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Sean Ellis was contacted by Dropbox founder Drew Houston in 2008 to help the company grow beyond its early tech-savvy user base and compete against much larger companies like Microsoft and Google entering the cloud storage space.
Ellis had previous success driving growth at startups like Uproar and LogMeIn through unconventional “growth hacking” techniques leveraging technology rather than traditional marketing.
At Uproar, he helped grow the company to one of the largest gaming sites by creating an embeddable single-player game widget that other sites could host, acquiring users at low cost.
At LogMeIn, which offered free remote access software but struggled with acquisition costs, Ellis had the idea to survey users who abandoned the product. This revealed people didn’t believe it was truly free, so they tested webpage variations clearly communicating there was no catch.
The book outlines Ellis’ “growth hacking” methodology developed over years helping startups like these drive breakthrough growth through innovative, low-cost techniques like leveraging technology, experiments, and unconventional channels rather than traditional marketing.
The author helped LogMeIn scale its search ads by improving conversion rates through experimenting with the install process, sign-up steps, and more. This allowed ads to become highly profitable.
Early tech companies like Hotmail, PayPal, LinkedIn grew rapidly by leveraging viral growth and network effects through simple programs like adding referral links to emails (Hotmail) or tools to easily invite contacts (LinkedIn).
The author coined the term “growth hacking” to describe this data-driven, experiment-based approach to rapid growth with low budgets.
He implemented this approach at Dropbox by surveying users’ emotional attachment to the product and finding word-of-mouth referrals were a major source of users.
The author worked with Dropbox to create a referral program offering storage space incentives. This led to a massive increase in referrals, growth from 100k to 4 million users in 14 months with no traditional marketing spent.
Other companies like Facebook also adopted focused growth teams applying experimentation and data analytics to continually test and optimize growth. This emerging approach disrupted traditional marketing models.
Facebook’s growth team was focused exclusively on experimenting with ways to break through plateaus in growth. Adding more resources allowed for more experimentation and faster growth.
A major breakthrough was developing a translation engine to translate Facebook into any language via crowdsourcing. This allowed for rapid international growth rather than slowly translating into top languages.
The growth hacking approach spread as Facebook employees moved to startups like Quora, Uber, Asana, and Twitter, bringing these methods with them. Other companies also adopted experiment-driven growth approaches.
Airbnb struggled to gain customers until they hit on a growth hack - integrating Airbnb listings onto Craigslist without authorization. This involved reverse engineering Craigslist and reproducing the posting process programmatically. It generated a huge increase in bookings without any advertising spend.
Growth hacking breaks down silos and assembles cross-functional teams to efficiently combine technical expertise, data analysis, and marketing to rapidly test and identify high-potential growth ideas. It’s driven the success of many high-growth startups and companies.
The passage advocates for growth hacking as a way for companies of all sizes to sustain growth and avoid stalls. Growth hacking involves cross-functional teams that break down silos, use qualitative and quantitative research to gain insights, and rapidly test and measure ideas.
Despite growth hacking’s effectiveness, there is no definitive guide on how to implement it. The authors wrote this book to be the first step-by-step playbook for putting growth hacking into practice.
It draws on the experiences of growth hacking innovators from successful tech companies and applies lessons to companies of any industry. The goal is an “unstoppable growth machine” through continuous testing, tweaks, and focusing on customer acquisition, retention and revenue.
Growth hacking benefits include complementing traditional marketing, optimizing approaches, and helping both startups and established firms survive disruption through rapid experimentation and adapting to technological changes. The book argues all companies today need growth hacking to sustain growth.
The passage discusses the need for companies to continuously test, update and improve their products to stay competitive and fend off new entrants. It uses Tesla as an example, who regularly sends software updates to cars to upgrade capabilities without waiting for new model releases.
Growth hacking allows companies to move faster through approaches like continuous customer testing, cross-functional teams, and real-time responses to the market. This speed is critical in today’s rapidly changing business environment.
Traditional processes with separated teams (product, marketing, engineering) are slow and create lag times. Growth hacking breaks down these barriers for more nimble responses.
Growth hacking helps companies better utilize customer data, which is often not fully tapped due to fragmentation across teams. An example from Walmart’s Savings Catcher app is provided.
Traditional marketing is facing challenges like rising costs, fragmented audiences, and ad blocking. Growth hacking provides alternatives to drive growth without reliance on these questionable traditional channels.
In summary, the passage discusses how growth hacking allows companies to move faster, better leverage data assets, and drive growth in new ways that avoid the problems plaguing traditional marketing approaches. Speed, data utilization and innovation are keys to competiveness according to the passage.
Early adoption of new digital marketing channels and platforms is important for companies seeking growth, but most are too slow to adopt promising new opportunities.
Growth hacking involves testing many small improvements through experimentation, not just one “silver bullet” solution. It requires cross-functional teams with programming, data analytics, and marketing skills.
The book aims to provide a methodology for systematic growth hacking through acquisition, activation, retention, and monetization of users/customers.
It will profile how companies like Facebook, LinkedIn, Uber, and others continually work to generate and test new growth ideas.
Myths about growth hacking include thinking it’s about one person or rule-breaking tactics. In reality it involves cross-functional teams doing creative problem-solving through data-driven experimentation.
Growth teams should work on product development, customer activation/retention, and monetization - not just acquisition. They are involved in all stages of the growth process.
The book is divided into an introduction to the growth hacking method/process, and a “playbook” with tactics for acquisition, activation, retention, monetization, and sustaining growth. It provides tools and examples of experiments from various companies.
Implementing growth hacking provides a systematic way for companies to discover and test opportunities for growth through collaboration and data-driven experimentation.

Pramod, the new head of product at a 50-person company, brought in Annabell Satterfield as a product marketing manager to help build out the mobile product and reignite growth.

Typically, the marketing, product, engineering, and data teams worked in silos. Annabell requested to work across teams to drive retention and monetization, not just acquisition. Her customer research found growth opportunities down the funnel.

Surveys revealed users hadn’t noticed the paid Pro version, so a prominent upgrade button was added, increasing revenue 92%. A prompt to leave positive reviews after the first torrent increased reviews 900% and downloads.

This cross-functional collaboration was uncommon but successful. The teams began regularly brainstorming hacks across departments to spur further growth.

