Tag Archives: growth

The Best Way to Drive Demand in Marketplaces is Hiding in Plain Sight

This essay was co-created with Dan Hockenmaier.

In marketplace businesses, the network is the product. If you’re not growing both supply and demand, the product generally isn’t getting better over time. And your network effects which compound organic growth are not getting better, and probably getting worse.

Most marketplace founders and leaders intuit this over time, so they obsess over growth metrics. In this essay we explore one channel that helps many marketplaces scale faster than others: supply driving demand. 

If a marketplace has the potential to use this channel and doesn’t, it leaves them unoptimized and susceptible to competition. But if they use it too much, it is no longer a classic marketplace and loses the compounding benefits of network effects. There is a Goldilocks Zone:

Driving between 10% and 40% of demand from supply is the Goldilocks zone to maximizing value of our a marketplace’s supply to grow demand. Obviously, other factors besides this determine the full value of a marketplace.

Exploring each case

Supply driving too much of demand (75-100%)

As Casey and Gilad Horev discussed in a previous essay, there are different types of marketplace models. In the SaaS-like network model, the supply is in charge of generating the majority of the demand, and the product acts as mainly a fulfillment or monetization vehicle for the supply. These products generally do not generate cross-side network effects. Because demand only comes to the product when supply tells them to, the product doesn’t feel materially better over time. Acquiring supply doesn’t get easier over time, because the product isn’t generating demand that supply has to come to the platform to access.

These models typically range from 75%-100% of their demand coming from supply, and include companies like Substack, Square, Eventbrite, Mindbody, and many others. Sure, they may generate some of their own demand over time, but not enough to unlock the types of cross-side network effects that power the best marketplaces to massive scale like Airbnb and Doordash. Generally, part of their strategy to grow is actually driving more demand that does NOT come from supply over time, and their value tends to come much more from how well the SaaS business can upsell new products over time.

The 50% threshold

Our rule of thumb is that a marketplace needs to generate at least 50% of the transactions for cross-side network effects to exist. If a supplier would get 50% more transactions through a platform vs. on their own, most rational suppliers would prefer that vs. the 15-25% higher margins they could get going directly to their customers and avoiding a marketplace fee.

This creates that attraction of supply to the product, decreasing acquisition costs. But it also means the selection for demand improves more quickly, making for better discovery, higher conversion, and generally higher frequency on the demand side. We see these elements in the best marketplace businesses. They have amazing cross-side network effects.

Supply driving very little demand (0-10%)

This isn’t ideal for the simple reason that supply driving demand is a great channel if you can get it to work. It is generally highly scalable (meaning its potential as a channel increases as supply grows) and relatively cheap (you are paying to incentivize your suppliers, which is generally cheaper than buying ads on Google or Facebook).

Can you build a great marketplace without supply driving demand? Of course. The company will just need other acquisition strategies they can leverage to be successful, such as virality, paid acquisition, sales, and user generated content, and they might need to spend more money to get to scale vs. marketplaces that can get demand side growth from supply.

The Goldilocks Zone (10-40%)

So, what is ideal? “Just right” appears to be driving your supply-led acquisition channel to north of 10% of demand, but less than about 40% where you start to see too much weakness in the company’s own demand drivers and resulting impact on its network effect.

(As an aside, the percentages in this essay should be thought of as transactions, not customers. If the suppliers are driving >50% of buyers in a marketplaces, but the marketplace is able to effectively cross-sell them to other suppliers on the marketplace such that transactions from this channel are <40%, that is likely just fine.)

At Grubhub, restaurants directly or indirectly drove about 30% of new demand-side acquisition at scale (and Doordash and Uber Eats have replicated this strategy to likely similar percentages). At Grubhub, we gave the restaurants a lower take rate for the orders they drove, but even so, restaurants driving customers to Grubhub became one of our most cost effective acquisition channels. At Faire, both sides of the marketplace refer their existing customers onto the platform because it is easier to manage orders with Faire’s free SaaS platform, and to get access to net 60 payment terms and free returns.

When to try it

Clearly not everyone can take advantage of this channel. Can you? It primarily boils down to three factors:

  1. Does supply have enough of their own demand and enough leverage over them to convince them to transact via the marketplace?
  2. Is supply sufficiently incentivized to do that via better fulfillment, lower costs, better tools, better data?
  3. Is that demand promiscuous enough where they value the access to other suppliers once on the marketplace?

