What Experiments Should a 1-5M Brand Run First?

You've gotten past the early struggles. Revenue is real. Customers are coming back. But you're also acutely aware of how tight the margins are.

Every dollar matters at this stage. A bad month can set you back significantly. A good month gives you runway to invest in growth.

This is exactly when learning to read your data becomes valuable... not as a nice-to-have, but as insurance against expensive mistakes. Before you scale ad spend or expand your product line, you need to understand what your data is telling you about what actually works.

Where You Are

Cash flow is a constant consideration. You've probably got a small team... maybe one or two people beyond yourself. Big investments require real deliberation.

You're finding product-market fit, but you're not certain you've nailed it. Some products sell well. Others sit in inventory. Some marketing channels work. Others drain budget without clear returns.

Success looks like this: building a foundation of knowledge about your customers before you scale. Exploring your data to validate assumptions, not just optimize metrics. Learning what works so you can double down confidently.

Your Testing Philosophy

Learn what works before scaling.

The biggest risk at $1-5M isn't running bad tests. It's scaling decisions that haven't been validated.

Imagine spending $50K on ads driving to a landing page with the wrong messaging. Or launching a subscription program that doesn't fit your customer behavior. These mistakes hurt a lot more at $1-5M than at $50M.

Exploration at this stage is about risk reduction. Every signal you investigate answers a question that could save you from an expensive mistake later. It's not about incremental optimization... it's about validating your assumptions while the cost of being wrong is still manageable.

5 Foundation-Building Signals to Explore

Where to start with price testing: Understanding the fundamentals before you scale.

1. Core Pricing Validation

Your data signal: Your prices were set early and haven't been revisited since you hit traction.

If you're seeing this pattern, there might be significant value hidden in your pricing. You probably set your prices when you launched. Maybe you looked at competitors. Maybe you did some basic margin math. But you've never actually explored whether customers would pay more... or if you're losing sales by pricing too high.

Before you scale, understanding your pricing could be crucial. A 10% price increase that doesn't hurt conversion is pure profit. A 10% price decrease that doubles conversion might be even better.

Why this could matter at $1-5M: You're about to invest in growth. Make sure every acquired customer is worth what they could be.

What you might explore: Current pricing vs. 10-15% higher vs. 10-15% lower. Explore across your best-selling products first to see what the data reveals.

Track this to understand: Profit per visitor, conversion rate, and margin impact. Find the price point that maximizes profit, not just revenue.

2. Discount Dependency Assessment

Your data signal: You're not sure how much of your revenue depends on discounts to convert.

If this describes your situation, it's worth investigating before the pattern becomes ingrained. Some brands train their customers to wait for sales. If that's happening to you, you need to know now... before you're trapped in a cycle of constant promotions.

This exploration isn't about optimizing discounts. It's about understanding how dependent your business is on them.

Why this could matter at $1-5M: Discount dependency erodes margin over time. Better to understand the situation now than discover it at $10M.

What you might explore: Run a period with your normal promotion calendar vs. reduced discounts vs. no discounts. See what the actual impact looks like.

Track this to understand: Profit per visitor, full-price conversion rate, and customer acquisition cost. How many customers will buy without a discount?

3. Shipping Strategy Foundation

Your data signal: You haven't systematically explored whether free shipping, thresholds, or paid shipping gives you the best profit.

This pattern often points to an opportunity worth investigating. Shipping strategy affects conversion, AOV, and margin simultaneously. The right answer isn't obvious... it depends on your products, customers, and competitive landscape.

Understanding YOUR optimal shipping strategy before you scale could be significant. Every order placed at scale will carry the weight of this decision.

Why this could matter at $1-5M: Shipping costs scale linearly with orders. A suboptimal strategy gets increasingly expensive as you grow.

What you might explore: Free shipping on all orders vs. free shipping threshold vs. flat rate vs. calculated shipping. Explore multiple threshold amounts to see how your customers respond.

Track this to understand: Profit per visitor (accounting for shipping costs), AOV, and cart abandonment rate. Learn more about shipping testing.

4. First Subscription Exploration

Your data signal: You sell products that customers might reorder, but you haven't explored whether subscription makes sense.

If you see this pattern, it could indicate an opportunity worth investigating. Subscription isn't right for every brand. But if it works, it transforms your business model. Predictable revenue, higher LTV, better unit economics.

Before building out a full subscription program, exploring whether your customers actually want it could save significant resources. A small exploration now saves you from building infrastructure for a program nobody uses.

Why this could matter at $1-5M: Subscription programs require investment. Validate demand before you build.

What you might explore: Subscription option available vs. subscription pre-selected vs. no subscription option. Explore on your most repeat-purchased products to see what resonates.

Track this to understand: Subscription take rate, first-order profit per visitor, and 60-day retention rate. High take rate means nothing if everyone cancels.

5. Channel-Specific Landing Pages

Your data signal: All your traffic goes to the same pages regardless of where it came from.

If you're seeing this pattern, there might be opportunities hiding in your channel data. A customer coming from a Google search has different intent than one clicking a Facebook ad. The messaging that resonates with each is different.

Exploring channel-specific experiences helps you understand which value propositions work for which audiences. This knowledge compounds as you scale your marketing spend.

Why this could matter at $1-5M: Your marketing efficiency determines how fast you can grow. Understanding what resonates with each channel improves every dollar spent.

What you might explore: Generic landing page vs. channel-specific messaging. For paid social, explore problem-focused vs. product-focused vs. social-proof-focused messaging to see what your audience responds to.

Track this to understand: Profit per visitor by channel, conversion rate by traffic source, and CAC by channel.

Your Data Points the Way

• Explore pricing before you scale. It's often the highest-leverage signal to investigate.

• Understand your discount dependency. It's better to know now than be trapped later.

• Discover your optimal shipping strategy. Every future order depends on this decision.

• Explore subscription appetite before building infrastructure.

• Learn what resonates by channel. This knowledge compounds as ad spend grows.

This stage is about building confidence in your fundamentals. Your data tells you what to test. These signals are starting points for your stage, not a checklist. Get these understood, and scaling becomes much less risky.

Start with pricing exploration. Everything else builds on knowing your prices are right. See how Intelligems helps brands at your stage.