A software company’s mobile app team implemented a feature that stopped background file transfers when a phone’s battery fell below 35% to preserve battery life. This proved very popular, increasing revenue by 47%.
The success of this team led to changes within the company. A marketing employee was moved to the mobile team as a product manager. Other engineers wanted to join the high-performing growth-focused team.
The team heavily leveraged data analysis, having a dedicated data analyst help design and evaluate experiments. This analyst was also moved full-time to the team.
The team’s success prompted the company to invest more in data science and grow its analytics team. Other product teams also started collaborating more closely with data analysts.
Through rapid experimentation, the mobile team grew the app to 100 million installs over 2.5 years. This altered the company’s culture to become more open, collaborative, and focused on growth hacking. It significantly increased revenue and established a new innovation process for the company.
Viral word of mouth and network effects are key mechanisms by which many social products grow as more people join.
A growth lead needs relevant industry expertise, strong leadership skills, and the ability to run experiments even when they fail. They must maintain enthusiasm while allowing room for failure.
Successful growth leads can come from engineering, product management, data science, marketing, or other backgrounds. For startups, founders often play this role.
Core roles on a growth team typically include product managers, software engineers, marketing specialists, data analysts, and product designers.
Product managers oversee the product and bring customer insights. Engineers contribute creativity and technical expertise.
Marketers provide expertise in different channels like content, email, SEO. Analysts design experiments and analyze results.
Designers improve experiment speed through design work. Small teams have one person per role, larger companies have more members.
An effective growth team has the right composition for its industry, size, goals, and experiment needs. Teams can be permanent or temporary depending on the company.
Companies take different approaches to organizing their growth teams - some have multiple focused teams like LinkedIn and Pinterest, while others have a single large team like Facebook and Uber.
When first starting a growth team, it’s a good idea to bring over one or two individuals from different departments to get it started and allow the team size to grow over time. Additional teams may form as the process is learned.
Example teams include IBM forming a team of 5 engineers and 5 marketing/ops staff focused on growing adoption of a product, and Inman comprising his team of a data scientist, 3 marketers, and a developer.
The growth hacking process involves continuous cycles of data analysis, idea generation, experiment prioritization, running experiments, and analyzing results to identify wins and guide next steps.
Regular growth team meetings (e.g. weekly) are important for managing experiments, reviewing results, and planning next steps in an agile manner.
Team members take on specialized tasks based on their expertise but also collaborate cross-functionally as needed for certain initiatives.
Executive sponsorship from a high-level leader is critical to give the growth team authority, resources, and priority to drive growth across the organization. Support from the top is key to sustained success.
Growth teams at companies can have two common reporting structures - product-led or independent-led.
Product-led teams report to a product management executive and focus on growing specific products. Companies like Pinterest and LinkedIn use this model.
Independent teams report directly to a VP of Growth and can work across all products. Companies like Uber, Facebook, and Walmart Labs use this model.
Setting up growth teams within an existing company can face initial resistance from teams with established responsibilities like marketing, product, and engineering. There are cultural adjustments as growth teams break down silos and change norms.
Effective growth teams require strong executive backing and collaboration with other teams to conduct experiments, overcome resistance to changes, and build trust in their mission of driving company-wide growth.
Growth experiments and resources needed can interfere with or take away from existing projects and priorities. For example, Annabell’s work on growth experiments at BitTorrent took time away from acquisition efforts.
As data demands increased, it strained the data team’s resources until more people were added.
Diverse backgrounds on growth teams can lead to differing perspectives that cause tension. Engineers prefer technical work over user impact. PMs focus on launches over last-minute changes. UX designers resist experiments that upset users. Marketers focus on vanity metrics over full funnel metrics.
These perspectives are ingrained in roles and training, making collaboration challenging even at startups.
Growth teams can ease tensions by incentivizing shared goals, making decisions strictly data-driven rather than assumptions, and proving strategies with experiment results that are hard to argue with.
As companies grow, growth teams should evolve too - adding more roles, creating subteams, or spinning off new teams to focus on specific initiatives. Outside experts can boost small startup teams.
Implementing growth hacking takes gradual adoption - starting narrowly focused and expanding successes to build enthusiasm for the process company-wide over time.
Many innovative products fail because they are not truly understood or seen as valuable by their target market. Companies push for growth too soon before ensuring the product is a “must-have” for customers.
It is important to identify the core value or benefit of the product to specific customers before launching major marketing efforts. No amount of advertising can make people love a substandard product.
Pressure from investors or internal targets can tempt companies to force growth prematurely through spending on marketing, but this usually does not work and often backfires.
Identifying core value requires rigorous analysis of user behavior to understand what aspect of the product customers truly value, which may not be obvious to the creators. The right target market may also need to be identified.
The story of BranchOut, a viral Facebook app that grew quickly but then collapsed, illustrates the danger of prioritizing growth over product experience. Many other products that relied too heavily on pushy marketing have also failed.
For sustainable growth, companies need to create an “aha moment” where customers truly understand the product’s value - like when Yelp pivoted to focus on user reviews which drove engagement and growth.
An “aha moment” is when users discover an irresistible benefit of a product that fills a meaningful need and drives word-of-mouth growth. Identifying the aha moment is key to knowing when a product is ready for aggressive growth efforts.
The Must-Have Survey is a simple way to determine if a product has achieved sufficient must-have status to warrant focus on growth. It asks users how disappointed they would be if the product disappeared and interprets results over 40% “very disappointed” as a green light for growth.
If results are below 40%, additional questions help identify improvements like alternative products users prefer, key benefits, recommendation patterns, ideal user types, and improvement ideas.
The survey is best targeted at active users to get informative feedback. Aim for several hundred responses for reliability. It is not recommended once major growth begins, as suggesting discontinuation could cause panic.
Overall, the Must-Have Survey provides a gating mechanism and insights to determine if a product is ready for a growth push or what changes are needed first to deliver a compelling aha moment.
Retention rate is an important metric for assessing whether a product has achieved must-have status. It measures the percentage of users who continue using the product over time, usually monthly.
Industry benchmarks can provide a baseline for comparing retention rates. Mobile apps typically see 10% retention after 1 month, while best apps retain over 60%. SaaS products have 90%+ annual retention.
To improve retention and achieve must-have status, companies should conduct additional customer surveys, interviews, and test product changes through experiments while deeply analyzing user data.
Getting out into the field to observe customers use prototypes and talk to them is important for uncovering true needs and pain points not realized sitting in the office.
Etsy grew through fieldwork like attending craft fairs to recruit sellers and understand what community and experience was most important to early adopters, then built tools and forums accordingly based on this feedback to fuel organic growth. Getting off the internet and into the field was key for Etsy.
Etsy spent little on customer acquisition and relied mainly on organic channels like social media and search to achieve strong growth leading up to their IPO. Their seller stores and listings were optimized for social sharing.
Tinder focused on gaining early adoption on college campuses by having team members visit campuses in person to demo the app to sororities and fraternities. This created a tight local network effect that fueled rapid growth through word of mouth.
PayPal surveyed eBay users via listings and forums to understand how they used PayPal on the platform. This helped PayPal build integrations that benefited both eBay users and its own growth.
Surveying even a few hundred existing users can provide useful insights into growth opportunities. Simple A/B testing of messaging, copy, etc. is an effective low-cost way to improve adoption and drive growth through small tweaks. Focusing experimentation on both product and marketing is important.
A/B testing tools make it easier for teams to experiment with things like ad copy, images, videos, buttons, etc. but the data is limited as it only shows surface-level metrics like click-through rates, not long-term user behavior.
Testing should go beyond just marketing and look at things like pricing plans, signup flows, core product experiences to improve retention, revenue, etc.
Engineers can help identify more complex tests within the product itself, like machine learning algorithms to optimize copy or major UI redesigns.
Larger, riskier tests should be backed by user research and data to minimize risk. Teams should balance big bets with incremental improvements.
To get valuable insights, companies need to collect the right user data across their whole digital and physical experiences, then connect and analyze different data sources to get a complete picture of user behavior.
Key is tracking what active, repeat users are specifically doing within the product to determine what makes them loyal versus other users.
Analyzing differences in user behaviors and attributes can reveal unexpected correlations that provide ideas for growth experiments. The most useful insights sometimes come from data that was not initially being tracked or analyzed.
Data analysis is crucial for discovering unexpected patterns and opportunities that can lead to product pivots and improvements. Companies like Instagram, Pinterest, Groupon, YouTube found success by analyzing user behavior data and making major changes based on what they learned.
Instagram originally started as a location-based social app but pivoted to focus solely on photo sharing after analyzing data and seeing that was the main feature people used. Pinterest pivoted from e-commerce to content discovery/sharing based on user data.
Groupon and YouTube also discovered more promising opportunities by analyzing user data of their original products, which led them to pivot their business models.
Other examples like HubSpot show that data analysis can validate problems aren’t with the product itself but how it’s introduced to customers. HubSpot discovered training improved retention.
Once a compelling user experience (“aha moment”) is identified, growth teams should focus on optimizing ways to get more users to experience it as quickly as possible, like tweaking new user flows. Getting this right is critical for sustainable growth.
Companies invest heavily in optimizing the new user experience through experimentation and data analysis to achieve fast growth foundations.

