The original growth hacker reveals his secrets | Sean Ellis (author of “Hacking Growth”)

Sean Ellis's Growth Hacking Secrets: Product-Market Fit, Retention, and Prioritization

Sean Ellis's approach to determining product-market fit revolves around a simple but powerful question: "How would you feel if you could no longer use this product?" If 40% or more of users say they'd be "very disappointed," it indicates strong product-market fit. This metric helps filter out users who see the product as a "nice-to-have" rather than a "must-have." Ellis emphasizes that improving retention is more about enhancing onboarding and user experience than making tactical adjustments.

Growth hacking, a term Ellis coined, is about sustainable growth strategies, not just one-off hacks. His ICE prioritization framework—Impact, Confidence, and Effort—helps teams focus on the most effective growth opportunities. In-product pop-ups are becoming less effective, and tools like CommandBar, an AI-powered navigation toolkit, are emerging as better alternatives for guiding users without annoying them.

Understanding must-have users is crucial at any stage of a company, even with an MVP. Retention cohorts provide a more accurate measure of product-market fit, but the 40% "very disappointed" metric offers early insights. Ellis also stresses the importance of focusing on reputation and learning over immediate earnings, as this can lead to long-term success.

If 40% or more of people say they'd be very disappointed if they can no longer use the product, you essentially have product-market fit.

The Sean Ellis Test: A Simple Question with Profound Impact

The Sean Ellis test is a straightforward but powerful tool for determining whether a product is a "must-have" for its users. The core of the test is a single question: "How would you feel if you could no longer use this product?" Users are given four options: very disappointed, somewhat disappointed, not disappointed, or not applicable (already stopped using the product). The goal is to identify users who would be very disappointed if they lost access to the product. These users are the key to understanding whether the product has achieved product-market fit.

Sean Ellis found that if 40% or more of users say they would be very disappointed, the product is likely to have a strong product-market fit. Products that hit this threshold tend to perform well, while those that fall below it often struggle. As Ellis puts it, "Once you got a high enough percentage of users saying they'd be very disappointed, most of those products did pretty well."

However, it's important to ignore feedback from users who say they would be somewhat disappointed. These users are signaling that the product is a "nice to have" rather than a "must-have." Focusing on their feedback can lead to changes that dilute the product for the core users who find it essential. Ellis warns, "If you start paying attention to what your somewhat disappointed users are telling you... maybe you're going to dilute it for your must-have users."

Improving retention, a key metric for any product, is often more about optimizing the onboarding experience than making tactical changes to the product itself. The right onboarding can help users quickly see the value of the product, which in turn improves retention. Ellis notes, "It's usually much more a function of onboarding to the right user experience than it is about the tactical things that people try to do to improve retention."

When it comes to improving activation—getting users to experience the core value of the product—Ellis suggests that there are specific strategies that can be employed, though the conversation doesn't dive into the details here. He hints at the importance of focusing on this area, asking, "What are like three or four things that you think people should definitely try to help improve activation?"

Sean Ellis is a key figure in the world of growth hacking. He coined the term "growth hacking," developed the ICE prioritization framework, and was an early advocate of freemium models. His work has had a profound impact on how startups assess product-market fit and drive growth. The conversation in this episode focuses on two main topics: how to know if you have product-market fit and what to do once you have it to drive growth. These two areas are closely linked, and Ellis has spent a significant amount of time working on both.

The 40% Rule and CommandBar's AI-Powered Solution

Pop-ups have become a common annoyance for users. Most people don't even read them anymore; they just want to close them as quickly as possible. Yet, product builders know that users need guidance to fully understand the product. The challenge is finding a way to help users without frustrating them. CommandBar offers a solution to this problem. Their AI-powered toolkit helps product, growth, marketing, and customer teams assist users in a way that feels natural and non-intrusive. Instead of relying on generic pop-ups, CommandBar uses AI to understand what users are trying to do. Users can describe their goals in their own words, and CommandBar provides personalized results, such as walkthroughs or specific actions. This approach makes the experience more intuitive and less annoying.

CommandBar also uses nudges, but these are based on user confusion or intent classification, making them far more effective than traditional pop-ups. This system works across web apps, mobile apps, and websites, and has been adopted by companies like Gusto, Freshworks, and HashiCorp. Over 15 million end-users have interacted with CommandBar, showing its wide-reaching impact.

Now, shifting to product market fit, there's a simple but powerful test known as the 40% rule. If 40% or more of users say they'd be very disappointed if they could no longer use the product, it's a strong indicator of product market fit. However, it's important to note that this is just a leading indicator. The ultimate test of product market fit is retention—whether users continue to use the product over time. Retention cohorts provide a more accurate measure, but they take time to analyze. The 40% rule offers a quicker way to gauge product market fit without needing a sophisticated analytics system.

