Tag Archives: retention

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.

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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.

Why Consumer Subscription Is So Hard, and What to Do About It

I was recently on Lenny Rachitksy’s podcast again, and one of the topics we discussed was consumer subscription business. I thought I would actually write down a lot of those thoughts and add some more depth for founders and employees working on these businesses. Don’t worry I got more marketplace content on the way as well 🙂

Shortly after I got into tech, investors started to fall in love with subscription business models, mostly on the B2B side. Across many different problems, subscription software sold over the internet seemed to produce dominant tech companies left and right. Even incumbents like Adobe and Microsoft rebuilt their businesses around these subscription models to unlock new growth. And unlike most trends in tech that start on the consumer side and then migrate into B2B over time, the subscription model actually did the reverse. So years after Salesforce, Shopify, etc. became behemoths, people started adapting consumer software to subscription models. Founders and venture capitalists would preach the gospel of predictable revenue and sustainable growth, powered by growth of apps sold over the App Store. These companies have largely under-performed their B2B counterparts, and I’ll use this post to explain why and how to produce superior returns in this category.

Why is B2B Subscription So Good?

While the pessimist would argue B2B subscriptions business actually aren’t that good either and more so looked good during a zero interest environment where “seat growth” was a given for most of your customers, I believe B2B Subscription has some durable advantages we will see play out even in the weakest of markets compared to many other business models. The average VC tweet storm or LinkedIn thinkfluencer will tell you it’s all about predictable revenue. And that’s kind of at too high a level to be instructive in my opinion. First off, revenue isn’t that predictable, but what does improve predictability is that your customers are more rational in their decisions. You tend to be able to evaluate which potential customers will become good sources of revenue, and won’t be terribly surprised by their usage of a product, whether they go out of business, will they grow their usage / seats / plans, etc. And most of these customers are sizable enough where the revenue from them creates very sustainable growth levers whether it be sales-, product-, or marketing-led growth, sometimes a combination of the three. But more importantly, when customers do grow, they create a phenomenon known as net dollar retention. So in any B2B subscription business, sure, some customers will churn. But the ones that do retain tend to invest more in your product over time, growing revenue per customer in a way that covers more than the churn of other customers. Many B2B subscriptions companies are seeing their first years of seats / usage contract due to the economy, but in general, outside of some bad years, net dollar retention will be a core feature of their growth model. 

What is different in Consumer Subscription? In short, everything. 

1. Churn will be higher. Average revenue per customer will be lower. Net dollar retention non-existent.

Consumers are not rational. It is very hard to predict who will churn vs. build habits around your product. So, on average, churn in consumer subscription businesses tend to be a lot higher. Also, the amount made per customer tends to be a lot lower as consumers have less spending power than businesses on average. What makes this fact even worse is even when you activate consumers effectively into paying users who build habits, you do not increase the amount of money you make from them over time outside of increasing prices. This means net dollar retention, the best feature of a B2B subscription growth model, is not even present in a consumer subscription growth model.

When you look at the consumer subscription products that have done the best at scale, they have won by spending an incredible amount of spend on content to prevent churn. These are companies like Amazon Prime, Netflix, and Spotify. That amount of spend on content is usually not replicable for startups.

2. Payments will be less optimized and more expensive

On top of this, mobile apps have become the most dominant product experience for consumer experiences, and the app stores take a significant percentage of subscriptions purchased through them, and prevent alternate forms of payment to avoid that tax. So the margin structure of these consumer businesses are significantly hampered compared to their B2B counterparts. To make matters worse, these app stores are also much worse at collecting payments. One way B2B growth teams help their companies improve is addressing involuntary churn through payment failures. Not only is Apple not near as good at this as, say, Stripe, Adyen, et al., because it controls the entire experience, your growth team cannot optimize it to improve payment failure rate at all. Fortunately, the lockdown on in-app payments is starting to break, allowing consumer companies to improve payment conversion on mobile web while improving margins at the same time, but it’s been a pretty annoying drag on an already difficult business model.

3. Customer acquisition is much harder, less scalable, and has fewer options

B2B subscriptions are sold to teams and companies at scale. Consumer subscriptions are sold to individuals and, at best, families. We already know that means the average order value is that lower. But what does that mean for your acquisition loops. Well, first off, sales is out of the question due its cost vs. the return of a subscription. Secondly, some of the viral and content loops that you see in B2B subscription like sharing a file, inviting a co-worker to collaborate / chat, etc. aren’t really possible in these consumer products. This mainly leaves paid acquisition as the lever for growth. You probably already know my position on paid acquisition as the main lever of growth, but let’s cover it quickly again.

Every company uses paid acquisition to target its best potential customers first. And this usually works with healthy payback periods. But, to scale, the company needs to target more and more customers who look less like the core customer over time. They respond to the ads less, convert worse on the landing page into trials, convert from trials to subscriptions worse, and retain worse after subscribing. All of this leads to predictable degradation in payback periods year over year until, eventually, you run out of people to acquire profitably and paid acquisition is no longer viable. Oh, and by the way, while this was going on Apple decided to torpedo all the ways you track effectiveness of paid acquisition too.

When we were evaluating investing the series A for Calm at Greylock, this is what spooked us. At the time, Calm had only decent annual retention. We could model out a time in the future when it would be impossible for them to grow based on their current numbers. Now, in Calm’s case, they are one of the few companies to improve their annual retention over time, which we’ll talk about in solutions to these issues below.

How to Solve the Systemic Challenges of Consumer Subscription

Now, I’m not going to just doom loop you and this post after ranting about how bad a business model consumer subscription is. I’m on the board of a consumer subscription company after all. Clearly, I believe there are solutions to these problems. Let’s talk through the best ways to fight these limitations and still create big outcomes.

