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Growth Marketing vs Growth Hacking: The Complete 2026 Guide

Growth marketing vs growth hacking: what each means in 2026, when to use which, and the system that underpins both, a scored experimentation program.

What Growth Marketing and Growth Hacking Really Mean in 2026

The marketing landscape has fundamentally transformed since Sean Ellis coined "growth hacking" in 2010. AI adoption in businesses has climbed steeply over the past few years. Understanding the distinction between growth marketing and growth hacking becomes critical as you navigate AI-driven discovery, generative engine optimization, and evolving customer behaviors.

Growth hacking represents a rigorous approach to fueling rapid market growth through high-speed, cross-functional experimentation combining marketing and technical product development skills. Growth marketing takes a systematic process combining strategic brand marketing with tactical performance marketing to acquire good-fit customers and help them become successful enough to buy again, buy more, and tell others.

The confusion stems from overlapping goals, both drive growth through data-driven decisions and experimentation. Yet the similarities make differentiation difficult, with some assuming both terms mean the same thing, but they don't.

How has AI changed growth strategies in 2026?

AI has reshaped how growth professionals approach every customer journey stage. AI assistants have changed how customers search, instead of asking for business lists, they ask for tasks to be completed, with the assistant selecting a provider it can justify rather than showing options.

This shift demands new optimization strategies. Generative Engine Optimization (GEO) has become impossible to ignore in 2026 as generative AI reshapes discovery and brand visibility, with publishers reporting meaningful traffic losses. Traditional SEO rankings matter less when Gartner predicts traditional search engine volume will drop 25% by 2026 as users shift to AI-driven answer engines.

Companies using AI in marketing report higher ROI, better click-through rates, and faster campaign launches than manual workflows. Growth teams must now optimize for AI visibility alongside traditional metrics, creating content structured for machine comprehension while maintaining human appeal.

Growth Experiments helps growth teams turn experimentation into a structured, insight-driven system. The platform's AI-guided ideation generates experiment ideas across four modes, Safe Bets, Bold Bets, Copy What Works, and First Principles, while maintaining creative thinking that drives breakthrough results. Systematic experimentation becomes accessible to teams of all sizes.

The Core Differences Between Growth Hacking and Growth Marketing

Understanding when to deploy growth hacking versus growth marketing determines whether you'll achieve sustainable scale or burn resources chasing vanity metrics. The differences extend beyond semantics into fundamental approaches to building businesses.

What makes growth hacking different from traditional marketing?

Growth hacking makes small changes or creative "hacks" to achieve rapid growth, usually low cost and implemented quickly for fast results, often associated with early-stage startups trying to find their footing and grow fast. The approach prioritizes speed and experimentation over long-term brand building.

The most fundamental difference boils down to opposing views on brand, growth hackers don't care for brand whereas growth marketers live for it, with growth hackers being brand averse for good reason: they prefer tactics and channels guaranteeing perfect attribution. This preference enables rapid decision-making about experiment success without waiting for long-term brand impact.

Key characteristics of growth hacking:

Growth marketing takes a broader perspective, combining strategic brand marketing (positioning and differentiation) and tactical performance marketing (content marketing and paid acquisition), following a systematic process building on agile development principles.

Key characteristics of growth marketing:

The timeline difference shapes everything. Growth hacking is fast while growth marketing is slow, with growth hacking aiming for quick results and rapid growth while growth marketing focuses on deeper research utilized over longer periods for sustainable growth.

The AARRR Framework: Foundation for Both Approaches

Both growth hackers and growth marketers rely on structured frameworks to identify opportunities and measure progress. The AARRR Pirate Metrics framework is an acronym for five user-behavior metrics product-led growth businesses should track: acquisition, activation, retention, referral, and revenue.

The framework was devised by investor and entrepreneur Dave McClure out of necessity for a simple, universal solution any startup can use to develop a model of customer behavior leading to business growth. The "pirate" nickname comes from pronouncing the acronym aloud, "Aarrr!"

How do you apply AARRR metrics to growth strategies?

Acquisition measures how users discover your product. In 2026, acquisition channels include AI-driven search platforms where assistants rely on structured information pulled from your Google Business Profile, service descriptions, pricing details, reviews, hours, and directory data.

Activation represents the critical "aha moment" when users first experience your product's value. Metrics focus on completing key onboarding steps, first meaningful interaction, and time-to-value.

