Breaking the Growth Rules: Shopify Bans KPIs, Optimizes for Churn, and Builds for 100 Years

Shopify's radical growth philosophy rejects conventional wisdom, revealing why 30-40% of successful experiments fail long-term and how thinking in decades creates sustainable competitive advantages.

Key Takeaways

• Shopify intentionally optimizes for churn to lower barriers for entrepreneurs, betting that a few massive winners will make entire cohorts profitable through power law dynamics
• 30-40% of experiments showing positive short-term impact have no long-term effect when measured 1-3 years later, revealing the danger of premature optimization
• Core product teams operate without KPIs or metrics, relying instead on taste and 100-year vision from leadership to guide product decisions
• Teams focus on absolute numbers rather than conversion rates to prevent local optimization that makes earlier funnel steps artificially harder
• Revenue model based on merchant success through payments rather than subscriptions enables patient capital approach to customer acquisition
• Long-term holdout experiments automatically ping teams with results at 3, 6, and 12 months to maintain accountability for real business impact
• Technical architecture decisions take priority over speed to market because "how" determines long-term strategic optionality more than "what"
• Hybrid growth model separates teams optimizing for different time horizons: core product (100 years), merchant services (medium-term), growth (short-term metrics)

Timeline Overview

• 00:00–02:30 — Archie's background: Introduction to Archie Abrams, VP of Product and Head of Growth at Shopify, leading over 600 people across product, design, engineering, data, and growth marketing
• 02:30–06:17 — Shopify's impressive growth: Overview of Shopify's scale representing 10% of US e-commerce and $235 billion in GMV, roughly the size of Finland's economy
• 06:17–08:43 — Shopify's unique approach to churn and retention: How Shopify intentionally lowers barriers to entrepreneurship, expecting most businesses to fail while betting on power law winners to drive success
• 08:43–11:08 — Monetization model and success metrics: Focus on cohort GMV over time rather than retention rates, with revenue tied to merchant success through payments versus pure subscription models
• 11:08–23:00 — Long-term experimentation and metrics: Framework for running long-term holdouts that automatically check experiment impact 1-3 years later, revealing frequent gaps between short-term and long-term results
• 23:00–26:42 — Examples of big wins that Archie's team has shipped: Specific growth experiments including monetary friction reduction and onboarding personalization that drove meaningful long-term impact
• 26:42–27:14 — Monetary friction: Definition and examples of reducing barriers through trial dynamics, incentives, and pricing strategies that enable more entrepreneurs to succeed
• 27:14–29:47 — Metrics: Philosophy of absolute numbers versus conversion rates to prevent teams from gaming local metrics by making previous funnel steps harder
• 29:47–33:03 — Shopify's growth team structure: Organization into Growth R&D (product, tooling, customer support) and Growth Marketing (paid acquisition, SEO, email, content) with clear functional separation
• 33:03–37:10 — Goal setting and forecasting: How teams measure success through incremental cohort value over long time periods with LTV/CAC guardrails rather than short-term conversion optimization
• 37:10–41:36 — Examples of long-term results within Shopify: Specific cases where payment failure notifications showed short-term lift but no long-term impact, and store templates showed opposite pattern
• 41:36–42:05 — Shipping neutral experiments: Philosophy of shipping experiments that show neutral short-term results if intuition suggests long-term value, based on 100-year thinking approach
• 42:05–48:04 — Building a hundred-year company: Toby's vision driving decisions with emphasis on technical architecture determining strategy, including detailed reviews of implementation approaches over speed to market
• 48:04–51:30 — Why Shopify doesn't use KPIs: How core product teams operate without metrics, using taste and conviction about building right technical foundations rather than optimizing specific KPIs
• 51:30–54:30 — Shopify's "Get shit done" framework: Internal project management system requiring group leader approval for all shipments, with detailed video reviews ensuring quality standards
• 54:30–58:48 — Cross-team collaboration: How growth team (metrics-driven) collaborates with core product team (taste-driven) through trust-building and quality standards rather than rigid boundaries
• 58:48–01:01:12 — The importance of an opinionated founder: Requirements for making taste-driven product development work, emphasizing need for strong founder opinions versus metrics-driven accountability
• 01:01:12–01:06:42 — Growth and sales integration: Challenges and learnings from adding enterprise sales motion to product-led growth, including attribution complexity and hybrid customer journeys
• 01:06:42–01:08:49 — Shopify's marketing structure: Distributed marketing teams embedded across functions rather than centralized CMO structure, enabled by strong founder brand intuition
• 01:08:49–01:11:09 — Insights on discounting from Udemy: Lessons about price signaling and aspirational purchasing behavior in education, where buying creates emotional progress regardless of course completion
• 01:11:09–End — Lightning round: Book recommendations, favorite content, product discoveries, life philosophy about planning with flexibility, and father's influence on leadership approach

