monetization potential. Pinterest’s feed of content related to your interests is a perfect model for integrating advertising and commerce, the two best consumer business models. Disappearing photos however was not a good fit for either of those models, and likely best lent itself to subscriptions and virtual goods, both largely unproven at consumer internet scale. Lastly, Pinterest grew adjacencies by making the product work better with interests in different local geos and in different categories e.g. travel vs. fashion. Snap had similar geographic growth, but had some additional format and product adjacencies.

Okay, so let’s look at how Pinterest and Snapchat grew their product offering over time. We’ll focus on the consumer, not advertiser side of the equation for this example, though obviously both companies built advertising products. The companies launched around the same time, and launched their second products around the same time. Pinterest significantly evolved how its core product worked, changing both the acquisition and retention loops over time. Acquisition shifted from a content loop built on top of Facebook’s open graph to a content loop built on top of Google with SEO. The way Pinterest retained users also changed, from seeing what your friends were saving to getting recommended the best content related to your interests regardless of who saved it. Snapchat did not evolve their core product nearly as much. But Snapchat’s second product was a lot more successful. Snapchat Stories was a huge hit. Pinterest around the same time released Place Pins, a map based product that did not find product/market fit and was deprecated. Both companies also launched additional new products in the coming years. Snapchat found new product success again with Discover, and Pinterest again failed building a Q&A product around saved content.

So wait a minute? You’re telling me Snap succeeded multiple times in product expansion where Pinterest failed, yet the companies are valued at about the same. What gives? Well, it turns out Pinterest didn’t need to expand into new products because its initial product had great acquisition, retention, and monetization potential, albeit with some evolution on how they worked. Iterating on its initial value prop was the unlock, not creating new value props. In fact, the new product expansion work outside of expanding countries and categories was a distraction that probably prevented the core product from growing faster. The company might be worth double if it had not spent so much time trying to develop new products. Snap, however, probably would not have survived without product innovation because its first product had low monetization potential. They needed those new products to work, and they did.

Let’s look at an example in SaaS. I had the pleasure of working with both Figma and Canva as they were developing. I was an advisor to Canva starting in 2017, and got to work with Figma while I was a growth advisor at Greylock, which led the series A investment. It’s a fascinating example of two design tools targeting entirely different audiences, basically designers and non-designers. 

At the time of their launch, Figma was in a competitive space with legacy products from Adobe, and many tech companies were using Sketch. Theoretically, Adobe’s Photoshop was the competitor for Canva, but it was much too complicated for laypeople to use, and much of Canva’s pitch was that it was Photoshop “for the rest of us”. Both could acquire users by having creators share their designs. Figma looked like it would be a much higher retention product as it was multi-player from the start, and applicable to larger businesses. Canva was more of a single player and SMB tool. As a result of this, it looked like Figma would monetize a lot better, with a classic per seat model selling to enterprises, with Canva having a lot of single user subscriptions. 

At the time, investors didn’t think the design market was one of the larger markets out there (they were wrong), but everyone did think the category was high growth. Both companies had some nice theoretical adjacencies in terms of formats they could work in, new products they could create, and platform potential.

Both companies evolved how they acquired users over time, layering in sales, and Canva got a huge boost from SEO. Both companies also evolved their retention strategies. Figma became a tool not just designers to collaborate, but for those designers to collaborate with their peers in engineering and product. Canva created lots of ways for users to not start from scratch with community-provided templates and stock photos to leverage. 

Figma launched its first new product in 2019, called Figma Community. It intended to create  a Github-like product for designers, or perhaps a Dribbble competitor. It has not reached the company’s expectations. Canva launched Presentations in 2021, and it has become a heavily used product. Both companies have continued to invest in delivering new value props. Figma launched Figjam, a Miro competitor in 2021. It has not become the Miro killer the company imagined as of yet. Canva launched its video product also in 2021, and it continues to gain traction, along with a suite of other enterprise bundle features more recently, like a document editor.

So on paper, it looks like Figma’s in a competitive space. Canva is not in a competitive space. But while Figma has had mediocre product expansion and is still being sold for potentially $20 billion, Canva is grinding on building out a suite to succeed. Why? Because Figma’s product/market fit so quickly surpassed what was on the market that products ceased to become competitive over time. And while Canva didn’t have competitors, it became a substitute to the big incumbents Adobe and Microsoft, forcing them to build copycats and respond. Canva as a point solution likely loses out to their bundles if they don’t expand their suite successfully.

Time and time again, we see two things. One, companies in the same space may need to think about new value prop development much earlier than other seemingly similar companies. Two, we see new products not inflect company growth when they are a random bet on innovation. New products tend to work when they have to work for the company to succeed. If you can still grow your core product, they rarely get the focus they need to succeed, and furthermore, might be a less efficient use of resources than continuing to grow the core business. Figma will not live or die on the success or failure of Figjam. Canva might need these new products to be successful to stay competitive long-term. 

Portfolio approaches that recommend some percentage of development on innovation vs. features vs. growth vs. scaling tend not to be where massively successful second products come from. Understanding your growth model, and betting big on new product development when you sense the company needs it, tends to be the more successful approach.

Okay, let’s look at a marketplace example. Here is where you see how marketplace strategy has needed to evolve over time. Older marketplaces like Grubhub were extremely profitable because they did not facilitate the transaction beyond payments. More recent startups like Instacart have needed to manage a significant component of the delivery of the value prop, which means its monetization potential out of the gate is much worse. 

Similar to Snap vs. Pinterest, Grubhub’s initial market was so large and so profitable that all new product value expansion did was limit the potential of the core product. Pickup cannibalized search results and lowered activation rates, especially in some key markets like LA that allowed upstarts to gain traction, notably Postmates.

Instacart’s initial product however required so many complex operations that it found it could not eke out real profits while paying groceries and pickers. But it could expand its network to CPG advertisers, replicating grocery market slotting fees in a digital product. So these companies had very different paths to similar market caps despite both being labeled marketplaces.

Last, but not least, let’s look at a consumer subscription example. Duolingo and Calm launched around the same time as consumer subscription apps. Both are in competitive spaces that struggle with retention because building new habits is hard for consumers. The language market is however considerably larger than meditation.

Both companies evolved their acquisition strategy over time, but Duolingo got a lot more leverage out of virality, keeping their acquisition costs much lower. Duolingo’s core product experience also got stronger over time through both data and manual improvement in lessons from user engagement, and laying in gamification tactics. Calm moved from web to app, and built in some daily habits that helped retention. 

What made Calm a much more interesting business though was the launch of Sleep Stories. Not only does expanding into sleep expand the target audience dramatically, it makes it easier for Calm to become attached to a durable habit. People have to sleep; they don’t have to meditate. Calm also was able to expand into B2B by selling Calm as a mental health benefit. Duolingo did not have the same success in new product expansion. While the core product continued to get better at covering more languages, new product efforts failed to create value, such as TinyCards in 2017. Yet, even with this fact, Duolingo appears to be a lot more successful than Calm, likely primarily due to the acquisition strategy and larger initial target market.

