Notes - Hacking Growth

February 4, 2025

Chapter 1: Building Growth Teams

The authors, Sean Ellis and Morgan Brown, begin by stating that the growth hacking method detailed in the book has been the engine behind the success of many of the fastest-growing Silicon Valley companies such as Pinterest, BitTorrent, Uber, and LinkedIn. The authors challenge the popular notion that these companies' success was due to a single brilliant idea, stating that it was actually achieved through methodical and rapid experimentation and analysis of user data.

The core elements of the growth hacking method include:

Despite the effectiveness of growth hacking, there has not been a definitive guide for practitioners, which this book aims to address. The authors emphasize that growth hacking is a fundamental new approach to market development that is not yet well understood. Sean Ellis, one of the leading innovators of the practice, is also the founder of GrowthHackers.com, a popular resource for information on the subject. The authors aim to provide a practical, step-by-step guide for implementing growth hacking in any company. The book is presented as a "playbook" that draws on the authors' experiences as well as the wisdom of other experts.

The book is divided into two parts:

The authors emphasize that growth hacking is a methodology that can be implemented by businesses of all sizes and in every industry. It is a data-driven approach that helps companies systematically use their data. Growth hacking is presented as essential for any company that wishes to achieve business goals with limited investment and maximum return on marketing dollars.

A collaborative approach between different departments is crucial for growth, but is not always the norm in many companies. Siloing business units can hinder innovation and slow growth. While research shows that collaboration is more important than individual talent for innovation, only a small percentage of senior executives consider their organizations effective at sharing knowledge across boundaries.

The role of the growth lead is discussed in detail. This role involves being a manager, product owner, and scientist. A growth lead is responsible for choosing the core focus area and objectives for the team. This focus helps to optimize results by preventing the team from pursuing ideas that don't contribute to their stated goal. The growth lead also ensures that the team is tracking the right metrics to measure and improve growth. Many companies focus on vanity metrics rather than metrics that indicate actual growth. A growth lead needs skills in data analysis, product management, and experiment design. They should also have familiarity with methods for growing adoption of the type of product they are working on as well as strong leadership skills to keep the team focused. The role is demanding, but with the proper tools and methods, it can be managed efficiently.

The book also outlines the role of a product manager on a growth team. Product managers oversee how the product and its features are developed. They are essentially the "CEO of the product".

The importance of data analysis is stressed. Data analysis should not be outsourced but should be a core function of the growth team. Companies should not rely too heavily on prepackaged programs with limited capacity to combine data and make discoveries. A skilled data analyst can be crucial for a growth team’s success.

Team members take on specialty tasks based on their area of expertise. For example, engineers will handle coding, designers will create design elements, data analysts will select the users for tests, and marketers will implement promotional experiments. User experience designers may collect and evaluate user feedback, which can lead to ideas for product changes.

The book underscores that growth teams need executive sponsorship to have the authority to cross departmental responsibilities. Without commitment from leadership, growth teams will face bureaucracy and inefficiency. Start-ups should have the founder or CEO leading growth, or have the teams report directly to them. In larger companies, teams should report to a high-level executive.

Two common reporting structures for growth teams are introduced: the functional (or product-led) model, where teams report to a product management executive, and the marketing-led model. A product-led team might focus on growing the user base for a specific product, or improving one aspect of the product, such as the onboarding process. For instance, at Pinterest, a growth team is dedicated to optimizing email messages and mobile push notifications.

The book explains how growth teams can ease tensions between different departments by ensuring that decisions are based on data rather than assumptions. Data-driven experimentation is crucial to overcome the "lore" that can hold back growth. At Qualaroo, for example, growth was previously limited by the belief that prices could not be raised; however, data showed that prices could be raised significantly while still attracting a new clientele.

Bringing in outside experts who specialize in user growth can be beneficial for smaller teams. However, the core responsibility for growth should not be outsourced. Consultants often lack the authority, time, or motivation to achieve sustainable growth.

The authors stress the importance of ensuring that a product is truly a "must-have" before focusing on growth. Spending resources on promoting a product that no one wants can be detrimental. It is also important to remember that word-of-mouth can be a double-edged sword.