The Churn Optimization Paradox

Shopify's most counterintuitive strategy involves intentionally optimizing for churn, directly contradicting conventional SaaS wisdom about retention being paramount. This approach stems from their fundamental mission to increase entrepreneurship on the internet rather than maximize customer lifetime value.

• The core philosophy recognizes that most new businesses fail, so Shopify focuses on lowering barriers to entrepreneurship rather than optimizing for merchant retention. Their goal is getting "as many people in the door trying their hand at entrepreneurship" with the understanding that many first attempts won't succeed.
• Power law economics drive this strategy because revenue comes primarily from payments tied to merchant GMV rather than subscription fees. A small percentage of merchants who become extremely successful generate enough revenue to make entire cohorts profitable, similar to angel investing portfolios where a few winners cover many failures.
• Success metrics focus on total cohort GMV over 3-5 year periods rather than traditional retention curves. They track how much gross merchandise value a group of merchants acquired in a given quarter generates cumulatively, understanding that this follows power law distribution where outliers drive most value.
• The monetization model enables this patient approach because Shopify grows with successful merchants through payment processing fees. Unlike pure subscription businesses that lose revenue immediately when customers churn, Shopify benefits when their few winners scale dramatically over time.
• This strategy requires different thinking about customer acquisition costs and payback periods. While most SaaS companies focus intensely on reducing churn to improve unit economics, Shopify can afford higher churn rates as long as their acquisition costs remain low relative to the eventual value created by successful merchants.

The lesson extends beyond e-commerce platforms: businesses with power law outcomes can optimize for volume and experimentation rather than retention when their monetization model aligns with customer success at scale.

The Long-Term Experimentation Reality Check

Shopify's commitment to long-term measurement reveals uncomfortable truths about growth experimentation that most companies never discover. Their systematic approach to tracking experiments 1-3 years after completion exposes the gap between short-term wins and sustainable impact.

• Automatic accountability systems ping experiment owners at 3, 6, and 12 months with updated results for every test run. This prevents teams from claiming victory based on early metrics and moving on without understanding true long-term impact on business outcomes.
• The shocking reality: 30-40% of experiments that show positive short-term impact demonstrate no long-term effect when measured a year later. This finding challenges the assumption that short-term lifts reliably predict sustained business value and suggests many growth wins are illusory.
• Purell effect explains many false positives where experiments appear successful initially but represent pulled-forward activity rather than incremental value creation. Payment failure notifications exemplify this pattern, showing immediate lift in recovered subscriptions but no long-term GMV impact because merchants who let payments fail weren't committed to entrepreneurship anyway.
• Discovery of valuable merchant segments represents the opposite pattern, where experiments show neutral or negative short-term results but unlock high-value customer cohorts over time. Store template experiments demonstrated this, showing no immediate conversion lift but significantly higher long-term sales for merchants who used pre-configured sections.
• Technical implementation enables this approach through cohort tracking rather than just A/B test measurement. They hold out 5% of users from quarterly changes and 50% splits for new merchant experiments, then track assigned cohorts indefinitely rather than just measuring during active test periods.
• Early warning signs for sustainable impact include looking as deep into the funnel as possible during short-term measurement windows. While not perfect predictors, downstream metrics provide better signals for long-term success than top-of-funnel conversion improvements.