The 40% rule wasn't always part of the process. Initially, it was just a way to filter feedback from users who truly cared about the product. Over time, a pattern emerged: products with a high percentage of users who would be disappointed without the product tended to succeed, while those with a low percentage struggled. This rule can be applied at any stage of a company, even with a minimum viable product (MVP). It helps identify "must-have" users and provides valuable feedback early on.

A real-life example illustrates the power of this approach. After leaving Dropbox, Sean worked with a company where only 7% of users said they'd be disappointed without the product. This was a worrying sign, especially since Sean had committed to helping the company grow over six months. However, by using the feedback from the survey, they were able to increase that number to 40% in just two weeks, setting the company on a much stronger path.

Lookout and Xobni: Growth Hacking Through User Insights

Lookout, a mobile security company, initially offered a range of features like data backup, phone location, firewall, and antivirus. However, when they ran a survey, they found that only 7% of users would be very disappointed without the product. Most of these users valued the antivirus feature, even though there was only one known phone virus at the time. People were already used to protecting their computers from viruses, and as smartphones became more like computers, they made the mental leap that their phones needed protection too.

With this insight, the team repositioned the product to focus on antivirus. This created a filter: users who didn’t care about antivirus wouldn’t convert, while those who did would. They also streamlined the onboarding process so that the first thing users did after signing up was set up the antivirus, followed by a message saying, "You’re now protected from viruses." This combination of setting the right expectations and delivering value quickly increased the "very disappointed" score from 7% to 40% in just two weeks. Six months later, it was at 60%. Lookout eventually became one of the early unicorns, reaching a billion-dollar valuation. Even as antivirus features became built into phones, the company’s early focus on user needs helped them stay responsive to market changes.

This approach is part of a broader growth strategy: identify the percentage of users who would be very disappointed without your product, understand what they value, and focus on that. This can lead to significant improvements in product-market fit without major product changes. Retention, often seen as difficult to improve, is usually more about onboarding users to the right experience than about tactical retention efforts.

A high "very disappointed" score is a good sign—it means you’ve created something people care about. But it’s not enough to grow the business. You need to dig deeper into who considers the product a must-have, how they use it, what problem it solves, and what they used before. One key question to ask is, "What is the primary benefit you get from the product?" Initially, this should be an open-ended question to gather a range of responses. In a follow-up survey, you can turn it into a multiple-choice question. Another important question is, "Why is that benefit important to you?" This provides context for why users value the product.

A similar approach was used with Xobni, an early YC company. Users who would be very disappointed without Xobni valued its ability to help them find things faster in their email. When asked why this was important, many users said they were "drowning in email." This insight led to a successful customer acquisition message: "Drowning in email?" followed by a description of Xobni’s benefits. Understanding the context behind the must-have benefit helps create a flywheel for sustainable growth.

Whether your "very disappointed" score is 7%, 40%, or 60%, the real value of the survey is in understanding what those users care about and doubling down on it. This should inform your product roadmap, onboarding process, messaging, and acquisition campaigns.

The 40% Rule and User Investment: Insights from Growth Hacking

Growth is about getting the right people to the right experience. Once users are in, the engagement loop should reinforce how they can experience the product’s benefits more frequently. This is the foundation of growth hacking. A key metric often used is the 40% threshold—if 40% of users say they would be very disappointed without the product, it’s a strong signal of product-market fit. However, this 40% isn’t a hard rule. It’s more about having a target that aligns the team. In early-stage startups, some people may feel the product is ready to grow, while others think it’s far from it. Having a clear target helps everyone get on the same page about when to scale.

The concept of product-market fit is crucial. It’s about knowing when to "step on the gas" and scale. The idea of "nail it, then scale it" has been around for decades, and it’s all about finding the right moment to grow. Before scaling, the team needs to understand what product-market fit looks like for their business.

False positives in the 40% test are rare. If users say they’d be very disappointed without the product, it’s usually a good sign. However, it’s important to understand what drives that disappointment. Sometimes, it’s not just the product’s utility but the investment users have made in it. This is where Nir Eyal’s "Hooked" model comes in. The final step in the engagement loop is user investment. When users invest time, effort, or emotion into a product, they are more likely to say they’d be disappointed without it.

A case study of Webs.com illustrates this. Despite being a commoditized product with competitors like Wix and Weebly, Webs.com scored 90% on the 40% test. This was one of the highest scores ever seen. The reason? Users had invested a lot of time in building their websites. They knew how to make changes and had spent time making their sites look beautiful. This investment made them reluctant to switch, even if better alternatives existed.

Switching costs also play a role in high scores. When users have invested a lot of time and effort into a platform, they are less likely to switch, even if there are better options available. This was also seen with Eventbrite. Event organizers had already set up their events on the platform, sent out invitations, and were managing their events. This investment made it difficult for them to switch to another platform, leading to a high score on the 40% test.

