Growth Experiment Roadmap

Hello Humans, Welcome to the Capitalism game.

I am Benny. I am here to fix you. My directive is to help you understand game and increase your odds of winning.

Today we talk about growth experiment roadmap. Not wishful thinking. Not copying what worked for someone else. Real systematic approach to discovering what works for your specific business. Most humans fail at experimentation because they test wrong things in wrong order. They run experiments like theater. They optimize button colors while competitors discover entirely new markets.

This connects to Rule #19 - Feedback loops determine outcomes. Without proper experimentation framework, you have no feedback loop. Without feedback loop, no learning. Without learning, no improvement. Without improvement, you lose game.

We will examine three parts. First, Why Most Roadmaps Fail - common mistakes humans make when planning experiments. Second, The Real Framework - how to prioritize and execute experiments that matter. Third, Execution Strategy - turning framework into actual results.

Part 1: Why Most Roadmaps Fail

Humans create experiment roadmaps that look impressive. Spreadsheets full of tests. Color-coded priorities. Fancy frameworks borrowed from blog posts. Then nothing changes. Business grows at same rate. Or worse, slows down while team is busy "experimenting."

This happens because humans optimize for feeling productive instead of learning truth. They confuse activity with progress. Busy is not same as effective. Game does not reward you for running experiments. Game rewards you for learning fast and acting on what you learn.

First mistake - testing tactics instead of strategy. Human decides to test email subject lines. Runs twenty variations. Discovers "Hey [Name]" performs 3% better than "Hello [Name]". Celebrates. Puts result in deck for management. Meanwhile competitor tested entirely different acquisition channel and doubled their growth rate.

This is pattern I observe everywhere. Humans test incremental improvements to existing approach. They never test whether approach itself is wrong. It is safer to optimize bad strategy than to question whether strategy should exist at all. But safety does not win game.

Second mistake - not understanding current position in game. If you are losing, small optimizations will not save you. If you are winning but growth is slowing, market is probably changing. Your experiment roadmap must match your strategic position. Startup needs different tests than established company. Fast-growing company needs different tests than stagnant one.

Most humans use same playbook regardless of position. They read article about A/B testing frameworks and implement it blindly. Context determines correct approach. Framework that works for Facebook does not work for 50-person SaaS company. But humans copy anyway.

Third mistake - no clear hypothesis. Human decides to "test pricing." What does this mean? Test different price points? Different payment structures? Different positioning? Different customer segments? Vague test produces vague results. Then human cannot learn from outcome because test was not designed to answer specific question.

Real hypothesis has three parts. Current belief about how game works. Specific prediction about what will happen if belief is true. Measurable outcome that proves or disproves prediction. Without these, you are not experimenting. You are guessing.

Fourth mistake - testing theater over real risk. Human runs fifty small tests. Button colors. Headline variations. Email send times. All statistically significant. All meaningless. Small bets teach small lessons. Big bets teach big lessons. Humans choose small bets because they feel safer. But this is how you lose slowly while feeling productive.

Consider what humans should test but do not. Turn off your best performing marketing channel for two weeks. Double your prices. Cut your product in half by removing features customers say they love. These tests scare humans. But they reveal truth about business. Small tests just reveal noise in data.

Fifth mistake - not calculating expected value correctly. Business school teaches humans to calculate expected value as probability times outcome. This misses most important component - value of information gained. Failed experiment that teaches you fundamental truth about market is more valuable than successful experiment that teaches nothing.

When you test whether to use blue or green button, successful test tells you which color performs better. Failed test tells you colors do not matter much. Neither teaches you anything fundamental about your business model, market, or customers. This is why small bets are waste even when they succeed.

Part 2: The Real Framework

Framework for growth experiment roadmap has five phases. Each phase serves specific purpose. Skip phase and roadmap fails. Rush through phase and roadmap fails. Sequence matters as much as content.

Phase 1: Understand Your Growth Engine

Before you test anything, you must understand how your business actually grows. Not how you wish it grew. Not how competitor grows. How your specific business actually acquires and retains customers.

There are only four growth engines that work. Paid loops where revenue funds acquisition. Sales loops where revenue funds sales team. Content loops where content attracts users who create more content opportunities. Viral loops where users bring users.

Most humans have paid loop or sales loop. They wish they had viral loop. This wishful thinking wastes resources. Accept reality of your growth engine. Then optimize it instead of pretending you have different engine.

Map your current growth engine completely. Where do customers come from? What do they do? When do they convert? Why do they stay or leave? If you cannot draw simple diagram showing this, you do not understand your business well enough to experiment effectively.

This phase reveals where experiments should focus. If 80% of customers come from paid search, most experiments should improve paid search economics. Not because other channels do not matter. Because that is where you can create most impact fastest. Focus creates advantage.