The methodology provides a framework any company can adopt: systematically revisit past "wins" after 6-12 months and honestly assess whether they created lasting value or temporary metric bumps.

The No-KPI Philosophy and Taste-Driven Decisions

Shopify's core product teams operate without traditional metrics or KPIs, instead relying on founder vision and taste to guide product development. This radical approach requires specific organizational conditions but enables long-term thinking impossible under quarterly metric optimization.

• KPIs and OKRs are "basically banned" for core product teams building the foundational commerce platform. Instead of optimizing specific metrics, decisions are made based on conviction about building the right technical foundation for 100-year commerce needs.
• The decision-making process relies heavily on Toby's technical vision and Glenn's deputized taste authority. Every project requires "OK2" approval from group leaders who review detailed implementation videos before anything ships, creating centralized quality control without traditional metrics.
• 100-year thinking drives technical architecture prioritization over speed to market. Detailed discussions about CSV importer implementation approaches matter more than rapid feature delivery because technical decisions determine long-term strategic optionality for the platform.
• Subjective decision-making creates both advantages and challenges. The upside includes building forward-thinking products and taking appropriate risks, while the downside involves extremely subjective debates about what constitutes "the right thing to do" without quantitative anchors.
• Quality control happens through concentrated taste authority rather than distributed metrics optimization. Glenn and a small group of leaders maintain consistent quality standards across all core product shipments, creating objective consistency within subjective decision-making frameworks.
• Success requirements include exceptionally opinionated founders with strong domain expertise and proven taste. Without these conditions, the approach degenerates into teams "building cool stuff in a haphazard way" without accountability mechanisms that metrics typically provide.

The model works at Shopify because Toby combines technical depth, long-term vision, and proven taste in commerce. Most companies lack these founder characteristics and benefit more from metrics-driven accountability systems.

Absolute Numbers vs Conversion Rate Optimization

Shopify's emphasis on absolute numbers rather than percentage improvements prevents the local optimization trap that plagues many growth organizations. This philosophical shift aligns team incentives toward total business impact rather than isolated funnel improvements.

• The conversion rate trap emerges when teams naturally break customer journeys into stages and optimize their specific funnel step's performance. This creates seductive local metrics that can be improved by making previous steps artificially harder rather than genuinely improving the experience.
• Absolute number focus means teams optimize for "more people getting activated" rather than "higher activation rates." This seemingly subtle shift encourages expanding funnel tops and reducing friction rather than constraining earlier steps to inflate conversion percentages.
• Perverse incentive prevention becomes critical as organizations scale because teams naturally want to show progress on their specific metrics. When a signup team optimizes signup-to-activation rates, the easiest path involves making signup harder to improve the quality of people who complete it.
• Total cohort value measurement aligns everyone toward the same ultimate outcome regardless of their specific funnel responsibility. Rather than optimizing local conversion rates, every team contributes toward absolute numbers of successful merchants produced over long time periods.
• CAC reduction often accompanies conversion rate decreases when teams focus on absolute numbers. Making earlier funnel steps easier typically hurts conversion rates but reduces customer acquisition costs more than proportionally, enabling higher volume acquisition.
• Implementation requires cultural change where teams celebrate absolute improvements even when percentages decline. Leaders must consistently reinforce that 1,000 activated users at 5% conversion beats 500 activated users at 10% conversion when acquisition costs allow for the volume approach.

This principle applies broadly beyond Shopify: any multi-step customer journey benefits from teams optimizing absolute throughput rather than local conversion rates to prevent internal competition that hurts overall business outcomes.

Building for 100 Years: Architecture Over Speed

Shopify's long-term orientation manifests most clearly in their prioritization of technical architecture over rapid feature delivery. This approach, driven by Toby's engineering background and 100-year vision, creates sustainable competitive advantages through platform flexibility.

