Are Banks Underpricing? Using the Kano Model to Find Money Left on the Table

Direct answer: Most banks and financial services firms leave money on the table not by mispricing headline rates, but by giving away delighter features for free and overinvesting in commoditized basic expectations customers no longer reward. The Kano Model helps you sort every product feature and fee into categories — must-haves, performance drivers, and delighters — so you can charge for what customers genuinely value and stop subsidizing what they take for granted. This is a segmentation-and-value exercise, not a rate table exercise.

Why pricing in financial services is really a value-classification problem

When a bank asks "are we underpricing?", the instinct is to benchmark rates against competitors. That comparison is useful but shallow. It assumes every feature carries equal weight in the customer's mind. It doesn't.

A retail depositor may treat mobile check deposit as invisible — until it breaks. A commercial client may pay a premium for same-day treasury reporting they'd never articulate in a survey. A wealth client may value a dedicated advisor far more than the 12 basis points you shaved off a fund fee to win them.

The Kano Model, developed by Noriaki Kano in the 1980s, was built precisely for this: to distinguish features that create satisfaction when present from features that only create dissatisfaction when absent. For a bank, that distinction is the difference between a fee you can raise and a fee that will trigger attrition.

The Kano Model applied to a bank's product and fee stack

Kano sorts every feature or service into five categories. For financial services, they map like this:

• Must-be (Basic): Expected as table stakes. FDIC/deposit insurance, functional online banking, accurate statements, fraud protection. Customers don't reward these — they punish their absence. You almost never win by charging more for these.
• Performance (One-dimensional): Satisfaction rises linearly with quality. Interest rates, transaction limits, transfer speed, cash-back percentages. These are where price competition lives, and where benchmarking matters most.
• Attractive (Delighters): Unexpected features that create outsized satisfaction. Proactive cash-flow insights, instant lending decisions, concierge treasury support, embedded FX tools. These are the most common source of money left on the table — banks build them, then bundle them free.
• Indifferent: Features customers don't care about either way. Legacy account types, rarely-used reporting formats. Cost centers with no pricing power.
• Reverse: Features some segments actively dislike — aggressive cross-sell prompts, mandatory bundles, paper-statement fees applied clumsily.

Running the walkthrough

Step 1 — Inventory your features and fees by segment. Retail, small business, commercial, and wealth clients will classify the same feature differently. Do not analyze them as one book.

Step 2 — Ask the two Kano questions per feature. For each item, survey or interview customers with the functional/dysfunctional pair:

• "How would you feel if this feature were present?"
• "How would you feel if this feature were absent?"

The answer pattern (from "I like it" to "I dislike it") tells you which of the five categories the feature falls into.

Step 3 — Plot and prioritize. You're looking for three moves:

1. Delighters you're giving away → candidates for premium tiers, packaging, or explicit pricing.
2. Performance features priced below the value they deliver → candidates for rate or fee adjustment, tested carefully against elasticity.
3. Indifferent features you overspend on → candidates to cut, freeing margin.

Step 4 — Watch the drift. Kano's most important insight for banks: today's delighter is tomorrow's must-be. Mobile deposit was a delighter a decade ago; now it's basic. If your pricing assumes a feature is still a differentiator, you're overpricing and will lose share.

What "good" looks like: a segmented map showing which fees can rise without attrition, which delighters justify a premium tier, and which spend to reallocate — tied to an elasticity assumption you can defend to a pricing committee or regulator.

How Percision helps — and when a spreadsheet or consultant is enough

Full disclosure: I write for Percision, an AI-powered strategic intelligence platform. Here's an honest read on where it fits.

Percision can run your business context through structured reasoning across 27+ frameworks — including Kano — and produce a board-ready classification of features and fees, scenario analysis on price moves, and financial intelligence (DCF impact, ratio benchmarking, warning-sign flags) in minutes rather than weeks. For a bank running a repricing cycle, that means moving from raw feature inventory to a defensible recommendation deck and Excel model with audit trails quickly. It's a co-pilot, not an autopilot — your treasury, risk, and pricing leads still own the decision and the regulatory framing.

Where Percision earns its place: you need a fast, structured pass across a large product stack; you want scenario modeling on margin impact; or your strategy team is stretched during a planning cycle.

Where it doesn't: if you have a single product line and a clean elasticity dataset, a spreadsheet and one analyst will do. If your challenge is deep regulatory pricing constraint, fair-lending exposure, or a contentious board negotiation, a specialist consultant or compliance counsel is the right call. Percision does not replace primary customer research — the Kano survey data must still come from your actual customers. And no tool should be the sole basis for a fee change that touches consumer-protection rules.

Use the tool to accelerate the classification and modeling. Keep humans on the judgment, the compliance, and the customer conversations.

FAQ

Q: Can the Kano Model tell us exactly how much to raise a fee? No. Kano classifies which features have pricing power. You still need elasticity testing and, for regulated products, compliance review to set the actual number.

Q: How often should a bank redo this analysis? At least annually, and whenever a delighter starts appearing across competitors — that's the signal it's becoming a must-be and losing pricing power.

Q: Do we need customer surveys, or can we infer categories internally? Internal inference is a starting hypothesis only. Kano's power comes from the functional/dysfunctional question pair asked to real customers per segment. Skipping that is the most common way this analysis goes wrong.

If you want to run a Kano-based pricing pass on your product stack quickly, Percision can turn your feature inventory and financials into a board-ready recommendation — with your team in control of every call.