1. Leverage network effects to solve retention and acquisition issues

My main selling points on network effects is that they allow your core product experience to get better faster than the customers you acquire get worse. The next generation of consumer subscription businesses tend to create product experiences that get better over time through either leveraging the data of their users or by offloading content creation costs to suppliers. Duolingo has done a masterful job of keeping retention high in a space with normally high churn because their lessons get better every day based on the feedback loop of their customer usage. We call that a data network effect. Spotify has exponentially more types of content (music, podcasts, video) and exponentially more artists than when it originally launched which attract more listeners. That’s a cross-side network effect.

Companies like Beek and Fable as startups continue to add new creators of content that improve selection or discovery of content for consumers, which keep them subscribed. But also, those creators make money based on overall subscription revenue of the app, so they become promoters of the app and a significant source of low cost acquisition. This allows these companies to rely a lot less on paid acquisition, and some don’t even have it as part of the mix at all. I also think there are many more opportunities to create multiplayer consumer experiences (or direct network effects) to drive low cost acquisition and better subscription retention because your friends or family keep pulling you back into the app. We mostly see this in the games category today.

2. Go multi-product earlier in your lifecycle to make the product stickier and raise price

It’s very hard for single product solutions to maintain the type of long term retention without network effects. So, if network effects do not make sense for the type of product value you’re delivering, launching new products to monetize your existing customers better and open up new customer segments can ease the burden on customer acquisition, raise monetization rates, and raise retention all at the same time. Calm was able to scale out its sleep stories product in a way that raised the retention rate of its meditation customer base as well as open up segments that were less interested in meditation. It turns out selling a product solution for something people have to do everyday (sleep) has a much bigger market than a habit a small percent of the world does (meditation).

3. Open up less saturated acquisition channels

Most consumer subscription business treat their content as their proprietary secret sauce and keep it under lock and key inside paid subscriptions. Much of the time, this means that content isn’t doing all it can to attract new customers who are not even aware of your product. Masterclass is a great example of re-using a lot of the amazing content they sell in their courses and repackaging it for search engines as a taste of what the full courses offer. This has allowed a company that historically 100% grew via paid acquisition to diversify its acquisition sources, bring down payback periods, and find new audiences. Spotify’s playlist sharing was a key growth driver in its early days as lists were shared among friends and publicly on the internet. Spotify and Hulu have also bundled their subscription model to find new audiences and to improve retention for both products.

Thinking about new platforms as well as channels works here too. Most subscription businesses start as apps, but the web opens up a new acquisition channel with significantly better margins because you don’t have to pay the in-app purchase tax. Many of the companies I have worked with have found ways to get conversion on the mobile web just as high as in app over time, or at least use annual subscriptions to dramatically improve payback periods.

4. Start selling to businesses (you knew this was coming, right?)

Well, this one is obvious, and basically the same suggestion as #3. Creating a B2B offering allows you to target a new customer business with a new acquisition loop in sales that can acquire hundreds to thousands of people at the same time inside companies. Headspace and Calm have done a good job of expanding into this model as an expansion from their consumer roots.

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I want to be very clear. These suggestions will not necessarily be a panacea, and they may not make a meaningful enough improvement to take your consumer subscription business beyond scale. Many of the companies I mentioned still may not have long-term success. This is why I advise founders to think through these challenges and opportunities during the zero to one phase of building their vs. being surprised how hard things will be in the growth stage. This is a very hard business to scale toward a venture outcome, and you want every possible thing you can to be working for you because so much of the default business model works against you.

Currently listening to my Avant Pop playlist on Spotify.

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.

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.

Announcing the next Retention Deep Dive, Growth Series, and something new

Over the last few years, I’ve worked with Brian Balfour (CEO Reforge, formerly VP Growth @ HubSpot) as a Growth mentor and contributor to the Reforge programs. These are part-time programs (no need to take time off work) specifically designed for experienced Product Managers, Marketers, Engineers, and UX/Designers in both B2B and B2C companies.

Today, Reforge announced their three upcoming programs this fall:

1) The Retention + Engagement Deep Dive program. I worked closely with Brian developing this program, which looks at every aspect of retention including activation, engagement, resurrection, and churn.

2) The Growth Models Deep Dive program. This is a new, detailed examination of a key growth topic Brian and I developed this year with Kevin Kwok.

3) The Growth Series program. This is Reforge’s flagship program that provides an overview of the key topics in growth that’s been 100% revamped to reflect today’s growth challenges.

Apply to Reforge (Takes ~5 minutes)

Each Reforge program runs from September 24th through November 16th. Seats always fill up fast, and I’m excited to be involved. I’ll also be doing some speaking and Q&A during the events.

Besides Brian, Kevin and myself, other hosts include Andrew Chen (General Partner @ Andreessen Horowitz), Shaun Clowes (VP Growth @ Metromile, former Head of Growth @ Atlassian), Dan Hockenmaier (former Director of Growth @ Thumbtack), Heidi Gibson (Sr. Director of Product Management @ GoDaddy), and Yuriy Timen (Head of Growth @ Grammarly).

About the Reforge Programs
These are all invite-only, part-time programs that last 8 weeks. Each program requires a time commitment of 4 – 8 hours per week. They’re designed for Product Managers, Marketers, Engineers, and UX/Designers in both B2B and B2C companies looking to accelerate growth in their companies and in their careers by developing a systematic approach to thinking about, acting on, and solving growth problems.

In addition to the course material, we’ll also hear from leaders in the industry through interviews, live talks, and workshops, including:

Fareed Mosavat, Growth @ Slack
Ken Rudin, Head of Growth, Search @ Google
Brian Rothenberg, VP Growth and Marketing @ Eventbrite
Ravi Mehta, Product Director @ Facebook
Mike Duboe, Head of Growth @ Stitch Fix
Josh Lu, Sr. Director, PM @ Zynga
Guillaume Cabane, VP Growth @ Drift
Matt Plotke, Head of Growth @ Stripe
Joanna Lord, CMO @ ClassPass
Gina Gotthilf, ex-Growth Lead @ Duolingo
Elena Verna, SVP Growth @ MalwareBytes
Kieran Flanagan, VP Growth/Marketing @ HubSpot
Naomi Pilosof Ionita, Partner @ Menlo Ventures
Nick Soman, ex-Growth Product Lead @ Gusto
Nate Moch, VP Growth @ Zillow
Simon Tisminezky, Head of Growth @ Ipsy
Steve Dupree, Former VP Marketing @ SoFi
See the full list here

Here’s some more detail about each program below:

About the Retention + Engagement Deep Dive
Retention + Engagement Deep Dive zooms in on one of the most important sub-topics of growth.