A common misconception about growth hacking is that it is primarily about building virality into products. While virality is a key tactic, it should only be deployed after the product has been deemed a must-have. Growth teams need to use rigorous methods to understand user behavior and discover the core value of their product. The core value of a product may not be what the creators originally envisioned, and it's up to the growth team to discover it.

Chapter 2: Determining If Your Product Is Must-Have

This chapter focuses on determining whether a product has achieved "must-have" status, which is essential for sustainable growth. The authors argue that a product must provide core value and elicit an "aha moment" for users to become loyal and for a company to achieve rapid, sustainable growth. Without this, marketing and virality efforts are ineffective.

The "Must-Have" Test: The authors present a two-pronged approach to assessing whether a product is "must-have":

Importance of the "Aha Moment": The "aha moment" is when users experience the core value of a product. The authors emphasize that identifying the core value is not always straightforward and may differ from the initial product vision. Sometimes, a valuable feature may be an afterthought or something quite different from the originally hypothesized core value. It's the growth team's job to discover this. Companies should also ensure that users experience the "aha moment" as quickly as possible.

What to do if a product is not "must-have":

Methods for discovering core value: The chapter suggests three methods to determine why the "aha moment" hasn't been achieved:

Data Analysis and Metrics: The authors emphasize that data analysis is crucial for understanding customer behavior and identifying opportunities for improvement. They criticize companies that rely too heavily on prepackaged programs like Google Analytics, which have limited capacity for combining data. They also warn against focusing on surface-level metrics and emphasize the need for a data analyst who can mine data for valuable insights. The goal is to identify behaviors that differentiate users who find the product "must-have" from those who don't. By analyzing the features most used by avid users and distinctive aspects of their behavior the team can identify correlations with higher engagement, purchases, and long-term use.

Product/Market Fit: Making a product compelling enough to pass the "must-have" test is a prerequisite for fast and sustainable growth, but not sufficient. Even great products will likely fail without a well-focused strategy to drive growth. Many products fail because they do not offer a compelling core product value to a large enough market beyond early adopters or they lack a well-designed strategy for growth.

Chapter 3: Identifying Your Growth Levers

The third chapter of "Hacking Growth" focuses on identifying and understanding the key metrics that drive a company's growth. It emphasizes that while common metrics are valuable, each business must determine its own unique set of growth levers. The chapter also discusses the importance of using data to gain insights into user behavior.

Fundamental Growth Equation The core concept of this chapter is the "fundamental growth equation," a simple formula that represents all the key factors that combine to drive growth for a specific company. This equation is unique to every product and business. It helps growth teams focus on the most critical signals amid a sea of data.

While all products share some common growth drivers like user acquisition, activation, and retention, the specific combination of factors that drives growth is unique to each business. Determining Essential Metrics To identify these essential metrics, a business should pinpoint the actions that most directly correlate with users experiencing the core value of the product. For example, for Facebook, this includes how many people a user invites to join their friend circle, how often they visit the site, and how much they post and comment. For Uber, an essential metric for riders is the number of rides completed.

It's crucial to track metrics for each step users must take to reach the "aha moment" and how often they are taking those steps. Companies should track not only the number of new users downloading an app, but also the number of rides being booked, the number of riders who return and rebook, and the frequency with which they are booking new rides.

Moving Beyond Basic Analytics While basic analytics like website visits and page views are important, they are not sufficient to reveal deeper insights. Metrics tracked through traditional marketing and prepackaged data dashboards, such as web visitors, page views, new and returning users, new sign-ups, and time spent on the website, are valuable, but it is more important to identify the specific metrics unique to the product.

The key mission at this stage is to differentiate the behaviors of customers who find the product "must-have" from those who do not. Analysts should look for features most used by avid users and other distinctive aspects of their behavior. By dividing customer data by attributes like demographics, job title, or mobile device, and examining how users are engaging with the product, correlations between attributes and behavior and greater levels of purchasing, higher engagement, and longer-term use can be found. For example, Netflix identified that customers who watched Kevin Spacey movies also had high engagement with the product.

Data Analysis and Experimentation Growth teams need to systematically track data on user behavior. Many companies focus on vanity metrics, such as the number of website visitors, which do not indicate actual growth. Instead, growth teams should focus on metrics that are directly related to product usage and revenue generation.