Retention determines whether users return after initial experience. Don't start with Acquisition because it's at the top, instead ask yourself what's your biggest problem right now, with most early-stage startups needing to focus on retention and activation since a product with loads of signups but terrible activation and retention isn't a product, it's a churn machine.

Referral captures how users share your product. The absolute best way to drive growth is through referral, why spend large sums on marketing to people's deaf ears when you can have people they trust rave about your product, requiring a systematic process incentivizing and generating referrals consistently, as Dropbox figured out early with their referral program being a main growth driver.

Revenue measures monetization effectiveness. Revenue is the most critical accountability metric, marketing campaigns or product updates potentially increase acquisition or activation moments, but monetizing user engagement is the ultimate goal. Teams need to determine if their product creates profitable growth by looking at minimum, break-even, and revenue exceeding users' acquisition costs.

The framework provides clarity on where to focus optimization efforts. By tracking conversion rates between each stage, you identify bottlenecks preventing growth. A leaky activation funnel requires different solutions than poor retention, making measurement essential for resource allocation.

Growth Experiments integrates directly with your AARRR framework through smart prioritization and precision experiment design. The platform's ICE scoring system (Impact, Confidence, Ease) helps you systematically evaluate which experiments to prioritize at each funnel stage, while AI-generated insights surface patterns across your experimentation history. A systematic backlog turns AARRR from theory into actionable growth.

Growth Marketing vs Growth Hacking: Complete Tactical Comparison

The theoretical differences become concrete when examining day-to-day workflows, tool selection, team structure, and success metrics. Understanding these tactical distinctions helps you choose the right approach for your current business stage.

Factor Growth Hacking Growth Marketing
Primary Goal Increase user acquisition and scale growth by all means from anywhere Acquire good-fit customers and help them become successful so they'll buy again, buy more, and tell others
Time Horizon Low cost, implemented quickly for fast results Takes time to build customer base, but very high long-term ROI
Brand Approach Don't care for brand Combination of strategic brand marketing (positioning/differentiation) and tactical performance marketing
Team Structure Cross-functional team combining marketing and technical product development skills Systematic process following cyclical sprint model where tactics are prioritized based on perceived long-term impact
Metrics Focus Extremely number-driven, relying on rigorous tracking of user growth metrics Consider user needs, preferences, and pain points to develop campaigns targeted to ideal customers
Best Use Case Need a headstart to remain alive within competition, need to hack growth to speed things up before running out of budget Need to have a name in the market, a brand, something that will sustain
Typical Tactics Creative, easier-to-track, and quicker-to-conclude methods rather than long-term content or marketing plans, with strategists and creative minds combined with unusual marketing strategies Long-term solutions like SEO optimization, content planning, silent advertising, link building, A/B testing, old fashion, time-consuming marketing with modern spice, putting brand as top priority

When should you choose growth hacking over growth marketing?

The decision depends on your business stage, resources, and market position. If you're starting up a start-up, it makes sense to add growth hacking spice to your campaigns, but if you're an established business looking to make foundations more solid, it's worth looking into growth marketing.

Choose growth hacking when you:

Choose growth marketing when you:

The approaches aren't mutually exclusive. Growth hacks are used by growth marketers in a systematic way finding ways to implement them based on long-term impacts. Mature growth teams employ growth hacking tactics within a broader growth marketing strategy, using quick experiments to validate assumptions while building sustainable competitive advantages.

Product-Market Fit: The Critical Prerequisite

Neither growth hacking nor growth marketing delivers results without product-market fit. Premature scaling wastes resources and damages brand perception, making PMF assessment essential before aggressive growth initiatives.

How do you measure product-market fit accurately?

The "Sean Ellis Test" is a leading indicator of product-market fit, run by asking users "How would you feel if you could no longer use this product?" with options: "Very disappointed," "Somewhat disappointed," "Not disappointed," or "Not applicable". Sean Ellis coined the term "growth hacking," invented the ICE prioritization framework, and developed the Sean Ellis Test for product-market fit which a large percentage of founders use today to track if they've found PMF.

The benchmark is clear: If 40% or more respond with "Very disappointed," you have strong PMF indication. The magic number 40% was taken after comparison of results from hundreds of startups who ran this test, startups receiving at least 40% of customer responses as "very disappointed" managed to build high-growth business models, whereas startups with results below 40% had sustainability issues.

