Tag Archives: product

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.

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 Three Personas: How Marketing, Product, and Analytics Attempt to Define The Customer

In my career, I’ve worked in marketing, product, and analytics. While the American Marketing Association defines all of that as marketing, the reality is those are rarely under the scope of one functional team, and the people in those groups see things very differently much of the time. One of the key ways this manifests that creates confusion for the organization is in the creation of personas. All three groups have their own ways to define personas that don’t tell the same story. And in many cases, these are called marketing personas even though they are very different. I’ll walk through each of them to try to define them separately, and talk about how to use each of them best and avoid common pitfalls.

The Analytics Persona
The analytics persona is the most straightforward of the group. The analytics persona is created by looking at clusters of users based on their usage and defining them based on that usage. At Pinterest, these were defined as core, casual, marginal, and dormant users. Core people came every day, casual people came every week, marginal people came every month, and dormant users had stopped coming to Pinterest altogether. This segmentation can be useful to see if your product is becoming more or less engaging over time. Key projects can include migrating people from, say, marginal to casual, by understanding the statistical differences between the two groups. Then, a product team might take something the casual group does that the marginal group does not and try to make the marginal group try to do that thing as well. Some of these differences are just correlation (or represent the people more so than the action itself being important), but some may be causal, and experiments like incentives or education to marginal users may help them become casual users.

The Product Persona
The product persona, just like the analytics persona, focuses on understanding existing users. In contrast to the the analytics persona, it relies on qualitative research to define who these people are, not just what actions they take. Someone that uses the product monthly can be the same persona as someone who uses it every day. When we built our personas at Grubhub, we had to use a mix of qualitative and quantitative research to define them. After a back of forth of customer calls and surveys, we were able to define four personas that used Grubhub based on two specific criteria: whether they ordered spontaneously or planned ahead of time, and whether they ordered for themselves or with others. When we mapped these personas back to our data, we were able to find that one segment was detrimental to serve because of high customer service costs, and that one segment was high potential, but low value currently because we had not built the right product for them yet. This helped inform our product strategy for the future.

The Marketing Persona
The marketing persona, unlike the first two personas, is usually a forward-looking persona: about who the company is going to try to reach. These are customers you want rather than customers you have. Marketing personas are common for product launches and market expansions since there are no existing users or data to build analytics or product personas.

The marketing persona exists to define a target market to go after. Marketers rarely try to target all people. They attempt to define a niche with specific needs (physical and emotional) and make their product attractive to that group. Marketers have many tools to define this persona. They pore over demographic and psychographic data and map competitive landscapes to spot opportunities for new markets. Since this persona is about targeting people outside the product, one common tool created during this process is a mapping of the target customer’s typical day. What they listen to on the radio on the trip to work, where they get their morning coffee, what they watch on TV during dinner, etc. From that, marketers identify opportunities to advertise to the target during one of those times to introduce them to the product.

Issues with Different Personas
All of these personas have pros and cons, and I recommend using them in tandem rather than a one size fits all approach. The danger with the analytics persona is it looks at people solely based on their activity and not also their motivations, their personalities, etc. There are key insights you will never find out from data that you can learn within ten minutes by talking to a customer. The product and the analytics personas also assume the current customers are the most important customers to understand. This is not always the case. Many times, you need to expand your market. Other times, focusing on existing users makes the product worse for incoming users.

Marketing personas also have their flaws. Since they are based on secondary data, marketers can sometimes invent personas that don’t exist or are too small to be important. This is why so many marketing teams create personas that are just cooler versions of themselves. It’s why whenever a fashion designer is asked who they design for, they say something like “a gallery owner in New York City.” They are maybe 500 of those people in the world. Now, I should be clear in that last scenario there is a blurred line between the marketing persona and the aspirations of the true persona, but you get the idea.

Marketing personas also can be unhelpful to some organic growth strategies or for products that have infinitely large markets since they are based on niches and targeting. When we tried to build marketing personas to target at Pinterest at around 50 million users, I asked, “What exactly are we going to do with this? We don’t spend money on advertising. Our targeting is defined by whoever searches for something on the internet that Pinterest is relevant for.” Similarly, does Google search have a target market? Sure, if you can call every single person with an internet connection a target. I bet you the Google Home team had a very specific target in mind for its launch though.

Personas of all types are also mistakenly used in lieu of personalization for many internet products. Email is a common example. Many companies still spend a lot of time creating dedicated emails for specific personas, segmenting lists, writing custom copy, and adding custom content. This frequently doesn’t makes sense in a world of personalization and one to one messaging. Pinterest has automated, personalized one to one messaging across email and push to over 200 million users. It doesn’t need to know what analytics, product, or marketing persona you are to be effective. This is not proprietary tech either. Many third party companies can help build this same type of system for smaller companies with less data.


Personas are very useful tools to help you identify opportunities to grow your business and better serve your customers. You should be using them in almost all phases of a company. Understanding the different types of personas, how you could use them, and how to prevent making mistakes with them is key to making sure they are worth the effort to define them.