In these scenarios, it is not good to assume you are one of these companies on the left side of the table where your initial product/market fit will have such a large addressable market and lack of competition that you can scale successfully without new product development. It is also dangerous to assume you will need a lot of new product innovation when your initial target market ends up being quite large. What I urge companies to do is dig deeper into the attributes in these tables for their company, and re-ask these questions every year as we have seen many of these market dynamics shift dramatically over time.

How to Know If New Products Are Successful

So when is a new product “successful”? Well, the answer, surprisingly, is not product/market fit. If you’ve read some of my work, you know I define product/market fit as satisfaction, normally measured by a healthy retention curve, that is through its own engagement or monetization able to create sustainable growth in new users for a significant period of time.

But second products don’t need to do all of that to matter. Whereas a new startup isn’t going anywhere unless it figures out acquisition and retention (and maybe even today monetization), new products may only need to influence one of the three to be successful. But the key is, they need to influence it for the overall company, not just the product itself. So if a second product has high retention and can effectively acquire new users, but can never inflect the growth of the overall business, it’s not successful. 

This is why developing a growth model above becomes so important. It can tell you if the new product is developing fast enough to inflect growth of the overall business, and when that might happen. And if it isn’t, you can understand what it will take for that to happen. This is something that confuses product teams that work on new products inside larger companies. By the frameworks they understand, the new product “is working.” It has product/market fit, it’s growing, etc., but it can never grow enough to really help the overall company.

Most companies struggle to understand when they need to start investing in adding new product value vs. just continuing to grow off the traction of their initial product/market fit. But it is becoming necessary earlier and earlier in a company’s lifecycle due to a confluence of factors. In order for us to get better at building great, enduring businesses, we need to talk about the types of expansions that matter for companies, and assess at an individual company level what is required for the new phase of growth. Modeling your growth really is a helpful start, and digging deep into understanding the competitive landscape, the acquisition, retention, and monetization potential of your current business, the size and growth of your market, and what your natural adjacencies is becoming critical to make the right calls at the right time regarding new product investment. New products work when they have to. It’s time to ditch outdated portfolio practices and innovation teams, and build modern approaches around when to start building, investing hard in building new products when it is the right time, and evaluating their success or failure properly.

Currently listening to my Early Dubstep playlist.

On Platform Shifts and AI

At TCV’s Engage Summit in 2022, I gave a take on finding your next wave of growth, which you can read here. Sam Shank, founder and CEO of HotelTonight, asked me the question that everyone asks growth people, “What new channels are you seeing new consumer companies take advantage of?” My answer was disappointing as it always is, “What new consumer companies? Discord is the last one I have seen grow organically for a long period of time. Tiktok spent many billions of dollars buying up every ad they could on Facebook, Snap, and Google properties. It’s not really replicable.” I then proceeded to explain that consumer companies tend to arrive in droves during platform shifts, and we haven’t had one since mobile. But AI could be coming (editor’s note: it did). Sam quickly pointed out that while AI is a potential technological platform shift, it is not a distribution platform shift. And it’s distribution platform shifts that create new consumer opportunities. I’ve thought about this conversation a lot, and think I have a better framework to both describe what Sam was describing, and what that means now that the technological platform shift clearly arrived when ChatGPT came out.

What separates a major platform shift from a minor platform shift is a platform shift that enables both a technological shift (new ways of making things possible) paired with a distribution shift (new ways of reaching people with it). The internet and mobile both created new technological and distribution shifts that enabled lots of new multi-billion dollar businesses to be created, whereas “cloud” as an example made new things possible without any new distribution (favors b2b innovation) and crypto arguably enabled new forms of distribution (tokens), but didn’t fundamentally make many new things possible with technology. So nothing can stand the test of time in that space. I’d argue the only companies that have found product/market fit in crypto are companies that either enable or catch grifters. A more pithy way of saying this is the crypto space has created more criminal convictions than companies with product/market fit. Other things VCs have hyped in the past as potential platform shifts have largely neither made interesting new things possible nor driven new distribution opportunities (NFC, VR, Internet of Things, et al.).

What I realized having gone through the internet and mobile platform shifts is that the technological and distribution shifts did not happen at the same time. Platform shifts that create both technological and distribution opportunities happen in a sequence, not all at once. The internet created websites, but the search engine wouldn’t come along until later to become the dominant form of distribution. Mobile created mobile apps, but it was Facebook mobile ads, not the App Store, that became the dominant form of distribution for mobile apps. So, AI has come out and definitely created a technological shift that enables new ways to solve problems that couldn’t be done before. But AI lacks a new distribution channel. ChatGPT is “not it”, as the kids would say. At least not yet.

So, today, that means the traditional distribution methods need to carry the distribution of AI innovation. This favors either: 

  • incumbents who already have distribution
  • startups that can leverage traditional channels such as sales, virality, user generated content, or paid acquisition because their product value is deeply innovative and very marketable

But, this may not be forever. As I mentioned before, we shouldn’t really expect new distribution shifts to have happened yet. The App Store launched in 2008, and even though there was fervor around discovering apps on the App Store for a while with the “there’s an app for that” campaigns, that fervor died as did most of the apps featured. It was when Facebook launched mobile ads four years later in 2012 that apps exploded into multi-billion dollar companies. This is similar to the internet. People started getting online around 1994. Google didn’t come out until 1998. Sure, there were search engines before that (Lycos, Yahoo!), but they lacked the predictable distribution of Google. Word of mouth can’t scale technological shifts alone. They need scalable distribution methods, and usually new ones that take time to become obvious.

So, as an operator, this feels like 1997 or 2008. The Google and Facebook mobile ads of AI haven’t come out yet. Most of the companies that exist will die in the next five years like the internet bubble as they lack sustainable business models and distribution, but there are a few that won’t (Amazon, Ebay, OpenTable et al. survived the internet bubble), and much of the next gen after this wave will become very large. And unlike the internet bubble, incumbents are on top of it and many will do quite well capitalizing on this shift. Some will get destroyed, of course.

We also can’t bet on a new distribution channel coming for AI though. With every generation, companies that reach massive scale have gotten more efficient at preventing other companies from growing on top of them, at least for free. Google created a scalable way for companies to grow both organically with SEO and by paying for it with Adwords, and it still works decades later. Facebook, after flirting with a similar strategy to Google, decided to charge companies for all distribution on its platform. So, if this distribution channel never materializes, expect the impact of AI at consumer scale to be mostly coming from consumer companies that already have consumer scale vs. a bunch of new Facebooks and Googles. I’m rooting for those new distribution angles myself though.

Currently listening to my Future Bass playlist.

Finding the Next Wave of Growth: S-Curves and Product Sequencing

I’ve had the pleasure of speaking at TCV’s Engage Summit the past two years. The Summit gathered ~40 CPOs and product leaders to chat through topics centered around product development and product-led growth. This year, topics ranged broadly from incorporating AI to deliver world-class consumer experiences to defining and measuring different forms of community-powered growth. I never posted my talk from last year, so I’m adapting it into a blog post here, and will do the same for this year’s talk in the following weeks and as well as some follow up questions from the Summit I’ve had a chance to ruminate on.

Also, I publish on Substack now! So subscribe here.

