Building NeedSonar in Public: Month 2 - Why We Pivoted Our Pricing

In month one, we shipped NeedSonar with a per-seat price of $29/mo. It felt safe. It matched what we saw in similar tools. By the end of month two, we had killed that price, replaced it with usage-based pricing plus a free tier, and learned four things about pricing for solo founders that we wish we had known on day one. This post is the unedited version of what happened, including the mistake. The numbers are real. The quotes are paraphrased. The lessons are the kind that only show up after you have charged the wrong price to enough people.

The Original Pricing: Per-Seat, $29/mo

We launched with a per-seat model at $29/mo. One user, one seat, $29/mo. Teams paid $29 per seat, with a five-seat minimum. The logic was simple: it was a familiar model, easy to explain, and the math was clean. Five seats at $29 was $145/mo, which felt reasonable for a team tool. The familiarity bias was the real reason we picked it. We had seen other tools do it. We copied the pattern without checking whether the buyer was the same.

The problem was that almost none of our users were teams. Of the first 200 sign-ups, 187 were solo founders or one-person shops. We were charging $29 to a buyer who was the only person in the company. That is a category error, and the data was telling us so from week two. The category error is the kind of mistake that is obvious in retrospect. At the time, we were too attached to the model to see it. The attachment cost us a month of suboptimal revenue and a churn rate that was 3x what we expected.

The other problem with per-seat is that it implies a future state the buyer does not have. When we said "$29 per seat, five-seat minimum," a solo founder heard "you think I will hire four people, and you are charging me for the fantasy." The framing was condescending without us realizing it. The interview data confirmed this pattern in 6 of the 8 calls. The remaining 2 were indifferent. None of them were positive.

The Interviews: Why Users Churned

In month two, we ran eight interviews with users who had signed up for the free trial and not converted, and with users who had converted and then churned within 30 days. The pattern was loud and consistent. The first three calls told us almost everything we needed to know. The remaining five confirmed it. The cost of the interview program was about 12 hours of our time and 8 hours of the users' time. The information density was higher than any other research method we have used.

The first pattern was that $29/mo felt like a lot for one person. A solo founder running a $500 MRR side project told us: "$29 is 6% of my revenue. I cannot justify it for a research tool I use twice a month." Another said: "I would pay $9. I would not pay $29. The tool is good, but it is not saving me that much time." The 6% of revenue number is one we have heard repeatedly since, and it is the rule of thumb we now use: a solo founder will pay up to about 5% of MRR for a tool that is core to the business, and 1% to 2% for a tool that is useful but not core. At $500 MRR, 1% is $5/mo and 5% is $25/mo. We were priced above the upper end of the range.

The second pattern was that the per-seat model implied future scaling that was not happening. When we said "you can add seats for $29 each," users heard "you think I will hire someone." The framing was wrong for the audience. The audience was a solo operator trying to keep the business alive, not a manager building a team. Every mention of "seats" was a reminder that we had built the wrong shape of product for them.

The third pattern was that the value of the tool was tied to volume, not to seats. A user who checked the dashboard every day was getting more value than one who checked it monthly, and we were charging both the same. The price was detached from the value delivered. The user who checked daily was underpaying. The user who checked monthly was overpaying and churning. The model was wrong for both segments, and the misalignment was the source of the churn.

"I am a solo founder. $29/mo feels like a lot for one person, even if I use it. I would pay $19 if I knew I was going to use it weekly." - u/solobuild on r/SaaS

The New Pricing: Usage-Based With a Free Tier

The new pricing has three tiers. The first is free, with 50 pain points per month, full dashboard access, and a daily digest email. The second is $19/mo for 500 pain points, priority crawling, and saved searches. The third is $49/mo for unlimited pain points, team seats, and an API. The free tier is the foundation. The middle tier is the most common upgrade. The top tier is for teams, which is a real but smaller segment of our users.

The free tier is not a trial. It does not expire. We made this decision after watching the data: trial users converted at 8%, free-tier users converted at 32% over 60 days. The free tier is a long game, and we are willing to play it. The conversion happens after the user has internalized the value, not during the trial when the user is evaluating. The free tier is a relationship. The trial is an audition. The audition model produces one-time conversions. The relationship model produces lifetime value.

The usage-based layer is the second decision. We charge by pain points scored per month, not by seats. A solo founder who checks the dashboard weekly is paying $19. A team that pulls 10,000 pain points a month is paying $49. The price is tied to the value consumed, which is the way modern infra tools price and the way our users expected to be charged. The mental model of "I pay for what I use" is now standard for technical buyers, and our buyers are technical. The per-seat model was a holdover from a different era.

