👋 Hey Reader, Dhruv here.

Hope you're having a great week.

This week, I came across a historical pricing change from Softr that made me stop and look at pricing differently.

At first, I saw a familiar pattern:

Prices changed.

But the more I looked at the pricing ladder, the more I thought:

What if this wasn't really a pricing experiment?

What if it was an acquisition + conversion + monetization experiment packaged as a pricing-page change?

That distinction matters.

Because as I'm training myself to think more like a Growth Marketer, I'm trying to look beyond individual metrics and understand how one product decision can influence the entire growth model.

So this week's Growth Note is about pricing architecture—and how I'd analyze an experiment like this.

Quick Recap

Here's the core observation from my LinkedIn post:

• Softr's historical pricing moved from $49 / $139 / $269 to $19 / $99 / $329.
• The entry and mid-tier prices decreased while the Business tier increased.
• That makes the change more interesting than a simple price increase or decrease.
• The experiment could potentially have been testing pricing architecture and customer segmentation.
• The right primary metric shouldn't necessarily be conversion rate—it could be revenue per visitor.
• ARPA can decrease while total revenue increases, so optimizing for customer value requires looking beyond one metric.

The interesting part is what happens when we treat the pricing page as a growth lever rather than simply a monetization page.

Behind the Scenes: I Started Looking at the Pricing Ladder Differently

My first instinct was to compare the old prices with the new prices.

That's useful.

But it doesn't tell us much about the strategy.

The more interesting question is:

The entry tier went from $49 to $19.

That's a significant reduction in the barrier to becoming a paying customer.

At the same time, the Business tier moved from $269 to $329.

That's a significant increase at the higher end.

So instead of thinking:

Old pricing → New pricing

I started thinking:

Lower entry friction → More customers entering → Better segmentation → Higher monetization from customers with greater willingness to pay

That's a very different way of looking at the same pricing page.

And importantly, it's still only a hypothesis.

We don't have enough evidence from the pricing change alone to say this was Softr's actual intention.

But it's a useful hypothesis for understanding the experiment.

Pricing Architecture Is Also Customer Segmentation

This is the part I found most interesting.

A pricing ladder isn't only telling customers:

It's also asking:

Consider two hypothetical customers.

Customer A

A small team exploring whether Softr solves their problem.

Their thought process might be:

A lower entry price could reduce that initial friction.

Customer B

A larger organization getting significant value from the product.

Their thought process might be:

Increasing the higher tier could potentially capture more willingness to pay.

So the pricing ladder can perform a segmentation function.

Low-friction entry for price-sensitive customers.

Higher monetization for customers with greater willingness to pay.

That's why I think pricing architecture deserves to be analyzed alongside acquisition and conversion—not only monetization.

The Metric Trap: Conversion Rate Can Mislead You

This is where I think Growth Marketers need to be careful.

Imagine the new $19 entry price increases paid conversion substantially.

That sounds great.

But what if most new customers choose the lowest tier?

Revenue might not increase as much as expected.

Now imagine the opposite.

Conversion rate decreases slightly, but more customers select higher-value plans.

Revenue could still increase.

That's why I wouldn't make:

Conversion Rate

the primary success metric.

I'd start with:

🎯 Revenue Per Visitor

Why?

Because it connects two sides of the funnel:

Visitors → Customers → Revenue

For example:

Scenario A

1,000 customers × $100 = $100,000

Scenario B

1,300 customers × $90 = $117,000

ARPA decreased by 10%.

But revenue increased by 17%.

If I only looked at ARPA, I'd conclude that monetization became worse.

If I only looked at conversion, I'd miss the revenue impact.

If I look at revenue, I get a much more useful business-level signal.

But Revenue Per Visitor Still Isn't Enough

This is where I'd go one level deeper.

A pricing experiment can create short-term revenue growth while damaging the quality of customers acquired.

For example:

A lower entry price might attract more customers.

But those customers could potentially:

• activate less frequently,
• churn faster,
• generate more refunds,
• upgrade less often,
• or require more support.

That's why I'd separate the metrics into primary success metrics and guardrails.

Primary Metrics

Revenue Per Visitor

Then I'd monitor:

• Paid Conversion Rate
• ARPA
• Plan Mix
• Upgrade Rate
• Expansion Revenue

Guardrails

I'd watch:

• Churn
• Activation
• Refund Rate
• LTV
• Customer quality

The important principle is:

🧪 Growth Experiment

Here's how I'd structure the pricing experiment.