So in summary, analyzing both qualitative and quantitative user data is important for discovering opportunities, validating strategies, and optimizing experiences - which leads to successful product pivots and growth foundations.

Everpix was a highly rated photo management app with early success - it had a large user base, high user engagement, and a very good conversion rate from free to paid subscriptions.
However, the founders failed to focus on growth strategies that would drive more users to pay for subscriptions. They spent too much time improving the product instead of finding ways to monetize existing users.
By the time they realized the need for growth, they had run out of funding. Several growth strategies like requiring contacts to sign up were considered but not tested.
Hiring a traditional marketer did not work either. Without stronger growth, they could not raise more capital from investors.
The key lesson is that founders need to shift focus from building the best product to analyzing metrics that drive growth and profitability. Rapid testing of high-impact growth strategies is important, not just improving the existing product.
A growth equation can help identify the key levers that influence growth specific to each business model. Focusing experiments on improving these critical metrics is more likely to lead to success than scattershot testing.
Choosing the right growth metrics is crucial for focusing efforts and resources. Metrics tracked through standard analytics may not be the most important.
A company’s unique growth equation depends on factors like inventory, traffic to product pages, conversion rates, average purchase value, and repeat purchases.
Core metrics capture actions that directly deliver a product’s core value to users, like number of friend connections for Facebook or rides booked for Uber.
It’s important to identify metrics for each step a user takes to experience the “aha moment” of a product’s value.
One metric should be selected as the “North Star” - the most important gauge of delivering customer value and success. This guides all growth experiments.
Good North Star metrics represent the core user experience, like messages sent for WhatsApp or nights booked for Airbnb.
The North Star may change as goals are achieved or a company’s needs shift over time, like Facebook switching from monthly to daily active users. Focusing on the right metrics is key for effective growth.
Companies like LinkedIn and Facebook have created separate product and growth teams, each with their own key focus or metric (North Star) to optimize, while still working towards the overall company goals.
LinkedIn’s growth teams focus on initiatives like network growth, search engine optimization, onboarding, international growth, and engagement.
Facebook originally focused on getting new users to connect with 7 friends in 10 days, but later evolved to priorities like growing advertisers and the international user base.
It’s important for growth teams to carefully choose the right key metric (North Star) to focus on in order to spend time and resources effectively. Aligning experiments and efforts around the North Star helps avoid wasting effort on irrelevant metrics.
Airbnb’s focus on increasing nights booked helped them efficiently test and implement professional photography, significantly boosting bookings. They avoided less effective experiments not directly linked to the North Star.
Companies need robust data collection and analysis capabilities to measure performance, understand user behavior, identify opportunities, and evaluate experiment results relative to the North Star. Getting these capabilities right up front is critical for effective growth experiments.
The passage discusses how marketing specialist Rob Sobers outlined a simple $9/month method for companies to create a user data tracking system using off-the-shelf tools.
While user data can show what users are doing, it has limits in explaining why users behave that way. Surveys and interviews can help uncover the reasons behind behaviors.
Continuing to talk to users after launch is important to provide feedback for experiments. Quantitative analysis should be complemented with qualitative user probes.
Data discoveries need to be reported accessibly across the organization to drive data-driven decisions. Complex spreadsheets may be daunting to non-analysts. Dashboards that clearly illustrate key trends are preferable.
Dashboards should focus on the most important metrics related to growth levers. Presenting metrics as ratios over time rather than static numbers makes trends clearer. Goals and variances from norms should also be indicated.
An example is given of how Twitter used cohort analysis and correlation analysis of user data to discover that users who followed 30 others had much higher retention rates, but they interviewed users to confirm the reasoning behind this pattern.
In 2007, Art Briles became the head coach of the Baylor football team, which was struggling.
Briles implemented a new high-tempo, no-huddle offense that allowed Baylor to run about 13 more offensive plays per game than competitors.
This extra playing time equated to nearly 2 extra games over a 10 game season, providing more opportunities to learn through experience what plays were most effective.
Running more experiments (plays) in a shorter time period allowed the team to learn faster and improve more quickly, leading to their success.
This “learning faster by learning faster” principle is also the goal of high-tempo growth hacking processes. The faster a team can run experiments, the more they learn and the faster they can improve.
It’s a numbers game - most tests fail or have inconclusive results, but a few produce big wins. Finding these wins through a high volume of tests is key.
Compounding small improvements over time through continual testing and learning can create big advantages. Even 5% improvements monthly can double impacts over a year.
The chapter discusses implementing a disciplined growth hacking process and cycle to efficiently generate, prioritize and run a high volume of experiments to maximize learning and results.
The growth team meeting kicks off the growth hacking process for driving more sales through a grocery chain’s mobile app. Initial analysis found the “aha moment” is convenience of ordering groceries for delivery.
The North Star metric is set as monthly revenue per shopper. Areas of focus are engaging more users and building a base of regular, high-spending shoppers.
In the Analyze stage, the data analyst crunches numbers and identifies patterns among top customers vs infrequent/non-users. Surveys further probe behaviors.
In the Ideate stage, all team members submit as many growth ideas as possible over 4 days, without self-censorship. Ideas follow a template and propose exactly what would be tested, why it may work, and how results will be measured.
The bank of submitted ideas covers ways to drive first purchases, more purchasing from existing users, and increasing order values. These ideas will be evaluated and selected from for the initial cycle of growth experiments.

