Pricing Experiments That Changed My Revenue

Pricing is the lever most solo founders are afraid to pull. I was too, for years. I'd set a price when launching a product and then leave it untouched, convinced that any change would alienate existing users. In 2022, I finally started running systematic pricing experiments across my products. The results were surprising, sometimes counterintuitive, and collectively increased my revenue more than any feature I've ever built.

The Starting Point

Across my products, I'd fallen into the same pricing trap that catches most technical founders: I'd priced based on cost-plus thinking. I calculated what it cost me to serve a user (hosting, API calls, bandwidth), added a margin that felt reasonable, and called it a price.

Aviation Infinity was priced at what I thought students could afford. Babonbo's take rate was set at what seemed fair compared to other marketplaces. ClickAi was priced based on the cost of the AI API calls required to generate a website.

None of these prices were based on the value users received. They were based on what I thought was fair, which is a polite way of saying they were based on my discomfort with charging more.

Experiment 1: The Price Increase That Increased Conversions

The first experiment I ran was on Aviation Infinity. I increased the monthly subscription price by 40%. I expected a drop in signups proportional to the price increase and hoped that the higher per-user revenue would compensate.

What actually happened was that signups increased. Not dramatically, but they went up by about 12% in the month following the price increase.

I spent a week trying to understand why. The answer, which I confirmed through user interviews, was that the original price was signaling low quality. Student pilots are making a significant investment in their training. Flight school fees are thousands of euros. Instructor time is expensive. In that context, a very cheap exam prep subscription looked like it couldn't possibly be comprehensive or high quality.

The higher price actually aligned better with the perceived value of the product. Students took the platform more seriously, were more likely to commit to using it consistently, and as a result, had better outcomes, which led to more word-of-mouth referrals.

This was the first pricing lesson: your price is part of your product positioning. A price that's too low can hurt you just as much as a price that's too high.

Experiment 2: Annual vs. Monthly Plans

The second experiment was introducing an annual plan for Aviation Infinity at a significant discount compared to twelve months of the monthly plan. Standard SaaS practice, nothing revolutionary.

The result was that 60% of new subscribers chose the annual plan. This was higher than I expected and had two important effects. First, it dramatically improved cash flow. Annual payments upfront meant I had capital to invest in the product months before I'd earned it on a monthly basis. Second, it reduced churn. Annual subscribers are committed for a year, and by the time their subscription comes up for renewal, they've typically gotten enough value that renewal is automatic.

The deeper insight was about commitment and behavior. Annual subscribers studied more consistently than monthly subscribers. They'd made a financial commitment, and that commitment translated into behavioral commitment. They used the platform more, progressed further, and were more likely to pass their exams, creating a virtuous cycle of satisfaction and retention.

Experiment 3: Removing the Free Tier

This was the scariest experiment. ClickAi had a free tier that let users generate one website with limited features. The theory was that free users would convert to paid plans after seeing the value. In practice, the conversion rate from free to paid was under 3%.

I replaced the free tier with a free trial. Users got full access to all features for seven days. After that, they needed to subscribe. The conversion rate from trial to paid was 18%, six times higher than the free tier conversion.

The reason, I believe, is that the free tier created a "good enough" category. Users with limited needs could stay on the free tier indefinitely and never felt the pull to upgrade. The free trial forced a decision: is this product worth paying for or not? And because the trial gave them the full experience, they could make an informed decision about the paid product's actual value.

I lost the free users who would never have converted anyway. I gained paying customers who had made an active decision that the product was worth the price. Net result: significantly higher revenue and a more sustainable business model.

Experiment 4: Geographic Pricing

Babonbo operates across multiple European markets with very different economic conditions. I was charging the same take rate everywhere, which meant the platform was affordable in some markets and expensive in others.

I implemented geographic pricing tiers, adjusting the take rate based on the local market. Higher in cities with higher rental prices (like Zurich and Paris) and lower in more affordable markets (like Lisbon and Krakow). The overall take rate was similar, but the distribution matched local economics.

The result was a significant increase in provider participation in lower-cost markets. Providers who had been reluctant because the take rate ate too much of their margin were now willing to list their equipment. More providers meant more availability, which meant more customer bookings, which meant more revenue despite the lower per-transaction take rate.

Geographic pricing is standard in global SaaS, but I'd been reluctant to implement it because of the complexity. In retrospect, the complexity was minor (a pricing table keyed by city) and the revenue impact was significant.

Experiment 5: Unbundling Features

This experiment was on ClickAi. Instead of a single plan with all features, I created three tiers: a basic plan for simple websites, a professional plan for business sites, and a premium plan that included AI copywriting and advanced design customization.

The counterintuitive result was that the professional plan, the middle tier, accounted for 70% of subscriptions. The basic plan was rarely chosen, and the premium plan attracted a small but profitable segment.

This is the decoy effect in action. The basic plan existed primarily to make the professional plan look like good value. The premium plan existed to make the professional plan look reasonable by comparison. The middle tier was always the intended target.

I'm not proud of using pricing psychology this way, but I'd be dishonest if I didn't acknowledge that it works. The important caveat is that each tier delivers genuine value at its price point. The psychology only works when the underlying product justifies the pricing.

What I Learned Overall

Running pricing experiments across multiple products taught me several things that I wish I'd understood years earlier.

Most solo founders underprice their products, often significantly. The discomfort you feel about charging more isn't a reliable indicator of what the market will bear. The only way to know your optimal price is to test.

Price communicates quality. In markets where buyers can't easily evaluate product quality before purchasing (which is most software markets), price is a primary quality signal. Pricing too low can literally reduce demand.

Pricing structure matters as much as pricing level. How you structure your plans, what's included, what's unbundled, whether there's a free tier, these structural decisions can have a larger revenue impact than the actual dollar amounts.

Annual plans are almost always worth offering if your product has ongoing value. The cash flow benefits alone justify the implementation effort.

And finally, pricing experiments are the highest-leverage activity a solo founder can undertake. A 30% price increase that the market accepts produces more revenue than any feature you could build in the same time.

The Ongoing Process

Pricing isn't a one-time decision. I now review pricing across all my products quarterly. Not necessarily changing it every quarter, but evaluating whether the current pricing still reflects the product's value and the market's willingness to pay.

The most important shift was psychological. I stopped thinking about pricing as something I owe to my users ("what's fair?") and started thinking about it as something that sustains the products they depend on ("what lets me keep building and improving this?"). Sustainable pricing is fair pricing, because a product that's priced too low to sustain development eventually stops getting better, and that serves nobody.