Retention and engagement separates those companies in the top 1% of their category. Every improvement in retention improves acquisition, monetization, and virality. But moving the needle on retention is hard.

This program takes a microscope to every aspect of retention, including:

  • Properly define, measure, segment, and analyze your retention
  • Find and quantify the three moments every new user goes through to create a long-term retained user
  • Construct a high performing activation flow from the ground up using detailed strategies across product, notifications, incentives, and more
  • Layer your engagement strategies to build a compounding growth machine at your company
  • Articulating retention and engagement initiatives across teams, as well as influencing how leaders think about retention in your company
  • Walk step-by-step through of lessons applied to dozens of examples from companies like Instagram, Zoom, Spotify, Everlane, Airbnb, Turbotax, Jira, Credit Karma, Blue Bottle
  • And more…

The Retention + Engagement Deep Dive is designed for growth professionals who are looking to zoom in on retention, either because their job is focused on retention, or because they already have an advanced working understanding of the quant and qual fundamentals of growth and are looking to build additional competency in retention and engagement.

Apply for the Retention + Engagement Deep Dive

About the Growth Models Deep Dive
The new Growth Models Deep Dive addresses an essential new skill and topic that every growth practitioner needs to understand. Your growth model is the essential tool that drives alignment, prioritization, strategic investments, metrics, and ultimately, growth. Without it, your team ends up setting faulty goals, focusing on sub-optimal initiatives, and running in opposite directions.

This program goes deep into growth models across companies. You will:

  • Learn how the fastest growing products actually grow (hint: the answer isn’t funnels)
  • Dissect how the fastest growing products like Uber, Slack, Dropbox, Stripe, Airtable, Instagram, Fortnite, Tinder, and others grow using growth loops
  • Learn the detailed components of 20+ growth loops
  • Systematically construct growth loops your product can use after analyzing the three qualitative properties of every growth loop
  • Assess gaps and uncover opportunities for growth by identifying, measuring, and analyzing your products existing growth loops
  • Complete a step-by-step walkthrough to build your quantitative model for a single loop and your entire product
  • Communicate actionable insights from your growth model to obtain buy-in from leadership and across teams
  • And more…

The Growth Models Deep Dive is designed for growth professionals looking to focus on growth modeling, either because their job requires modeling their company or product’s growth or because they’re in a leadership role. It’s especially useful for growth leaders looking to influence leadership, set a team’s direction, and rally colleagues using growth models.

Apply for the Growth Models Deep Dive

About the Growth Series
The Growth Series is a comprehensive overview of the key topics in growth. The program is designed to help you accelerate growth of your product, company and your career by creating a prioritized list of retention strategies, building your quantitative growth model, and much more. Plus, the Reforge team spent +100 hours collecting feedback, investigating new growth concepts with experts, and analyzing the latest strategies coming out of top companies to completely overhaul the content with new topics, frameworks, and relevant examples.

During the Growth Series, you’ll learn:

  • Going from understanding one or two pieces of your growth model to understanding how the entire system works together
  • Evaluating the key components of growth (acquisition, retention, monetization) and how they feed one another
  • How to construct a holistic growth model, bringing together all the components of the funnel
  • How to understand and evaluate the user motivations behind the levers in your growth model
  • Running a continual, self-reinforcing experimentation process to execute against your growth model and user psychology
  • Learn how to properly call, dissect, and analyze an experiment, plus implement the results across your team
  • And more…

The Growth Series is designed for practitioners who already know the basics of growth and are figuring out how to take the next step. Participants are assumed to have knowledge about A/B testing, ad buying, and other fundamental tactics, and are ready to take on the bigger challenge of thinking about the entire picture of growth and forming a coherent and compelling strategy.

Apply to the Growth Series

Feature/Product Fit

Through various methods, Silicon Valley has drilled into the minds of entrepreneurs the concept of product/market fit. Marc Andreessen says it’s the only thing that matters, and Brian Balfour has an amazing series of posts that talk about how to find it. But what happens after you find product/market fit? Do you stop working on product? I think most people would argue definitely not. Post-product/market fit, companies have to balance work creating new product value, improving on the current product value, and growing the number of people who experience the current value of the product. While I have written a lot about how to do that third step, and even wrote a post about thinking about new product value, I haven’t written much about how do that second part.

What is Feature/Product Fit?
Every product team tries to make their core product better over time. But sadly, at most companies, the process for this is launching new features and hoping or assuming they are useful to your existing customers. Companies don’t have a great rubric for understanding if that feature is actually valuable for the existing product. This process should be similar to finding product/market fit, but there are some differences. I call this process feature/product fit, and I’ll explain how to find it.

In product/market fit, there are three major components you are searching for. I have written about my process for product/market fit, and Brian Balfour, Shaun Clowes, and I have built an entire course about the retention component. To give a quick recap from my post though, you need:

  • Retention: A portion of your users building a predictable habit around usage of your product
  • Monetization: The ability at some point in the future to monetize that usage
  • Acquisition: The combination of the product’s retention and monetization should create a scalable and profitable acquisition strategy

Feature/Product Fit has a similar process. We’ll call this the Feature/Product Fit Checklist:

  • The feature has to retain users for that specific feature
  • The feature has to have a scalable way to drive its own adoption

Feature/Product Fit has a third requirement that is a bit different: the feature has to improve retention, engagement, and/or monetization for the core product.