Here is a summary of the idea description for submitting the “Shopping List” feature to the grocery app product manager:

The idea is to build a “shopping list” feature that will allow users to store a list of items from their prior purchases so they can easily reorder them. This feature is targeted at all existing app users. It aims to increase repeat purchases and the rate of repurchasing by making it more convenient for users to save and reorder their favorite items.

The shopping list feature would be added to the app’s navigation menu, making it accessible for every user. An initial test with a small group of users would be done before rolling it out more widely.

The hypothesis is that by making it easier for shoppers to view and reorder past purchases, the number of repeat purchases will increase, potentially by 20%.

Metrics to track the impact include the number of users utilizing the shopping list feature, the number of items saved per list, the number of repeat purchase orders, the repurchase rate, and the average order size of repeat purchases. This will help assess if the feature is achieving the goal of improving key metrics like revenue per user.

The passage discusses using a scoring system called ICE (Impact, Confidence, Ease) to rank and prioritize potential experiments for a grocery mobile app team aiming to increase revenue per user.
Impact measures how much the idea is expected to improve the key metric. Confidence is the idea generator’s belief it will work, based on evidence. Ease measures resources/time needed.
The team reviews scores before a meeting, discussing modifications. They aim to run a mix of high impact and easier tests.
Scores are not perfect but provide a starting point. Lower scoring ideas can sometimes end up being most successful.
The team selects experiments to run that week based on high impact and ease scores. Others are slated for future testing dates based on preparation needs.
In this example, the team chooses a first-time shopper promotion and improved free delivery visibility to test first due to high impact and ease. A complex shopping list feature is considered for future testing.
The goal is to prioritize experiments collectively through a collaborative selection process to optimally use time and resources.

The passage discusses the importance of cross-functional collaboration when preparing and deploying experiments. It notes that those leading the experiments will need to work with other teams within their company to set up the experiments properly.

As an example, it describes how a marketing team member working on a shopping app experiment would collaborate with the graphic design, email, and data analytics teams. The graphic design and email teams would help create promotional materials, while the data analyst would identify the control and experiment groups and ensure results can be tracked.

It also notes that when experiments are ready to launch, the growth lead should notify other teams so there are no surprises. And if issues arise that could delay an experiment, the team member in charge should inform the growth lead as soon as possible.

The overall message is that running effective experiments requires coordinating across functions within an organization. Proper preparation and communication are important for experiments to deploy smoothly.

The weekly standup meeting for the grocery app team would review metrics like average revenue per shopper and number of shoppers completing purchases to assess whether things are going well or if any improvements are needed.
The growth lead highlights key positive and negative factors, such as improvements from tests/experiments or drops in performance hurting growth.
The current growth focus area is discussed, like whether the focus is staying the same on user acquisition or shifting to retention/monetization. Short-term objectives are also noted.
10 minutes are spent reviewing testing activity from the previous week, including the number of tests launched versus the tempo goal and any tests that were delayed.
15 minutes are spent going over lessons learned from analyzed experiments, including preliminary and final results and implications for further actions.
15 minutes are spent selecting the next set of growth tests from nominated ideas, with discussion of each idea’s merits and assigning tests to team members to own.
5 minutes check the pipeline of new growth ideas and recognize top contributors to inspire more idea generation.
The growth hacking process can produce improvements quite quickly, sometimes within just two weeks, through a cycle of testing, analyzing results, and retesting based on learnings.

Here are the key points about crafting compelling marketing messages and customer acquisition from the passage:

It’s important to achieve “language/market fit” where the marketing messages resonate well with the target audience. This includes the language used across all marketing channels like emails, ads, product descriptions, etc.
The messaging needs to directly address customer needs and provide a clear benefit of how the product will improve their lives, all within 8 seconds to capture short attention spans.
Steve Jobs’ phrase “1,000 songs in your pocket” for the original iPod is cited as an example of compelling messaging that reframed how people thought about mp3 players.
Crafting effective language is difficult and not an exact science. Growth hacking uses A/B testing to rigorously test different messaging options.
A/B testing of messaging is easy to do using tools that can swap out website copy or test different email/ad subject lines and calls to action.
Upworthy tests headline options for viral potential by sharing two versions of content on Facebook in similar regions and seeing which generates more clicks and shares.
Customer feedback from surveys, social mentions, reviews can provide ideas for messaging to test in acquiring new customers. The goal is reducing acquisition costs through optimized messaging.
To find the most effective language to market a product, talk directly to customers and ask how they describe the product’s value. Also read customer support calls, forums, and reviews.
Small changes to language can have a big impact on growth. Examples are changing “store photos online” to “share photos online” and “find a date” to “help people find a date.”
Testing language changes is a low-effort way to experiment. But results may lead to deeper changes, like positioning or the product itself.
Learning what language resonates helped founders like Sophia Amoruso define their brands’ identity as they grew.
Unlike portfolio investments, diversifying marketing across many channels is usually not best for growth. Focusing “more wood behind fewer arrows” through testing is better.
To find best channels, prioritize a few high-potential options from categories like viral, organic, paid. Conduct discovery experiments before optimizing the top channels. Content remains a top organic channel.