The timing of the 40% survey is also important. The best time to ask the question is after users have had meaningful engagement with the product. Ideally, they should have used it multiple times and recently, but not so far along that they are overly invested. A random sample of users who have used the product within the last week or two is ideal. This ensures that the feedback is from users who are still engaged but not so invested that their responses are skewed by their commitment to the platform.

Surveying Activated Users and the Path to Product-Market Fit

When evaluating product success, the focus should be on users who have "activated"—those who have been using the product for a couple of weeks. These are the people who have truly experienced the product, not just those who landed on the homepage, signed up, or saw a demo. It’s crucial to gather feedback from users who have had enough time to engage with the product meaningfully. For example, if you’re testing updates to the onboarding process, you should only survey users who went through the new onboarding. This ensures that the feedback is relevant to the changes you’ve made.

However, there are limitations to using this kind of score. It doesn’t work well for one-off products, like a movie or a workshop. Asking someone how they’d feel if they could no longer watch a movie they just saw or attend a workshop they just completed doesn’t make sense. In these cases, an NPS (Net Promoter Score) question is more useful to filter feedback from people who love the product and those who are detractors.

There’s also a risk of startups over-relying on the score and prematurely declaring product-market fit. The real question is whether people who get through the onboarding process and experience the product love it. If they do, and you can retain them, that’s a sign of product-market fit. But if the onboarding is bad and users still don’t like the product after getting through it, then it’s a core product issue. There’s some debate about whether fixing onboarding is part of achieving product-market fit. If users who get through onboarding still don’t like the product, it’s a deeper issue. But once you know users love the product, you can focus on optimizing onboarding and other aspects.

Once product-market fit is validated, the focus shifts to growth. This involves optimizing the speed to value, ensuring users have the right prompts to return to the product, encouraging user referrals, and fine-tuning the revenue model. Customer acquisition comes last because it’s difficult, and if you’re not efficient at converting, retaining, and monetizing users, you’ll struggle with acquisition.

The Evolution of the "Disappointment" Question and Its Impact on Growth

The conversation begins with a question about adjusting the 40% threshold for customer satisfaction, particularly in different cultural contexts. For instance, Nubank in Brazil uses a 50% threshold, possibly because Brazilians tend to be more optimistic. Sean Ellis acknowledges that cultural differences can influence how people respond to satisfaction surveys, with some cultures being more optimistic or pessimistic. However, he emphasizes that the 40% threshold is not a rigid rule. He doesn’t make decisions solely based on whether a product hits a specific percentage. For example, a product with 37% might still be worth pursuing, while a product with 70% might not be if there’s no clear growth path. It’s more nuanced than just the numbers.

Ellis then shares the origin of his famous "How would you feel if you could no longer use this product?" question. It all started at Xobni, where he was trying to get more honest feedback from senior management, who are notoriously hard to satisfy. Initially, he used a standard satisfaction question, but he realized that senior management would always give lukewarm responses. To get a more honest answer, he flipped the question to focus on disappointment rather than satisfaction. This approach worked much better, and he continued using it at Dropbox and other companies. The insights he gained from this question were far more useful than those from traditional satisfaction surveys.

Ellis also reflects on the culture in Silicon Valley, where people often get excited about technology for its own sake. He contrasts this with his more practical approach, emphasizing that a product needs to provide real value to succeed long-term. Just being "cool" isn’t enough. Even at Dropbox, where early adopters initially flocked to try the product because it was new and exciting, Ellis noticed a shift over time. After six months, the majority of users were no longer early adopters but people who only used products they found genuinely useful. This shift demonstrated that even early adopters would only stick with a product if it provided real utility.

In essence, the "disappointment" question became a powerful tool for Ellis to gauge a product’s true value to its users, cutting through the noise of cultural optimism or the allure of new technology.

Execution Challenges, Sample Sizes, and Strategic Decisions in Product-Market Fit

Once you hit the 40% threshold—where 40% of users say they'd be very disappointed without your product—it tends to stick. However, this doesn't guarantee success. Companies can still fail even after reaching this point, and the issue often shifts from product-market fit to execution. Not everyone is good at execution, and even with product-market fit, failure is still possible. The risk of creating something people care about is high, and getting to product-market fit is hard and somewhat random. As Sean Ellis puts it, "I haven't seen it really fade back down, but I've seen companies fail despite having it."

When it comes to gathering reliable data, a sample size of at least 30 responses is considered the minimum. This number was initially self-learned but later validated by experts. Even with a small sample size, like 10 people, you can still get useful insights, but it's not enough to go to market. Ellis recalls, "I need at least 30 responses, and I just thought I randomly made up a number." This approach was further validated by the co-founder of SlideShare, who had a PhD in survey-related fields. She praised the methodology, which was largely driven by Ellis's curiosity and the desire to reverse-engineer failure.

Once product-market fit is achieved, the challenge often becomes execution. The number one reason for failure is that people don't care about the product, so if you have 40% of users who would be disappointed without it, you're likely down to an execution challenge. As Ellis notes, "The number one reason for failure would be that people don't actually care about the product."

