I remember the first time I suspected a subset of our users would pay more for a premium feature: the support tickets kept coming, product tours showed repeated engagement, and a few customers even asked, “How can we get this faster?” That curiosity is the seed of every strong pricing experiment. If you want to prove customers will pay more for a SaaS feature, you need a methodical, low-risk approach that turns signals into evidence. Below I’ll walk you through how I build pricing experiments that actually demonstrate willingness to pay—step by step, practical and actionable.
Start with a clear hypothesis
Any experiment needs a crisp hypothesis. Don’t start with “We should charge more.” Start with something like:
Make it measurable: define the target audience, the price point(s), the expected lift, and the timeframe. A hypothesis gives you a guardrail so you aren’t chasing vanity metrics.
Choose the right experiment type
There are multiple ways to test price sensitivity. I usually pick one of these depending on risk and technical constraints:
Each method has trade-offs. A/B tests scale and provide clean data quickly but require traffic and engineering support. Sales-led experiments give qualitative insight and can validate higher prices for fewer accounts.
Define your metrics and success criteria
Focus on business-impact metrics, not just clicks:
For instance, if your baseline upgrade rate is 3% and you want to detect a 1% absolute lift to 4%, you'll need a much larger sample than if you expect a 3% to 6% jump.
Design the experiment flow
Clarity in user experience is crucial—if users are confused, the data is noisy. Here’s a typical flow I use for a feature price test:
Keep the UI clean and use consistent messaging across variants. Use anchoring—show the original price struck-through next to the new price—only if it fits ethically with your positioning.
Run a pilot and collect qualitative signals
Before ramping to full A/B, run a small pilot. I often do this via manual sales outreach or targeted emails to power users. The goal is to gather qualitative reasons for acceptance or rejection. Ask questions like:
These responses refine positioning, messaging, and sometimes the price structure itself. If many prospects say “we’d pay but need integration A,” you’ve uncovered a product or packaging issue rather than pure price resistance.
Analyze results with rigor
When the test is running, track conversions and revenue by cohort and variant. A simple analysis table helps—here’s a template I use:
| Variant | Visitors | Payers | Conversion Rate | ARPU | Revenue |
|---|---|---|---|---|---|
| Control (current price) | 10,000 | 300 | 3.0% | $25 | $7,500 |
| Variant (new price) | 10,200 | 360 | 3.53% | $30 | $10,800 |
Compute uplift in conversion and revenue per visitor, and check statistical significance. But don’t stop at p-values—look at the distribution of purchase sizes, churn predictions, and feedback.
Watch for cannibalization and long-term effects
Charging more for a feature can push some customers into churn or downgrade. I always segment results by customer value:
Track 90-day retention for the buyers from the experiment. A short-term revenue spike is not a win if it materially raises churn or reduces lifetime value.
Iterate and scale
If the experiment shows a positive lift in revenue without unacceptable churn, scale it gradually. Tactics I recommend:
Examples I’ve used
I once tested a “priority export” feature at a mid-market analytics startup. We offered it as a $15/month add-on and as part of a $90/month “Pro” tier. The A/B test showed a small lift in individual add-on purchases but a larger uplift when we introduced the $90 tier, driven by perceived value and simplified billing. We combined quantitative A/B results with sales feedback and ultimately increased ARPU by 18% without a meaningful rise in churn.
Another time, for an HR SaaS product, we used a preorder model: we let customers reserve early access for a $99 refundable deposit. The impulse to reserve, plus the stakeholder conversations it triggered, offered stronger signal than a pure A/B test and helped us justify enterprise pricing with implementation fees.
Building a pricing experiment that proves customers will pay more is part science and part storytelling. You need the numbers, yes, but also the right narrative that communicates why the feature is worth the extra cost. Run small, learn fast, and let qualitative feedback guide your quantitative decisions.