So in summary, customer input, A/B language testing, and channel prioritization are emphasized as growth hacking approaches to refine marketing fit and drive faster scaling.

The passage describes a method for prioritizing which distribution channels a company should experiment with first. It involves making an initial cut by considering the demands of the business model and target user behaviors.

Some key factors to consider include whether users are actively searching for the product/service online, which social networks they use, and identifying complementary products they buy.

The passage then outlines a framework for prioritizing channels to experiment with. It involves scoring each channel on a scale of 1-10 based on cost, targeting ability, control, input time, output time, and scale. Channels are then ranked and prioritized based on their average scores.

As an example, a grocery store app growth team analyzes user data and decides to leverage their website more and try Facebook ads. They consider factors like user behaviors and competition. They develop hypotheses and channel experiments to test, like improving app promotion on the website, emailing existing loyal customers, and adding a pop-up on the website.

The method provides a systematic way to use data and consider key criteria to select the highest potential channels to start experimenting with. The grocery store example illustrates how it can be applied in practice.

Here are the key points about viral user acquisition approaches:

Viral user acquisition relies on users sharing or referring your product to their personal networks. Effective viral loops provide strong incentives for users to do this, such as referral programs that offer discounts.
Creating truly viral growth is challenging and requires initial experimentation and continuous optimization. There is no magic formula. Viral loops work better for some products than others, depending on how easily the product can be shared or referred.
Merely having a referral program is not enough - the product itself still needs to deliver real value to users in order to generate word-of-mouth sharing. The product experience needs to be compelling enough for users to want to refer it to others.
Different types of viral loops can include things like referral programs, digital sharing tools like email signature lines, mechanics within the product experience itself that motivate sharing, and more. The best approach depends on the specific product.
Triggering widespread viral growth often requires an initial investment in user acquisition via paid channels or organic efforts to get the first set of users on board before viral effects can really take hold. Viral user acquisition alone may not be enough for growth.

So in summary, viral user acquisition relies on user incentives and product value, but realizing significant growth through virality typically requires experimentation, optimization efforts over time, and often a combination with other acquisition tactics. There is no quick and easy formula.

Viral growth relies on both catchy/appealing packaging (headlines, visuals, etc.) and high-quality content that people truly enjoy and are motivated to share.
There are two types of virality - traditional word-of-mouth sharing, and instrumented virality via built-in sharing mechanics. The best products see growth from both.
When adding sharing mechanics, focus first on building a great product experience before instrumenting virality. Products like Hotmail and Dropbox had sharing built seamlessly into core experiences.
Achieving a viral coefficient above 1, while desirable, is extremely difficult to sustain. More realistic goals focus on experimenting with various sharing loops to optimize payload, conversion rates, and frequency.
Sharing incentives work best when they provide value to both referrers and recipients. High payloads require more compelling incentives.
The best sharing loops leverage network effects, where more users improve the experience for all. Examples include social networks and marketplaces.
User experience must remain positive - tricks that annoy or deceive users into oversharing can backfire long-term. Simplicity and double incentives work best.
The goal is to increase activation rates by getting more new users to experience the “aha moment” for the product, which makes them want to continue using it.
The first step is to map out the user journey and identify each point on the way to the aha moment. This could include things like signing up, adding payment info, browsing products etc.
The second step is to analyze data at each point to see where drop-off is occurring and what might be hindering the user from progressing.
The third step is to experiment with different hacks to improve activation at the problem points identified. Things like simplifying onboarding, providing quick wins, optimizing triggers etc.
Best practices that have worked for companies include optimizing the new user experience, offering tutorials/tips, using push notifications strategically as triggers to re-engage users.
Triggers need careful testing to get the timing, frequency and messaging right so they don’t irritate users but gently encourage continued use.
Constant experimentation is required as most successes come from unexpected discoveries, not predetermined strategies. Data should guide which experiments to prioritize.

So in summary, it’s a process of mapping the user journey, analyzing drop-off points, and experimenting with different approaches to get more users all the way to the aha moment.

The growth team at a grocery delivery app analyzes usage data to understand where users are dropping off or losing interest in the funnel from downloading to purchasing.
They create a conversion funnel report showing the percentage of users completing each step like downloading, searching, adding to cart, creating an account, purchasing, etc.
The data reveals major drop-offs when adding to cart and checking out, low search volumes, and high rates of purchase for those using a new shopping list feature.
This indicates checkout may need streamlining, search should be encouraged earlier, and the shopping list feature is promising.
Before experimenting, the team will survey and interview users to better understand reasons for behaviors seen in the data.
The goal is to identify high-potential experiments like simplifying checkout or promoting search and shopping lists to boost activation and conversions.
Analyzing usage funnels and drop-offs is a key method growth teams use to spot issues and focus experimentation on addressing major friction points.
Companies should survey customers to better understand issues they are facing rather than making assumptions. Surveys allow customers to provide open-ended feedback.
To survey customers who abandoned a process, pop-up surveys can be used as they are leaving a page. Short surveys with questions like “What prevented you from signing up?” are most effective.
Surveying recent customers who completed a purchase can also provide insights, as they encountered the same obstacles as those who abandoned.
Case study of HubSpot’s Sidekick product - Segmenting and analyzing data revealed activating users was higher when they used work emails. Feedback also surprisingly found people didn’t understand how to use the product. Many experiments failed but finally showing a message after installing helped activation.
There are no shortcuts - companies need to map the customer journey, analyze drop-off points, conduct surveys, and continually experiment based on learnings to find effective activations hacks for their specific product and customers. The goal is to reduce friction points preventing users from accomplishing their goals.
Friction refers to any impediments, annoyances or difficulties users experience when interacting with a product. Developers are often unaware of the friction in their own products since they know the systems so intimately.
Even small amounts of friction can discourage users and send them away. The key is to continuously look for ways to reduce friction.
A simple formula ties conversion rates to user desire for a product and the friction experienced: Desire - Friction = Conversion Rate.
Optimizing the new user experience (NUX) is important since it’s a unique first impression. The NUX landing page should communicate relevance, value and have a clear call to action.
Testing techniques like single sign-on (e.g. via Facebook login) and “flipping the funnel” by letting users experience a product before signing up can significantly reduce friction and improve conversions.
Sometimes a small amount of strategic friction may be needed to guide users, as seen in Airbnb experiments prompting earlier sign-ups to tailor search results better. The goal is to balance friction reduction with moving users through the desired process.
The first Airbnb experiment testing sign-up prompts saw an increase in sign-ups but a drop in bookings, indicating the prompts caused friction.
Further analysis found the increased sign-ups led to other valuable outcomes like more invites and wish list additions.
The team’s next experiment reduced friction by dropping the frequency of prompts to every 5 pages instead of each page. This eliminated the negative impact on bookings while maintaining most of the sign-up gains.
Adding additional explanatory text to prompts backfired, likely distracting users instead of encouraging sign-ups as intended. This showed how subtle friction can deter actions.
Through multiple experiments optimizing various design elements of the prompts, the team found the optimal approach to balance improved sign-ups and other metrics.
Pinterest successfully tested personalizing their onboarding process for new users by asking them to select topics they are interested in upfront. This resulted in a 20% increase in user activation rate by showing them relevant content first.
Questionnaires at signup, like asking 5 multiple choice questions about interests or problems to solve, can increase commitment while personalizing the experience. Neil Patel saw a 281% increase in leads by testing this.
Gamification, like offering “missions” and rewards for actions as in Adobe’s Photoshop trial, increased trial to purchase conversions by 4x. But gamification can backfire if rewards are not meaningful or relevant.
Triggers like push notifications and emails are powerful but must be used carefully to avoid annoyance. The effectiveness depends on how much they motivate action and the relevance to the user’s interests and stage in the product lifecycle. Experimentation is important to get triggers right.
Triggers are cues that prompt users to take an action. Their effectiveness depends on the user’s motivation and ability at the time, according to BJ Fogg’s Behavior Model.
Motivation considers factors like knowing the caller ID, while ability means how convenient it is to take the action. Triggers work best when both are high.
Experiment with trigger types cautiously and follow platform rules. Not all users opt into notifications, so impact varies.
Strong triggers relate clearly to core product value, like sale alerts in a shopping app. Irrelevant triggers irritate users.
Test different trigger types like facilitators, signals, sparks based on user motivation/ability levels.
Consider persuasion principles like reciprocity, liking, social proof, authority, scarcity when crafting triggers.
The most powerful triggers form habits and internal motivation for long-term use and engagement.