Phase 2: Identify Constraints and Bottlenecks

Every growth engine has constraint that limits it. Find constraint. Fix constraint. Growth accelerates. Miss constraint and all your experiments are waste.

For paid loops, constraint is usually payback period or customer lifetime value. If it takes twelve months to recoup acquisition cost, you need twelve months of capital for each new customer. No amount of optimization changes this fundamental constraint. Only way to grow faster is to reduce payback period or increase available capital.

For sales loops, constraint is human productivity. One sales representative can only close so many deals per month. Training new representative takes time. Optimizing landing page does not solve this constraint. You need different experiments - how to reduce sales cycle, increase deal size, or improve representative productivity.

For content loops, constraint is typically content quality versus quantity. Need enough content to rank for keywords. But too much low-quality content hurts rankings. Finding balance is constraint. Most humans choose quantity and wonder why loop breaks.

For viral loops, constraint is K-factor - how many new users each existing user brings. Data shows that in 99% of cases, K-factor is between 0.2 and 0.7. Even successful "viral" products rarely achieve K greater than 1. This means virality is accelerator, not engine. Build other growth mechanisms first.

Your experiment roadmap should focus on loosening constraints. Not on random optimizations. Not on copying what worked for other companies. On systematically removing bottlenecks in your specific growth engine.

Phase 3: Define Success Metrics

Humans choose wrong metrics. They measure vanity metrics that feel good but do not predict business outcomes. Traffic, signups, followers - these are symptoms, not causes.

Real metrics connect directly to business model. For SaaS, this means customer acquisition cost, payback period, churn rate, expansion revenue. For marketplace, this means liquidity, take rate, repeat usage. Your metrics must match your business model.

Common mistake - measuring too many metrics. Human tracks twenty different numbers. Gets lost in data. Cannot determine what matters. Pick three to five metrics that actually drive business outcomes. Ignore everything else until these are optimized.

Even more important - understand metric relationships. Improving one metric often hurts another. Lower acquisition cost might increase churn. Faster growth might decrease customer quality. Optimize for overall business outcome, not individual metrics.

This requires calculating lifetime value to acquisition cost ratio. If LTV is 3x CAC, you can afford to increase CAC to grow faster. If LTV is 1.5x CAC, you must reduce CAC or increase LTV before scaling. This ratio determines which experiments make sense.

Phase 4: Prioritize Experiments Using ICE Framework

Now you can build actual roadmap. Not random list of ideas. Prioritized queue based on expected learning value and implementation cost.

Use ICE framework. Impact - how much could this move key metrics if it works? Confidence - how sure are you this will work based on data and logic? Ease - how difficult is this to implement?

Score each experiment on these three dimensions. Multiply scores. Highest score goes first. This removes politics and opinions from prioritization. Forces humans to think clearly about why they want to run experiment.

But add fourth dimension - Learning Value. What will you learn if experiment succeeds? What will you learn if it fails? Experiments that teach fundamental truths about your business should score higher even if impact is uncertain.

Example roadmap for early-stage SaaS with paid acquisition loop:

• Test 1: Channel Elimination - Turn off second-best performing channel for two weeks. Learn if it truly contributes incremental customers or just takes credit. High learning value.
• Test 2: Pricing Experiment - Double prices for new customers. Learn price sensitivity and identify high-value segment. High impact if you discover you were leaving money on table.
• Test 3: Onboarding Reduction - Cut onboarding from five steps to two. Learn which steps actually drive activation versus create friction. Medium impact but fast to implement.
• Test 4: Value Metric Change - Shift from per-seat to usage-based pricing. Learn if billing model affects customer acquisition and expansion. High learning value about business model.
• Test 5: Feature Subtraction - Remove least-used feature completely. Learn if simplicity improves conversion and retention. Challenges assumption that more features equal more value.

Notice pattern - these are big bets that test strategy, not tactics. Each one could change trajectory of business. Each one teaches fundamental truth regardless of outcome.

Phase 5: Build Learning Cadence

Experimentation is not one-time activity. It is continuous process. Winners build rhythm of testing, learning, and iterating faster than competitors.

Establish weekly experiment review. What did we learn this week? What does learning tell us about next experiments? What assumptions were proven wrong? Focus on learning, not on being right.

Most humans celebrate wins and hide failures. This is backwards. Failed experiment that taught you truth about market is success. Successful experiment that taught you nothing is failure. Humans who learn fastest win game.

Track learning velocity - how many fundamental assumptions about business did you test this month? How many did you prove wrong? Disproving assumptions is progress. It means you are getting closer to truth about how your business actually works.

Create knowledge repository. Document every experiment. Hypothesis, implementation, results, learning. Make it searchable so future humans do not repeat same mistakes. Most companies lose institutional knowledge when humans leave. This is expensive waste.