Currently listening to Big Loada by Squarepusher.

Product Visionary vs. Product Leader

Many people want to work in product management. One of the most common questions I receive is how to break into product management. It’s a hard question for me to answer, because 1) there is no default path (the same is true for trying to land a business development role), and 2) most of these people really don’t know what they’re asking for. My most common response is, “Are you sure? Product management can kind of suck.” The reason for the dichotomy of people who haven’t done product management finding it so alluring, and people who have done it cautioning people trying to get in is the difference between what I call a product visionary and the product leader.

Product visionaries are who we all hear about in the press. They are the people who come up with brilliant products that go viral or solve real needs in the market that no one else thought of. They appear to be masters of finding product/market fit. I’ve been lucky enough to work with a few of them in my previous jobs and a few in the portfolio at Greylock. These tend to be founder/CEOs, and they generate brilliant insights that create product opportunities others don’t see. Ev Williams is on his third breakout product in Medium after Blogger and Twitter. Ben Rubin created two products that hit product/market fit in two years with Meerkat and now Houseparty.

Anyone working in technology hears these stories, and they think the shortest path to that sort of glory is becoming a product manager. They are excited to get to a role where they can drive the vision of a product, even if it’s only one part of the company. This excitement is exacerbated by the commonly propagated myth that the product manager is the mini-CEO of their product. The reality is that in 90+% of cases, product management is not about being a visionary. It’s about being a leader.

What does a product leader do at a tech company? It’s actually very little of creating a vision and a strategy from scratch. It’s about helping everyone understand what the vision and strategy is. It’s about communicating to the entire team why the company is doing what it is doing. It’s about building a process that helps a team execute on that vision. It’s about when there are competing visions, aligning and motivating the team to focus on one, and getting people to disagree and commit (including sometimes yourself). It’s about looking at data to measure if product changes are having a positive impact on the customer and the company’s growth. It’s about talking to users to understand why they’re doing what they’re doing, and the problems they still face even though your product exists. It’s about mentoring more junior people on your team, across product as well as engineering, design, and analytics. And they don’t have to listen to you, so you have to use influence rather than authority to be successful.

In the scaling phase of a startup, it’s product leadership that drives performance, not vision. Vision is needed early to find product/market fit and plot a course to scale, and then the less that vision wavers over time the better. This vision is usually done by the founder/CEO. The reason founders hire product managers and VPs of Product is not to set vision, but to help execute the vision. Don’t get me wrong; that will sometimes mean coming up with solutions to problems your customers face. If a company is scaling by having the founders solve all the customers’ problems instead of product teams, it will struggle. But much of the time, it will be wrangling the ideas of the individual engineers, designers, and analysts on your team and matching that to an overall vision set by the founder(s). It’s very rarely your ideas you’re executing on as a product leader, and it shouldn’t be.

I also don’t want to make it sounds like I am devaluing visionaries. They are, of course, critical in finding the initial idea(s) that create a growing company and maintaining a vision for that growing company. Having visionaries also becomes more important as you saturate your core market and need to tackle new value propositions to drive new growth opportunities. That is the ideal time for non-founder visionaries to enter a growing company. These are not going to be typical product managers or VP’s though. They are usually ex-founders. The best outcomes for these people entering an organization that is scaling is giving them a team and space to experiment with ideas until they find their own product/market fit, where a business unit is built out around that vision with product leaders to help them scale.

As you’re thinking about your business, think about whether you need a product visionary or a product leader. Most founder/CEOs are already visionaries, so they need a product leader to help execute. Some businesses minded founders need the opposite to be successful. If you’re thinking about product management, think about whether what you really want to be a is product visionary instead of a product leader. In that case, it might be a better idea to start a company than take a role expecting to execute on your vision and instead managing other people’s visions.

Currently listening to Ambivert Tools Vol. 3 by Lone.

Why Focus Is Critical to Growing Your Startup, Until It Isn’t

When I was a teenager, I told my dad about a friend and his dad and how they had seven businesses. He immediately replied, “And none of them make money.” I thought it was an extremely arrogant thing to say at the time, but later, I realized it might be the smartest piece of advice he ever gave me.

When I joined Grubhub, I quickly noticed the founders were incredibly good at staying focused. They said we were building a product for online ordering for food delivery — and only delivery — not pickup, not delivery of other items, not catering, and that’s all we would do for a long time. I remember thinking, “but there’s so much we could do in [XYZ]!” I was wrong. By staying focused on one thing, we were able to execute technically and operationally extremely well and grow the business both very successfully and efficiently. When we added pickup functionality four years later, it proved not to be a very valuable addition, and hurt our conversion rate on delivery.

If you have product/market fit in a large market, you should be disincentivized to work on anything outside of securing that market for a very long time. There is so much value in securing the market that any work on building new value propositions and new markets is destructive to securing the market you have already validated.