Businesses like Grubhub, Faire, and Eventbrite have all three qualities. Uber and Airbnb are examples that fail on the first point. Most peer-to-peer marketplaces like Poshmark fail on the second because suppliers would much rather transact off platform via something like Venmo, Paypal, or Cash app to save fees. Upwork and Thumbtack fail on the third because once a buyer has a supplier they trust, they tend to stick with them.

If your business meets all three criteria and you’ve been banging your head against a wall on SEO or paid acquisition, using supply to drive demand is the first thing you should try.

Just don’t over-rotate. If you’re not careful you can accidentally pivot a marketplace business with cross-side network effects into a SaaS-like network that does not have cross-side network effects. You will then find you struggle with slower growth, generally lower take rates, and a weaker relationship with the demand side of the marketplace.

How to pull it off

Here are three things to focus on to enable this channel at scale.

1/ Make it clearly better for suppliers to transact through your marketplace vs. through other marketplaces or directly with their customers. Examples:

  • Low or no fees for seller-referred transactions Examples: Faire Direct, Grubhub’s $1 fees for website ordering, Etsy Share & Save
  • Better payment terms than offline transactions receive e.g. get money faster or pay slower Examples: Faire net 60 terms
  • More efficient workflows when transactions are processed through the marketplaces that save the supplier time Examples: Eventbrite’s Mailchimp integration
  • Take on financial risk for these transactions suppliers would need to manage on their own if processed outside the marketplace. Examples: Turo’s car insurance, Bounce BounceShield

2/ Create a referral incentive for supply that is meaningful but still hits your payback thresholds. Notes:

  • Make sure to measure the value of seller-referred customers separately from other acquisition channels. They tend to be lower lifetime value due to the lower fees mentioned above and perhaps sub-par onboarding to the full marketplace value
  • If this incentive is shared with demand, fraud detection is necessary to maintain effective payback periods over time

3/ Create marketing tools that make suppliers better at attracting more transactions. Marketplaces are generally more sophisticated marketers than suppliers at scale, and suppliers know this. Examples:

  • Website builders or embedded checkouts into suppliers’ own websites that are better optimized for conversion. Examples: Eventbrite’s Embedded Checkout, Shopify Shop Pay
  • Optimize supppliers’ Google Local presence. Examples: Grubhub, Bounce
  • Email & SMS marketing tools. Examples: Eventbrite Email Campaigns, Zillow Contact Manager
  • Pooled performance marketing data from all customers on people most likely to convert and tooling to more easily target them. Examples: Etsy Offsite Ads, Eventbrite Boost

If you can do these three things, you will be well on your way to creating one of the most powerful demand acquisition channels for marketplaces.

Be sure to subscribe to my Substack to catch future posts.

Currently listening to Casey’s 2023 playlist.

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.

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.

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.

How to Justify “Non-Sexy” Product Investments

A common issue leaders in product management, design, or engineering face is justifying investment in the “non-sexy” stuff. What is not sexy can differ by company, but usually the sexy things are new products and few features. Non-sexy things include general user experience improvements, performance, developer velocity, infrastructure, technical debt, and, fortunately less than it used to be, growth. I’ll walk through some frameworks and examples from my career on how to drive excitement and investment in these critical areas that may not be properly valued or staffed currently in your organization. But I urge everyone I can in product to develop the intuition to support these initiatives without making teams jump through hoops to justify these investments.

User Experience

The most common path product teams are on today is that they go from feature to feature trying to add new functionality, never confirming their feature actually adds value, and never improving features over time or updating experiences to be more modern as the world evolves. Designers complain about how stale certain experiences get over time, but improvements never make the roadmap. Product managers think designers are whining about things that aren’t important versus their current OKRs. 

Why are the designers right in this instance? Well, they aren’t always. It is possible to over-design and do things that feel good and look excellent, but don’t materially help your customers or the business. Polishing too often can be just as bad polishing too little as you don’t deliver enough new value for customers. While over-polish does happen, why designers are mostly right is they intuit something about product/market fit that is hard to measure on a metrics dashboard: that expectations of customers increase over time. Another way to say that is product/market fit has a positive slope. If you do not consistently improve your product or feature, and customer expectations continue to increase, your product or feature can fall out of product/market fit over time. Many business strategists talk about companies being in a Red Queen effect with their competitors. This means they have to run really hard to stay in the same place competitively over time. But what many product teams misunderstand is that they are in a Red Queen effect with their customers to maintain product/market fit as well. Consistently improving the user experience helps products stay above that positive sloping curve of product/market fit. Let’s visualize this by borrowing a graphic from my product/market fit essay.