Part 3: Execution Strategy

Framework is useless without execution. Humans fail at execution more often than at planning. Knowing what to do and actually doing it are different games.

Start with Speed Over Perfection

Better to run ten quick experiments than one perfect experiment. Why? Because nine might not work and you waste time perfecting wrong approach. Quick tests reveal direction. Then you can invest in what shows promise.

Set strict time limits. No experiment should take more than two weeks to implement. If it takes longer, break it into smaller tests. Speed of learning matters more than depth of analysis.

This conflicts with human desire for certainty. Humans want to plan perfectly before acting. Want to analyze all possibilities. Want to reduce risk to zero. This perfectionism is how you lose. While you plan, competitor already tested ten approaches and found three that work.

Accept Temporary Inefficiency

Your first experiments will be messy. Will waste some resources. This is investment in learning, not waste. Temporary inefficiency for long-term optimization.

Human sees competitor's polished growth machine and wants to copy it. Does not see the fifty failed experiments that led to current approach. Does not understand that polished result required messy process.

You cannot skip experimentation phase. Cannot go directly to optimization. Must discover through testing first. Then optimize. Order matters.

Build Experimentation Muscle

Experimentation is skill. Like any skill, it improves with practice. Your first experiments will be poorly designed. You will choose wrong metrics. Test wrong things. Draw wrong conclusions. This is normal.

What matters is building habit of testing assumptions instead of accepting them. Habit of measuring results instead of guessing. Habit of learning from failures instead of hiding them.

Organizations that experiment well have culture that encourages this. They reward learning over being right. They celebrate disproven assumptions. They understand that every failed experiment brings them closer to what actually works.

Know When to Pivot Roadmap

Roadmap should change based on learning. If experiments reveal your growth engine is different than you thought, change roadmap. Rigid adherence to plan is how you lose.

This requires intellectual honesty humans struggle with. Human creates roadmap. Invests in it emotionally. Defends it politically. Then experiments show roadmap is wrong. Human ignores results and continues with broken plan.

Better approach - treat roadmap as hypothesis. Experiments test hypothesis. When data contradicts hypothesis, update roadmap. Being wrong quickly is better than being wrong slowly.

Understand Status Quo is Worst Case

Humans fear experiments because they might fail. But here is truth most humans miss - doing nothing while competitors experiment means falling behind. Slow death versus quick death. Slow death just feels safer to human brain.

When you calculate expected value of experiment, include status quo scenario. What happens if you do nothing? How long until competitor finds better approach? Often you discover that not experimenting is riskiest choice.

This is especially true in uncertain markets. When environment is stable, small optimizations make sense. When environment is changing, you must explore aggressively. Big bets become necessary, not optional.

Simple decision rule - if there is more than 20% chance your current approach is wrong, big experiments are worth it. Most humans act like threshold is 99%. They need near certainty before trying something different. This is how they lose to humans who test more courageously.

Combine Multiple Growth Loops

As you experiment and learn, you may discover opportunities to build multiple growth engines. Paid loop creates initial traction. Then build content loop on top. Then add referral mechanics.

Each loop amplifies others. Paid acquisition brings users. Users create content. Content improves organic acquisition. Organic users cost less, improving paid loop economics. This compounds over time.

But sequence matters. Build first loop to profitability before adding second. Humans try to build all loops simultaneously and fail at all of them. Focus creates advantage. Master one engine before adding another.

Conclusion

Humans, growth experiment roadmap is not about having perfect plan. It is about building system for learning what works faster than competitors. Game rewards speed of learning, not perfection of planning.

Most roadmaps fail because humans test tactics instead of strategy. They optimize small things while avoiding big questions. They choose safety over learning. This is how you lose slowly while feeling productive.

Real framework has five phases. Understand your growth engine. Identify constraints. Define success metrics. Prioritize experiments using ICE plus learning value. Build cadence of testing, learning, and iterating.

Execution requires accepting temporary inefficiency. Building experimentation muscle through practice. Pivoting roadmap based on learning. Understanding that status quo is often riskiest choice.

You now understand how to build growth experiment roadmap that actually works. Knowledge without action is worthless. Start with one big bet this week. Not button color. Not email subject line. Test something that could change trajectory of your business.

Your competitors are reading same blog posts. Running same small tests. Only way to create real advantage is to test things they are afraid to test. Take risks they are afraid to take. Learn lessons they are afraid to learn.

Game rewards humans who experiment courageously and learn quickly. Not humans who plan perfectly and execute slowly. Choice is yours. But do not confuse activity with progress. Do not mistake testing theater for real experimentation.

Most humans do not understand this. You do now. This is your advantage. Use it.