This last part can be a bit confusing for product teams to understand. Not only do the products they are building need to be used regularly and attract their own usage to be successful, they also need to make the whole of the product experience better. This is very difficult, which is why most feature launches inside companies are failures. What happens when a feature has retention and adoption, but does not increase retention, engagement, or monetization for the company? This means it is cannibalizing another part of the product. This might be okay. As long as those three components do not decrease, shipping the feature might be the right decision. The most famous example of this is Netflix introducing streaming so early in that technology’s lifecycle, which cannibalized the DVD by mail business, but was more strategic for them long term.

What is a Feature Team’s Job?
You would be surprised how many core product features are shipped when the new feature decreases one of those three areas. How does this happen? It’s very simple. The team working on the feature is motivated by feature usage instead of product usage, so they force everyone to try it. This makes the product experience more complicated and distracts from some of the core product areas that have feature/product fit.

If you own a feature (and I’m not saying it’s the right structure for teams to own features), your job is not to get people to use that feature. Your job is to find out if that feature has feature/product fit. You do this by checking the three components listed above related to feature retention, feature adoption, and core product retention, engagement and monetization. During this process, you also need to determine for which users the feature has feature/product fit (reminder: it’s almost never new users). Some features should only target a small percentage of users e.g. businesses on Facebook or content creators on Pinterest. Then and only then does your job shift to owning usage of that feature. And in many teams, it’s still not your job. The feature then becomes a tool that can be leveraged by the growth team to increase overall product retention, engagement, and monetization.

Mistakes Feature Teams make searching for feature/product fit
Feature teams commonly make mistakes that dissatisfy the third component of feature/product fit at the very beginning of their testing.

  • Mistake #1: Email your entire user base about your new feature
    Your users do not care about your features. They care about the value that you provide to them. You have not proven you provide value with the feature when you email them early on. Feature emails in my career always perform worse than core product emails. This inferior performance affects the value of the email channel for the entire product, which can decrease overall product retention.
  • Mistake #2: Put a banner at the top of the product for all users introducing the new feature
    New features usually target specific types of users, and are therefore not relevant to all users. They are especially irrelevant to new users who are trying to learn the basics about the core product. These banners distract from them, decreasing activation rates. It’s like asking your users if they’re interested in the Boston Marathon when they don’t know how to crawl yet.
  • Mistake #3: Launch with a lot of press about the new feature
    PR for your feature feels great, but it won’t help you find feature/product fit. PR can be a great tool to reach users after you have tested feature/product fit though. It should not happen before you have done experiments that prove feature/product fit. And it will not fix a feature that doesn’t have feature/product fit.

Many features won’t find feature/product fit
Many of the features product teams work on will not find feature/product fit. When this happens, the features need to be deleted. Also, some older features will fall out of feature/product fit. If they cannot be redeemed, they also need to be deleted. If you didn’t measure feature/product fit for older features, go back and do so. If they don’t have it, delete them. Some of our most valuable work at Pinterest was deleting features and code. A couple examples:

    • The Like button (RIP 2016): People did not know how to use this vs. the Save button, leading to confusion and clutter in the product

    • Place Pins (RIP 2015): Pinterest tried to create a special Pin type and board for Pins that were real places. As we iterated on this feature, the UI drifted further and further away from core Pinterest Pins and boards, and never delivered Pinner value

  • Pinner/Board Attribution in the Grid (RIP 2016): Attributing Pins to users and their boards made less sense as the product pivoted from a social network to an interest network, and cluttered the UI and prevented us from showing more content on the screen at the same time

How do I help my feature find Feature/Product Fit?
All features should be launched as experiments that can test for feature/product fit. During this experiment, you want to expose the new feature to just enough people to determine if it can start passing your feature/product fit checklist. For smaller companies, this may mean testing with your entire audience. For a company like Pinterest, this might start with only 1% of users. The audience for these experiments is usually your current user base, but can be done through paid acquisition if you are testing features for a different type of user.

I’ll give you a few tactics that have helped the companies I’ve worked on find feature/product fit over the years. Most good product development starts with a combination of data analysis and user research. User research should be involved at the right times to add the most impact, prevent confirmation bias, and determine what components users are struggling to see the value of. For example, when we launched the Grubhub mobile app, we saw in the data when people used the current location feature, their conversation rate was lower than people who typed in their address. This turned out to be an accuracy problem, so we turned that component until off until we were able to improve its accuracy.

In research, we saw people were having trouble figuring out which restaurants to click on in search results. On the web, they might open up multiple tabs to solve this problem, but this was not possible in the app. So, we determined what information would help them decide which restaurants were right for them, and started adding that information into the search results page. That included cuisine, estimated delivery time, minimum, star rating, number of ratings, On the surface, the page now feels cluttered, but this improved conversion rates and retention.

Since Grubhub is a transactional product, we were able to leverage incentives as a strategy to help find feature/product fit. Our early data showed that people who started to use the mobile app had double the lifetime value of web users. So, we offered $10 off everyone’s first mobile order. This transitioned web users to mobile for the first time and acquired many people on mobile first. The strategy was very successful, and the lifetime value improvements remained the same despite the incentive.

Grubhub also uses people to help find feature/product fit. Since mobile apps were new at the time (and we were the first food delivery app), we monitored any issues on social media, and had our customer service team intervene immediately.


Not all complaints are this entertaining.

At Pinterest, we launched Related Pins in 2013. For Pinterest, we did not have a revenue model at the time, so tactics around incentives and people do not make as much sense. One thing we did instead was use notifications to drive feature/product fit. Once this algorithm was developed, after you Pinned something, we could now email you more Pins you might like that are related to that Pin. These emails were very successful.