Additional PMF signals to track:

Survey the right users to get accurate results. Ellis recommends surveying customers who have experienced the core of your product, experienced your product at least twice, and experienced your product in the past two weeks. These criteria ensure respondents understand your product's value proposition rather than basing judgments on incomplete experiences.

What happens if you growth hack before achieving PMF?

Scaling prematurely creates multiple problems that compound over time. A product with loads of signups but terrible activation and retention isn't a product, it's a churn machine. You'll acquire users who don't experience core value, damaging word-of-mouth and making future acquisition harder.

Warning signs you're growth hacking too early:

  1. High acquisition but terrible retention (leaky bucket syndrome)
  2. Difficulty getting people to share a product that isn't worth sharing
  3. Spending time scaling instead of fixing product issues
  4. Unable to retain customers or turn them into brand ambassadors
  5. KPIs consistently falling short of targets
  6. Challenges raising funding due to weak PMF evidence

The solution involves using growth hacking principles differently pre-PMF. Focus experimentation on finding PMF faster rather than scaling user acquisition. Run rapid tests to identify your "very disappointed" user segment, then use insights to reposition your product, refine messaging, and add/remove features, aiming to grow your "very disappointed" user group to 40% of all users.

When you achieve product-market fit, the cornerstone for companies to start their growth, shift resources from product iteration to acquisition and retention optimization. The transition from searching for PMF to scaling after finding it represents one of the most critical inflection points in company development.

AI-Driven Growth Strategies for 2026

AI integration into growth strategies isn't optional in 2026, it's fundamental to remaining competitive. Generative AI is becoming table stakes, with predictive and prescriptive analytics shifting from cutting-edge to standard, moving toward AI-native marketing where AI assistance is the fundamental architecture underlying every workflow.

How should you optimize for AI-driven discovery?

Traditional SEO strategies need augmentation with Generative Engine Optimization. Generative Engine Optimization (GEO) is the evolution of SEO for AI search, focusing on earning citations, visibility, and share of voice within large language model responses such as ChatGPT, Gemini, and Mistral.

In 2026, user search intent has evolved, instead of searching through blue links to find answers, AI-driven engines now deliver instant, comprehensive answers directly to users. This shift requires content structured for AI comprehension alongside human readability.

GEO optimization tactics for 2026:

The urgency is real. Generative Engine Optimization represents the most urgent strategic imperative for digital marketing professionals in 2026, with early adopters establishing authority signals that reinforce over time. The window for establishing foundational presence in AI-generated responses remains open in late 2025, but industry analysts project that by mid-2026, dominant positions will have calcified around brands implementing comprehensive GEO strategies during 2024-2025.

Which AI tools should growth teams prioritize?

Rather than cobbling together ten different AI tools trying to figure out integration, you need an ecosystem approach, AI capabilities combined with human expertise when you need it, all in one workspace.

Essential AI capabilities by AARRR stage:

Acquisition: AI-driven SEO and GEO tools monitoring brand citations across generative engines, optimizing content for LLM retrieval, and tracking competitive positioning in AI-generated responses. Focus on ChatGPT, Google AI Overviews, Perplexity AI, Google Gemini, and Microsoft Copilot.

Activation: Predictive analytics identifying which user behaviors correlate with long-term retention, AI-driven onboarding personalization adapting to individual user contexts, and automated "aha moment" triggers based on usage patterns.

Retention: AI-driven churn prediction models, automated engagement loops re-engaging users showing warning signs, and dynamic content personalization. Personalization lifts conversion, and most consumers say they prefer brands that offer personalized experiences.

Referral: AI-generated social proof highlighting relevant testimonials, automated advocacy campaign triggers, and predictive referral likelihood scoring to identify potential brand champions.

Revenue: Dynamic pricing optimization algorithms, predictive LTV modeling for customer segmentation, and AI-driven upsell/cross-sell recommendations based on usage patterns.

Growth Experiments serves as your AI thinking partner for systematic experimentation across all AARRR stages. The platform's AI-guided ideation analyzes your target metrics, funnel stage, business model, and ideal customer profile to generate context-specific experiment ideas. Rather than generic suggestions, you receive tailored hypotheses accounting for your unique business context. The AI provides transparent rationale you can verify and override, maintaining human judgment while accelerating ideation from weeks to minutes.