So in summary, effective triggers prompt action when motivation and ability align, by relating to product value and following persuasion principles, to drive user engagement over the long run. Careful experimentation is important.

Retention is critically important for a company’s profitability and success. Losing customers is very costly due to the high costs of customer acquisition.
Growth teams use growth hacking techniques to rapidly test and deploy different retention strategies. This allows them to quickly determine which strategies are most effective at reducing churn and re-engaging lapsed users.
A example is given of a grocery delivery app seeing declining usage after the first month. Growth hacking allows fast testing of ideas like increased notifications, in-app promotions, or new features to find the best strategy.
The compounding value of retention is that longer term customers spend more over time and drive more referrals. This allows reinvesting profits into growth. Companies like Amazon that achieve very high long term retention see tremendous business success as a result.
Growth hacking helps companies home in on the most promising retention “bets” early to stop customer defections. The key is to rapidly deploy and learn from small tests of different retention strategies.
Providing high quality products/services that continuously solve customer needs is fundamental to retention. But retention can erode over time, so ongoing effort is needed to maintain engagement through new features, promotions, etc.
Growth teams should monitor customer retention and look for early signs that retention may be eroding over time. However, their goal shouldn’t just be stability - they should actively experiment with ways to continually increase retention.
Retention happens in three phases: initial, medium-term, and long-term. The initial phase is critical for conversion; medium-term focuses on creating habits; long-term ensures customers see ongoing value.
Good retention means customers return frequently based on the type of product/industry. Benchmarks against competitors and typical usage patterns are important to assess performance.
Retention metrics may measure repurchase rate, frequency of return visits, or how long until the next purchase. They vary by product but should show retention improving over time like Evernote’s “smile graph.”
Growth teams have tools to improve each phase, from onboarding tohabit-forming tactics to new features that refresh perceived value. Their goal is continual improvement, not just stability.
Churn rate refers to the percentage of customers or subscribers lost over a given period of time, usually monthly or annually. It is the inverse of the retention rate.
Tracking cohorts, or groups of customers segmented by when they joined, is an important way to analyze retention data more granularly. Common ways to segment cohorts include month of acquisition or sign up.
Charting cohorts over time allows companies to identify trends, such as higher churn for customers acquired during certain campaigns or time periods. It can reveal issues needing further investigation.
Comparing retention curves of different cohorts visually on one graph makes patterns more apparent. For example, a sharp drop-off for newer cohorts compared to earlier ones.
Further segmentation can be done based on engagement metrics like numbers of purchases, visits, or content consumed. This helps uncover correlations between usage behavior and retention.
Identifying drop-off points through cohort analysis informs experiments to improve initial retention, such as refining the new user experience or getting users engaging with core value faster. Triggers can also prompt continued engagement but not replace experience improvements.
Building habits is an important part of the retention phase of growth. The goal is to make use of a product a regular habit, whether daily, weekly or less frequent.
Habit formation relies on rewarding customers for continued use through what’s called an “engagement loop.” Triggers like push notifications remind users to take actions that yield valuable rewards.
Amazon Prime is a successful example, as free shipping and quick delivery provide clear rewards every time members shop, reinforcing the habit.
Growth teams should experiment with different reward types and triggers to measure what drives the most engagement and retention for their specific product.
Rewards don’t need to just be financial - social recognition and experiences can also be powerful motivators to form habits. Brand ambassador programs give social status and perks to top users.
Both tangible rewards like discounts as well as intangible ones like status should be tested to keep users engaged over the long run.
American Express provides elite customer programs with exclusive offers, travel/concierge services to make wealthiest customers feel special and retain loyalty.
TheSkimm grows its referral program by rewarding referrers with public recognition, branded merch, networking events for hitting referral targets.
Companies recognize customer achievements through notifications, like Fitbit/Runkeeper fitness milestones or Medium article recommendations, driving retention.
LinkedIn/Twitter notify users of engagement on their posts by others, motivating continued use.
Companies now offer mass customized experiences using large customer databases and personalization tools, like Walmart recommending tailored products.
Machine learning further enhances customization, like newsletters tailored individually without human input based on reading patterns.
Pinterest rapidly tests countless engagement message variants across languages through its Copytune program.
Companies promote “coming soon” new features to retain users awaiting updates, like Netflix with new seasons/shows or Apple with new devices.
HBO’s Rome production costs were offset by subscribers retaining accounts pending the anticipated new show’s premiere.
A streaming company has acquired the rights to a popular new series that won’t be available for 3 months.
They want to test if notifications about the upcoming show lead to higher subscription renewals in the lead up to its release.
They will run an A/B test, notifying some existing subscribers (experiment group) about the new show via email, while others get the normal experience (control group).
They will compare retention rates between the two groups to see if “Coming Soon” messages are effective.
If the messages help retain more subscribers who watch similar shows, the company will make “Coming Soon” notifications a permanent part of their customer communications strategy.