The chapter also stresses the importance of combining all data sources to track customers throughout the entire experience. It highlights the need for a data analyst who can delve into the data to make discoveries. Simply reviewing data from prepackaged programs is not enough, it is also important to connect various data pools. A standout data analyst can be the difference between a growth team wasting time and finding valuable insights.

Importance of a Unified Data Store A unified data store helps identify areas for experimentation and design better experiments focused on improving key growth levers. The Facebook growth team stopped all growth experiments for a month in 2009 to focus on improving their data tracking and collection. After this project, they could see what each user was doing, allowing them to create more focused experiments. Smaller companies can more easily combine their data thanks to the availability of tools and services.

Limitations of Data and Importance of Qualitative Research The author notes that even detailed data has limits. Data analysis can tell you what users are doing, but not why they are behaving that way. To understand the "why," it is important to conduct user surveys or interviews. For example, if many users are leaving a website when trying to use a certain feature, it may be due to usability issues, but further user research would be needed to understand the underlying causes.

Accessible Reporting The chapter emphasizes the importance of reporting data findings in an accessible way. Even if the right metrics are tracked and analyzed, the results won’t lead to action if the team can’t understand them. Visual dashboards can help team members focus on key trends and metrics. These dashboards make it easier to share findings with the company, encouraging more participation in the growth effort. The author notes the importance of focusing team members’ attention on key trends and metrics using dashboards. The author also recommends sharing data in a way that is judicious and keeping in mind which reports are sent out beyond the growth team. The reports should be insightful and actionable, rather than just “data puking.”

Dashboards should report only on the most important metrics that map to growth levers. Metrics should be presented as ratios rather than static numbers, and they should be accompanied by indicators showing if they are above, below, or on par with past performance.

Case Study: Twitter The chapter uses Twitter as an example of how identifying growth levers, conducting in-depth analysis, and supplementing it with customer queries can optimize growth. The growth team discovered that users who followed at least 30 other users became long-term fans. The team used cohort analysis to divide users into groups by common traits, like the month they joined. This led to the discovery that users who followed 30 or more accounts were more active and engaged with the platform. Twitter then refined its ways of suggesting people for users to follow.

Chapter 4: Testing at High Tempo

This chapter focuses on how to run rapid-fire experiments to achieve compounding wins. The volume and pace of experiments will vary depending on company size and resources, but it is crucial to have a disciplined process to create a pipeline of ideas and prioritize them efficiently. This helps to ensure continuous testing without errors in execution or getting bogged down in brainstorming.

The authors have developed a four-part cycle with simple tools to ensure that a growth team operates as a fast-tempo experimenting machine.

The Growth Hacking Cycle

The growth hacking cycle includes data analysis and insight gathering, idea generation, experiment prioritization, running the experiments, and then analyzing the results and deciding on next steps in a continuous loop. This cycle should be completed consistently, preferably every one or two weeks, and is managed by a weekly one-hour growth team meeting.

Preparing for Liftoff

Before launching into the cycle, an initial team meeting is required to explain the process. The growth lead should clarify the roles and expectations of each team member. The methods for generating and prioritizing ideas should be explained, and the data analyst should share the results of initial analysis. The growth lead should present the key growth levers, the North Star metric, and the area of focus for the team. The team should set the goal for the volume and tempo of experiments to launch each week and data analysts and engineers are best suited to make initial assessments.

Generating Ideas

Over four days following the team meeting, all members should submit as many ideas as possible for hacks, even if they seem crazy. Team members should contribute ideas based on their expertise but not be limited by it. For example, a user experience designer might propose changes to screen displays, while a marketing member might focus on different ways to encourage users to make their first purchase, and engineers might come up with ideas to enhance product performance.

Each idea should include a detailed description and a hypothesis.

Prioritizing Ideas

The authors recommend using a scoring system to prioritize ideas, assessing impact, confidence, and ease of implementation.

The growth lead reviews the initial scores before the team meeting and may suggest modifications based on past experience. The team should use the score as a guide, not as the absolute rule for test prioritization. The growth lead must act decisively to keep the team moving.