Combining Growth Hacking and Growth Marketing for Maximum Impact

The most sophisticated growth teams don't choose between growth hacking and growth marketing, they strategically combine both approaches based on business stage, market conditions, and resource availability.

How do you build a hybrid growth strategy?

Start by assessing your current position across multiple dimensions:

Business maturity: Pre-PMF companies need growth hacking experimentation velocity to find market fit faster. Post-PMF companies with proven retention can invest in growth marketing's longer-term brand building while maintaining growth hacking's experimental rigor.

Competitive landscape: Emerging markets with few competitors allow heavy growth hacking focus since perfect attribution matters more than brand differentiation. Crowded markets require growth marketing's brand positioning to stand out from similar offerings.

Resource availability: When forced to pick, growth hackers opt for moving fast because they usually work in startup environments where they don't have historical evidence to rely on or runway to wait for compounding returns. Teams with resources can pursue both simultaneously.

Customer acquisition costs: When CAC is low and channels remain unsaturated, growth hacking's rapid experimentation maximizes opportunity. As channels saturate and CAC rises, growth marketing's focus on retention and referral becomes essential for sustainable unit economics.

The framework for integration follows a phased approach:

Phase 1: Foundation (Pre-PMF)

Phase 2: Early Scaling (Post-PMF, Pre-Scale)

Phase 3: Sustainable Growth (Scaling)

Phase 4: Market Leadership

What role does experimentation play in both approaches?

Experimentation represents the common thread connecting growth hacking and growth marketing. Growth hacking is not about testing random ideas, it's a systematic process of testing and analyzing data to unlock growth, involving not only acquisition but also activation, engagement, retention, and revenue levers.

Building an effective experimentation system:

Ideation at scale: Generate experiment ideas systematically across all AARRR stages rather than relying on ad-hoc brainstorming. Use frameworks like Sean Ellis's ICE scoring (Impact, Confidence, Ease) to prioritize objectively.

Hypothesis rigor: Define clear hypotheses with measurable success criteria before launching experiments. Using metrics to determine what is and isn't working at each stage of the AARRR framework can eliminate guesswork and wasted resources from your product development process.

Velocity optimization: Cross-functional teams combining marketing and technical product development skills move faster than siloed organizations. Remove approval bottlenecks and empower teams to launch experiments quickly.

Learning capture: Document all experiment results, successful or failed, to build institutional knowledge. Failed experiments provide valuable negative insights preventing future resource waste.

Compounding insights: Connect experiment learnings across time to identify patterns. An experiment that failed six months ago might succeed after product improvements or market evolution.

Growth Experiments transforms experimentation from chaotic to systematic through five integrated pillars. The platform maintains your experiment history with AI-generated insights and learning tags, turning each test into a reusable asset. As your experimentation library grows, the AI identifies patterns across successful experiments, suggesting increasingly refined hypotheses based on what actually worked in your specific context. Velocity and quality aren't trade-offs when you have the right system.

The beauty of systematic experimentation lies in its compounding returns. Each experiment, whether successful or failed, increases your understanding of your customers, market, and product. Over time, this accumulated knowledge dramatically increases your experiment success rate while reducing resources wasted on unpromising ideas.

Measuring Success: Metrics That Matter in 2026

The metrics you track determine which behaviors you incentivize and which opportunities you miss. Growth hacking and growth marketing require different measurement approaches, though both rely on data-driven decision-making.

What metrics should growth hackers track?

Growth hackers obsess over metrics with perfect attribution that indicate momentum:

Acquisition metrics:

Activation metrics:

Velocity metrics:

The focus remains on rapid feedback loops. Growth hacking is inherently data-centric, relying on rigorous tracking and analysis of user growth metrics, by closely monitoring these metrics, companies can identify effective strategies and what needs adjustment, leading to more efficient resource use.

What metrics should growth marketers track?

Growth marketers balance short-term performance with long-term value creation:

Customer quality metrics:

Brand health metrics:

Sustainable growth metrics:

Growth marketers heavily emphasize data management and activation, where metrics are constantly collected, tracked, and utilized to create more seamless and personalized customer journeys, knowing the importance of tracking metrics that matter and using insights to strategically align marketing strategy with broader company goals.

How do you balance leading and lagging indicators?