After identifying different use cases through open-ended questions, the decision often comes down to whether to focus on a niche with a highly loyal customer base or a broader market with less intense loyalty. This is a strategic decision that depends on factors like funding and long-term goals. Ellis explains, "Do you want that intensely loyal group or the much broader group that's maybe a bit less, but almost there?"

If a company has raised a lot of money, they may need to go after a larger market, even if it's not as loyal. However, starting with a niche and expanding later is also a viable strategy. "Once I have traction in that market, I can start to try to appeal to some other markets," Ellis suggests.

Ultimately, the preference is usually to focus on a passionate customer base because the biggest competition is irrelevance. Being deeply relevant to a smaller group gives a better chance of long-term success. "Your biggest competition when you're really innovating is just being irrelevant," Ellis emphasizes.

Survey Tools, Product-Market Fit, and Retention: Insights from Sean Ellis

Sean Ellis has used a variety of survey tools over the years, including SurveyMonkey and Qualaroo, a survey business he sold to private equity. For him, the key is ensuring that the survey experience is pleasant for the customer and that the data is easy to work with. He recalls a time at Bounce when they used Intercom’s survey tool, which provided a poor customer experience. This made him more sensitive to the quality of the survey experience, emphasizing that it’s not about being tied to a specific platform but about what works best for both the customer and the data.

One of the most common questions founders ask is whether they have product-market fit. Surveys can provide a leading indicator, but they don’t give the full picture. Retention cohorts are essential for understanding if users continue to engage with the product. If customers who say they’d be disappointed without the product are churning, it’s a sign that product-market fit might not be as strong as initially thought. Sean advises paying attention to retention cohorts while growing the business, and if churn is high among those who claim they’d be disappointed without the product, it’s time to reassess.

Nubank offers a compelling example of how to operationalize this approach. Before launching any new product, they wait for 50% of users to say they’d be disappointed if the product didn’t exist. This threshold is applied even at the feature level, ensuring that only must-have features are included. This method works well for Nubank because they have millions of users, allowing them to ask a small percentage of their user base without overwhelming them with repeated questions.

For those struggling to increase their product-market fit, Sean points to Superhuman as another example of a company that used the survey approach effectively. Superhuman focused on the core benefit that must-have users valued and then looked at users who were "somewhat disappointed" but still focused on that core benefit. They figured out what those users needed to turn the product into a must-have for them. This approach allowed them to improve the product for on-the-fence users without diluting it for must-have users.

Sean warns against focusing too much on users who are "somewhat disappointed" because it risks diluting the product for must-have users. If you start tweaking the product based on feedback from these users, you might end up with a product that’s good for everyone but great for no one. Superhuman found a way around this by staying true to the core benefit and improving the product for users who were close to becoming must-have users, without compromising the experience for their most loyal customers.

Sean Ellis' Growth Hacking and Product-Market Fit Insights

Sean Ellis wasn't directly involved with companies like Superhuman or Nubank, but his work had a significant influence on them. He had written about his approach in his 2017 book, and it had been available for a while. Back in 2012, he teamed up with Kissmetrics to publish a survey template on survey.io, making it freely available to the community. While Kissmetrics used it for lead generation, Sean's primary goal was to contribute something valuable to the community. Over time, various companies found unique ways to use the template.

The process Sean recommends for understanding the benefits users find in a product starts with a follow-up survey to those who would be "extremely disappointed" if the product were no longer available. The first question is open-text, asking users to describe the primary benefit they get from the product. After gathering these responses, a second survey is sent to a different group of users, offering multiple-choice options based on the initial responses. The next question asks why the benefit is important to them, helping to dig deeper into the value the product provides.

When survey.io was eventually closed down, Sean moved the template to PMFsurvey.com. This version includes additional questions, such as asking users what they would use if the product were no longer available. This question is particularly useful for identifying whether the product is a "must-have." If users can easily switch to an alternative, it suggests the product may be seen as a commodity. To be a true "must-have," a product needs to be both valuable and unique.

Sean also clarifies a common misconception about the term "growth hacking." When he coined the term, he didn't mean a series of one-off hacks. Instead, he was referring to the process of scrutinizing every action a company takes and evaluating its impact on growth. At the time, many companies were focused on awareness-building, but Sean believed that startups, in particular, needed to focus on acquiring customers and ensuring they continue using the product. He admits that "growth hacking" might not have been the best term, but it did open up a conversation about rethinking growth strategies.

Today, Sean spends most of his time helping companies figure out sustainable growth strategies, not just short-term hacks. His approach is deeply rooted in product-market fit, which is essential for long-term growth. The goal is to continuously improve the product and grow faster, rather than relying on quick, temporary solutions.