After product/market fit, most companies’ obsession is not thinking about how to create their next amazing product. Their obsession is thinking about growth. Specifically, how do I get this product I know is valuable in the hands of everyone it can be valuable to. Most companies have a primary acquisition loop that drives this scalable growth, and unfortunately, there aren’t that many acquisition loops that really scale. Even when they scale, they eventually asymptote, and companies need to find new ways to grow. This can be new growth loops for the same product, or entirely new products. In this post, I’ll explain how to think about the timing of that, and show some of the successes and failures of my career.

As I have discussed in previous essays, product/market fit can be hard to interpret at the time. When you find product/market fit, problems don’t go away. Customers don’t stop complaining. In fact, they complain more, because they like the product enough to care. What they stop doing is leaving. And you start being able to acquire more of them in a scalable way i.e. an acquisition loop.

Because of this and other factors, when you find product/market fit, you can’t stop iterating. Product/market fit has a positive slope. If you find product/market fit and don’t continue to make the product better, a rising competitive landscape and customer expectations can have you fall out of product/market fit over time. But when you do achieve product/market fit, while you don’t stop iterating, your portfolio of what you work on needs to change.

At Reforge, we talk about the four types of product work. Once you find product/market fit, zero to one product/market fit work goes away entirely for a while as your portfolio shifts to different types of product work:

  • Features: improve current product/market fit
  • Growth: connecting more people to existing product/market fit
  • Scaling: being able to scale the product to more users and more teams internally
  • Product/Market Fit Expansion: new segments, markets, and eventually products


The growth work in particular becomes a major focus, strengthening and discovering new acquisition and engagement loops. Most companies when they find product/market fit with their first product only have one acquisition and engagement loop that is successful, and the job of most of the team is to refine and scale those loops. At Pinterest, I was originally in charge of building a new acquisition loop built on top of Google. It looked like this:

We eventually re-architected our engagement loops to be based around personalization instead of around friends. When you stitch these acquisition loops and engagement loops together, it creates a more complicated growth model that looks like this:

The acquisition loop now feeds new users into a personalization loop that increases engagement over time, and emails and notifications reinforce that loop by distributing relevant content to users outside the product to bring them back. The entirety of Pinterest for the first few years I was there was tuning these loops in one way or another. Eventually, the company needed to layer in new advertiser focused loops to monetize, but I’ll skip that detail for now.

When I arrived at Eventbrite, the company was a lot more mature than when I started at Pinterest. But similar to Pinterest, it started with one acquisition and engagement loop driving its growth.

Creators market their events to bring in new ticket buyers. Many of those ticket buyers, once introduced to Eventbrite, start creating events themselves. And when event creators are successful at selling tickets, they come back and create more events. But Eventbrite didn’t stop there. It kept investing in making its overall growth model stronger.

Why did they do this? Well, all growth loops eventually asymptote. If you get good at modeling your loops, which basically takes the diagrams above and turns them into spreadsheet based forecasts of the impact to your business, you can start to predict when they will stop driving the growth the company needs. Modeling both helps you predict when those asymptotes will happen and unconstrain those loops by finding their bottlenecks and alleviating them. At Pinterest, we 5x’d conversion rate into signup over time, and doubled the activation rate of signups to engaged users over time as a couple of examples.

Some constraints in your growth loops can’t be fundamentally unconstrained by optimization though. The company requires either new growth loops or new products to acquire, retain, or monetize better. Modeling your loops helps you start investing in building out those new growth loops or products well in advance of when you need them to sustain your growth, because of course developing them takes much more time than improving a current loop. We think of this as sequencing different S-curves of growth.

By my arrival as an advisor by 2017 and CPO by 2019 at Eventbrite, the company had layered on many more acquisition loops onto its original loop to continue to grow, creating a much more complicated growth model.

Now, I know this looks complicated, but all that is really going on here is Eventbrite took its monetization of ticket sales and re-invested all of that money into new acquisition loops to bring in more event creators (sales, paid acquisition, content marketing). Also, Eventbrite took the increasing scale of event inventory created on the platform and started distributing it themselves to drive more ticket sales per event to places like Google, Facebook, Spotify, and its existing base of millions of people who had bought tickets to previous events.

People don’t talk enough about how much S-curve sequencing work went on at all these successful companies, so I wanted to give you a taste of what it looked like across my experience at Grubhub, Pinterest, and Eventbrite because it’s a lot, and a lot of it didn’t work. Let’s start with Grubhub summarizing ten years of decisions that both helped and hurt Grubhub as it scaled to be a public company (+’s show up where I think the decision helped, and -’s where I think the decision hurt):

  • 2004: Grubhub co-founder collects menus of Chicago neighborhood restaurants, scans them, and puts them online (+)
  • 2005: Grubhub expands to cover all of Chicago (+)
  • 2006: Grubhub launches online ordering from restaurants (+)
  • 2007: Grubhub optimizes sales model and expands into second market (+)
  • 2008: Grubhub unlocks demand side channels and refines expansion playbook (+)
    • Grubhub launches Boston and New York (+)
    • Grubhub landing pages for restaurants that deliver to X start ranking well on Google (+)
    • Grubhub unlocks paid acquisition to drive demand (+)
  • 2009: Grubhub scales market launch playbook (+)
    • Grubhub switches from flat fee to percentage model (+)
  • 2010: Grubhub launches pickup (-)
    • It doesn’t find product/market fit and hurts delivery use cases (-)
    • Grubhub now launching at least one new market per month (+)
  • 2011: Grubhub acquires Campusfood and launches restaurant websites (+)
    • Grubhub acquires Campusfood to expand to many college markets (+)
    • Grubhub acquires Fango to build in-restaurant tech (+)
    • Grubhub launches restaurant websites to drive in-restaurant growth (+)
  • 2013: Grubhub acquires Seamless (+)
  • 2014: Grubhub goes public and starts building a delivery network to compete with Uber and Doordash (+)
    • It doesn’t matter as those companies raise billions of dollars to destroy Grubhub’s network effect (-)

What you can see here is despite a successful outcome of an IPO and $7.6 billion exit, Grubhub made a lot of mistakes. If you strip those mistakes out, the sequencing of S-curves looks like:


The main lessons that matter here to me are that Grubhub tried product expansion too early with pickup. But market expansion became a major strength and well oiled machine through sales and SEO expertise as well as strategic M&A. That strategic M&A failed them, however, in responding to the threat of delivery networks. Grubhub was integrating its largest acquisition when Doordash and Uber Eats rose to prominence, and while Grubhub acquired over a dozen companies, it never acquired the one that was truly disruptive (Doordash).