Pinterest also used the product to drive feature/product fit. We launched the algorithm results underneath the Pin page to start, and interaction when people scrolled was great. But many people didn’t scroll below the Pin. So, we tried moving them to the right of the Pin, which increased engagement, and we started inserting related Pins of items you saved into the core home feed as well, which increased engagement.

The Feature/Product Fit Checklist
When helping a product find feature/product fit, you should run through this checklist to help your feature succeed:

  • What is the data telling me about usage of the feature?
  • What are users telling me about the feature?
  • How can I use the core product to help drive adoption of the feature?
  • How can I use notifications to help drive adoption of the feature?
  • How can I use incentives to help drive adoption of the feature?
  • How can I use people to help drive adoption of the feature?

When confirming feature/product fit, you need to ask:

  • Is the feature showing retention?
  • What type of user(s) is retaining usage of the feature?
  • How do I limit exposure to only those users?
  • What is the scalable adoption strategy for the feature for those users?
  • How is this feature driving retention, engagement, or monetization for the overall product?

Every company wants to improve its product over time. You need to start measuring if the features you’re building actually do that. You also need to measure if existing features are adding value, and if not, start deleting them. Asking these questions when you build new features and measure old features will make sure you are on the path to having features that find feature/product fit and add value to users and your business.

Thanks to Omar Seyal and Brian Balfour for reading early drafts of this.

Currently listening to Breaking by Evy Jane.

The Email Marketing and Notifications Evolution Inside Companies

“Stop sending emails like a marketer. Start sending email like a personal assistant.”

I’ve communicated this line in a lot of my presentations on growth, but I haven’t talked about in depth the evolution on how to get there. There is a clear evolution that companies follow in terms of evolving their emails and notifications, from not sending them at all, to sending one-off blasts to their entire audience, to creating a lifecycle, to having a holistic personalized messaging platform. Having worked on these systems at my last two companies, I thought it would be beneficial to outline these transitions for people earlier along in their process.

Phase 0: We Hate Email

“We hate email, so we don’t send it to our customers.”

Almost every company I have worked with has communicated this at some point in its early stages, and it’s always wrong. Except for very specific groups, people don’t dislike email; they dislike bad email. The path to figuring what email your customers would like to receive is largely to ask what kind of value do they see in my product, and can I deliver that value in email form.

Phase 1: Mass Promotional Email + Personalized Order Notifications
If you are a transactional service, you have to send personalized order notifications, so that is where most companies start. At some point later, companies start sending mass emails to their broader audience about certain things like new features, discounts, etc. This effort will show improvements in key metrics, but it is very unsophisticated.

Promotions train users to wait for promotions to order, decreasing profitability. Also, with this approach, marketing is assuming a cadence for the customer instead of adapting to the customer’s cadence (and ultimately improving customer cadence to increase lifetime value).

If you are engineering constrained, there are some simple optimizations to this approach that will improve your performance:

  • Develop additional emails intended to drive habit formation (instead of just timely purchase). Examples include trending items, item sales, new merchants added, recommended items
  • If emails are successful, test them as push notifications as well
  • Take existing confirmation emails, and add marketing messages to them (other things to buy, set up a re-order, most popular items, etc.)
  • Send every non-transactional email and tweak to confirmation emails as an experiment with an enabled and control group to prove impact on lifetime value vs. unsubscribes/push permissions/app deletions

Phase II: Moving to Lifecycle Messaging
In Phase II, these additional emails and notifications that have been successful in Phase I start to form an automated program that consistently drives additional engagement from customers. In order to address messaging fatigue, these email and notification templates are managed to a frequency per month based on the team’s expected value of a good customer. This frequency is not based on data, but if everyone used the product properly, what would ideal frequency look like. The goal is to use messaging to remind people of the service and reinforce the habit. They are paired with personalized discount emails intended to drive new use cases and increase frequency.

These emails and notification templates are also managed against each other, so messaging does not get stale. For example, if you have three templates outside of confirmation emails, and you sent template 1 last week, you would attempt to send templates 2 and 3 before sending template 1 again. Also, each week, these emails have new subject lines to present them from looking like the same email as the previous weeks.

Phase III: Holistic, Personalized Messaging
As the Phase II approach flattens in terms of the additional impact it can drive, companies shift toward a more holistic, personalized system. This is a considerable investment, which we made at Pinterest. Essentially, product and engineering determine for each customer:

  • The right content
  • The right time to send it (day of week and time or day)
  • The right amount (how many emails and pushes to send)
  • The right channel (email, push, or both)

This requires a team to develop a log of every email/push sent to a subscriber, when it was sent, when it was opened, when it was clicked, what downstream engagement occurred from clicks, and which template it was. All emails and notifications are run through the same experiment dashboard as product changes to understand the impact on all key metrics. From this, it needs to determine:

  • The best day(s) of week and time(s) to send messages to each user
  • A prioritization of the templates to send based on historical click through rates and/or purchase rates
  • How many messages per a generic time frame maximizes lifetime value of each user

This usually starts via a rules based approach, and eventually becomes powered by machine learning. If you lack enough historical data on a user to do this, for example new users, you group people who used to look like those users as a segment i.e. previous new users and look at the best performing approach for them. Email can no longer be considered marketing at this point. It is considered an extension of the core product.

The team also starts optimizing deliverability through choosing better message transfer agent partners and segmenting IP addresses for different templates to isolate issues. The team may also start investing in more advanced security measures like DMARC.

This is a considerable investment, which is why most companies only start building this once there are sending millions of emails a day with a lot of history from operating in the first two phases originally. At this point, companies know the value of email, and can justify the investment.

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In my opinion, every company should end up at phase III at some point. The question is how long it takes to get there. This varies based on engineering constraints, scale, and how long it takes emails and notifications to flatten off in terms of additional engagement by the previous phases. Outsourcing this to a marketing technology company is also very problematic as it requires access to all of your user data, and any migration of data from system to system slows down performance. At a certain scale (like Pinterest), it is not even possible.