 

In the above graphic, the customer expectations line is the point at which customers stop complaining about elements of a product. That is not the target for product/market fit. The target for product/market fit is the purple line where customers stop leaving a product. Teams invest in products and features to get them above the purple line, but failing to continue to invest in them beyond that point means expectations for product/market fit will eventually exceed what has been built without continued investment. 

The dotted line is a worst case scenario as it happens in a way that is not measurable, but once those hard to define lines cross, every metric gets worse. So, in prioritizing user experience improvements that scale with customer expectations, the net effect you see is no impact in business metrics. But the effect of not doing these investments means business metrics will decrease over time. This practically means that teams that make investments feel like the investment didn’t “pay off”, but in reality it prevents the possibility of dramatic issues for the product down the line.

On the growth team at Pinterest, Kaisha Hom and Lindsay Norman on the growth design team intuited this, but had trouble convincing a very metric-oriented team on the value of this investment. Eventually, we decided that one of our key results would be a quarterly audit (and refresh if needed) of our top five user flows. The expectation was no material impact on growth, but instead prevented potential growth issues down the road. 

At Eventbrite, we have gotten a little more sophisticated in how we manage this. Adele Maynes, who leads our research team, helped craft a survey that measured different components of our product/market fit, including:

  • Ease of discovery
  • Ease of use
  • Ability to self-serve
  • Product fit
  • Likelihood to recommend

We also created this survey for some of our key features inside the product so we can understand their feature/product fit better. Our new strategy is to be a fantastic self-service experience that rivals the best SMB tools on the market, but we know we have a long way to go to get there. Investing in user experience is a key driver of this strategy, and these scores help us know if our overall product and specific features are on the right track. CRPX is now one of the top level key results for the product team.

Sample analysis of Eventbrite’s Creator Product Experience Score (CRPX)

Performance

Performance, roughly meaning how long it takes for software products to become usable to customers who load them, tends to become a problem at scale without concrete investment. Products become bloated, the number of different types of users and use cases multiply across countries and categories, and the number of frameworks engineers are leveraging to deliver experiences rises exponentially. We actually have pretty good data externally on the impact of performance. There are many studies that show additional milliseconds of load time impact things like conversion to purchase and engagement on many websites and apps. 

A big problem is actually addressing performance issues at the start tends to be measuring it well, across different pages, apps, countries, use cases, etc. Obviously, this is normally the place to start. But sometimes even shockingly high metrics in certain countries or at the edges can’t motivate teams to scrap their current OKRs for performance work. 

On the growth team at Pinterest, we were struggling with some performance issues of our home grown frontend framework. After trying to rally the company around this work and failing, we decided to leverage our skills to prove out the value of this work. A small team of engineers led by Sam Meder decided to work part-time on a performance initiative just for our logged out experiences, migrating to React, server side rendering, lazy loading, spriting – all the usual suspects from a frontend performance perspective. They ran these changes as AB tests to show the impact on user engagement and key business metrics. The result was a 30% decrease in user-perceived wait time, which resulted in double digit= increases in traffic from Google and conversion rate to signup. The impact was enough to get our CEO to push this as an organization-wide initiative the following quarter.

Developer Velocity

Shortly after I joined Eventbrite, I ran into Omar Seyal on the street. Omar was the Head of Core Product at Pinterest at the time. As I said hello and asked him how things were going, Omar, always to the point, remarked, “Pinterest doesn’t understand leverage!”. He then went on to say how he was struggling to get Pinterest to invest in its infrastructure so that engineers could move faster. In my head, I thought, he doesn’t know how good he has it compared to Eventbrite. Startups, or companies that emerge from startups, tend to prioritize new customer value and growth at all costs. This not only can create a lot of technical and design debt that will slow companies down for years to come, but it also prevents them from seeing “what got you here won’t get you there.” At a certain point in a startup’s lifecycle, it has to shift from growth at all costs to balancing growth and long-term scalability. Yes, you could spend 90% of your time building new things when you were small, but that won’t work when you’re big and have dozens of things to maintain. 