Okay, let’s do Pinterest in the same format:

  • 2010-2011: Founder visits DIY/Craft Meetups and convinces Influencers to start “Pin It Forward” Campaign (+)
    • This gets people to learn how to use the “Pin It” functionality in their browser (+)
    • Pinterest uses Facebook Sign-In to bootstrap network of friends as more people join the platform (+)
  • 2011-2012: Pinterest leverages Facebook Open Graph to share every Pin into users’ Facebook feeds (+)
    • Pinterest starts to amass enough content to make discovery, not saving, primary value prop (+)
    • Retention and frequency of use improve (+)
  • 2013: Facebook turns off Open Graph and growth stops (-)
  • 2014: Pinterest fails to unlock growth with new products, but does unlock User Generated Content distributed through SEO (+)
    • Pinterest launched a maps product, a Q&A product, and a messaging product, and all fail to drive growth (-)
    • Pinterest finds another channel in Google to distribute its high quality content to after Open Graph turned off by Facebook (+)
    • Users come in with less match to existing network, so friend graph ceases to drive ongoing discovery. Retention decreases. (-)
  • 2015: Data network effects kick in (+)
    • While friend graph ceases to work, Pinterest now has the scale of content to recommend great content just based on users’ interests. Moves to interest, not friend based discovery. Retention improves again. (+)
    • Pinterest pauses all U.S. work to make sure we unlock international markets (+)
    • Pinterest tries to re-ignite user sharing and fails (-)
  • 2016: Pinterest crosses 50% international active users (+)
    • Focus shifts to building advertising business to make money (+)
    • Growth team starts seriously experimenting with paid acquisition as new channel (+)

Despite Pinterest being worth *check’s today’s stock price* $21.5 billion on the public markets today, you still see a lot of the mistakes we made. Too much new product development that didn’t pan out and too much trying to regain what we had lost vs. leaning into new areas that were working. Network effect products rely less on new product innovation unless it’s the only way to monetize. And Pinterest tried the harder expansion before the easier ones. Market and category expansion tend to be much easier than product value expansion. But, Pinterest did make a very successful pivot from direct network effects to data network effects and from Facebook to Google as the primary distribution channel. When you strip the failures out, our success looks like the following sequence:

Okay, for the last one, let’s do Eventbrite:

  • 2006: Eventbrite launches to allow event creators to accept payments online (+)
  • 2007: Event creators start putting $0 in the payment field to create free tickets, driving huge awareness (+)
  • 2008-2012: Eventbrite builds more features to help event creators run their business and includes them in ticket fee (-)
  • 2012: Eventbrite builds sales team to scale to more upmarket event creators (-)
    • Eventbrite launches new countries with sales-led strategy (-)
    • These countries never build the self-serve growth motion of the U.S.
  • 2016: Eventbrite launches consumer destination to help consumers find events (+)
    • SEO landing pages featuring events in different cities become large drivers of ticket sales (+)
    • Eventbrite begins scaling emails to consumers of events they might be interested in (+)
  • 2017: Eventbrite launches packages and acquires Ticketfly to move upmarket into the enterprise music segment (-)
    • Packages makes Eventbrite more money in the short turn, but drive churn and less acquisition over time (-)
    • Many Ticketfly customers are a poor fit for Eventbrite from a service / functionality perspective. Segment is low growth. (-)
  • 2018: Eventbrite acquires Picatic to build developer platform (-)
  • 2020: Pandemic hits, and Eventbrite rewrites strategy to focus on independent, frequent creators and help them grow
    • Focus on self-service and helping creators drive demand
    • Cancel separate music product and developer platform
  • 2021: Eventbrite launches Eventbrite Boost, a suite of tools to help creators improve their own marketing
  • 2022: Eventbrite launches Eventbrite Ads to help event creators reach more consumers searching for events on Eventbrite

Since this shift is happening in real time, I’ll describe the S-Curve sequencing Eventbrite was investing in as of the end of my full-time role. The value prop is shifting from payments and ticketing to helping event creators grow their ticket sales. Eventbrite has launched new pricing with tools like marketing tools that help event creators get better at their own email and performance marketing as well as let them get more distribution inside Eventbrite’s platform. The revenue from this will help drive more investment in the consumer product side of Eventbrite, which hopefully drives more consumers looking to Eventbrite to find things to do and buying more tickets from our creators.


Hopefully you see from these examples that sequencing S-Curves to drive growth of companies over the long term is not only quite difficult, but the craft of doing it is under-developed. All three of these companies made some critical successful moves as well as major mistakes that set them back years. I hope that by studying these and other examples startups can get smarter about how they sequence their S-Curves and drive long term success for their companies. In my next two posts, I’ll go deeper on how to think about how platform shifts like AI affect this and publish a lot more on when and how to invest in building your second product successfully.

Currently listening to my Rhythym & Bass playlist.

And don’t forget to get on my Substack list for future posts here.

New Podcast with Lenny Rachitsky

I recently joined Lenny Rachitsky on his podcast for the second time. We covered a lot of interesting topics, such as why Grubhub ultimately lost to Doordash, over-reliance on frameworks and research in product management, and a deep dive on network effects, SaaS to marketplace transitions, and why consumer subscription is so hard. Listen on your favorite platform below.

Podcast Links

Youtube:

Spotify:

Apple

Currently Listening to Raven by Kelela.

Founder Intuition vs. Team Expertise vs. Customer Expertise

When founders of startups start to hire employees to work on various parts of the business, it tends to be uneasy for both the founder and the employee early on. The founder may have done that job in some capacity before they hired for it, but they are not an expert. The incoming employee may bring more expertise, but they don’t know the business yet. The founder is going to have a lot of opinions as is the employee, and they won’t necessarily match. In this essay, I’ll talk about how to think about balancing founder intuition vs. team expertise, and how that changes over time. I find that this same balance is true for customers vs. founders when they start businesses too, so we’ll cover that as well.

Generally, the biggest mistake founders can make when starting to hire a team is defer too many decisions too quickly to new employees. This is most painful with new executives, but can also be damaging when hiring new individual contributors. Founders frequently convince themselves that this new person they hired is an expert on this topic, and they should defer to them. The opposite actually tends to be true. The founders are experts on the business. And incoming employees should defer to them until they are confident they have become more of an expert on a certain aspect of the business.

The opposite mistake tends to happen later on as the business grows. The founders have now staffed the company with a lot of great talent who have had time to learn the business, have impact, build processes, know customers really well, etc. Meanwhile, the founders are scaling a bigger business and getting further away from the details. Founder intuition becomes less reliable because the founder(s)’ advantage of having spent more time on the problem goes away. Their thoughts become perhaps dated. And they won’t really know it. This degradation of founder intuition also happens at different times for different parts of the business.

Great founders start to back away from relying on founder intuition when they see the expertise developing on the team, or are proven wrong in a meaningful way by the team that makes them start to question their often blind faith in their own judgment. Moving forward, founders have to calibrate how their intuition stacks up against team expertise on every topic of the business to know how much to intervene vs. let the team drive. Think of this as a simple graph.

It’s incredibly hard to accurately graph where the founders and team are on this graphic for every topic within the company, and when they cross. Normally, founders tend to navigate this based on factors like personal interest, what areas of the company they perceive to be doing well or not, etc. Having worked with lots of founders myself as a leader, advisor, investor, or board member, I default to founders needing to operate differently at different stages. On a bunch of different axes, I have mapped how I think companies optimally behave as they grow (changes in italics).