The operational discipline is harder with usage-based pricing. We have to meter everything, display the meter, send warnings at 80% and 100%, and have a clean upgrade path. The billing infrastructure is more complex than flat-rate. We use Stripe Billing for the metering, and we built a small in-app usage page that shows the user where they are in their tier. The page is one of the most visited pages in the app, which is a good sign. Users want to know what they are paying for and how close they are to the next tier.

Week 1 Reaction

In the first week of the new pricing, the numbers moved the way we hoped. Free sign-ups were up 38% week over week. Free-to-paid conversion in the first seven days was 4.1%, up from 1.4% under the old model. The biggest change was that users who hit the free-tier limit started upgrading, which had not happened under the trial model. The 50-pain-point limit was tuned to be generous enough for casual use and tight enough that heavy users would notice. The notice moment is the upgrade moment.

One user emailed us on day four: "I was about to cancel after the trial. The free tier changed my mind. I will upgrade when I need the volume." Another said: "I cannot believe how much better this feels. I am paying for what I use, and the free tier is actually useful." The emails were unexpected. We had assumed the pricing change would be neutral or negative. The reaction was positive across the board, including from users who were paying the same amount.

The churn in week one was zero. The previous week, under the per-seat model, we had two paid cancellations. We are not declaring victory on a week of data, but the directional signal is clear. The churn rate appears to have dropped by an order of magnitude. The free-to-paid conversion has tripled. The sign-up rate has increased. All three changes are consistent with the hypothesis. We will know more after 30 days.

The Four Lessons

Lesson 1: Match the price to the buyer. Per-seat pricing assumes teams. If your buyers are solo, usage-based or flat-fee pricing is more honest. We were charging teams prices for what was, in practice, a solo tool. The lesson generalizes: the price should match the buyer's reality, not the founder's mental model. The mental model is a hypothesis. The buyer is data. Lesson 2: Free tiers beat trials. A free tier with a real limit converts better than a 14-day trial with full access. The reason is that a free tier is a long-term relationship. A trial is a one-time audition. We were running auditions. Now we are running relationships. The relationship model produces 4x the conversion rate of the audition model in our data, and the lifetime value is higher because the user has more time to integrate the product. Lesson 3: Tie price to value delivered. Usage-based pricing aligns cost with consumption. The users who get the most value pay the most. The users who get the least value pay the least. The old model had a solo user paying the same as a heavy user. That is bad pricing. The new model has both segments paying a fair amount, and the segments upgrade naturally as their usage grows. The upgrade path is the most valuable part of the model. Lesson 4: Interview churned users, not just happy ones. The eight churn interviews gave us more insight than 50 usage metrics. The pattern was clear within the first three calls. We should have done this in month one, not month two. The cost of waiting was 60 days of suboptimal pricing. The cost of running the interviews was 20 hours. The return on the interview was 30x. The math is obvious in retrospect. It was not obvious at the time.

What We Killed to Get Here

We killed the per-seat model, the team minimum, and the 14-day trial. We also killed the "pro annual" plan, which was $290/yr, because it was anchoring users to a higher number than they would otherwise pay. The simpler the structure, the easier the decision. The buyers in our segment are not procurement officers. They are founders making a 30-second decision. Every additional plan is friction. Three tiers is enough. Two is better. We may collapse to two in month three.

The internal fight that produced the kills was harder than the customer interviews. Two of the team members were attached to the per-seat model because it was what they knew from previous companies. The interviews were the evidence that broke the attachment. The pattern in early-stage companies is that opinions are sticky until data replaces them. The data from the interviews was the replacement. Without the interviews, we would still be defending the per-seat model in our monthly review.

What Month Three Is About

Month three is going to be about onboarding. The conversion data tells us that the free tier works, but the activation data tells us that many free users do not reach their first "aha" moment in the first session. We are going to redesign the onboarding flow, run a 50-user usability test, and document the result. The next post will cover what we changed, what we measured, and what we still got wrong. The bet is that activation is the next bottleneck, and the bet will be tested against the data.

The other thing month three is about is patience. We are now at the point where the only thing standing between us and a $10k MRR business is execution. The market is real. The product is working. The pricing is right. The onboarding is the next layer. The compounding is starting. The temptation to add a feature, launch a new channel, or raise a round is real. The discipline is to do the boring work and let the compounding happen. The boring work is the work that compounds.

If you want to follow along, we post monthly updates on the blog and ship in public.

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