If you’re not at Pinterest’s level of sophistication, don’t dismay. Very few companies are. Just start to think about the long term evolution, and when is the right time to push for a step change in email and notification performance vs. continued optimization. It’s a big investment to shift from phase to phase, but the returns are usually worth it, and the impact of these emails and notifications in the current phase, and the struggle to improve their performance, should be what drives that decision to make additional investment to get to the next phase.

Currently listening to XTLP by μ-Ziq.

Align Revenue to the Value You Create

“We want to create more value than we capture.”*

Tim Kendall, the former President of Pinterest, repeated those words at an all hands to describe our strategy for monetization a few years ago. My role as an advisor to Greylock’s portfolio companies allows me to work with many different types of businesses: consumer social, marketplaces, SaaS, etc. I’ve come to realize this saying describes an optimal strategy for a lot more than just an ad-supported revenue model. It should actually be the guiding light for most subscription software businesses.

Align Revenue To The Value You Create
One of the most common questions I receive from subscription businesses is when to ask for a signup and when to start charging customers. In freemium businesses, the slightly different question is how aggressively you upsell the paid product, and how good you make the free product. If you talk to entrepreneurs, you will get definitive answers from them, but they are frequently the opposite of each other. “You should never give away your product for free!” “You’ll never succeed without a free trial!” “Ask for credit card upfront! People won’t take the product seriously.” “Never ask for a credit card upfront! You’ll shoo too many people away.” The default answer I gave to entrepreneurs after hearing all of this feedback is that it depends on the business and needs to be tested.

As I researched more into the problem, these questions actually seemed to be the wrong questions to be asking. Harkening back to Tim Kendall’s advice, I started asking entrepreneurs, “What is the path to actually creating value from your service for your customers? How long does it take, and what actions need to be accomplished?” In other words, very similar advice to what is a successful onboarding? Once you learn that, you can determine how to capture some of the value you create.

Capture Value For The Business After Value Has Been Created For The Customer
When your product is subscription based, the prime time to ask for a subscription is after a successful onboarding occurs. It frequently is based on usage, not time. Dropbox is a famous example. The product is free up to a certain amount of storage. Once a user hits that amount of storage, they cannot add more files to Dropbox without paying. This storage amount also happens to be around the point where Dropbox becomes a habit, and represents real switching costs to find another way to share files across devices. So their conversion rates to paid are very high without any sort of time-based trial period. They don’t have a free product and a paid product; they have a free introduction to their paid product, and it becomes paid as soon as value has been created for the customer.

Your company may not have a long time to demonstrate value though, which may force your product to change to display (and capture) value more quickly. For startups based on search engine traffic, people reach your page with intent at that moment, and you frequently learn that this initial session is your only chance to convert them. So you push for a signup during that session after showing a preview of the value you can provide.

That is what we implemented at Pinterest, and it worked well, but it definitely created backlash from users for whom we had not yet created enough value. Once Pinterest was relevant on search engines for multiple topics, we saw people come back multiple times, and pulled back the signup walls on first visit. At that point, Pinterest was confident users would come back and thus focused on demonstrating more value before asking for signup.

Don’t Try To Capture Value In A Way That Reduces Value Created
It’s interesting to map the revenue growth of Dropbox to Evernote over the same time period. Evernote allows you to store an unlimited number of files and only makes you pay for advanced features like offline storage, storing large files, and (later) sharing on more than two devices. These features would have actually increased value created and switching costs if they were free, because Evernote’s value prop is about being able to access notes everywhere. If Evernote had instead mined their data and seen that people stick around after, say 50 notes, that would probably have had more effective monetization.

You only want to hide features from free users if they do not create habits or virality. Hiding sharing functionality before payment never makes sense because it introduces more people to the product for free. Hiding functionality that helps create retention also doesn’t make sense because you can always upsell retained users, but you can never upsell users who did not see the value and therefore don’t come back.

Decreasing Churn Is Long Term More Important Than Maximizing Conversion
Many people will decry that this strategy actually reduces revenue. In the short term, this sentiment is likely to be true. Decreasing churn might have a lower conversion rate upfront, but it aligns to long term successful retention. Churn rate is usually one of the biggest barriers to long term growth, so it’s worth thinking about this type of strategy even if it has a short-term decrease in revenue. It can be much harder to re-acquire someone after they have canceled, than charge someone for the first time who has been receiving regular value because you charged them for value you didn’t create.

What usually happens when a company captures more value than they create is they will have high revenue growth for a period of time (with a lot of investor enthusiasm), followed by a flattening of growth and then a steep revenue decline. This happens because revenue growth is a lagging indicator. Usage growth is the leading indicator. When usage lags revenue, this predicts churn. As you churn more and more users, it becomes harder and harder (and eventually impossible) to replace those churned users with new users to keep revenue metrics flat. Look at Blue Apron’s valuation to see this playing out currently as subscribers start to decrease for the first time year over year.

You Want Your Revenue Model To Align As Closely As Possible To The Value You Create
Lastly, as you start charging customers to capture value you create, you want your business model to align to the value that is being created. Email marketing tools have mastered this. Email marketing tools’ value is based on reaching customers with messages. Most email marketing tools charge on a CPM (i.e. a price for every thousand emails you send via their platform). As your email volume increases, they continue to drop the CPM. This make these companies more money because customers are sending a lot more email over time. But it actually becomes more valuable to the customer as well, because email is now cheaper on a per unit basis to send.

Compare this to Mixpanel, a product analytics tool. Mixpanel charges per event, and their value is delivering insights based on data from events being logged on your website or mobile app. The more events that are tracked in Mixpanel, the more insights the customer can receive, and the stickier the product. Since Mixpanel is charging per event though, a weird calculus emerges for the customer. The customer has to ask if tracking this event is worth the cost because not all events are created equal. Meaning the customer has to decide which data is important before they use the product. So, Mixpanel’s revenue model actually hurts its product value.