Phase 1: Starting Phase 2: Scaling Phase 3: Expanding
Founder makes decisions Founder starts to delegate decisions Founder empowers team completely
Speed > Precision Speed with some precision Precision > Speed
Generalist > Specialist Specialist = Generalist Specialist > Generalist
Done > Perfect Done > Perfect Done Perfect Trade Off
Focus > Breadth Focus > Breadth Breadth Focus Tradeoff
Execution > Strategy Execution > Strategy Strategy = Execution
Hungry > Seasoned Hungry > Seasoned Seasoned > Hungry
Cheap > Robust Cheap Robust Trade Off Robust > Cheap
Teamwork > Process Some process Process First
Doers > Managers Doers with some doer-managers Managers + Specialists

Obviously, this table can be a bit crude, and understanding when the company is shifting between phases is not always apparent to everyone inside the company. But it provides a default guide on when to delegate and empower teams as a company grows. I find that just asking the question of what phase the company is in builds good awareness on how one might want to be operating at the moment.

If you are a startup employee or leader, you have to respect founder intuition greatly. The company would not have gotten to the point you could have even been hired had that intuition not served the company well. But when you’ve really spent the time to understand the business and you or your team can start to have better judgment than the founder, it’s important to signal that confidence and get alignment on the operating model shifting. It will not be an easy conversation, but worthwhile to have.

What can you gain from such a conversation? First, you make the above graph visible to the founder in a way they may not be aware. Second, you can calibrate where each of you think you are on this graph. If the founder believes their intuition still serves the business better on a topic than the expertise you have built, then you can have a conversation about what would signal those two lines crossing to change how decisions are made in that area? Keep in mind, in some cases, that answer may be that there will never be a signal that changes how much control the founder wants to exert in an area. Companies are not democracies, and founders have the right to run the company any way they want. If they want to drive decisions on what tech stack the company uses or which segments are interesting, they will. If you don’t like it, you should join another company. But most founders want to do what is best for the company, and giving them the signal on where it’s valuable for them to lean in vs. that potentially being unhelpful is worth figuring out.

On the flip side, if the founder is putting decisions on you or your team where the founder would be better fit to make the calls themselves, tell them! There is no shame in admitting you’re not yet equipped to make the calls and empowering the founder to make more direct decisions for a while. This is not an admittance of incompetence. It’s driving clarity on decision-making rights that are optimal for the business. If you’re still asking the founder to make those same calls a year later though, expect them to think you aren’t developing the expertise you should be.

What is ironic about founders and employees facing this split between intuition from expertise is both have to do that same analysis with the company’s customers. When startups are small, most founders and employees tend to think the customer knows best. This may be experienced by changing the product based on customer feedback, allowing customers to experiment with different ways to use the product that help them become successful, etc. But as a company scales, it starts to see every way customers use a product, and what works best. For a marketplace like Airbnb, it might be seeing that hosts that provide toiletries get higher ratings, or for Eventbrite it might be seeing that emailing past attendees of an event a certain time before the next event maximizes their chances of buying another ticket. Then, the company’s job switches from observing what customers are doing and adopting them to teaching customers what best practices the company is aware of that will make them more successful.

So, in summary, founder intuition is extremely valuable, and new employees and leaders should learn to leverage that vs. ignore it because they’ve seen things before. But founder intuition does ebb over time in most areas as founders scale up the company, and of course, teams get a lot smarter as they spend time on deep company problems. This also happens with your customers over time. Having a dialog about where teams are in this journey is important to helping startups scale, clearing a path for teams to have maximum impact, and for leveraging founders’ scarce time in the areas that are most highly leveraged.

Currently listening to my 2022 playlist.

Finding Product/Market Fit with Network Effects

I have written a lot about product/market fit in the past. Whether you are a founder, product or growth leader, being able to recognize and measure product/market fit is a critical tool to make the best decisions on driving success of a company. In the post I linked to above, I make a case on the two metrics you really need to understand about product/market fit: retention and acquisition, and that product/market fit is really about finding a level of satisfaction (usually measured through retention) that will drive scalable acquisition. If you apply this model to a SaaS business, the application is usually pretty straight forward. Measure the retention of your customer, and does either the virality or monetization unlock scalable acquisition loops like sales, performance marketing, referrals, or content. Many founders struggle to apply this same framework for network effect businesses though, and it’s easy to see why. Frankly, it is just much harder. In this post, I’ll break down the nuances of product/market fit for network effects businesses to understand if you’re on the right track.

When a network is a key component of the value prop you are trying to create, and things aren’t going well, it’s hard to understand which is the real issue preventing the product from reaching product/market fit:

  • Is the product’s value prop not valuable enough?
  • Is there just not enough people or content to make it valuable yet?
  • Conversely, could there be too many people or too much content to retain the value?

I’ll break down some additional insights to product/market fit for different types of network effects to make the product/market fit mystery easier.

Cross-Side Network Effects (Marketplaces and Platforms)

Cross-side network effects can be painfully difficult to find product/market fit for because you are building out a product for two different types of customers that have to align. It can feel like building two companies at the same time. Fortunately, there are some easy ways to navigate this puzzle to make the journey to product/market fit less amorphous. The first is that for businesses like marketplaces, it tends to be easy to understand how to constrain the audience initially. The first thing to understand is that there a spectrum of network effects from global to local:

  • Global Network Effect: every seller makes the product better for every buyer and vice versa e.g. Ebay, Airbnb
  • Local Network Effect: every seller within certain area makes the product better for every buyer within certain area and vice versa e.g. Grubhub, Ritual

Based on whether you are pursuing a global or local network effect, you can usually either constrain the audience by category or geography to start: Grubhub started in one neighborhood in Chicago, for example. Amazon started with just books.

Assuming you pick the correct geographic or categorical limit, you can test the value prop and scaling of supply and demand to find liquidity, which is a synonymous term with product/market fit for marketplaces. There is almost always a quality and quantity element to liquidity. If you understand it, you can then measure product/market fit by just analyzing retention and acquisition for both sides of the market.

Platforms are more complicated in the beginning, but eventually follow the same logic as marketplaces; they are just less likely to have a geographical component at the core. Whereas marketplaces need supply to attract demand and are willing to do unscalable things to generate that initial supply like paying drivers to sit idle for Uber and Lyft or delivering from restaurants who are unaware they are on your platform like Postmates and Doordash, platforms tend to have to be the initial supply themselves. Let’s take a look at a couple of different examples. Nintendo has had some of the most successful hardware platforms in gaming across decades. One of their secret sauces is that they seed supply on their platform with games they build themselves that they guarantee are high quality. Now, they can afford to do that with games that are based on incredibly valued IP like Mario, Zelda, and Pokemon. This creates initial demand for a new hardware platform like, say, the Nintendo Switch, that then attracts third party developers to build games for the platform as well. Then, Nintendo can look at demand side retention through purchases of games, and supply side retention through creation of multiple games. This helped them understand that platforms such as the Wii and the Switch had strong product/market fit, whereas the Wii U and GameCube much less so. 

The success of the Nintendo Switch was partially due to the console launching with one of the best reviewed games of all time from a beloved franchise: The Legend of Zelda: Breath of the Wild.