There is an interesting switch in the mindset of a startup that needs to occur when a startup hits product/market fit. This group of people that found product/market fit by creating something new now have to realize they should not work on any new value propositions for years. They now need to work on honing the current product value or getting more people to experience that value. Founders can easily hide from the issues of a startup by working on what they’re good at, and by definition, they’re usually good at creating new products. So that tends to be a founder’s solution to all problems. But it’s frequently destructive.

If a product team can work on innovation, iteration, or growth, they need to quickly shift on which of those they prioritize based on key milestones and value to the business. In this scenario, it’s important to define what innovation, iteration, and growth mean. In this context:

  • Innovation is defined as creating new value for customers or opening up value to new customers. This is Google creating Gmail.
  • Iteration is improving on the value proposition you already provide. This can range from small things like better filters for search results at Grubhub to large initiatives like UberPool. In both cases, they improve on the value proposition the company is already working on (making it easier to find food in the case of Grubhub, and being the most reliable and cheapest way to get from A to B in the case of Uber).
  • Growth is defined as anything that attempts to connect more people to the existing value of the service, like increasing a product’s virality or reducing its friction points.

I have graphed the rollercoaster of what that looks like below around the key milestone of product/market fit.

Market Saturation
The time to think about expanding into creating new value propositions or new markets is when you feel the pressure of market saturation. Depending on the size of the market, this may happen quickly or slowly over time. For Grubhub, expansion into new markets made sense after the company went public and had signed up most of the restaurants that performed delivery in the U.S. The only way the company could continue to grow was to expand more into cities that did not have a lot of delivery restaurants by doing the delivery themselves.

All markets are eventually saturated, and that means all growth will slow unless you create new products or open up new markets. But most entrepreneurs move to doing this too early because it’s how they created the initial value in the company. Timing when to work on iteration and growth and when to work on innovation are very important decisions for founders, and getting it right is the key difference to maximizing value and massively under-performing.

Product-Market Fit Requires Arbitrage

One of the most discussed topics for startup is product-market fit. Popularized by Marc Andreessen, product-market fit is defined as:

Product/market fit means being in a good market with a product that can satisfy that market.

Various growth people have attempted to quantify if you have reached product-market fit. Sean Ellis uses a survey model. Brian Balfour uses a cohort model. I prefer Brian’s approach here, but it’s missing an element that’s crucial to growing a business that I want to talk about.

First, let’s talk about what’s key about Brian’s model, a flattened retention curve. This is crucial as it shows a segment of people finding long term value in a product. So, let’s look at what the retention curve shows us. It shows us the usage rates of the aggregation of users during a period of time, say, one month. If you need help building a retention curve, read this. A retention curve that is a candidate for product-market fit looks like this:

The y axis is the percent of users doing the core action of a product. The x axis in this case is months, but it can be any time unit that makes sense for the business. What makes this usage pattern a candidate for product-market fit is that the curve flattens, and does fairly quickly i.e. less than one year. What else do you need to know if you are at product-market fit? Well, how much revenue that curve represents per user, and can I acquire more people at a price less than that revenue.

If you are a revenue generating business, a cohort analysis can determine a lifetime value. If the core action is revenue generating, you can do one cohort for number of people who did at least one action, and another cohort for actions per user during the period, and another cohort for average transaction size for those who did the core action. All of this together signifies a lifetime value (active users x times active x revenue per transaction).

Now, an important decision for every startup is how do you define lifetime. I prefer to simplify this question instead to what is your intended payback period. What that means is how long you are willing to wait for an amount spent on a new user to get paid back to the business via that user’s transactions. Obviously, every founder would like that to be on first purchase if possible, but that rarely is possible. The best way to answer this question is to look with your data how far out you can reasonably predict what users who come in today will do with some accuracy. For startups, this typically is not very far into the future, maybe three months. I typically advise startups to start at three months and increase it to six months over time. Later stage startups typically move to one year. I rarely would advise a company to have a payback period longer than one year as you need to start factoring in the time value of money, and predicting that far into the future is very hard for all but the most stable businesses.

So, if you have your retention curve and your payback period, to truly know if you are at product-market fit, you have to ask: can I acquire more customers at a price where I hit my payback period? If you can, you are at product-market fit, which means it’s time to focus on growth and scaling. If you can’t, you are not, and need to focus on improving your product. You either need to make more money per transaction or increase the amount of times users transact.

Some of you might be asking: what if you don’t have a business model yet? The answer is simple then. Have a retention curve that flattens, and be able to grow customers organically at that same curve. If you can’t do that and need to spend money on advertising to grow, you are not at product-market fit.

Other might also ask: what if you are a marketplace where acquisition can take place on both sides? If you acquire users on both sides at the wrong payback period, you’ll spend more than you’ll ever make. Well, most marketplaces use one side that they pay for to attract another side organically. Another strategy is to treat the supply side as a sunk cost because there are a finite amount of them. The last strategy here is to set very conservative payback periods on both supply and demand sides so that in addition they nowhere near add up to something more than the aggregate lifetime value for the company.

Currently listening to From Joy by Kyle Hall.