Sean Ellis' Approach to Growth: From Divisive Naming to Activation Focus

Sean Ellis reflects on the name "growth hacking" and how it was divisive from the start. He believes that sometimes divisive names are better because they stand out. He wanted to name the approach he and companies like Facebook, LinkedIn, and Twitter were using for growth. He sat down with friends, came up with the name, and it stuck. From day one, it was divisive with different groups, but that was part of its strength.

When a company scores low on the Sean Ellis test, the first step is to focus on getting as many people as possible to experience the product in a way that makes them feel they would be very disappointed if they could no longer use it. This is not just about acquisition but about shaping the first user experience. The hardest part often sits inside the product team, figuring out how to make the product a must-have.

The next step is to build a flywheel around the must-have value. First, understand what makes the product a must-have, then figure out a metric that captures units of that value being delivered. This is the "north star metric." The team must decide on this metric together. After identifying the north star metric, the next step is to diagram all the ways to grow that metric. This involves looking at onboarding, the "aha moment," activation, engagement loops, and referral mechanisms.

Once the current state is captured, the focus shifts to identifying the biggest opportunities for improvement. These are the high-leverage opportunities, and the team should run experiments against them. The sequence Sean Ellis prefers is to start with activation, as it is critical and often overlooked. The product team is usually focused on the roadmap, and the marketing team is focused on bringing in new users, but the first user experience often falls through the cracks. After activation, the focus moves to engagement, referral, and getting the revenue model right. Only after these pieces are working well does he focus on acquisition.

Even though acquisition is not the first focus, Sean Ellis still thinks about acquisition hypotheses early on. He wants to have two or three viable ideas for acquiring customers before getting involved with a company. The acquisition side is very competitive, and if a company is not efficient at converting, retaining, and monetizing customers, it will not find scalable, profitable acquisition channels.

Many companies do the opposite of what Sean Ellis recommends. They start by driving growth to the product, then fix onboarding, then figure out how to make money, and referrals come later. Sean emphasizes that focusing on onboarding and activation first is crucial because it is highly correlated with retention and creating must-have customers.

The Critical Role of Activation and the LogMeIn Breakthrough

Users are at their highest risk of being lost before they reach the "aha moment"—the point where they experience the core value of a product. They may be intrigued by the initial promise but remain skeptical. Until they hit that must-have experience, they are likely to drop off. Many companies focus on capturing contact information, like emails or phone numbers, but this only means you’ll have to reacquire them later. The real focus should be on collapsing the time to value, getting users to that key moment as quickly as possible.

A perfect example of this is LogMeIn. Initially, the company struggled to grow beyond spending $10,000 a month profitably. The core issue was that 95% of users who signed up never actually used the product’s main feature—remote control sessions. This meant that monetization was limited to the 5% who did use it, severely restricting growth. The CEO made a bold decision: freeze all product development and focus entirely on improving the signup-to-usage rate. The entire product, engineering, and marketing teams were redirected to this goal.

In just three months, they improved the signup-to-usage rate by 1000%, going from 5% of users engaging with the product to 50%. This change allowed them to scale from $10,000 a month to $1 million a month, with a three-month payback on marketing dollars. Even more impressively, 80% of new users started coming in through word of mouth, a clear sign that the product was delivering value.

When it comes to improving activation, the key is not just trying random tactics but deeply understanding the problem that’s preventing users from engaging. For example, after LogMeIn had already made significant improvements to their onboarding process, they found a demand generation channel that looked promising but had a 90% drop-off rate at the download step. After more than 10 failed A/B tests, someone suggested simply asking users why they didn’t download the software. The response was eye-opening: "This seemed too good to be true. I didn’t believe this was free."

Armed with this insight, they made a simple but effective change. They gave users a choice between downloading a free version or a trial of the paid version, with a big checkmark next to the free option. This small adjustment led to a 300% improvement in the download rate, making the channel viable.

This success wasn’t just about one big breakthrough. It was the result of combining qualitative research—asking users directly—with inspiration from other companies. For instance, they studied instant messengers, which were also downloadable software with millions of users, to see how they handled the download and install process. The final result was a series of small gains that, together, made a massive difference.

Improving Activation, Conversion, and Growth Engines

To improve activation and conversion rates, the first step is to drill into what’s stopping users from progressing. Ask them directly: “Why did you bounce here? What did you think this was going to be? Why didn’t you end up using this?” Understanding these points of friction is crucial. Additionally, look for inspiration from other products, and ensure your product’s positioning is clear. For example, if you’re offering antivirus software, make it obvious: “We’ve got the best antivirus software.”

There are two main levers to drive conversion: increasing desire and reducing friction. Sometimes, it’s as simple as reminding users of the benefits they’ll get along the way.

A good example of a complex funnel is LogMeIn. Users had to go to a different computer to experience the product, which created multiple points where they could drop off. Initially, the team wasn’t intentional about designing each step, but later, they studied the data to see where they were losing the most users. They then focused their tests on those areas, trying to understand why users were dropping off and coming up with solutions.