On the software side, Shopify and WordPress have spent decades building their own SaaS features to allow the platform to become stronger and stronger with other features built by external developers they would never consider in their roadmap. Now, the majority of the value may be generated by third party developers, but a lot of first party development was required to get them far enough along in customer acquisition to make it attractive for so many developers to build on top of their platforms. You can read more about building successful platforms here.

Data Network Effects (Personalization and Recommendations)

For products that expect to create a better experience through data, the journey to product/market fit is usually about collecting enough data to provide a compelling experience. For some products, this takes a massive amount of time. Pinterest, for example, started as more of a tool for individual users to collect cool things from the internet before it could become a real recommendation engine for things related to your interests based on the wisdom of the crowd. Tinder can recommend great matches after just a few swipes. After I joined Pinterest, we had to make a hard pivot to onboard new users based on topics instead of their friend graph as we realized there was stronger product/market fit as a content recommendation engine vs. a social network. This creates an issue for geographical expansion. By default, we would not have enough data to understand local tastes. To rectify this, we had to reconfigure our feeds to emphasize the local content we had, in the local language, and in the local way of measuring (no more imperial measurements!). And in some markets, the focus needed to be gathering content so we could even have enough local content to display.

Direct Network Effects (Communication Tools and Social Networks)

For many network driven businesses, especially in the social realm, there is a Goldilocks zone of user experience. Too few people or content, and there isn’t enough content to consume or people to communicate with. Too many people or too much content, and it becomes overwhelming, or generates too many notifications. When Yogi Berra said, “”Nobody goes there anymore. It’s too crowded.”, it’s easy to imagine he was talking about Clubhouse, not a local restaurant at the time.

Clubhouse downloads per month. This graph is not up and to the right.

For a marketplace, there is usually a clear way to constrain the audience to generate appropriate retention metrics to evaluate product / market fit. Geography or category is the dominant mode for this in marketplaces. For SaaS, it’s a specific target audience. But social or content driven networks eventually want to scale to everyone, and a lot of times founders or leaders can’t tell which should be the first audience that will hook onto it. The better your thesis, the easier it is to evaluate e.g. Harvard only with Facebook, midwestern moms with Pinterest, USC with Snapchat. So the dominant strategy for building direct networks effect business is to have a strong thesis for the target market, prepare to be surprised both who the target market and right features are, and shift to another type of network effect over time. Outside of communication tools, it’s usually my belief that direct network effect products are actually cross-side network effects products where it’s just too early to understand who the different sides are. So, founders have to understand the user data or have a strong vision for what type of cross-side network effect or data network effect can be created to scale product/market fit over time. Do this, or risk becoming like a lot of other flash in the pan social network fads that failed to scale. 


My previous post on product/market fit mentioned the Ries vs. Rabois model on finding product/market fit, which you can think of as testing vs. vision. While both concepts have value, attempts at finding product/market fit with network effects businesses tend to reward more of the Rabois model, or else the testing you do will be inconclusive on the reasons why something isn’t working between the core value prop vs. the size of the network. Finding product/market fit for these types of businesses is much harder, but can be much more rewarding in terms of scale if successful. 

Currently listening to my Hipster House / Lofi House playlist.

Podcast with Lenny Rachitsky

Lenny Rachitsky recently launched Lenny’s Podcast, and I was happy to be a guest. We talk about how to communicate upward, different product design strategies for complex products, what it means to be a product leader, and much more. I’ll expand on some of these in upcoming posts. You can listen to the podcast here or on Spotify below.

Currently listening to my Downtempo House playlist.

Should You Pay Attention To Competitors? It Depends On Your Company’s Conflict.

I don’t remember much from high school literature classes, but one of the key frameworks I do remember is the different types of conflicts in storytelling. Now, the internet is confused over how many types of conflicts there are. Much like the 4P’s of Marketing are now 7, scholars are adding more types of conflict in storytelling over time. When I was taught this, however, there were three we focused on; whether the protagonist was fighting against:

  • nature
  • another person
  • themselves

Why am I talking about high school literature frameworks? Because every company has a conflict as well. The type of conflict a company is in will determine how you think about competition. I’ll describe these conflicts in more detail, how they apply to companies, and how to think about what to do in each situation.

Company Vs. Nature

Every founder I speak with can name dozens of competitors. That does not mean they are in conflict with another company. Take Grubhub, for example. When I joined, we had raised $1 million in venture capital, and had three competitors all about the same size, but with different strengths and strategies. But this was not a competition about who would become the leader in online ordering for food delivery. This was a competition against nature and to make food delivery more attractive to consumers, and if they were ordering delivery, competing against calling restaurants on the phone. At the time, 99% of people were not ordering food online.

Most startups are in a conflict against nature. There is a status quo in the market – or some other type of barrier to adoption like, say, a global pandemic (“too soon!” shouts my co-workers) that has to be overcome for any type of company in the space to be successful. Normally, startups engage in trench warfare to grind against the status quo over time. Online ordering for food delivery went from a very fringe thing to a completely normal thing that everyone does. Mike Evans, Grubhub co-founder, famously said, “we were an overnight success ten years in the making.” Occasionally, nature provides catalysts to growth as well. Just talk to any telemedicine startup, grocery delivery service, or remote work tool about what happened to them when the pandemic started. The technology already existed for all those companies, but consumers needed a forcing function to accelerate adoption.

If your main goal is to grow the category and make people want its value prop in general, obsessing over competition isn’t very helpful. This is how we felt at Grubhub. We didn’t care too much about competitors at all and focused on our customers. This prevented us from wasting precious time analyzing our competitors instead of our customers, which is what would really help us be successful. Eventually, Grubhub acquired those initial competitors or made them irrelevant. When we acquired those companies, we found they spent a lot of time thinking about us. Now, one can make the counter-argument that that is because we were winning. This is a very important point. If any competitor working to grow a category is being materially more successful than another, you may shift into another type of conflict.

Company Vs. Another Company

In many markets, companies fight vehemently against competitors. Companies are embroiled in a Red Queen effect, where each company is trying to out-innovate or outwork others in the market to gain market share. Think of Uber vs. Lyft as a recent example. Startups frequently think they are in this type of conflict when they are not. I have seen quite a few startups emphatically compete before they are even sure the category will be successful. In many of those cases, it would be better to cooperate and grow the category faster by being coordinated. Stripe and Shopify are an interesting example of this. The categories of ecommerce and online payments are growing so fast that instead of competing with each other, they have tightened their partnership to make sure the category continues to grow quickly. Uber and Lyft however tracked each other’s moves, and responded in kind to new product launches, pricing changes, and market launches. Both of these moves look correct in hindsight. Uber and Lyft’s market, while growing fast, would ultimately be capped by consumer transportation needs, and lean toward a winning take most model due to the strength of the local network effects involved in the model. While Uber and Lyft have been competitive, they ultimately saw value in presenting a unified position on local regulations and working together to ensure its services would remain available throughout cities worldwide. So company vs. company can change depending on the fight back to vs. nature.

Company Vs. Self

The third type of conflict within a company is one many founders and employees seem to forget: competing with themselves. In this type of conflict, the primary fear of the company is not that the market doesn’t unlock or that a competitor will take your opportunity; it’s that the opportunity isn’t realized because the company cannot execute on the strategic vision. This type of conflict can be dominant for many reasons:

  • Internal politics
  • Lack of focus
  • Execution issues e.g. technical and process debt

Investors frequently call this “execution risk.” A company in conflict with itself means the vision is definitely technically possible (hence, not technical risk), but the company struggles to build toward it either due to being unfocused, people internally competing vs. cooperating, or building is very difficult due to technical or process issues. These types of companies can be appealing to certain types of executives who think they can fix the underlying execution issues. The reason for this is that if these companies do everything right, they win. This is certainly true. But it is not easy. Evernote is a recent example. Technical debt slowed their progress to a crawl, so the new CEO spent two years rebuilding everything. During this time, growth was slow, and more competitive risk emerged with companies like Notion, Coda, and Roam Research. If the company is finished with its conflict with itself, it likely finds itself in conflict with other companies now.

It is tempting to say in this example that Evernote should have paid a lot of attention to these competitors early on, but I think that would have been a mistake. The entire issue with Evernote seemed to be that they spent too long building new things on top of a technology stack that became unbearable to maintain. Movements in the market have no impact on fixing that core problem.

The type of conflict you are facing affects a lot of how you build and what you focus on as a company. Spending some time to think through where the real conflict is can help focus the company on the right activities to win. This can affect how much you invest in marketing, the focus of the product roadmap, and even organizational structure. Tackling the appropriate conflict is where the real leverage is in growing a company.

Currently listening to my Vocal Tones playlist.

Casey’s Guide to Finding Product/Market Fit

As a product leader with a background in growth, it’s surprising how much what I actually end up working on is product/market fit. Product people should only be focused on growth i.e. connecting people to the value of a product once they’ve confirmed the product is delivering value. So it’s important to have a strong understanding of what product/market fit looks like before investing in growth. Founders and product leaders struggle with answering this question however, and advice and blog posts on the internet frequently espouse that you’ll know product/market fit when you see it, and that all of a sudden everything will start working. It’s not very actionable advice. I don’t claim to be the world’s foremost expert, but this is what I learned through scaling multiple startups, launching new products, and advising and investing in dozens of companies.

The Quantitative Approach to Product/Market Fit

I define product/market fit as satisfaction that allows for sustained growth. Satisfaction is tricky to understand because, well, customers are rarely truly satisfied. Jeff Bezos frequently drops pearls of wisdom in his shareholder letters, and my favorite of them is the following:

One thing I love about customers is that they are divinely discontent. Their expectations are never static – they go up. It’s human nature. We didn’t ascend from our hunter-gatherer days by being satisfied. People have a voracious appetite for a better way, and yesterday’s ‘wow’ quickly becomes today’s ‘ordinary’.

To put that in product/market fit parlance, product/market fit has a positive slope. The expectations of the customer continue to increase over time, and in fact, total satisfaction is likely an asymptote impossible to achieve. So what is product/market fit then? Product/market fit is not when customers stop complaining and are fully satisfied. They’ll never stop complaining. They’ll never be fully satisfied. Product/market fit is when they stop leaving. Represented visually, customer expectations are an asymptote a product experience can rarely hope to achieve, but product/market fit is a line a product can jump over and try to maintain a higher slope than over time.

All products start below a theoretical product/market fit line and some cross that line and work to stay above it over time.

So, for most businesses, instead of measuring satisfaction, measuring retention is the best signal of product/market fit. Measuring retention is pretty easy. Perform a cohort analysis, graph the curve over time and see if there is a flattening of the retention curve. As I’ve discussed before, what you measure for your retention curve matters quite a bit though. For your product, there is usually a key action the customer takes in a product that best represents product value. For Pinterest, it was saving a piece of content. For Grubhub, it was ordering food online. For your product, there is also a natural frequency to product use. For Pinterest, we eventually determined that people would browse sites for topics of interest on a weekly basis. For Grubhub, people usually ordered food once or twice a month. Once you have a key action and a designated frequency, the cohort graph should have the key action as the y axis and the designated frequency as the x axis. This does not mean companies should ignore other measurements of satisfaction, but to understand definitively what product/market fit looks like, this is the best start.

This graph measures a group of users and how many of them perform the key action of your product over time. There will usually be a stark drop at the beginning, but for products with product/market fit, a percentage continue to find value consistently.

If you’ve been around startups long enough, you’ve undoubtedly seen startups with retention of customers that struggle to grow. If you’re not growing, you definitely do not have product/market fit. So product/market fit cannot be measured by retention alone. That retention has to create sustainable growth, which means the rate of retention matters. Why does the rate of retention matter? Well, most acquisitions of new customers come directly from retained customers through a few key acquisition loops. Either retained customers: 

  • talk about the product to others to attract them to the product
  • invite people directly to the product to attract them to the product
  • create content that they or the company can share to others to attract them to the product
  • make the company enough money that the company can invest that money into paid acquisition or sales loops with a healthy payback period to attract them to the product

I’ve seen many founders misunderstand this, looking for growth hacks to drive growth or a PR bump. These types of tactics are only useful if they help you sequence to a sustainable growth strategy, but they rarely are sustainable growth strategies directly. This is why other measurements of satisfaction can still be important. If it doesn’t exist, the first two of these loops are impossible to drive growth from.

Sustainable growth is measured by one or more of these loops growing the size of your monthly acquisition cohorts month over month with a flattened retention curve. A flattened retention curve of your key action at the designated frequency plus month over month growth in new customers is the best way I have found to measure true product/market fit. If the rate of retention can’t support acquisition loops that continue to scale new users, retention needs to be improved to find product/market fit. Some companies can scale with 10% retained users, and some may need 40%, all depending on the strength of the acquisition loop. The graph below represents one month’s cohort retention over time vs. monthly growth in new users.

Consistent flattening of a month’s retention curve over time plus growth in new users every month is true product/market fit.

What if satisfaction of your product cannot be measured by retention? This happens. Some products are one time use or extremely infrequent in nature. In that case, I like to use custom surveys to measure the level of satisfaction depending on the nature of the product. Rahul Vohra describes the process they used at Superhuman for this, and it is excellent. Don’t forget to measure new user cohorts month over month though. Acquisition becomes even more important in these scenarios.

The Paths to Product/Market Fit

It takes most products a couple of years to find product/market fit. If you are not there yet by the above measurement framework, don’t be alarmed. The first step is to understand how far along you are and the approach to improve. There are two stages of the push toward product/market fit:

  • Building enough of the product vision so that the product is valuable and ready for customers
  • Getting customers to understand and receive the value you’re targeting for them

If you are in the first phase, you generally aren’t allowing customers to use the product anyway to measure product/market fit. There are two main schools of thought for how long you should stay in the first phase, which I will oversimplify into calling the Eric Ries model and the Keith Rabois model (unfair to both of them), and they are diametrically opposed on many axes including how long you build before you allow customer access. Thinking about these axes will help you understand what to do next and what successful companies did in this phase. I will outline some of the main differences below.

This is certainly oversimplified, but should help give an idea of the spectrum of things to consider with a new product. Let’s break these down in a little more detail.