The key idea is that the company wants to test if generating excitement and awareness about an upcoming new show through targeted emails can encourage higher subscriber retention in the months before the show is released. An A/B test will help them quantify the impact of these “Coming Soon” notifications.

Here are the key points from the passage:

Companies can increase retention and revenue by targeting “lost” or inactive customers with re-engagement campaigns like emails highlighting recent content. Testing found this approach increased site returns by 29.4% over a control group.
However, these campaigns need to be tested carefully to avoid annoying customers and further alienating them. Companies should accept when a user is truly not coming back.
The goal of acquiring, activating, and retaining customers is ultimately to increase lifetime value (LTV) by earning more revenue from each customer over time.
Growth teams often overlook opportunities to increase monetization and focus only on acquisition and activation.
To identify opportunities, companies should map their entire customer journey and monetization “funnel” to highlight revenue opportunities and barriers.
Typical pinch points include shopping cart abandonment for e-commerce and plan/pricing pages for SaaS. Ads can also be a monetization weakness if too intrusive.
Analysis should segment customers into cohorts like high/low spenders to see where opportunities exist to increase average revenue per user or customer.

The key idea is that growth teams should systematically analyze customer data and test ways to increase retention, repeat purchases, upgrades, and other monetization metrics to boost lifetime customer value over time. Careful re-engagement can bring back some inactive users as well.

Companies should segment users based on engagement metrics like time spent on site, pages viewed, videos watched, etc. to understand patterns in ad revenue.
Users should also be segmented by location, demographics, acquisition source, device used, purchases/features used, visit frequency, etc. to analyze correlations with revenue.
HotelTonight analyzed bookings by WiFi vs cellular users and found cellular users booked more due to difficulty comparing sites on spotty data connections. They targeted cellular-only users.
E-commerce companies should analyze cohorts by spend amount, number of items/orders, purchase timing, repeat purchase rates, etc. to design experiments increasing revenue.
For SaaS, analyze users by business type to understand willingness to pay for higher plans/features. Education/nonprofit users of SurveyMonkey spent less, so they offered discounts to convert to paid.
Look at monetization differences between countries due to payment preferences and business model understandings.
Identify primary customer segments and design experiments like communications,landing pages and offers to drive revenue from each segment.
Survey customers to understand desired product improvements, features or plan levels by segment to provide most value and drive purchases.
The passage discusses how companies can survey users to help decide which new features to build. It gives the example of BitTorrent asking users to rank potential new features to help prioritize development.
recommendations and personalization are effective monetization tactics. Customized recommendations are generated using algorithms and data on individual users’ purchases and behaviors as well as aggregated data from similar users.
Amazon is cited as a leader in developing powerful recommendation engines. More basic recommendations can be generated using the Jaccard index to identify products commonly purchased together.
Personalization requires sensitivity to avoid unwanted intrusion into users’ lives. Target faced backlash for inadvertently revealing a teenager’s pregnancy through targeted coupons.
Companies should test personalization through small experiments before widespread rollout to gauge user response and refine approaches.
Pricing products optimally is challenging. Growth teams can help by conducting research to identify an optimal pricing range for testing and considering psychological pricing tactics like charm prices that end in 9.
Pricing is a key growth lever that requires continual testing and optimization. Factors like presentation of prices and multiple pricing options can impact customer behavior.
For SaaS products, it is recommended to survey customers asking what price points are too high, borderline expensive, a good deal, and too cheap. The answers map onto a graph to determine an ideal pricing testing range.
Other data like costs, competition, and customer personas should also inform pricing experiments run quarterly.
For usage-based software, charging should align with the customer’s perceived value and scale with usage, using a “value metric” like number of contacts or page visits.
Dynamic pricing based on many factors can be used for e-commerce but risks backfiring if not implemented carefully, like when Orbitz charged Mac users more based on data.
Ad-based products should experiment with pricing different ad types and placements to maximize user engagement and profits through an auction model that targets the highest bidders. Ongoing optimization is important.
Advertisers that make higher-quality ads will get their ads shown more prominently and be incentivized to bid more, which benefits both advertisers and the advertising platform.
Smaller publishers without bidding systems should experiment with pricing their ad inventory based on supply and demand to maximize revenue growth.
Pricing relativity refers to how people’s price perceptions are influenced by other options offered. Offering a “decoy” option can drive customers to choose a higher-priced option by making its value seem relatively better.
Teams should experiment with decoy options to understand how customers perceive value and which options they are likely to choose. Both lowering and raising prices through experimentation can provide insights, as customers may be less price sensitive than assumed.
Testing pricing changes requires coordination across teams to avoid inconsistencies and protect the customer experience. Sales teams need visibility into experiments.
Overcoming the “penny gap” where customers don’t want to pay anything can require making a product initially free and monetizing through other means like in-app purchases or upgrades.
Stomization refers to offering a basic free version of a mobile app and monetizing through upgrades, premium features, subscriptions, virtual goods, etc. rather than charging upfront.
Revenue can significantly increase even if the vast majority of users don’t pay, as upgrades from a small percentage can drive substantial profits.
It’s important to optimize strategies for persuading more free users to upgrade, such as displaying premium features they can’t access or subscriptions to unlock them.
Experiments with virtual currencies, combinations of revenue streams, and monetizing user data through subscriptions or partnerships can also work.
Understanding consumer psychology principles like reciprocity, commitment, social proof, and scarcity can inspire new experiments to boost monetization and revenue. Simple tactics based on these principles have been shown to dramatically increase purchases and engagement.
Having social proof like many happy customers on a shopping site resulted in a 44% increase in revenue for those shoppers who likely felt more comfortable with so many endorsements.
Growth teams can increase visibility of social proof along the purchase path, like testimonials, prominent customer logos, product results, and numbers of current shoppers/bookers to validate customers’ decisions.
The principle of authority explains why celebrity endorsements are common and why brands founded by celebrities are often successful. Growth teams can experiment with featuring influential endorsers.
We are more likely to buy things recommended by people we like. Leveraging the principle of liking, like using real customer photos, can increase purchases.
Triggering fear of missing out (FOMO) by using scarcity as a tactic, like limited time deals or limited inventory, can drive purchases since customers feel pressure to buy before it’s too late.
Driving growth requires constant experimentation across acquisition, activation, retention and monetization to avoid stalls from issues like failing to innovate, acknowledge competition, or update products based on changing customer needs and markets. Relentless growth requires perpetual focus on all fronts.
Companies can experience growth stalls for numerous reasons, such as becoming distracted by new products/markets, losing key talent, or becoming complacent in marketing efforts.
Overreliance on certain channels like Facebook or Google can backfire when those platforms tweak algorithms or rules, as was the case for apps like Viddy that lost most of its users.
The author’s company GrowthHackers experienced a growth stall when they ran fewer than 10 experiments in a quarter due to complacency. They refocused on running many weekly experiments and saw traffic increase 76% as a result.
Growth teams need to constantly run experiments and avoid prioritizing administrative tasks over growth work. They also should double down on successful experiments/channels instead of moving on too quickly to find new growth levers. Pushing further in proven areas can generate more growth that teams may leave on the table by not fully exploiting successes. Maintaining a rigorous testing schedule is key to avoiding stalls.
When a growth tactic starts working, teams should double down and intensely experiment with optimizing it further rather than moving on. Examples from GrowthHackers newsletter signups are provided.
Teams may think they’ve tapped out a lever when really they just need better analytics data. Investing in improved tracking allows for more refined experiments.
In addition to optimizing existing channels, teams should experiment with new acquisition channels over time to avoid growth stalls.
Bringing in fresh perspectives from other teams/departments can generate new idea through open collaboration.
Teams should regularly test “moonshots” - significant redesigned of existing successful features/tactics, not just optimizations, to break out of local maxima.
Both continual optimization and bolder experiments are needed together to drive major growth leaps, not just incremental wins. Pinterest and Uber examples given.