When it comes to defining an activation metric, start qualitatively. Ask yourself: when has the user had a good enough experience to understand the product? In LogMeIn’s case, if users didn’t do a remote control session, they hadn’t really used the product. Look for a correlation between this activation metric and long-term retention, but remember that experimentation is needed to prove causation. There might be multiple activation moments, and it’s important to choose something that gives users a good taste of the product early on. Avoid setting activation metrics too far down the user experience, like requiring users to use the product 100 times. Ideally, users should experience value in the first session or day.

An interesting example comes from a game company where the traditional funnel was flipped. Instead of requiring users to sign up first, the games were used as advertisements on other websites. Users would start playing the game, and then be prompted to register to enter a prize drawing. This strategy was similar to YouTube’s growth approach, but it was implemented two years before YouTube.

When it comes to growth engines, there are four main ways to grow:

1. Sales
2. SEO
3. Virality/Word of Mouth
4. Paid Growth

Most companies rely on one of these engines for the majority of their growth. For example, Bounce uses both SEO and physical signage to attract customers. People searching for luggage storage often start with Google, but many also discover Bounce by walking past a sign that says, “Store your bag here for $5 a day.” This combination of SEO and physical presence helps Bounce capture a wide range of customers.

Demand Generation, Acquisition Channels, and the Power of Talking to Customers

Demand generation and demand harvesting are two distinct approaches to customer acquisition. Demand generation is about creating awareness in the right context, like seeing signs at the right place and time. Yelp, for instance, grew by placing stickers in restaurants, while DoorDash likely followed a similar path. On the other hand, demand harvesting captures existing demand, such as when people search for something on Google. Paid and organic search are typical demand-harvesting strategies.

When helping a business decide which acquisition channels to focus on—whether paid, SEO, or sales—the key is to identify realistic customer acquisition angles. For B2B companies, a sales team is often necessary, but for others, it’s about finding two or three channels that make sense. For example, Dropbox grew through user-get-user loops, while LogMeIn relied heavily on paid search. The difference in approach stemmed from the nature of the products and the market context. Dropbox couldn’t rely on search because no one was looking for cloud storage at the time, whereas LogMeIn capitalized on a competitor, GoToMyPC, which was spending millions on radio and TV ads, creating latent demand. LogMeIn disrupted this by offering a freemium model, inserting themselves into the flow of people searching for what they had heard about on TV.

Each business has unique opportunities, and what worked for one company might not work for another. For Dropbox, user-get-user loops made sense, but for LogMeIn, paid search was the key. The challenge is to figure out what’s unique about each business and how that opens up channel opportunities. Often, people get stuck trying to apply what worked in their last business to the next one, but after enough experience, it becomes clear that each situation requires a fresh approach.

One of the most powerful tactics for figuring out the right approach is simply talking to customers. Asking them how they found the product and how they typically find similar products can provide invaluable insights. Initially, the speaker was focused on quantitative data and testing, believing that all the answers could be found through analytics. However, a VC pushed him to start talking to customers regularly, and this led to much better experiments. Over time, he realized that a blend of qualitative and quantitative research produces the best results. Relying too much on one or the other can limit the insights you gain.

The speaker also played a key role in developing Dropbox’s referral program. The idea of a double-sided referral program, where both the referrer and the referred are incentivized, came from previous experiments at Zabni and advice from a friend who had tested similar programs. This approach proved to be the most effective. Interestingly, at LogMeIn, the speaker had considered introducing incentivized referrals but decided against it. At the time, 80% of new users were coming through word of mouth, and with over 100 million devices connected to the system, he was afraid that adding an incentive might disrupt the existing growth engine.

Dropbox, Freemium, and North Star Metrics: Insights from Growth Hacking

Dropbox's referral program is often cited as one of the most legendary and copied referral programs, alongside PayPal's. However, many companies trying to replicate it miss a crucial point: Dropbox already had a strong referral rate before the program was introduced. The referral program acted as an accelerant, but it couldn't have fixed a product that people weren't already talking about. As Sean Ellis puts it, "It's a great accelerant when it's already working, but it can't fix it if people don't want to talk about your product."

When it comes to freemium models, the key to success lies in making the free version of the product so good that it generates word of mouth on its own. The premium version, on the other hand, must be significantly better and differentiated enough to justify the upgrade. Many companies focus too much on the premium version and neglect the free one, leading to weak word of mouth. "People are so worried about the second part that they make the free version not very good, and then they're surprised when word of mouth isn't very strong."

Product usage cycles also play a significant role in growth. Some products, like Airbnb, have natural usage cycles that don't lend themselves to daily engagement. "You're not going to use it every day unless you're like a vagrant," Ellis jokes. However, Facebook made a strategic shift from measuring monthly active users to daily active users, which had a profound impact on how the team approached engagement. "Once Facebook was on a daily active user goal, the team suddenly had a lot more incentive to think about, 'How do I bring people back every day?'"