A belief Omar and I share is that developer velocity is the purest form of leverage in a software company. So, it follows, investments in things that make developers more productive are the highest leverage investments a company can make. Sure, those investments don’t translate into customer value directly, but they enable each developer to build more customer value. That can mean more features, more experiments for a growth team, whatever the company needs to maximize long term growth. The key question I think non-developers fear is that these are just quality of life investments and don’t actually meaningfully improve the amount of value to customers. After all, you’re spending less resources on value to customers in the short-term whenever you look inward at internal tools.

What we did at Eventbrite to confront this narrative is we built a measurement plan and a goal. First, we measured the amount of downtime our developers experience on a quarterly basis for various issues. We then stated that with investment we could decrease that downtime, freeing up more capacity to build value for customers. We then set a goal. By making these investments, James Reichardt and Dan Peterson, our leaders in platform engineering and product, argued we could free up the equivalent of 15 new engineers’ worth of capacity at the company. In the end, those investments freed up 18 engineers’ worth of capacity. We confirmed this with “end of sprint” reporting on different teams on the amount of what they were able to deliver. If those numbers aren’t improving over time, you’re probably under-investing in projects related to developer velocity.

Developer Downtime

Engineering downtime was actually trending upward, but by working on our tooling we were able to save hours per engineer per week.

Growth

As much as I’ve written about the rise of growth teams and how growth teams work, the concept of investing in things that help connect customers to value instead of building new value is still pretty nascent in the software world. I speak to product managers and leaders all the time that are struggling to get investment in areas that could help inflect their growth. We definitely faced some of these same issues when I started at Pinterest. While we had a dedicated growth team, many parts of our growth model felt under-optimized, but also hard to measure or justify investment for.

One of these areas was search engine optimization. A few months before I got to Pinterest, Pinterest had “no indexed” the entire site, leading Google to email us to confirm that was what we wanted (it wasn’t). Anna Majkowska jumped onto the problem, but was only able to secure a few part-time engineers to help her. I joined shortly after as the PM, and we worked together on a plan to improve SEO for Pinterest as we believed that to be a large growth opportunity. The problem was we were on a growth team that ran every change as an AB test to show the improvement in growth. With SEO, you can’t run AB tests because it’s one Googlebot instead of millions of separate users. Julie Trier, a part-time engineer on the team at the time, said we had to develop an SEO experiment framework like we use for other parts of the growth team, and set out to build it. With this initial version, instead of showing different users different experiences, we changed parts of the experiences on some pages and not on others and measured the traffic change from SEO. The framework worked, and helped us justify SEO investments by showing how much extra traffic we received from making changes. 

More traffic was great, but the issue was that users from Google would just look at all our cool content and leave. Conversion rates were very low. Conversion was managed by another team. So I went to them and explained the opportunity. They said they were busy working on a home page overhaul and couldn’t look at it. So I said we’ll take on the work ourselves. By then Jean Yang had joined the SEO team and ran an experiment that increased traffic, but decreased sign ups. How that was possible was by making a new page available to Google, we removed a signup modal blocking logged out users from accessing it. It turns out people signed up when they saw that modal, so we hypothesized maybe we could trigger that modal when you clicked on an image when you didn’t have an account. Also, we thought the other thing that indicates you like what you’re seeing and should sign up besides a click on an image is scrolling down and viewing more images. We already restricted Google from seeing more than 25 images on a page, so we decided to make the same change with users, with a modal coming up from the bottom saying to sign up to see more. 

It took Jean two days to implement the experiment, and the result was a 50% increase in conversion rate to sign up. Every graph at the company kinked up as a result. I got a message from Tim Kendall, our Head of Product, asking “What did you do??”. I thought he might fire me, but instead he used the data to go raise more money at a higher valuation showing investors we could inflect our growth. Don’t under-estimate the power of proving it by going rogue or making the measurement investment others think isn’t worth it. It can turn subjective conversations into objective ones very quickly. The team grew dramatically after this with Julie eventually leading a platform team for growth tools.

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These are just a few examples of how teams were able to make the investment case and prove the value of “non-sexy” projects to make a big impact. Of course, what tactics work for you will depend a lot on your company’s culture, but one thing that will likely mimic these stories is teams working together to make both the case and execute on the investment. Building products is a team sport, and the more cross-functional the support you achieve, the more likely you are to succeed. 

