The default path we didn’t take
At a retail-tech SaaS I worked at, we rebranded the product — new name, new visual identity, new positioning, new website and social presence built from scratch. Somewhere in that process, the pricing conversation started the way it starts almost everywhere: open three competitor pricing pages, eyeball where they land, pick a number somewhere in the middle, call it “market rate.”
We stopped that conversation before it went anywhere. It’s the single decision from the entire rebrand I’d defend most confidently, because it was the one built on the least amount of guessing.
Why competitor pricing pages are a trap
Competitor pricing tells you what another company believes their buyer will pay, filtered through their own cost structure, their sales motion, their brand equity, and their existing customer base’s switching cost. None of that transfers cleanly to a company mid-rebrand, with a new name buyers don’t recognize yet and none of the trust an established competitor has already banked.
We still did deep competitive intelligence — feature matrices, positioning gaps, messaging audits — because understanding what competitors offered and how they talked about it mattered for product and GTM decisions. What we refused to do was let their price tag become our starting point. Anchoring to a competitor’s number answers the wrong question: it tells you what they charge, not what your buyer will actually pay for your specific version of the value proposition.
What we did instead
We didn’t run a formal, single-instrument willingness-to-pay study. What we ran was messier and, in hindsight, more honest: a continuous willingness-to-pay process built on real conversations rather than a survey instrument.
Sales was already running when I joined, and I spent a significant amount of time sitting in on live sales calls — watching how prospects actually reacted to a price in the moment, not what they said they’d pay in the abstract. That unfiltered, in-context reaction turned out to be more reliable than anything a structured questionnaire would have told us, because it was revealed behavior, not a hypothetical answer.
Alongside that, we ran direct interviews with existing customers, and we used competitive intelligence — feature matrices, positioning gaps — to shape our initial bundling. None of this happened in a single clean phase before launch. It happened continuously, in parallel with ICP and persona work, as we onboarded new customers and learned more.
The output was never a single number handed over by a research report. It was a live feedback loop — sales call data, customer interviews, and real market response, compounding into a clearer picture of what different segments would actually pay.
Building the price ladder from segments, not features
The common approach to SaaS tiering is to bundle features into “Basic / Pro / Enterprise” buckets and hope the segmentation falls out naturally. We built it the other way around. The willingness-to-pay signal we gathered — through sales conversations and customer interviews, not a formal survey — showed us where the real price ceilings sat for different buyer segments, and the tiers were built to match those segments, not to create artificial scarcity that pushes people toward an upsell.
That distinction matters more than it sounds. Feature-bundled tiering optimizes for extracting more from each customer. Segment-based tiering, built from real buyer signal, optimizes for actually landing the price a given buyer is willing to pay in the first place — which matters enormously more when you’re a newly rebranded product still building trust.
Ability-to-pay mattered as much as willingness-to-pay
Our buyer base skewed heavily SMB, running on a freemium model. For that segment, budget reality was often the harder constraint — not whether they were willing to pay, but whether they structurally could. That distinction shaped the final tiers as much as any single conversation did. It’s a detail formal WTP frameworks sometimes miss: a segment can want your product and still have no room in their budget for it, and no amount of clever pricing psychology changes that math.
Pricing below the incumbent, on purpose
The signal pointed to a clear answer: price around 20% below the established mid-market players, and roughly 50% below the category leader. That wasn’t a discount strategy or a “let’s undercut everyone” reflex. It was where real buyer behavior told us our ceiling sat once we accounted for the trust deficit of a smaller, newly rebranded product asking someone to switch from something they already knew.
Pricing lower than an incumbent without any signal behind it is just guessing downward. Pricing lower than an incumbent because sales conversations, customer interviews, and repeated iteration showed that’s genuinely where the value-to-trust ratio lands for your buyer is a decision you can defend in a board meeting — even if the process that got you there wasn’t a textbook study.
Letting the rebrand carry the price
The launch didn’t apologize for the number. New name, new identity, new website, new social presence — every touchpoint was built around the same positioning that justified the price, not around explaining why it was lower than the leader’s. Buyers weren’t told “we’re cheaper because we’re new.” They were told why this product, at this price, was built for exactly their use case.
Results
+28% website traffic, +13% social engagement, +12% MQL growth in the period following the rebrand.
The takeaway
Pricing research gets treated as something that only counts if it’s a formal study — a survey instrument, a fixed sample, a clean report. Done properly, willingness-to-pay work doesn’t have to look like that. Sometimes the most reliable signal is sitting in your sales calls and your actual market response, if you’re willing to iterate against it instead of guessing once and moving on. We repriced three times before we got it right. That’s not a weaker process than a formal study — it’s a different kind of rigor, built on revealed behavior instead of stated preference.