A critical tool for guiding growth is the North Star Metric, which should reflect the value delivered to customers rather than focusing on revenue. The metric should be something that can grow over time and correlate with revenue growth, but revenue itself shouldn't be the North Star. "Revenue shouldn't be the North Star Metric, but as you grow value across your customer base, you should be able to grow revenue at the same rate."

Examples of effective North Star Metrics include Airbnb's "nights booked" and Uber's "weekly rides." These metrics focus on the value delivered to customers, not the money made from bookings or rides. "At Airbnb, our North Star Metric was nights booked... Uber obviously weekly rides."

The Evolution of Growth Hacking and Prioritization: Insights from Sean Ellis

Sean Ellis highlights the importance of tracking daily active users (DAUs) over monthly active users (MAUs) to better understand user engagement. DAUs provide a more accurate reflection of how often users are interacting with a product, while MAUs can give a false sense of growth when aggregated over time. As Sean puts it, "If you're just kind of taking an aggregate number over time, it always looks like it's going up." This focus on engagement frequency is crucial for understanding real user behavior.

When it comes to North Star metrics, Sean didn’t explicitly think about them during his time at Dropbox and Eventbrite, but he was always focused on what made the user experience valuable. For Dropbox, it might be more about file access than just file hosting. For Eventbrite, weekly tickets sold could be a better metric than weekly events, as it reflects actual success. "Weekly tickets would be more likely to reflect, events are going to be happy if they're selling tickets," he notes.

Growth strategies have evolved significantly since Sean first started. In the early days, simply being data-driven in customer acquisition was enough to succeed. Testing and creativity in marketing were key differentiators. "When I first started just being data-driven on customer acquisition was enough to win," Sean recalls. However, today, most marketers are data-driven, and the challenge has shifted to optimizing the entire business, including conversion, retention, and monetization. This requires cross-functional collaboration between marketing, product, sales, and customer success teams, which is difficult to achieve. "Now you're talking about the overlap between marketing, product... customer success, and those teams are not used to working together," Sean explains.

Sean is particularly focused on how to get cross-functional teams to work together on growth. It’s a huge advantage when it works, but it’s hard to implement, especially in later-stage companies. Early-stage companies that adopt this approach tend to be more successful. "Very few later-stage companies have been able to make much progress in replicating that type of approach," he says.

Sean also created the ICE framework (Impact, Confidence, Effort) to help teams prioritize work. He believes that RICE (which adds Reach) is unnecessary because reach is already part of impact. "Reach is a super important part of impact. And so I think it's already factored in the I in ICE," he argues. Sean prefers simplicity and thinks that adding complexity doesn’t necessarily improve results. ICE was designed to help teams prioritize ideas systematically and avoid frustration when ideas aren’t chosen. "If you're having people submit ideas and you can't tell them why their idea was not chosen, they're just going to get upset," he explains.

Finally, Sean emphasizes the importance of running a high-velocity testing program. More testing is generally better, but the quality of tests also matters. "More testing is better. No, it doesn't just work like that. I mean, better tests are better than bad tests," he clarifies. ICE helps teams prioritize tests and ideas efficiently, ensuring that everyone understands why certain ideas are pursued over others.

AI's Role in Growth Hacking and the Power of Asking the Right Questions

Sean Ellis believes that AI is poised to revolutionize growth hacking, particularly in how it models potential outcomes for experiments. He suggests that AI could either enhance or even replace the ICE framework (Impact, Confidence, Ease) by improving the accuracy of predicting outcomes. AI's ability to model probabilities will make it a powerful tool for growth teams. As Sean puts it, "AI is going to actually change our ability to model out potential outcomes on experiments."

On a personal level, Sean has found a practical and fun way to use AI in his daily work. He often receives requests for advice but doesn't always have the time to craft thoughtful responses. To manage this, he turns to ChatGPT, asking it, "How would Sean Ellis answer this?" The AI generates a draft response, which he then tweaks. This method allows him to respond to more people efficiently. "I get a lot of people asking me for advice... I go to ChatGPT and say, 'How would Sean Ellis answer this?'" he explains.

Beyond personal use, Sean sees AI as a potential solution to a common problem in growth teams: the tension between product and marketing departments. These teams often resist taking direction from each other, but AI could provide neutral, data-driven recommendations that are harder to ignore. "It's a lot harder to let ego get in the way when it's kind of dispassionate recommendations from a system," Sean notes. This could help companies overcome internal barriers and focus on growth.

Another area where AI could make a significant impact is in the analysis of experiments. Sean observes that once companies reach a high velocity of experiments, the bottleneck often shifts to the analysis phase. He believes AI will play a crucial role in speeding up and improving this process. "The bottleneck ends up happening more on the analysis side. And I think AI will help a lot with that as well," he says.