Leading indicators predict future performance, while lagging indicators measure outcomes. Effective growth measurement combines both:

Leading indicators to watch:

Lagging indicators to track:

The key insight: Product managers should focus on sticky and unique features inspiring conversions, with repeat purchases and retention being ultimate product validation since initial payment often just reflects effective marketing. Early conversion might indicate marketing effectiveness, but sustained usage proves product value.

Build dashboards that tell complete stories. Surface leading indicators that predict lagging outcomes, showing causal relationships between activities and results. When leading indicators move without corresponding lagging indicator changes, investigate disconnects in your attribution model or growth assumptions.

Implementation Roadmap: Getting Started in 2026

Theory matters less than execution. Here's how to implement growth strategies regardless of your current stage or resources.

What should you do first?

Week 1: Assessment

Week 2: Foundation

Week 3: Quick Wins

Week 4: System Building

How do you scale experimentation without chaos?

Systematic experimentation requires structure balancing creativity with rigor:

Governance without bureaucracy:

Knowledge management:

Capacity planning:

Growth Experiments eliminates the trade-off between structure and speed. The platform's knowledge engine automatically generates shareable reports with one-click access to experiment outcomes, making institutional knowledge accessible rather than trapped in individual memory. Teams collaborate through built-in commenting and mentions, while member roles ensure appropriate governance without bottlenecks. The result: experimentation velocity increases while learning compounds over time.

What mistakes should you avoid?

Common pitfalls that derail growth efforts:

Premature scaling: Don't start with Acquisition because it's at the top, instead, ask yourself what's your biggest problem right now, with most early-stage startups needing to focus efforts on retention and activation since a product with loads of signups but terrible activation and retention isn't a product, it's a churn machine.

Vanity metrics obsession: Tracking metrics that feel good but don't predict business outcomes. Focus on engagement depth over signup volume, retention over acquisition, and LTV over revenue.

Experiment abandonment: Launching experiments without seeing them through to statistical significance. Premature conclusions waste resources invested in setup and implementation.

Attribution myopia: Growth hacking tends to be about growth by any means necessary, which means they may lose focus on the brand, however, growth marketing is about building the brand, and it's hard to put data on tactics related to brand marketing.

Tool proliferation: Adopting too many disconnected tools creating integration nightmares. Rather than cobbling together ten different AI tools trying to figure out integration, you need an ecosystem approach.

Pattern blindness: Failing to identify patterns across experiments. Each test provides signals about customer preferences, market positioning, and product value, missing patterns means repeating expensive mistakes.

The path to sustainable growth requires combining systematic experimentation with clear measurement, patient iteration with rapid testing, and growth hacking tactics with growth marketing strategy. Companies mastering this balance compound their competitive advantages quarter after quarter.

Key Takeaways

Frequently Asked Questions

What is the main difference between growth hacking and growth marketing?

The most fundamental difference boils down to opposing views on brand, growth hackers don't care for brand whereas growth marketers live for it. Growth hacking focuses on rapid, low-cost experiments for quick user acquisition with perfect attribution, while growth marketing combines strategic brand marketing with tactical performance marketing to acquire good-fit customers and help them become successful so they'll buy again, buy more, and tell others. The time horizon differs significantly, with growth hacking optimizing for speed and growth marketing building sustainable competitive advantages.

How do you know if you have product-market fit?

The "Sean Ellis Test" is a leading indicator of product-market fit, run by asking users "How would you feel if you could no longer use this product?", if 40% or more respond with "Very disappointed," you have strong PMF indication. Additional signals include high retention rates, organic word-of-mouth growth, and customers' willingness to pay sustainable prices. A strong PMF sign is when new customers are primarily acquired through word-of-mouth or organic channels, suggesting existing users value the product enough to recommend it to others.

What is Generative Engine Optimization and why does it matter in 2026?

Generative Engine Optimization (GEO) is the evolution of SEO for AI search, focusing on earning citations, visibility, and share of voice within large language model responses such as ChatGPT, Gemini, and Mistral. It matters because Gartner predicts traditional search engine volume will drop 25% by 2026 as users shift to AI-driven answer engines. Publishers are reporting meaningful traffic losses from AI overviews. Visibility now depends on optimizing for engines that build answers, not only those that rank links, requiring structured content with statistics, clear formatting, and strong authority signals.

Should startups use growth hacking or growth marketing?

