A New York Fed analysis has found that tariffs added 2.9 percentage points to consumer goods prices, giving economists and merchandisers a common number to argue about. The figure matters beyond macroeconomics: it is the kind of precise, attributable claim that AI assistants are likely to retrieve and repeat when shoppers ask why a product costs more. How that number is framed will shape consumer expectations for the next several quarters.
Key Conclusions
A pass-through estimate is a distribution, not a single fact. The 2.9 point average conceals wide variation across categories, and any answer that presents it as a uniform markup will mislead. For brands, the practical implication is to publish category-level explanations with the same rigor as the aggregate, because the aggregate will be quoted at exactly the moment a shopper is comparing two specific items.
This is a generative engine problem before it is a pricing problem. AI answers are assembled from retrievable claims, and a well-sourced single number travels further than a nuanced paragraph. September retail sales rose 6.7% year over yearHigh prices push September retail sales , largely reflecting price, so the question "why is everything more expensive" is being asked at scale. Whoever supplies the clearest sourced explanation controls the narrative, and that includes retailers explaining their own shelf prices.
How a Pass-Through Number Travels
It gets compressed into a headline
Research findings are usually reported as a single figure because a single figure is memorable. The compression strips the conditions: which categories, which time window, which import share. By the time the number reaches a shopper, it has become "tariffs made everything 2.9% more expensive", which is both roughly right and operationally useless. Brands should expect this compression and plan for it rather than fight it.
It becomes a justification
Once a number is public, it becomes available as a justification for any price change, including ones unrelated to tariffs. That is not dishonesty so much as convenience, and it is why price-increase communication needs its own evidence discipline. A brand that cites a tariff figure for a cost increase driven by something else will eventually be caught by a customer who checks.
It shapes elasticity assumptions
If shoppers believe increases are externally caused, they tolerate them longer than increases attributed to margin expansion. That belief has a measurable effect on elasticity, which is why the framing of cost stories affects volume. Retailers that explain cost drivers with category specificity tend to retain more volume than those that repeat a generic inflation line.
Best Practices
Publish category-level sourcing
Instead of one aggregate statement, publish the pass-through estimate by category with the import share and the observation window attached. This gives journalists and AI systems something precise to retrieve, and it reduces the chance that a single number is applied to a product it does not describe. The cost is modest; the benefit is durable.
Separate cost explanation from price defense
Explaining why costs rose is different from arguing that a price is fair. Mixing the two weakens both. Keep cost communication factual and time-bound, and keep value communication focused on what the product delivers. When the two are separated, a price increase can be explained without inviting a debate about margin.
Common Mistakes
The first mistake is quoting an aggregate figure for a specific product. Category variation is large enough that the aggregate can be wrong in both directions. The second mistake is treating a research estimate as a settled fact, when pass-through is measured with assumptions that can change as sourcing shifts.
The third mistake is ignoring how AI assistants will summarize the claim. If a brand's own pages do not contain a clear, sourced statement, the assistant will use someone else's, and that version may be less favorable. The fourth mistake is assuming that consumers do not check. Price-comparison tools and AI shopping agents now make verification cheap, and an unsupported claim is discovered faster than most teams expect.
The Answer Layer as a Pricing Surface
Search and shopping assistants are becoming the first place a price question is asked. When a shopper asks why a category got more expensive, the answer they receive sets the range of acceptable prices before they ever open a product page. That makes the answer layer a pricing surface, and it means the quality of a brand's published evidence has a direct commercial return.
Retailers have a similar opportunity at the category level. A clear, sourced explanation of cost movements, published alongside the assortment, gives the assistant something accurate to quote and gives the shopper a reason to trust the shelf price. Research on Gen Z, Gen Alpha and the AI-ification of shows younger shoppers already relying on AI tools to compare gifts, which means the explanation will be read before the product is seen. Evidence, in this environment, is merchandising.
Summary
The 2.9 point pass-through estimate is useful precisely because it is specific, and dangerous precisely because it will be generalized. Publishing category-level sourcing and separating cost explanation from price defense keeps the number honest and keeps the narrative in the brand's hands.
In an answer-driven shopping journey, the sourced explanation is not a public relations artifact. It is the input that determines what a customer believes a fair price looks like. Brands that publish that input clearly are effectively setting the reference point, and the reference point is where every price negotiation begins.
Data Sources
- New York Fed analysis of tariff pass-through Tariffs pushed up consumer goods prices
- US September retail sales High prices push September retail sales
- Gen Z and Gen Alpha shopping behavior Gen Z, Gen Alpha and the AI-ification of
FAQ
Does the 2.9 point figure apply to every category?
A: No, it is an average; category-level pass-through varies widely depending on import share.
Why do AI assistants repeat the aggregate?
A: Because a single sourced number is more retrievable than a nuanced distribution.
How should brands communicate cost increases?
A: With category-level sourcing, a stated time window and a clear separation from value messaging.
What is the commercial risk of a vague claim?
A: Verification is now cheap, so unsupported claims are discovered quickly and damage trust.
Is the answer layer really a pricing surface?
A: Yes, because the first explanation a shopper receives sets the acceptable price range.
