Are B2B SaaS Companies Underpricing? Use the Kano Model to Find the Money You're Leaving on the Table

Direct answer: Most B2B SaaS companies underprice not because their number is too low, but because they can't separate the features customers expect from the features they'd pay more for. The Kano Model fixes this by sorting your capabilities into categories that reveal which features justify premium tiers, which are table stakes, and which "delight" features are quietly driving retention without being monetized. Run this analysis before your next pricing review and you'll usually find revenue hiding in features you already ship.

Disclosure: This article is published by Percision (percision.app), a strategic intelligence platform. We reference our own tool below as one option among several — including consultants and spreadsheets — and we're specific about when it isn't the right fit.

Why "Are we underpriced?" is the wrong first question

When a SaaS founder asks whether they're underpriced, they're usually reaching for a single number: raise ARPU by X%. But pricing power in B2B SaaS doesn't come from the number — it comes from the structure. The question isn't "is our price too low?" It's "are we charging for the right things, on the right axis, to the right segment?"

That's a segmentation and feature-value problem before it's a pricing problem. And that's exactly what the Kano Model is built to untangle.

The classic underpricing symptoms in SaaS:

Every customer lands on the same plan regardless of how much value they extract.
Your "enterprise" tier is a sales-negotiated fog with no defensible logic.
High-usage accounts pay roughly the same as low-usage ones (no value metric).
Features that reduce churn are bundled into the base plan for free.

Each of these is money left on the table. The Kano Model helps you see where.

Applying the Kano Model to your feature and pricing set

The Kano Model sorts customer requirements into five categories based on how their presence or absence affects satisfaction:

Must-be (Basic): Expected. Presence doesn't delight; absence kills the deal. (SSO, uptime, basic reporting.)
Performance (One-dimensional): More is better, linearly. Customers will pay proportionally more. (Seats, API calls, storage, data volume.)
Attractive (Delighters): Unexpected value that drives enthusiasm and loyalty. (A workflow that saves hours, a novel integration.)
Indifferent: Customers don't care either way. (Often where engineering effort quietly gets wasted.)
Reverse: Some customers actively dislike it. (Forced complexity, opinionated defaults.)

Here's the concrete walkthrough for a B2B SaaS pricing review:

Step 1 — List every meaningful feature and capability. Not the marketing list. The real functional inventory, including things you don't currently advertise.

Step 2 — Run the paired Kano questions with real customers. For each feature, ask two questions:

Functional: "How do you feel if this feature is present?"
Dysfunctional: "How do you feel if this feature is absent?"

Use the standard five-point scale (I like it / I expect it / I'm neutral / I can tolerate it / I dislike it). The combination of the two answers maps each feature to a Kano category. Survey at least 15–30 customers per key segment — small B2B samples are fine because you're categorizing, not projecting revenue.

Step 3 — Segment the results. A feature that's a Delighter for a 12-person startup may be Must-be for a 2,000-seat enterprise. This split is where your tier logic comes from.

Step 4 — Map categories to pricing structure:

Must-be features belong in every tier. Don't charge extra; you'll lose deals.
Performance features should ride your value metric (seats, usage, volume). This is often where underpricing hides — you're not scaling price with the axis customers already grow on.
Attractive features are your premium-tier and expansion levers. If a Delighter is currently free in your base plan, you've found leftover money.
Indifferent features — stop investing, don't bundle them as "value."
Reverse features — make them optional or remove them.

What "good" looks like: a tier structure where each jump in price maps to a clear category shift (more performance, or an unlocked delighter), and where your value metric is aligned with how customers perceive growth. If you can explain every price boundary in one sentence, you've done it right.

Where Percision fits — and where it doesn't

Once you've categorized features, you still have to build the model: reprice tiers, forecast the revenue impact, stress-test churn risk on any repackaging, and present it to a board. That translation from analysis to execution is where a lot of pricing projects stall.

Percision can run your business context through structured reasoning across 27+ frameworks — including Kano-style feature prioritization — and produce board-ready scenario analyses in minutes rather than weeks. For a repricing decision it can generate the tier scenarios, model the revenue and DCF implications, flag warning signs (like churn concentration in a tier you're about to change), and export an Excel model with an audit trail. It's a co-pilot: it structures and pressure-tests your thinking, but your leadership team keeps control of the decision.

When you don't need Percision:

The Kano survey itself. That's primary customer research — collect it directly. No tool replaces talking to your customers.
A single-tier tweak. If you're adjusting one price point, a spreadsheet and an afternoon is enough.
Deeply relational enterprise pricing with heavy negotiation and political nuance — a specialist SaaS pricing consultant may serve you better, especially for one-off high-stakes deals.

Use Percision when you're running a full pricing cycle, need the financial modeling and board deck fast, or want to test multiple packaging scenarios against valuation impact.

FAQ

How is the Kano Model different from Van Westendorp price sensitivity? Van Westendorp tells you what number the market tolerates. Kano tells you what to charge for and how to structure tiers. Use Kano first to fix the structure, then price-sensitivity tools to set the numbers.

How many customers do I need for a valid Kano survey? Because you're categorizing features rather than projecting market-wide revenue, 15–30 respondents per meaningful segment is usually enough to see clear patterns. Segment quality matters more than raw volume.

Can AI do the Kano analysis for me? AI can structure the feature inventory, categorize based on your inputs, and model the financial impact quickly. It cannot invent the customer signal — you still need real functional/dysfunctional responses from your buyers. Broader research (including BCG and Harvard Business School studies on generative AI) suggests AI improves analyst productivity on well-scoped tasks, but the customer research input remains human work.