— 

It’s easy for subscription businesses to get attracted to the allure of short term revenue. The goal of your business is first to create value. The creation of that value and the understanding of how it’s created allow for more optimal and sustainable revenue generation opportunities. Don’t pursue short term revenue opportunities that prevent the customer from understanding the value your company creates. When you are generating revenue, you want to align that revenue model to how value is created for your customer. If you’re not sure, err on the side of creating more value than you capture rather than the opposite. This leads to long term retention and the maximization of revenue.

Naomi Ionita, General Partner at Menlo Ventures and former growth leader at Invoice2go and Evernote, and I talk more about this and other subscription growth problems in the Greymatter podcast.

*This quote I believe originally stems from Brian Erwin.

Currently listening to Shape the Future by Nightmares on Wax.

What Are Growth Teams For, and What Do They Work On?

This blog post was adapted from a presentation I did recently. Hence, slides. Don’t say I didn’t warn you.

I receive a lot of questions about growth teams. Naturally, there is a lot of confusion. Is this marketing being re-branded? Who does this team report to? What is the goal of it? What do they actually work on? When do I start a growth team for my business?

The purpose of growth is to scale the usage of a product that has product-market fit. You do this by building a playbook on how to scale the usage of a product. A playbook can also be called a growth model or a loop.

The first question you should ask before asking about growth is if you have product-market fit?

The traditional definition above is qualitative, and if you’re like me, you like to have data to answer questions. The best way to get that data for most businesses is to measure retention.

The best way to identify the key action is to find a metric that means the user must have received value from your product. The best way to understand the frequency on which you should measure that metric is how often people solved this problem before your product existed. Let’s look at some examples from my career.

For Pinterest, a Pinner receives value if we showed them something cool related to their interests. The best way to determine if the Pinner thought something we showed them was cool is that they saved it.

For Grubhub, this was even easier to determine. People only receive value if they order food, and when we surveyed people, they ordered food once or twice a month (except for New York).

Once you have a key metric and a designated frequency, you can graph a retention curve or a cohort curve. If it flattens out, that means some people are finding continual value in the product. But that is not enough.

Brian Balfour has a great post on this, which he calls product-channel fit.

If you’ve been around startups for a while, you might remember this tweet from Paul Graham. It talked about the fastest growing startup Y Combinator has ever funded. It is a graph of revenue growth from $0 to $350,000 per month in just 12 months.

The startup was Homejoy, an on demand cleaning service. Investors liked this graph, so they gave the company $38 million to expand.

20 months later Homejoy shut down. From a post-mortem of the company, I highlight the following quote.

If you discounted to get to product-market fit, you didn’t get to product-market fit. Product-market fit is not revenue growth, it’s not growth in users, it’s not being #1 in the App Store. Product-market fit is retention that allows for sustained growth.

So, I though product teams were in charge of creating a product people loved, and marketing teams were in charge of getting people to try the product. What changed?

What changed is an acknowledgement of what actually drives startup growth. There are three main levers. Phase I is simultaneously the most important and the least understood. In Phase I, you change the product to increase its growth rate. Some changes include improving onboarding, helping the product acquire more customers through activities like virality or SEO, incresing the conversion rate, et al.

These initiatives are “free” in that they don’t require an advertising budget. Their cost is the opportunity cost of a product team’s time. They are measurable in that you can create an experiment and understand the exact impact of the change. They are also scalable in that if you make a change that, say, improves your conversion rate, and it has a certain amount of impact, it likely will have that same impact tomorrow, weeks from now, and potentially even years from now.

The other two phases are what we traditionally think of as marketing. Performance marketing initiatives, like buying ads on Facebook or Google, are also measurable and scalable, but scale with an advertising budget. Brand marketing usually requires an even larger ad budget, and is harder to measure or scale. The time frame over which brand marketing works takes years, and can be hard to confirm. If you do create a PR campaign or a TV ad that seems to work on a more immediate time frame, it can be hard to scale. that is because brand marketing always requires new stories to keep people’s attention.

This is why marketing can’t be in charge of all growth initiatives. They don’t have access or capability to the most important ones. They might know they need to improve the site’s conversion rate or get more traffic from referrals, but they don’t have access to the product roadmap to get them prioritized appropriately, and if they do get engineering and design help, they don’t have the expertise in working with them to build the best solutions.


Perhaps what’s more important to understand in the difference between marketing and growth is how the traditional marketing funnel changes with startups. Above is the traditional marketing. This model is based on the old school model of product development before the internet in spending a lot of money to make people want things.

Startups by definition should be making things people already want. When you do that, you can invert the funnel and focus on people that already want the product or people that are already using it. This is more effective on a small (or no) budget.

When you translate that into tactics, you see how product-driven growth initiatives dominate the top of the list of priorities. It does not mean you won’t work on performance marketing or brand marketing, but that they usually become important later on in a product lifecycle as an accelerant to an already sustainably growing company.

So I spent a lot of time explaining why growth is different from marketing. How is different from product?

Growth teams don’t create value. They make sure people experience the value that’s already been created.

The most common examples to start a growth team to address are:

  • improving the logged out experience (for conversion or SEO)
  • sending better emails and/or notifications
  • increasing referrals or virality
  • improving onboarding

SEO and onboarding are harder places to start because their iteration cycles are much longer than the other areas.

Growth teams don’t start by finding mythical VP’s of Growth to come in and solve all of your problems. They are usually started by existing employees at a company (or founders) that really understand the company and what’s preventing it from growing faster. They report to their dedicated functions, but sit together to focus on problem solving.

To find out which area you work on after you have the team, you have to analyze the data. For example, at Pinterest, they originally wanted me and my team to work on SEO. What we saw was that while there was a lot of opportunity to get more traffic via SEO, a bigger issue was the conversion rate from that traffic. So we decided to work on conversion instead.