• Technical architecture determines strategy more than feature roadmaps because implementation choices create or constrain future optionality. How something is built matters more than what is built because architectural decisions compound over decades while features are replaceable.
• Detailed implementation reviews happen every six weeks where Toby and group leaders examine every R&D project's technical approach. These sessions focus extensively on whether teams are building with sufficient flexibility and optionality for unknown future requirements.
• Platform thinking drives build-versus-buy decisions toward creating reusable infrastructure rather than quick solutions. CSV importer discussions exemplify this approach, where the team spends extensive time deciding between open source libraries, internal development, and first-party app approaches.
• Long-term value creation justifies slower initial delivery when architectural decisions enable compound benefits over time. Taking longer to ship features becomes acceptable when the technical foundation supports rapid iteration and scaling for decades.
• Founder-driven technical leadership requires exceptional depth and proven judgment about technology trends and business evolution. Toby's continued coding and hands-on technical involvement enables informed architectural decision-making at scale.
• Quality control extends beyond user experience to include technical debt management and platform coherence. The goal is building commerce infrastructure that can adapt to unknown future requirements rather than optimizing for current feature delivery speed.

This approach works for Shopify because their platform business model benefits from architectural flexibility, they have patient capital, and Toby provides exceptional technical leadership. Most companies should prioritize speed over architecture unless they have similar conditions.

Hybrid Team Dynamics: Metrics Meets Taste

Shopify's unique organizational structure creates productive tension between growth teams focused on metrics and core product teams driven by taste. This hybrid approach requires careful relationship management but enables both short-term performance and long-term vision.

• Intentional organizational tension emerges from structuring teams with different time horizons and success criteria. Growth teams optimize short-term metrics while core product teams build for 100-year visions, creating natural disagreements that must be managed constructively.
• Trust-building becomes essential when teams with different philosophies must collaborate on shared product experiences. Growth teams earn credibility by shipping high-quality implementations rather than quick metric wins that compromise user experience.
• Quality standards provide common ground where both teams can agree on minimum acceptable implementation quality. Even when disagreeing on features, teams align on ensuring anything shipped meets Shopify's user experience standards.
• Boundary negotiation happens organically rather than through rigid interface definitions. Growth teams can touch any part of the product but must work with core teams to ensure implementations align with long-term technical architecture and design principles.
• Human relationships matter more than process frameworks for resolving conflicts between different team philosophies. Success depends on individual relationship-building and mutual respect rather than structural solutions.
• Examples of productive collaboration include the wizard principle, where growth teams want onboarding tools but core teams resist separate wizard flows. The solution involves bringing wizard benefits into actual product experiences rather than creating parallel onboarding paths.

The model succeeds because both teams respect each other's goals and constraints while finding creative solutions that serve both short-term growth and long-term platform vision.

Shopify's radical approach to growth challenges fundamental assumptions about metrics, retention, and product development. Their success demonstrates that conventional growth wisdom may not apply when business models align with power law outcomes and patient capital enables long-term thinking. The key insight is that sustainable growth often requires optimizing for different metrics than traditional SaaS playbooks suggest, particularly when platforms benefit from volume experimentation rather than retention optimization.

Practical Implications

• Consider optimizing for volume and experimentation rather than retention when your business benefits from power law outcomes and low acquisition costs
• Implement long-term experiment tracking that automatically reports results 6-12 months after initial measurement to validate sustained impact
• Focus teams on absolute numbers rather than conversion rates to prevent local optimization that hurts overall funnel performance
• Ship neutral experiments when intuition suggests long-term value, especially if short-term metrics don't capture true business impact
• Prioritize technical architecture decisions over speed to market when building platforms that need long-term flexibility and optionality
• Create hybrid team structures that balance short-term metrics optimization with long-term vision when both capabilities are essential
• Measure success through cohort value over multi-year periods rather than monthly retention curves for businesses with delayed monetization
• Establish quality standards and trust-building processes when teams with different philosophies must collaborate on shared products
• Consider distributed marketing teams embedded with business functions rather than centralized CMO structures when founders have strong brand intuition
• Audit past "successful" experiments after 6-12 months to understand which interventions create lasting value versus temporary metric improvements
• Use incrementality testing over attribution modeling to understand true causal impact of marketing channels across complex customer journeys
• Build experimentation platforms that enable long-term holdout groups and automatic result reporting to maintain accountability for real business outcomes