The key message is that growth teams should not only optimize existing wins but also double down on them, improve their data insights, try new channels, collaborate openly, take “moonshot” risks with major redesigns/changes, and balance optimizations with bolder experimentation.

The book shares lessons and strategies for accelerating growth from Sean Ellis and Morgan Brown’s experiences working with startup companies.
Early-stage companies need to focus on experimentation and customer feedback through a minimum viable product approach.
Growth hacking blends traditional marketing techniques with new technology to optimize for growth. It utilizes things like viral growth, engineering, data analysis, and more.
Examples of growth hacking success stories include PayPal, Dropbox, Facebook, Airbnb, LinkedIn, and Uber. They all focused heavily on customer acquisition and experimentation.
As companies scale, growth becomes more challenging due to the costs of acquiring new customers. Sustaining growth requires continually refining growth strategies and adapting to changing market conditions.
The book aims to help companies optimize for growth at all stages by sharing tangible tactics and frameworks from companies that have succeeded in achieving rapid and sustainable growth.

Here is a summary of the Electrek article:

Tesla is building a new growth team from scratch ahead of the highly anticipated launch of its Model 3 electric sedan.
The team will focus on demand generation, customer acquisition, and other growth initiatives.
Tesla has hired ex-Facebook growth manager Tanner Buff to lead the team.
Other recent hires include Andrew McKean from Uber’s growth team and Mike Harrigan from Eaze, an on-demand marijuana delivery service.
Tesla experienced huge demand for reservations after unveiling the Model 3, receiving over 325,000 reservations in the first week.
The new growth team will help Tesla convert reservations to actual orders and sales as production ramps up for the Model 3 over the next two years.
Building a growth team from scratch allows Tesla to implement best practices from Silicon Valley tech companies as it enters a new phase of higher volume production and sales with the Model 3.

Here is a summary of the article “rtup Is Going Out of Business,” The Verge, November 5, 2013, theverge.com/2013/11/5/5039216/everpix-life-and-death-inside-the-worlds-best-photo-startup:

Everpix was a photo startup that offered automatic photo organization and sharing features. It was considered one of the best photo startups.
The article profiles Everpix’s founder Peter Pachal and how he grew the company quickly with innovative technology and features. However, Pachal struggled to monetize the popular service.
Everpix’s free users grew to over a million photos uploaded per month. But Pachal had difficulty convincing people to pay for additional storage or premium features.
The company ran low on funding and could not raise another round to continue operations. Pachal was forced to shut down the service in November 2013, much to the disappointment of Everpix’s many loyal users.
The article examines the challenges of monetizing a consumer photo service and questions if the space is even viable given the dominance of free options from Google, Apple, Facebook and others. It’s presented as a case study of a “best-in-class” startup that still struggled to survive.

That covers the main summary points about the challenge faced by Everpix in monetizing its photo organization service, which ultimately led to the startup shutting down despite having many engaged users. Let me know if you need any clarification or have additional questions.

Here are summaries of the key points from the selected articles:

The article from Bloomberg discusses how Amazon pioneered and perfected same-day delivery of products through its Amazon Prime membership program, creating an unmatched customer experience of instant gratification.
The Quora response explains how Yelp initially grew its user base by posting reviews on various websites and forums to attract initial users, then utilized a critical mass threshold where the site became more valuable and attracted more users the larger it grew.
The academic paper studies how anonymity affects the competition for volunteers on online review platforms, finding that some level of real name identification increased the size and quality of user contributions.
The Points Guy article provides details on the exclusive Amex Centurion credit card program and the luxurious benefits it provides to high spending customers.
TechCrunch discusses how theSkimm engaged millennial audiences through its fun, conversational daily newsletters covering politics and current events in short, shareable segments.
The Nieman Lab article examines how theSkimm achieved over 1.5 million newsletter subscribers through passionately engaging its readership in discussion and building a sense of community.
The Unbounce blog post provides examples of how behavioral emails targeting customer actions and inactions can be used to increase engagement and retention.
The interview discusses personalizing the customer experience and optimizing websites for conversion based on individual user behavior and preferences.
The Call To Action conference talk outlines how moving from static homepages to personalized experiences increased user engagement for several companies.
The KPCB blog post lists some tactics for “retention hacking” or using behavioral data to address at-risk customers and keep them engaged.
The summary focuses on articles related to growth strategies, optimizing the user experience, and leveraging customer data to improve retention and uncover new growth opportunities.

#book-summary

Hacking Growth How Today's Fastest-Growing Companies Drive Breakout Success - Sean Ellis & Morgan Brown