Testing Rules of the Road

Each experiment has an opportunity cost, so the idea selection and testing method are important. A poor test means a lost opportunity, and bad data can lead the team down the wrong path. It's essential that every experiment produce statistically valid results. The data analyst is responsible for applying testing rules. The authors recommend two rules of thumb:

Analyzing Results

Analysis of test results should be conducted by the analyst or the growth lead. Results should be written up in a test summary that includes:

This summary should be shared with the growth team via email and added to a database of test summaries. This database, called the knowledge base, should be easily searchable for teams to revisit and consider variations.

The Weekly Growth Meeting

The weekly growth meeting is a one-hour meeting that helps manage the growth hacking cycle.

Chapter 5: Testing at High Tempo

This chapter discusses how to run rapid-fire experiments to produce compounding wins. The chapter emphasizes that big successes in growth hacking come from a series of small wins that accumulate over time. Each learning leads to better performance and ideas, which in turn leads to more wins.

Tempo Picks Up Over Time

The volume of experiments a growth team can run depends on the size of the company and resources. Leading growth teams may run 20 to 30 experiments a week or more, while early-stage startups may launch only one or two tests a week. Regardless of the company size, a disciplined process is crucial to maximize the number of experiments and gains. This process ensures continuous testing without sloppy execution, invalid results, or getting bogged down in debates about which ideas to try.

The Growth Hacking Cycle

The authors have developed a four-part cycle with tools to help a growth team operate as a fast-tempo experimenting machine. The stages of this process include:

Preparing for Liftoff

Before starting the cycle, an initial team meeting should be held to explain the process. The growth lead clarifies each team member's role and how they are expected to work collaboratively. The methods for generating and prioritizing ideas are also explained. The data analyst should share the results of their initial analysis, and the growth lead presents the key growth levers, the North Star metric, and the team's focus area. The team should also set a goal for the volume of experiments they can manage weekly.

Example of a Growth Hacking Cycle

The chapter provides an example of a grocery app team that goes through the first stages of this cycle. The data analyst summarizes user data, while the marketing expert conducts user surveys and interviews. The growth lead then writes a summary highlighting the shared characteristics of regular shoppers versus those who have not made many purchases. The team members are tasked with contributing ideas for hacks to improve revenue from app users, and all ideas are considered.

Idea Submission

Ideas should be submitted with a brief name, a description that addresses the "who, what, where, when, why, and how" of the idea, and a hypothesis of the expected outcome. The product manager of the grocery app team comes up with the idea to build a "shopping list" feature to store prior purchases for easy reordering. The description explains who the feature is for, what will be created, where it will be implemented, when it will appear, the rationale behind the idea, and a recommendation of the test to be done. The hypothesis for the shopping list feature could be: "By making it easier for shoppers to view and reorder previously purchased items, the number of people who make repeat purchases will increase by 20 percent."

Prioritization

The submitted ideas are scored based on their potential impact, confidence, and ease of implementation. The growth lead reviews the initial scores and may suggest modifications based on their experience. It’s important not to get bogged down in trying to fine-tune the score; it serves as a guide for prioritization.

Testing Rules of the Road

Each experiment comes at the expense of another, so care must be taken in the selection of an idea and how it is tested. A poor test slows the team down, and bad data can lead the team down the wrong path. Every experiment must be designed to produce statistically valid results. The data analyst is responsible for applying these rules to experiments. The chapter notes two rules of thumb: (1) keep tests simple, with one variable, and (2) avoid changes that are too complex to be easily understood or replicated.

Back to Stage 1: Analysis and Learning

The analysis of test results is conducted by the analyst or the growth lead. The results are written in a test summary that includes:

The Weekly Growth Meeting

A weekly one-hour growth meeting should follow a standard agenda and is managed by the growth lead. This agenda should include:

The team compiles this information with the meeting agenda and shares it beforehand, with the growth lead responsible for keeping the team on track.

Chapter 6: Hacking Activation

This chapter focuses on how to improve the rate at which new users become active users of a product or service. The authors emphasize that even after a product has achieved "must-have" status, it's crucial to ensure that new users quickly experience its core value. This process is called activation, and it's not a one-time event but a series of steps that a new user takes to engage with the product.