👀 Observation

The pricing architecture changed asymmetrically.

The entry and mid-tier prices decreased while the Business tier increased.

🚧 Potential Bottleneck

The previous pricing structure may have created friction for smaller customers while potentially under-monetizing higher-value customers.

Important: This is a hypothesis, not a confirmed explanation for Softr's change.

💡 Hypothesis

A wider pricing ladder could reduce entry friction while increasing monetization from customers with higher willingness to pay.

🧪 Experiment

Test multiple pricing architectures against the existing structure.

For each variation, measure not only whether visitors convert, but which customers convert, which plans they select, and how much revenue they generate over time.

I'd specifically compare:

Architecture A

Low → Medium → High

vs.

Architecture B

Very Low → Medium → Premium

Then I'd analyze customer behavior across each tier.

📊 Success Metrics

Primary:

• Revenue Per Visitor

Secondary:

• Paid Conversion
• ARPA
• Plan Mix
• Upgrade Rate
• Expansion Revenue

Guardrails:

• Activation
• Churn
• Refunds
• LTV

Framework of the Week: The Pricing Experiment Scorecard

Here's a framework I'd use whenever evaluating a SaaS pricing change.

1. Acquisition

Did the new pricing reduce the barrier to becoming a customer?

Look at:

• Visitor → Signup
• Signup → Paid
• Cost per acquired customer

2. Conversion

Did more qualified users become customers?

Look at:

• Paid conversion
• Trial conversion
• Demo → Paid
• Plan selection

3. Monetization

Did the business capture more value from customers?

Look at:

• Revenue per visitor
• ARPA
• Revenue per customer
• Expansion revenue
• Plan mix

4. Retention

Did the customers acquired through the new architecture stick around?

Look at:

• Activation
• Churn
• Retention
• Refunds
• LTV

5. Expansion

Did customers naturally move toward higher-value plans?

Look at:

• Upgrade rate
• Expansion revenue
• Time to upgrade
• Feature adoption by plan

This gives you a much more complete view than simply asking:

Additional Insights

1. Pricing pages are acquisition assets too.

We usually categorize pricing under monetization.

But pricing can influence acquisition.

A prospect who sees "$19/month" may be willing to start a conversation that never would have happened at "$49/month."

So pricing can influence the top of the funnel indirectly.

2. Plan mix can be more informative than average price.

Suppose ARPA falls.

That doesn't automatically mean the experiment failed.

I'd want to know:

Why did ARPA fall?

Was it because:

• More customers selected the entry tier?
• Existing customers downgraded?
• The customer mix changed?
• More small businesses entered the funnel?

The reason behind the metric matters as much as the metric itself.

3. Higher pricing can communicate positioning.

A higher Business tier isn't necessarily just about extracting more revenue.

It can also influence how customers perceive the product's positioning.

A pricing ladder communicates who the product is designed for and how much value the company believes it can provide.

Again, that's a hypothesis worth testing—not something we can conclude from the price change alone.

4. Pricing experiments need time.

A customer who signs up today might upgrade three months later.

Another customer might churn after two weeks.

So evaluating pricing entirely on immediate conversion can produce misleading conclusions.

I'd want to compare cohorts over time.

Acquisition → Activation → Conversion → Expansion → Retention

That's where the real economics become visible.

5. The best pricing experiment might not change the price.

Sometimes the bottleneck isn't price.

It might be:

• unclear value proposition,
• poor plan differentiation,
• confusing feature limits,
• weak social proof,
• excessive complexity,
• or the wrong default plan.

Before changing prices, I'd first ask:

That's probably the first experiment I'd want to validate.

Weekly Challenge 🧪

Pick one SaaS pricing page you use or follow.

Spend 20 minutes answering:

1. Who is the cheapest plan designed for?
2. Who is the most expensive plan designed for?
3. What behavior is the pricing ladder encouraging?
4. Which plan appears to be the "default" choice?
5. What metric would you use to determine whether the pricing architecture is working?

Then ask yourself one final question:

Your answer will tell you a lot about how you think about Growth.

One Question for You

When you evaluate a SaaS pricing experiment, what's the one metric you would prioritize first?

Conversion rate, ARPA, revenue per visitor, LTV—or something else?

Hit reply and tell me why.

I'd genuinely like to compare how other Growth Marketers approach this trade-off.

— Dhruv