As I relay these types of stories, it’s easy for people to say something to the effect of “sure, that worked at Pinterest or Eventbrite, but it could never work here” without realizing the point of the story is that almost all companies have these types of issues. The question is whether you are willing to put in the work to try to change the narrative to help your company grow. Those that do typically are rewarded and reward their customers in the process.

Currently listening to my Trip-hop playlist on Spotify.

Why Product Leaders Fail

I’ve yet to meet a fellow Chief Product Officer or Head of Product say, “yeah, I’m crushing it right now.” In my conversations with fellow product leaders, there’s even a meme that’s started to form around product leadership roles. Effectively:

“Yeah, you just try to put some points on the board before you inevitably get fired.”

So, if a typical CPO feels their role is about trying to survive a couple of years i.e. long enough to help the business a little bit, what is causing that? Why is it so hard to endure as a product leader?

I would say there are three common failure modes depending on how far along the company is. The earlier stage of a company it is, the more likely the answer is going to be misalignment with the founder/CEO. What no one tells product leaders when they accept product leadership roles is that nine times out ten the founder and CEO still wants to drive the product vision. They want you to help execute that vision. And as founders scale, their founder intuition ebbs in effectiveness in comparison to product expertise in-house, but it takes a long time for founders to accept that. That transition period can be very rocky.

For later stage companies, the more likely answer is that the CPO is really only good at one type of product work, and the type of product work needed for the business changes over time. This can manifest in two ways: The product leader has skills that don’t match the type of job needed today, or as they execute, the skills needed change, and the leader cannot adapt. Not only is the product leader not good at that new job, they are also less likely to be interested in it.

Let’s talk about each of these failure modes and what both product leaders and CEOs can do to make them less likely to happen.

Failure Mode #1: Who Owns Product Vision?

Founders tend to have insanely good intuition about their customers and products because, let’s face it, no one has spent near as much time thinking about them and their problems. When startups tend to hire product leadership, it’s their first time hiring this type of leader. Interview processes can be clumsy or unoptimized, with the founder still figuring out how to articulate the real need. Commonly, what happens during this process is both the founder and prospective product leader end up jamming together on the future product vision. Both sides love this engagement, but for the founder, it’s not an effective test on how much the prospective product leader will help the founder, and for the product leader, it can give a false impression that they will have a ton of say in the product vision.

If at all possible, founders should leverage outside expertise to structure the recruiting and interview process for this type of role. One of the key questions to get explicit on before the process begins is what role will this leader play in setting the product vision? For non-product/eng/design founders, they may be asked to define it. For founders with product, eng, or design backgrounds, that is typically not the case until the company becomes much larger. Product executives usually play a consulting or execution role in a vision that is founder led. Founders need to tell candidates which one it is, and candidates need to ask. Neither of these happen as often as they should. 

Once founders understand their answer to this question, they need to vet for the appropriate skills. If what you really need is help executing on the vision, don’t spend much of the interview process jamming on the vision. Sure, candidates need to understand the vision, but what you really need to learn is are they comfortable receiving a vision from you and bringing it to life in the myriad ways that are difficult. 

Failure Mode #2: Does the Expertise Match the Type of Product Work Needed?

Different companies require very different approaches to their product strategy to be successful over time. Most of us understand there are different types of product work. There is tech and process scaling, there is building new products to find new product/market fit, there’s building new features and iterating on the user experience to strengthen current product/market fit, and there is growth work to get the maximum number of users to experience the product/market fit that exists. Traditionally, product leaders lean toward being experts in one or another. For example, I am definitely most known for my expertise in growth.

Founders often lack the understanding of what type of product challenge they are actually facing when they attempt to hire a product leader. Network effects businesses tend to focus more on growth because more users make the product stronger in a much more meaningful way than new features. DTC ecommerce companies / brands are always launching new products. SaaS businesses tend to need to launch lots of new features over time. Hiring a product leader that wants to build new features all the time into a network effects business likely isn’t going to work that well.

Failure Mode #3: Can the Product Leader Adapt as Needs Change?
Even if founders hire the leader with the right skills at the right time, as companies scale, how much weight they put on these will need to change over time. Today, the product leader’s job is to be what the business needs them to be. So while the old school product leader is a specialist, the new school product leader needs to be a chameleon, who can balance a portfolio across scaling, new product work, feature work, and growth weighted toward the needs of the business, and re-weight it considerably over time as business needs change rather than leave once business needs change. That’s hard, but inevitably how product leaders have to evolve to be successful over the long term within a company.

As a growth oriented leader, I am actually spending more of my time at Eventbrite on scaling, features, and new product expansion work, because that is what the business needs right now.

—

Product leadership is incredibly hard. Both founders and product leaders can eliminate some of what makes it so difficult by aligning on expectations before hiring roles, and on aligning which problems the organization is focused on right now. It is then up to product leaders to be able to evolve as the product needs change over time. They are both the best equipped to understand when needs change and help the organization change with them.

Currently listening to Rare, Forever by Leon Vynehall.

The Kindle and the Fire

Entrepreneurs ask me about how to grow all the time. They ask about SEO, about virality, and about increasing conversion, about onboarding. They ask about hiring local teams, building up new functions, and hiring executives. What almost all of them miss is that all of this is context specific on what stage of company they are in. There are two types of growth strategies: non-scalable strategies to get to scale and scalable strategies. I have come to call these kindle strategies and fire strategies. I’ll explain a little bit more about that, and how to think about them.

Kindle strategies can be very unsustainable. Their only goal is to get the company to a place where more sustainable strategies are available. The classic example is in marketplaces. Marketplaces only work when their cross side network effects kick in. Those network effects can only kick in once you get to liquidity. Once network effects do kick in, they can generally be optimized for very sustainable growth. When I left Grubhub, my former co-workers started complaining to me about how much Doordash, Uber Eats, and Postmates were spending on paid search and promotions. They were losing tons of money. There was no way any of those companies ever made this money back from consumer LTV. I told them that wasn’t their goal. Their goal was to get enough consumers so the cross-side network effect kicked in. Those companies were paying their drivers no matter what. Might as well spend money to make sure they’re actually delivering something. And these companies would be willing to almost spend an infinite amount to get the cross-side network effect going because of its value to the company.

I’ve never seen a company as aggressive with promotions as Postmates besides perhaps Homejoy.

This does not only exist in marketplaces however. Superhuman, for example, does live calls or meetings with most people who sign up. This will never scale if they have millions of users, but until either their LTV increases to make this strategy profitable, or they improve self-serve onboarding, they are doing it, because no one would retain without it. You’re willing to do anything it takes in kindle strategies no matter how silly it might seem long term. This is what Paul Graham is talking about when he says “do things that don’t scale.”

Airbnb sold politically themed cereal to raise money for the startup in the early days.

For kindle strategies, it’s more important they work quickly rather than sustainably or efficiently. A lot of early stage companies, for example, ask me about SEO. SEO, unless you have very clever hacks for content and authority, is a fire strategy. It takes too long to work. It might be a great fire strategy to sequence to, though. A lot of companies also ask me about local teams in each market they launch. This can be a great kindle strategy, but it’s generally a terrible fire strategy. Read more from me on this topic here.

Fire strategies by definition need to be sustainable. They need to be able to scale the company 100x and set up a long-term profitable business. So a question all entrepreneurs need to be asking when they pursue their kindle strategy is what fire strategy am I sequencing to. Pinterest used DIY meetups to sequence to influencer blog campaigns to sequence to virality to get enough content where it could scale via personalization on the retention side and SEO on the acquisition side. Most startups won’t go through that many sequences. I’ve seen many companies that have a successful kindle strategy, but it’s not sequencing them into any eventual fire strategy. For example, I spoke with an automotive startup that hacked SEO to get answer box results for their content to get initial users. That eventually caps on how many users it could drive, and it didn’t sequence them to something greater. They eventually shut down.

There aren’t that many fire strategies. Those that involve network effects are generally the best. Sales, paid acquisition, virality, and user generated content with a scalable distribution channel are the most common ways to create sustainable loops to either scale a network effect or scale a product that doesn’t have network effects. This is sobering when I tell entrepreneurs this. There just aren’t that many ways to scale, and almost all involve having extremely good retention as well. Otherwise, you won’t have the profit in the system to invest in sales or paid acquisition, or you won’t get enough users to invite or create content to attract more users.

There are many ways to sequence to a network effect or some other sustainable growth loops, but there are not that many ways to scalably grow without these loops. If you want to learn more about these strategies, it’s exactly what we cover in the Reforge Advanced Growth Strategy course.

Currently listening to Perception by Grant.

What Type of Company Are You and the Growth 2×2

At Reforge, we’ve written about how companies actually grow, and built an entire program around it. Most companies, when they talk about how they grow, will usually pick from one of the following terms:

  • Sales Driven e.g. Oracle, Workday
  • Marketing Driven e.g. Hubspot, Moz
  • Product Driven e.g. Atlassian, Github
  • Engineering Driven e.g. Google, Palantir
  • Product + Sales Driven e.g. Slack, Stripe
  • Marketing + Product Driven e.g Uber, Amazon
  • Sales + Marketing Driven e.g. Drift, Salesforce

Most people can’t reasonably answer why they are one of these types, but there are reasons. If people inside these companies have thought enough about it, they might understand the market to have attributes that force these styles:

  • Sales Driven: Custom value props and big customers
  • Marketing Driven: Need to make a “space” that doesn’t exist yet and convince people they need it
  • Product Driven: High viral quotients and/or word of mouth from product differentiation
  • Engineering Driven: Solving hard technical challenges creates markets
  • Product + Sales Driven: Large customer spread with bottoms up adoption
  • Marketing + Product Driven: Marketing fuels network effects
  • Sales + Marketing Driven: Custom value props and big customers and need to make a “space”

That was extremely simplistic, but hopefully you get the idea. The larger a company grows, the more likely singular definitions like this start to break down though. Companies launch multiple product lines that require different distribution models, and these product lines typically build on top of each other that gain from the intersection of them. In the Advanced Strategy course, we teach how to model these systems of loops that power such companies.

What growth teams sometimes miss is that optimization is not always the answer to a growth problem. It may require a new product or building a sales team. Modeling your loops to understand your constraints to pick tactics that alleviate those constraints takes some time to do well. A framework I’ve used for more quick and dirty decision making is using data companies usually have on hand: whether a tactic is reliable or not historically in driving growth i.e. you can predict the output from an investment or not, and how fast the payback period is.

For those who aren’t familiar, payback period is one of the most important metrics you can track for growth. It’s very simple. Given this investment today, how long will it take before I recoup that investment in profit from the customers it impacts. For example, a customer you acquire in Adwords for $10 might take you six months to make $10 in profit, subtracting all marginal costs. So our payback period would be six months.

Most executive teams can start to plot where in this 2×2 tactics seem to sit. For example, brand marketing at all but the most sophisticated marketing driven companies is something that has a slow payback and is not reliable. Similarly, innovative product development focused on entirely new products or value props usually sits in that category. For other tactics, where they sit in this 2×2 may vary. On Pinterest’s growth team, for a more specific example, efforts in product driven growth around UGC content distributed through SEO, conversion optimization, email marketing, and activation were very predictable and had quick payback periods, but viral growth was unreliable. At Uber, I imagine viral growth via incentivized referrals was very reliable, but SEO was not. Now, it’s important to remember that within reliability is a sense of scale that matters for the business. If a tactic gives you .1% growth, and only 10% improvements matter, it actually isn’t a reliable lever. An alternative is to make a three dimensional chart where reliability is separate from impact, and ain’t nobody got time for that.


A sample Growth 2×2 for just Pinterest’s growth team

In the long run, model your loops well and find the constraints. In the short term run, maximize efforts on reliable and quick payback activities until you hit diminishing returns. Then, think about moving excess resources into things that are reliable, but have longer payback periods. And think about how anything with short paybacks that are unreliable can become more reliable.

What you’ll quickly realize in the case where you have built a proper growth model or you’re short term optimizing based on this 2×2 above is the opportunities are rarely siloed to one function. You can’t even build this 2×2 if you don’t have many functions represented. So start talking to all the functions of your company to map the opportunities to grow better so that you can grow faster.

Currently listening to Simplicity is the Ultimate Sophistication by Matthieu Faubourg.

What Is Good Retention: An Exhaustive Benchmark Study with Lenny Rachitsky

At the end of 2019, I presented Eventbrite’s product plans to the board for 2020. These plans included a lot of the goals you likely have in your company: improvements in acquisition, activation, and retention. One of our board members asked: “I understand these goals for the year. But long term, how high could we push this retention number? What would great retention be for Eventbrite?”

I actually didn’t have a great answer. Soon after, I was chatting with Lenny Rachitsky, and we decided to embark on a holistic study across the industry to ask “what is great retention?” across business models, customer types, etc. Lenny surveyed a lot of the top practitioners in the industry across a variety of companies, and we’re happy to share the results here. You can see the raw data below, but I recommend reading Lenny’s analysis here. Done? Good.

Why is retention so damn important?
Why are Lenny and I spending so much time researching retention? Because it is the single most important factor in product success. Retention is not only the primary measure of product value and product/market fit for most businesses; it is also the biggest driver of monetization and acquisition as well.

We typically think of monetization as the lifetime value formula, which is how long a user is active along with revenue per active user. Retention has the most impact on how many users are active and lengthens the amount of time they are active. For acquisition, retention is the enabler of the best acquisition strategies. For virality or word of mouth, for example, one of the key factors in any virality formula is how many people can talk about or share your product. The more retained users, the more potential sharers. For content, the more retained users, the more content, the more that content be shared or discovered to attract more users. For paid acquisition or sales, the more retained users, the higher lifetime value, the more you can spend on paid acquisition or sales and still have a comfortable payback period. Retention really is growth’s triple word score.

What are effective ways to increase retention?
Okay, so you understand retention is important and want to improve it. What do you do? Well, at a high level, there are three types of efforts you can pursue to increase retention:

  1. Make the product more valuable: Every product is a bundle of features, and your product may be missing features that get more marginal users to retain better. This is a journey for feature/product fit.
  2. Connect users better to the value of the product that already exists: This is the purpose of a growth team leveraging tactics like onboarding, emails and notifications, and reducing friction in the product where it’s too complex and adding friction when it’s required to connect people to the value.
  3. Create a new product: Struggling to retain users at all? You likely don’t have product/market fit and may need to pivot to a new product.

We discuss these strategies in a lot more depth in the upcoming Product Strategy program coming soon from Reforge, and if you really want a deep dive on retention, we build the Retention & Engagement deep dive.

Why does retention differ so much across categories?
One question you might be asking yourself is why does retention differ so much by different categories? This was the impetus for the initial research, and why I couldn’t give a great answer to our board. Every company has a bunch of different factors that impact retention:

  • Customer type: For example, small businesses fail at a much higher rate than enterprise businesses, so businesses that target small businesses will almost always have lower retention.* This does not make them inferior businesses! They also have many more customers they can acquire.
  • Customer variability: Products that have many different types of customers will typically have lower retention than products that hone in on one type of customer very well.
  • Revenue model: How much money you ask from customers and how can play a big role in retention. For example, a customer may be more likely to retain for a product they marginally like if it costs $30 vs. $300,000. A product that expands revenue per user over time can have lower retention than ones that have a fixed price.
  • Natural frequency: Many products have different natural frequencies. For example, you may only look for a place to live once every few years (like my time at Apartments.com), but you look for something to eat multiple times of day (like my time at Grubhub).
  • Acquisition strategy: The way a company acquires users affects its retention. A wide spread approach to new users may retain worse than carefully targeting users to bring to your product.
  • Network effects: Network effects may drive retention rates up more over time vs. businesses that do not have these effects. For example, all of your friends on Facebook or all of your co-workers on Slack makes it hard to churn from either product whereas churning from Calm or Grammarly is entirely up to you.

* In those businesses, the business failing and churning as a result is called “involuntary churn”, though that can also mean a payment method not working for someone who wants to retain in other models.

BONUS: Why are Casey’s benchmarks for consumer transactional businesses lower than others?

For the demand side of transactional businesses, where the retention graph flattens is more important to me than the six month retention rate. And unlike other models, these businesses can take longer than six months to have their graphs flatten. Also, for marketplaces, one of the two common models along with ecommerce in this category, a healthy demand side retention rate is very dependent on what supply side retention looks like and acquisition costs. For example, since Uber and Lyft have to spend so much time and money acquiring drivers due to a low retention rate, in order for their model to work, demand side retention either has to be high or demand side acquisition has to be low cost. For a business where supply side retention is high and acquisition costs are low, demand side retention can be lower, and the company can still be very successful. Etsy and Wag I imagine fit more into this model.

Currently listening to We All Have An Impact by Boreal Massif.