Then we had to figure out what to work on. Jean, an engineer on the team, had recently run an experiment that gave us a key insight. So, we said, we could use this same modal when people clicked on Pins. Clicking on a Pin could show enough interest in Pinterest for you to want to sign up.

The other thing people did when they liked what they saw scroll to see more. So, we decided to try stopping them where we stopped the Google crawler, and asking them to sign up then.

It took Jean two days of work to launch this experiment, and it resulted in a much bigger impact than expected.

So that’s an example of finding a conversion issue in you data, and putting together a really scrappy experiment to try to improve it. What else can growth teams work on? Here are some examples from my time at Pinterest, and some best practices we’ve learned.

Usually, the biggest area a growth team focuses on improving is retention. That’s right; growth teams are not just about acquisition. Retention comes from a maniacal focus on improving the core product, which I define as core product, not growth, work. Where growth comes in is reducing friction to experience that core product. Simplifying how the current product works usually has much more impact than adding new features. New features complicate the product, making it harder for new people to understand.

So how do you simplify the core product? Well, you have to have data to understand what people do, and pair it with qualitative research to understand why they do it. We spent countless hours at Pinterest putting laptops in front of non-users watching them sign up for the product to figure out why people didn’t activate.

At Grubhub, data pointed out that Grubhub was an S curve when it came to both conversion and retention. This graph is a (now very old) graph of conversion rate in Boston based on how many restaurants Grubhub returned when you searched your address. After 55 results, conversion rate essentially doubled for new and returning users.

Qualitative research gave us different insights. When we asked users why they didn’t use Grubhub more often, they would say, “it’s expensive.” We thought that was weird, because Grubhub wasn’t charging them anything. What they meant is that delivery was expensive due to minimums and delivery fees. So, we went back to our restaurants, convinced a few to try lowering their minimums and fees to see if increased volume could make up for lower margin. When it did, we creates case studies to help convince other restaurants.

At Pinterest, we simplified the signup and onboarding flow. What used to be a flow that required five steps was now three with one of them pre-filled and the other two optional. What we did do was introduce friction that we knew made it more likely a Pinner would find content they care about. This was asking them which topics interested them before showing them a feed of content.

We also realized that the more content people see, the more likely they will find something they like, which will lead to retention. So, we removed content around Pins that was non-critical, like who Pinned it to what board and how they described it. All of these increased activation rates.

We also contextually educated people on what to do next when they were onboarding. There is a common saying that if you need to add education to your design, it’s a bad design. It’s pithy and sounds smart, but it’s actually dangerous. My response is that a design with education is better than a design that doesn’t educate.

Search engine optimization has been a really great lever for organic growth for every company I’ve worked on in my career. It’s not for every business though. People need to already be searching for what you do. That alone isn’t enough though. You need to be an authority on the subject, which Google determines by relevant external links to your domain and your content. You also need to be relevant to what was just searched.

We worked on improving both of these at Grubhub. When Grubhub launched new market, by definition we weren’t locally relevant yet. So we would go to local blogs and press outlets and tell them we were launching there, and that we wanted to give their readers $10 off their first order. All they had to do was link to a page where the discount would auto-apply. After a while, that page would have enough local links so that even though the promotional discount was over, it would still rank #1 for the local delivery terms e.g. “san francisco food delivery”.

For relevance, Grubhub knew which restaurants delivered where, their menu data, and reviews from real people. So we aggregated them into landing pages for every locale + cuisine combination e.g. ‘nob hill chinese delivery”.

We applied the same landing page strategy at Pinterest. While Pinners had created boards on their favorite topics, it was one person’s opinion on what was relevant for a topic. Pinterest has repin data globally for every topic, so we knew what the best Pins were across the Pinterest community. So we created topic pages with the best Pins, and they performed better than individual boards on search engines and with search engine users.

We also worked a lot on emails and notifications on the Pinterest growth team. Emails are a key driver of retention. They won’t solve your retention problem, but they will certainly help if you do them right. At every company I have been at, people hated email and didn’t want to send them to their customers. When they finally did, they saw lifts. You are not your customer. You get more email than they do. Emails help them if they’re connected to the core value of why they use your product. Emails are not helpful if they’re pushing a marketing message.

At Pinterest, I made this mistake. I set up campaigns with emails that explained all of the things Pinterest could do. People don’t care about what Pinterest can do. They care about seeing cool content related to their interests. We needed to stop sending email like a marketer, and start sending email like a personal assistant. So we replaced those emails with popular content in topics of interest for each Pinner, and our retention increased.

Then, we built a system around it. Each Pinner likes different content, at different times, and different amount of it. So we learned for each Pinner what content they liked, when they liked to receive emails and notifications (based on when they opened them), and how much they liked to receive based on testing different volumes and seeing open rate impacts.

If you’re testing emails and notifications, you can test manually first, then automate and personalize. What I have learned at Pinterest and Grubhub is what seems to be worth testing. At Pinterest, one engineer tested 4,500 different subject lines, resulting in hundreds of thousands of additional weekly active users. Around the same time, we spent three months redesigning all of our emails, and it had no impact on usage.

A common issue I see with growth and marketing teams is they think that emails and notifications can only have positive impact. This is not true. You have to measure the lift in usage vs. the unsubscribes (and the impact of an unsubscribe) and app deletions. Those will impact usage, and you need to know how.

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Growth teams have a clear purpose, and that purpose makes sense only if you have first found product-market fit. Once you have that, you will find traditional product and marketing lacking in their ability to help scale usage of your product. That’s where growth teams come in. Growth teams use data and qualitative research to help understand the frictions that prevent more people from finding the value in your product. That can mean acquisition, but it can also mean reducing friction in the core product, working on conversion or onboarding, or finding ways to remind existing users about the value you’re creating. If you have questions about growth teams, don’t hesitate to reach out to cwinters@greylock.com.

This presentation was made in conjunction with @omarseyal, who is awesome.

Currently listening to Everybody Works by Jay Som.