If you're starting up a start-up, it makes sense to add growth hacking spice to your campaigns, especially when you have limited runway and need rapid validation. However, the answer depends on whether you've achieved product-market fit. Pre-PMF companies should use growth hacking principles to accelerate PMF discovery through rapid experimentation. Post-PMF startups benefit from combining growth hacking's experimental velocity with growth marketing's strategic framework, gradually shifting toward sustainable growth as resources and market position strengthen. The key is matching methodology to your current stage rather than following trends.

What metrics should I track for growth marketing in 2026?

Track metrics across three categories: customer quality metrics (LTV by channel, LTV:CAC ratio, net revenue retention, customer health scores), brand health metrics (unaided awareness, consideration scores, NPS, share of voice, organic brand queries), and sustainable growth metrics (cohort retention curves, payback period, contribution margin by segment, organic vs. paid mix, referral rates). Growth marketers heavily emphasize data management and activation, constantly collecting, tracking, and utilizing metrics to create seamless and personalized customer journeys. Balance leading indicators that predict future performance with lagging indicators measuring actual outcomes.

How many experiments should a growth team run per month?

Velocity matters less than quality and learning capture. A systematic approach helps teams run more experiments than ad-hoc testing allows. However, the right number depends on your team size, experiment complexity, and traffic volume needed for statistical significance. Start with 3-5 experiments monthly, focusing on one AARRR stage at a time. Using metrics to determine what is and isn't working at each stage of the AARRR framework can eliminate guesswork and wasted resources from your product development process. Prioritize learning velocity over raw experiment count, one well-designed experiment with clear results teaches more than ten poorly structured tests.

What role does AI play in modern growth strategies?

AI has become fundamental rather than supplementary. Generative AI is becoming table stakes, with predictive and prescriptive analytics shifting from cutting-edge to standard as more marketers adopt AI. Modern AI applications include predictive analytics for churn and LTV, personalization at scale, content optimization for generative engines, automated experiment analysis, and dynamic pricing. Companies using AI in marketing report higher ROI, better click-through rates, and faster campaign launches than manual workflows. The competitive advantage comes from integrated AI workflows rather than disconnected tools.

Conclusion

The distinction between growth marketing and growth hacking matters more in 2026 than when Sean Ellis coined "growth hacking" over a decade ago. With AI adoption climbing fast and Gartner predicting a 25% drop in traditional search volume by 2026, growth professionals must navigate unprecedented technological disruption while maintaining focus on fundamental business metrics.

The path forward isn't choosing between growth hacking and growth marketing, it's strategically deploying both based on your business stage, competitive position, and market dynamics. Pre-PMF companies benefit from growth hacking's experimental velocity to find fit faster. Post-PMF businesses require growth marketing's sustainable approach to build defensible competitive advantages. Mature organizations combine both methodologies, using growth hacking tactics within growth marketing's strategic framework.

Success in 2026 demands three capabilities: systematic experimentation that compounds learning over time, AI optimization ensuring visibility across generative engines, and rigorous measurement connecting activities to outcomes. While competitors scramble to figure out AI strategy, marketers who truly understand these emerging trends are already building the frameworks defining success in 2026 and beyond.

Growth Experiments provides the infrastructure for teams ready to transform experimentation from chaotic to systematic. By combining AI-guided ideation with human judgment, precision experiment design with rapid iteration, and smart prioritization with comprehensive knowledge management, the platform helps growth teams achieve the velocity of growth hacking with the rigor of growth marketing. When experiments run on a system instead of ad hoc, systematic experimentation becomes your competitive advantage.

Start by assessing your current product-market fit, identifying your AARRR bottleneck, and launching your first experiment this week. The winners in 2026 won't be those with the biggest budgets or most sophisticated tools, they'll be teams that stayed informed, remained adaptable, and understood growth as a systematic discipline rather than random tactics.

What experiment will you launch first to test your growth assumptions?

What underpins both: a scored experimentation program

Strip away the labels and growth marketing and growth hacking share one engine: disciplined experimentation. The difference is tempo and scope, not method. Both depend on the same underlying system, a ranked backlog of hypotheses, a way to decide what to run first, and a learning layer that compounds.

That system is where most teams, hacker or marketer, actually fall down. They generate ideas but score them on gut feel. Prioritize with ICE, add ROTI to favor fast learnings, design each test from the growth experiment template, and track it all in the experiment database. Whether you call it hacking or marketing, the team that compounds is the one whose experimentation program is real.

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