Time To Market

The Ries model emphasizes talking to customers early and often to understand what to build, and whether what you have built is actually solving problems for them. The Rabois model is so driven by the vision of the founders of the company that customer feedback is less important than building what the founders have envisioned before any customers interact with it.

Success can be achieved by both modes. In the food delivery space, Tony Xu and the founders of Doordash and Arram Sabeti, the founder of Zerocater, famously used spreadsheets and founder manual labor to prove out their early models before investing in a lot of technology to scale. Shishir Mehrotra, the founder and CEO of Coda, spent four years building the product before any public launch, and now the company is growing quickly. In reality, companies that take longer to reach the market usually have alpha or beta customers that are still driving a feedback loop into the product. Coda used employees at Uber, for example. 

Focus

The Ries model tends to target a customer segment and attempt to find out their pain points to build something valuable. The Rabois model starts with a strong vision of both a problem and a target solution and works to build that from the start. The Ries model is common in enterprise business where customers just want to deeply understand data scientists or general contractors or some other segment, identify a problem they deal with a product solution could solve, then build and test with that segment. Here, you’re more likely to see some early pivots as companies try out different solutions to the same problems or target different problems of the same type of customer. We incubated a few of these types of companies while I was at Greylock with success, but it was rarely the first iteration that was successful.

One issue with the Ries model here is successful product/market fit rarely avoids all speed bumps. Without a strong vision and motivation by the founders and being “in love with the problem”, those speed bumps can instead appear to be roadblocks causing companies to change direction too early, causing a lot of “failing fast”, which is just a more acceptable way to say failing. The Thumbtack founders are a good example of grinding through these moments and not giving up on eventual product/market fit.

Many founders with vision put out a product experience and eventually find a market interested in that product solution. TikTok and Houseparty didn’t initially set out to build a product for children. Clubhouse started blowing up in Atlanta, and its founders are both in Silicon Valley. Square and Faire, however, had pretty strong ideas of their initial target markets and did find product/market fit with those markets.

Initial Launch Goals

The only goal of launching a product in the Eric Ries model is to generate feedback from the target customer. These launches are generally limited in size to receive enough signal from the target customers to know if something is working or not. The goal of a launch in the Rabois model is to achieve the initial vision that sparked the creation of the product. Launches are usually broader in this model, because the product may be looking broadly for a market that will be attracted to the product solution being launched. Even if there is a strong thesis for a target market, launches are more likely to go big to try to reach the maximum amount of the market possible, because the product should be ready to provide them value day one. In network driven businesses, a bigger effort out the gate may be required to reach the liquidity necessary for people to experience the product/market fit. In marketplaces, this may be sequenced by targeting supply or demand first, but with direct network effects, it is more driven by volume. Twitter and foursquare both launched at SXSW to get initial liquidity correctly. At Grubhub, whenever launching a new market, we would target to sign up 50 restaurants in the first two weeks to have a good selection to show consumers. 

Changes to Vision

The Eric Ries model is very flexible on vision, and companies on this side of the spectrum frequently pivot around customer needs quite a bit. Instagram, Twitter, Groupon, Slack, Pinterest, GOAT and many other successes started in one direction, and through either failure, seeing only part of their initial idea work, or fresh thinking altered their product and mission dramatically to find product/market fit. In the Rabois model, a singular vision drives the company from the start. Opendoor started with a mission to remake real estate through data science and instant sales, and has not strayed much from that vision. 

I think this is an area where despite all the news we hear about successful pivots that leaning more towards the Rabois model is a dominant strategy. Blindly trying out a bunch of startup ideas is like being in a dark room and feeling around for a door. A successful vision can turn on a light to that room so everyone can see the door and run toward it. Even many of those major pivots were guided by a strong, albeit new, vision from their founders.

Growth Strategy

The Eric Ries model thinks about product change as the main way to unlock growth in the early stages. This requires a tight feedback loop between customers and the team so the team can tweak and adapt and potentially built totally new things to please potential customers. You’ll typically see these types of companies leverage growth hacks that are long-term unscalable, but could get the initial product to more people efficiently. The Rabois model implies product changes will be more difficult. The product vision is usually taxing, so relying on product change to grow is very expensive. Rabois prefers to leverage a strong go to market model and heavy marketing muscle to scale usage of the product. Neither of these models go particularly in depth on scalable growth loops, and of course I believe thinking in terms of the scalable loops you’re unlocking is a dominant strategy.


Risks

It’s weird to talk about failure modes in starting companies as most companies do fail. But there are tendencies of failure types in each model that founders should be familiar with as they are avoidable. The Eric Ries model tends to lead to either iteration to no eventual destination or a lot of “failing fast” that feels like progress and isn’t. We saw a lot of this with startup studios five or so years ago. I can’t recall one of them creating an enduring company. The Rabois model has entirely different risks. This is where you can find solutions looking for a problem, like many crypto pitches you hear these days (how many ICO’s found product/market fit? Is there even one?). Some memorable failures in this area in the past are Color Labs and Clinkle. These companies can spend so much effort selling what they have really hard without ever confirming people want it. Startups in this area can also become so consumed in their vision they fail to actually launch. Magic Leap is a recent example of this in the AR space. The Rabois model can also hide some of the key insights to unlock the market if not careful. Instagram and Pinterest only had a small part of their original visions work, so they pivoted to focus on just that element to build massive businesses.

Applicable Company Types

While these models can be used for any type of business, they tend to lean towards certain models. The Eric Ries model is very common in enterprise businesses because founders are confident certain segments have day-to-day problems that aren’t solved and money to pay to create a reliable business model, so they learn all about that segment to find that problem with the willingness to pay to solve. The quick to launch elements are common in marketplaces where matching can be done manually to validate liquidity before investing in a lot of technology. The Rabois model is more common for hardware because iteration has such long timelines, and consumer models where typically founders have new habits or interactions they need to convince a broad market to try.

So Which Should You Use?

Hopefully, you see from these examples that either extreme is pretty dangerous, and even those that vehemently believe in one of these models over the other seems to have selective memory as to when companies veered away from their approach when appropriate. Founders should build a strong opinion over which parts of which model they need to apply to maximize the chance of finding product/market fit for their business. My personal belief is a strong vision combined with market feedback is a pretty dominant combination of these two approaches whereas many of the other axes depend on the product you are building. I actually find both models fairly weak on the growth strategy side. “Sell tickets” ignores how the product is frequently the most effective way to unlock growth through strong acquisition loops. Assuming new products equals new growth is a “build it and they will come” fantasy unless you get lucky.


The reason to understand what product/market fit looks like is of course important for founders of companies. While measuring the product/market fit line directly is impossible, measuring whether a company is above it or not is possible by measuring retention curves and new user growth. If the retention curves every month flatten and new user numbers increase at a healthy payback period, you can feel confident you have product/market fit. If you can’t say this for your product, I hope I’ve been able to give you some axes to understand what you need to work on and how to apply it to your specific product. The next problem is to scale it and keep as customer expectations continue to grow. If you want to learn more about these concepts, we expand on them dramatically in the Reforge Product Strategy program.

Currently listening to my Scrambled Eggs playlist.

Thanks to Kevin Kwok and Emily Yuhas for feedback on drafts of this post.