However, Sean's insights aren't limited to AI. He also emphasizes the importance of asking the right questions at the right time, a lesson he learned from Oleg Yakubenkov, a former data scientist at Meta. According to Sean, many business problems arise because people aren't asking the obvious questions. "It often comes down to asking the right question at the right time in how you figure things out," he explains.

In his workshops, Sean frequently encounters this issue. People often struggle to find good answers because they aren't asking the right questions. For example, instead of jumping to conclusions, they should ask users why they aren't downloading the software or identify who considers the product a "must-have." "As soon as they have, like why aren't users downloading the software? Let's just ask them that question," Sean advises. This approach can help companies hone in on product-market fit and drive growth more effectively.

Insights from Sean Ellis on Books, Presenting, Movies, Products, and Life Mottos

Sean Ellis has two go-to book recommendations. First, Presenting to Win, a book that has been around for a long time but has significantly improved his presentation skills. He often recommends it to other speakers when sharing the stage. The second is Hooked by Muriel, which he always suggests to others.

When it comes to presenting, Ellis emphasizes that confidence stems from having well-organized information. The key is to spend most of the time preparing the presentation itself. As the author of Presenting to Win advises, if you have an hour to present, spend 55 minutes creating the right presentation and only 5 minutes practicing it. This approach ensures that the delivery is confident and effective.

In terms of entertainment, Ellis has been enjoying the Olympics, admiring the athletes who dedicate years of hard work for a brief moment of performance. He also recently watched Blackberry, a movie that tells the story of the rise and fall of the Blackberry phone. The film highlights how Blackberry initially had strong product-market fit, but lost it when the iPhone disrupted the market. The movie also explores how egos can complicate things as success grows. Ellis draws parallels between Blackberry and another movie, Tetris, which he also found fascinating.

On the product front, Ellis is a fan of the Pack Gear Hanging Suitcase Organizer. Having traveled nearly 100,000 miles this year, he finds it incredibly useful for keeping his clothes organized and making travel more efficient. The product allows him to keep his clothes folded in an insert that fits into his suitcase, which he can easily pull out and hang up upon arrival.

Ellis also shares a life motto that has guided him well: focus on reputation and learning over earnings. He recalls a time when he refunded two companies after sensing that they weren’t fully satisfied with his contributions. He valued his reputation more than the money, estimating his reputation to be worth $5 million, far more than the $20,000 he refunded. Interestingly, the VCs who introduced him to these companies were the first to offer him term sheets when he later raised money for his own company. This experience reinforced his belief that reputation opens doors to more learning and opportunities, which in turn enhances reputation further.

Sean Ellis' Indirect Role in TikTok's Success and His Approach to Advising Companies

Sean Ellis shared a fascinating story about his indirect role in TikTok's early success. During a recent trip to Singapore, he met with TikTok's original growth team. This team had been in place since the app's inception, and they revealed that much of their early growth strategies were based on Sean's writings. Interestingly, this was before his book "Hacking Growth" was published, so they were referring to his blog posts. Sean was humbled by the acknowledgment, saying, "It felt really good to know that I played some kind of role in TikTok." However, he was careful not to overstate his involvement, noting that while he had helped many unicorns, TikTok was one of the biggest.

The moment was bittersweet for Sean, as it coincided with Congress discussing a potential ban on TikTok. He joked that maybe if the TikTok team hadn't read his work, Congress wouldn't be "wasting their time on TikTok bans."

For those interested in learning more about Sean's work or reaching out to him, he directs people to his website, Seanellis.me, where he links to all his projects and provides contact forms. He also mentions LinkedIn as another way to connect. One of the projects he highlights is GoPractice.io, a platform that offers a simulated environment for learning growth. Sean was involved in the data-driven product management and user growth programs on the platform. He also plans to offer a special discount for Lenny's listeners on his website.

When it comes to advising companies, Sean prefers to get involved early, ideally just after they have achieved product-market fit. He enjoys working with companies that are pre-scale but showing signs of traction. His approach is hands-on, and he typically works with a company for three to six months, fully immersing himself in the team. "The sweet spot for me on companies that I go hands-on with are ideally pretty early just after they get to product market fit," he explains. However, this intense involvement often leads to burnout, so Sean takes breaks between projects to focus on lecturing and workshops.

See You in the Next Episode

The phrase "See you in the next episode" serves as a casual and friendly sign-off, signaling the end of the current discussion while promising more content in the future. It maintains a conversational tone, typical of podcast formats, where the speaker directly addresses the audience. The brevity of the phrase leaves no room for additional context, focusing solely on the idea of continuity and encouraging listeners to return for future episodes.

Conclusion

Growth hacking should prioritize sustainable strategies over temporary fixes, with a focus on user satisfaction and retention. Efficient onboarding, understanding customer needs, and leveraging data from retention cohorts are key to achieving and maintaining product-market fit.