The authors outline three essential steps to identify high-impact activation experiments:

The chapter provides several best practices for increasing activation, such as:

The chapter also discusses the use of triggers, which are prompts that urge users to re-engage with a product. The authors emphasize that the rationale for triggers should be to alert users to an opportunity of clear value to them. They also suggest using the six principles of persuasion outlined by Robert Cialdini, which include reciprocity, commitment and consistency, social proof, authority, liking, and scarcity, when crafting triggers. The authors distinguish between external triggers, which are prompts from the company, and internal triggers, which are habits that users develop.

Chapter 7: Retention: How to Keep Customers Coming Back

This chapter focuses on customer retention, emphasizing its critical role in achieving profitability for businesses. The authors argue that high retention rates are crucial because acquiring new customers is expensive. They cite research indicating that a 5% increase in customer retention can lead to a 25% to 95% increase in profits. Additionally, retaining customers enhances the effectiveness of word-of-mouth and viral marketing efforts, as longer-term users have more opportunities to share and recommend the product or service.

The chapter addresses how to identify and prevent customer churn, or defection. The authors suggest that growth hacking is an ideal method for managing customer retention, using the example of a grocery app that experienced a slide in usage after the first month. They argue that before trying to re-engage users, a company must assess the core drivers of retention. Fundamentally, retention depends on delivering a high-quality product or service that meets customer needs and is considered must-have. A stable retention curve is a strong indicator of achieving product/market fit, in contrast to a curve showing increasing customer abandonment.

The authors then discuss tactics for both initial and long-term customer retention. For initial retention, strategies are similar to those used for improving activation, such as refining the new user experience. Triggers like mobile notifications and emails can also help reinforce the product's value, though companies should avoid over-reliance on these tactics.

A key concept is habit formation, where products become integrated into users' routines. The chapter uses Amazon's Prime program as an example of a powerful habit-creating mechanism because it provides both the rewards of free shipping and the instant gratification of two-day delivery. This encourages frequent purchases, reinforcing the value of the Prime subscription. The authors state that enhancing the perceived value of rewards generally leads to greater retention. This can be achieved by offering a variety of rewards and encouraging action to receive them, which builds habit and perceived value. The authors suggest using cohort analysis to identify avid users, what features they use the most, and what rewards result in the highest retention rate.

Three strategies are highlighted for boosting habit formation and retention:

For long-term retention, the authors recommend a two-pronged approach:

  1. Optimizing current features and rewards: It's crucial to ensure the product continues to provide value and delight through refinement and enhancements.
  2. Introducing new features: Companies should be judicious when adding new features, avoiding "feature bloat" which can overcomplicate the product and obscure its core value.

Chapter 8: Monetization: How to Make Money from Your Customers

The ultimate goal of acquiring, activating, and retaining customers is to generate revenue, and ideally, to increase the lifetime value (LTV) of customers over time. Many growth teams focus on acquisition and activation but miss the potential for growth through effective monetization.

Mapping the Monetization Funnel

Analyzing Customer Cohorts

Customer Segmentation and Personas

Expanding Product Offerings

Customizing Offers With Data

Balancing Customization with Privacy

Pricing Strategies

Price Sensitivity

Social Proof

Growth teams should use data analysis to map the monetization funnel, identify opportunities and barriers, segment customers into cohorts, understand customer needs, and experiment with pricing strategies, all while respecting privacy.

Chapter 9: A Virtuous Growth Cycle

This chapter emphasizes that driving growth is a continuous process, not a one-time task. The most successful companies sustain their growth by constantly pushing for more, leveraging their achievements, and seizing new opportunities. The authors caution against complacency, stating that in today's competitive market, stagnation leads to decline. This chapter serves to highlight the importance of maintaining the growth hacking mindset across all the growth stages, from acquisition to monetization, and the need to avoid common pitfalls that lead to growth stalls.

The authors use Facebook as an example of a company that has sustained its growth over a decade due to its growth teams' relentless efforts. Facebook’s growth team, started with just five people, is now responsible for the seemingly unstoppable growth of the largest social network of all time. This highlights that continuous growth is achievable with a well-implemented growth strategy.

Avoiding Growth Stalls:

Key Actions to Sustain Growth: The chapter outlines several steps that companies can take to maintain a virtuous growth cycle:

The Virtuous Cycle: