Price elasticity without the PhD

Our point of view: most businesses don't lack the sophistication to understand their customers' price sensitivity. They lack the habit of asking the question properly.

Every price decision is a bet on elasticity: if we move the price, how much volume do we lose or gain? In practice, that bet is usually placed on instinct, on last year's experience, or on whoever argues most forcefully in the pricing meeting. At the other extreme, some businesses commission an elaborate model, receive a single impressive-looking number, and then struggle to act on it. Both approaches share the same flaw. They treat elasticity as something you either guess or outsource, rather than something you learn.

Fear is the most expensive assumption

The most common elasticity estimate in business is unspoken: "if we put prices up, customers will leave". Sometimes that is true. Often it is a fear inherited from one bad experience years ago, or from a sales team that hears every complaint and none of the silent acceptance. The cost of that fear is invisible, because it shows up as increases that were never tried.

The opposite error is just as real. Price cuts made to "win share" often win volume that would have arrived anyway, or that never pays back the margin given away.

Illustrative example

Illustrative example. A payments business hasn't changed its fees for small merchants in four years. Everyone agrees they are probably too low, but the sales director is certain an increase would trigger an exodus, and nobody can prove otherwise. Meanwhile, the business has years of history it has never examined: a regional price change that went ahead in one country but not another, a fee threshold that happens to split similar merchants into two groups, and thousands of quotes with outcomes recorded in the CRM.

When those records are finally read with care, the picture is calmer than anyone expected. Merchants in the market that saw the earlier increase behaved much like those that didn't. Win rates barely varied across the fee threshold. The evidence doesn't say "raise prices anywhere"; it says some segments are far less sensitive than the business feared, and one is genuinely fragile. That is a far better basis for a decision than either the old fear or a single model output.

What you'd notice

• Price decisions are justified by anecdote: "customers won't accept that".
• Past price changes were never reviewed afterwards against anything comparable.
• Quote outcomes, or reasons for lost deals, are not recorded consistently.
• Elasticity is discussed as one number for the whole business.
• Success is judged on volume or conversion, rarely on margin.
• A model exists, but its outputs aren't used because nobody trusts or understands them.
• The same debate about whether to raise prices comes back every year, unresolved.

Why it matters

Price is the most powerful profit lever most businesses have, and elasticity is what determines whether a price move creates value or destroys it. Learning it properly changes the quality of commercial decisions far beyond any single price change. It makes increases less frightening and cuts less reflexive. It gives boards and investors something more convincing than conviction.

Good evidence does not require a research department. Some of the most elegant public work on price sensitivity, such as the NBER paper Using Big Data to Estimate Consumer Surplus: The Case of Uber, was built on the simple insight that a business's own pricing rules can reveal how customers respond. Ron Kohavi and Stefan Thomke's HBR article The Surprising Power of Online Experiments makes a similar case for disciplined experimentation. Fairness and regulation matter too: in consumer markets, pricing sits alongside rules such as the EU's Unfair Commercial Practices Directive.

Takeaway

Elasticity isn't a number you look up. It is something a business learns, segment by segment, from evidence it often already holds. The businesses that price well aren't the ones with the cleverest model. They're the ones that stopped guessing and started asking the question properly.

Examples marked illustrative are hypothetical. Third-party figures are from the sources linked.