How inflation impacts different businesses
A pricing leader deciding how to pass through this year's cost increases is really choosing among three levers: raise the price, shrink the pack, or cut a quality input. Which lever a segment will tolerate depends on the industry and the SKU tier, not a single company-wide elasticity: construction and transportation absorb cost shocks directly, grocery and CPG shrink the pack, and luxury has leaned on price until buyers started pushing back.
- Construction and transportation absorb direct cost shocks; grocery and CPG shift toward pack architecture; luxury has relied on price increases now meeting buyer resistance.
- Shrinkflation added 12% to 32% in per-unit price by category between 2019 and 2024, averaging 14.8% among major national brands, across a limited set of tracked categories (GAO).
- Quality cuts (skimpflation) draw a harsher consumer penalty than equivalent price or size changes (Journal of Consumer Research), making it the sharper reputational risk.
- Stated price sensitivity in surveys does not predict actual switching behavior; only a randomized choice experiment isolates which lever a segment will actually tolerate.
- Current guidance favors granular, segment-specific pricing, protecting key-value SKUs while passing cost through on secondary items, over blanket increases (McKinsey).
Why does inflation hit industries so differently?
Inflation hits industries differently because each sector has a different cost structure and a different lever available to pass that cost through. Construction and transportation companies face direct input cost shocks in materials, fuel, and labor, with few places to hide the increase, so cost typically flows straight to invoiced price or gets absorbed into thinner margins. Grocery and CPG brands sell packaged goods where the pack itself is variable: they can hold the sticker price and shrink the container instead. That's what happened across major national brands from 2019 to 2024, with per-unit price increases ranging from 12% for paper towels to 32% for coffee, a range measured across a limited set of tracked categories, not the full grocery aisle (GAO). Luxury brands operate in a category where price signals status, and that strategy is now running into buyers who have grown price-sensitive enough to push back. None of these are the same elasticity curve, and none respond to the same lever.
The three pass-through levers: price, pack, and quality
Every business facing input cost inflation chooses among three mechanisms, whether or not the choice is explicit: raise price, shrink the pack, or cut a quality input while holding price and size constant. These are not interchangeable. Shrinkflation is a distinct demand-response channel from a straight price increase, with its own elasticity curve documented in peer-reviewed modeling (Shrinkflation and Consumer Demand, Marketing Science). Skimpflation means cutting an ingredient, a fabric weight, or a service-level detail while holding price and pack constant, and it produces the harshest consumer reaction of the three, because buyers read a quality cut as more unfair than a price hike or a size reduction of the same economic magnitude (Journal of Consumer Research). The lever a business picks changes not just near-term margin but the switching risk it takes on.
Why skimpflation is becoming the sharper reputational risk
Skimpflation is overtaking shrinkflation as the riskier lever because consumers penalize a quality decrease more severely than a size decrease or a price increase of equivalent economic value (Journal of Consumer Research). Shrinkflation is visible on the shelf if a buyer checks the label. A quality cut, a thinner formula, a cheaper input, a shorter service window, often shows up only at the point of use, after the purchase decision is already made. That timing gap makes it reputationally dangerous: buyers feel misled rather than merely price-conscious, and the reaction doesn't surface until real usage, past the point a survey would have caught it.
What the conventional "segment by stated sensitivity" playbook misses
The standard playbook segments customers by self-reported price sensitivity, then raises prices selectively on the segment that claims to care less. It misses that self-reported segments and actual purchase behavior diverge, and that demand isn't moving as one block to begin with. In McKinsey's 2025 data, 75% of consumers report trading down in at least one category while 39% simultaneously intend to splurge in another (McKinsey State of the Consumer 2025). A survey question like "how sensitive are you to a 5% increase" captures attitude, not the causal effect of a specific price or pack change on a specific purchase decision. Willingness-to-pay figures pulled from these surveys run high relative to what buyers actually pay, unless the design is incentive-aligned, because there's no real cost to overstating tolerance on a questionnaire. That gap between attitude and behavior is what made shrinkflation and skimpflation blindsiding so common: brands read survey tolerance for a "small" change and missed how buyers actually reacted to a thinner formula or a smaller box at the point of choice.
How does a randomized choice experiment resolve the say-do gap?
A randomized choice experiment resolves the say-do gap by putting buyers in front of actual product configurations, varied by design, rather than asking them to self-report a hypothetical reaction. The causal identification comes from the randomized manipulation in the experiment design itself, not from the statistical method used to analyze it afterward. Discrete choice models such as McFadden discrete choice, Mixed Logit, and ICLV are the estimators applied to the resulting choice data. A flat logit model carries the independence-of-irrelevant-alternatives assumption, which matters when the question is which SKU a buyer substitutes toward, and Mixed Logit or ICLV relax that assumption when substitution patterns are part of what's being tested. Run properly, this setup separates what a segment says it will tolerate from what it actually does when price, pack, and quality level are each varied independently.
On validation: our best configuration reaches 87% of the measured human ceiling on one study, a 0.832 rank correlation against the published human result, where two independent samples of real humans reach 0.959 against each other. Across all 43 studies that passed the design filters, the mean is 0.73 (causal fidelity paper). That's a replication result against past studies, not a guarantee for a market that hasn't been tested yet. It carries a standing limitation: some of those published studies may sit in a model's training data, which is why the replication protocol runs against held-out design variations rather than treating a single match as proof. A confidence interval produced from a simulated experiment covers the effect within that simulated population; it is not an unconditional bound on what the real market will do. Current benchmark results by method and study are on the leaderboard, and the underlying validation approach is covered in more depth on the methods and validation hub.
Which lever fits which business?
| Lever | Typical industry | How consumers react | Reputational risk | Best for |
|---|---|---|---|---|
| Price increase | Luxury, services, B2B contracts | Visible immediately; tolerated when tied to a clear status or quality signal, resisted once buyers turn price-sensitive (McKinsey) | Moderate, rises sharply once resistance sets in | Segments where price itself signals quality and volume isn't the growth driver |
| Pack shrink (shrinkflation) | Grocery, CPG, household goods | Often unnoticed at purchase, caught later at the shelf or in unit-price comparisons; effect size varies 12% to 32% by category (GAO) | Moderate, mostly a trust cost when discovered, not an immediate switch | Categories with strong per-unit price transparency risk but low willingness to pay a higher sticker price |
| Quality cut (skimpflation) | Grocery, CPG, food service, subscription services | Discovered at use, not at purchase; judged more unfair than an equivalent price or size change (JCR) | Highest of the three; sharpest negative reaction | Nowhere as a default move; only defensible when the change is disclosed and the segment's actual tolerance has been tested |
What should a senior pricing buyer do this quarter?
Current guidance has already moved away from blanket price increases toward granular, segment- and channel-specific pricing: pass cost through on secondary and tertiary items while protecting the key-value SKUs that anchor a buyer's price perception (McKinsey). What that guidance doesn't close is knowing, for your specific segments and SKUs, which of the three levers each group will actually tolerate before it switches, versus what they'd claim to tolerate on a survey. That's a testable question, not a judgment call. Run a randomized choice experiment across price, pack, and quality variants for the SKUs where the decision matters most. Use the discrete choice model output to find where each segment's switching threshold actually sits. Don't infer it from a stated-sensitivity score. Examples of this kind of segment-level pricing work are in the case studies.
As a next step, list the SKUs where you're weighing a price, pack, or quality change this quarter, and run a small choice experiment on the two or three that carry the most switching risk before committing company-wide. If you want a second set of eyes on the design, the team is reachable through meet.