Mini-lecture: Conjointly's guide to inflation
Inflation re-entered the forecasting conversation in 2026, and a pricing or insights leader deciding how to respond needs one thing Conjointly's mini-lecture on inflation doesn't deliver: a causal number. Conjointly's guide walks through the standard toolkit for tracking and reacting to price pressure, sentiment indices, Van Westendorp, Gabor-Granger, conjoint pricing studies. That toolkit tells you what people say about prices; it does not tell you what a specific price increase does to demand, with a confidence interval attached. This mini-lecture covers the same ground Conjointly's does, and adds the step it skips: a randomized experiment that returns a causal price elasticity instead of a stated-preference guess.
- Conjointly's guide, like most pricing-research vendors in 2026, recommends higher-frequency sentiment tracking plus Van Westendorp, Gabor-Granger, or conjoint studies to find new price points as costs rise.
- Those tools measure stated intent, not an actual reallocation of spending, and the underlying sentiment signal is itself documented as biased (ECB working paper 2642).
- The decision this article owns: replace the pricing guess with a randomized experiment, analyzed with discrete choice models, that returns a causal elasticity and a confidence interval.
- On the one published Subconscious causal-fidelity benchmark, the best configuration reaches 87% of the measured human ceiling on a single study (0.832 against 0.959); the mean across all 43 studies that passed design filters is 0.73.
- Next step: name the price move you actually need decided this quarter, and price a randomized test of it before committing budget to another sentiment read.
What Conjointly's mini-lecture gets right
The conventional advice is not wrong, it is incomplete. Tracking inflation sentiment at higher frequency than a quarterly index makes sense when prices are moving fast: the Conference Board, the University of Michigan survey, and the NY Fed's Survey of Consumer Expectations exist precisely because quarterly cadence is too slow for a live cost shock. Layering a Van Westendorp or Gabor-Granger study on top to probe new price points, sometimes alongside a conjoint-based portfolio trim of underperforming SKUs, is standard practice across pricing-research vendors in 2026. None of that is a mistake. The mistake is stopping there and treating the output as a demand forecast.
What sentiment trackers and stated-preference tools can't tell you
Sentiment and stated-preference studies measure how people talk about prices, not how they would reallocate spending at a specific price point, and the talk itself is a documented biased signal. ECB research on consumer inflation expectations finds that average consumer inflation perceptions run systematically above actual inflation, with an attention asymmetry: price increases get weighted more heavily than decreases when people form their estimate (ECB working paper 2642). A Van Westendorp curve built on top of that biased perception inherits the bias. Any willingness-to-pay number it produces should carry its own caveat: stated willingness to pay runs high relative to incentive-aligned or revealed-preference designs, because a survey respondent faces no real budget constraint when answering. A sentiment index or a stated-preference curve can tell you that people feel prices are rising faster than they are. It cannot tell you what share of demand a $2 increase moves versus a $1 increase, because it never ran the counterfactual.
Why is inflation a live forecasting problem again in 2026?
Inflation stopped being a settled, post-2023 story this year because both the direction and the monitoring toolkit are under active revision. PIIE flags renewed upside risk to US inflation in 2026, reframing it as an open forecasting problem rather than a resolved one (PIIE, 2026). RBC Economics published a 2026 practitioner framework for which price indicators to watch and how often, which is itself evidence that the standard monitoring cadence needed retooling this year (RBC Economics, 2026). For a buyer, that combination means the sentiment-tracking half of the conventional playbook is getting more attention right now, not less, which makes it easy to mistake a faster read of sentiment for a better read of demand.
Can a language model replace the consumer panel?
Not on its own, at least not yet, and the Bank of England has already tested it. A 2026 Bank of England working paper ran GPT-3.5 against the Bank's own Inflation Attitudes Survey and found the model replicates some household-level regularities, income, housing tenure, food-price sensitivity, but concluded the model "lacks a consistent model of consumer price inflation" (Bank of England, 2026). That result came from matching aggregate perception patterns, not from a causal validation design: no randomized manipulation, no held-out study, no confidence interval on the match. It shows a language model can echo how people describe inflation. It does not show a language model, or any synthetic panel, can substitute for a designed experiment that isolates what a price change does to choice.
The one decision: measure a causal elasticity, not a sentiment score
The decision this article is arguing for is narrow: run a randomized experiment on the specific price move under consideration, and analyze it with a discrete choice model, so the result is a causal elasticity with a confidence interval rather than a stated preference. McFadden discrete choice, Mixed Logit, and ICLV are estimators. They are not, on their own, causal methods; the identification comes from randomizing the price or attribute levels each simulated respondent sees, not from the model that reads the resulting choices. That distinction matters when the model is a flat multinomial logit, which assumes independence of irrelevant alternatives: add a near-substitute SKU at the new price point and a flat logit can still hold implausibly stable shares between the existing options. Mixed Logit relaxes that assumption by letting preference weights vary across the simulated population, which is why it is the workhorse for substitution questions, not the plain logit.
| Sentiment tracker | Stated-preference pricing study | Randomized experiment, discrete choice model | |
|---|---|---|---|
| What it measures | How people describe expected inflation | What respondents claim they'd pay or tolerate | What a randomized price or attribute change moves in choice |
| Output | An index level or trend | A hypothetical price point or acceptable range | A causal elasticity with a confidence interval |
| Causal identification | None | None; no counterfactual price is tested | From the randomized manipulation in the design |
| Known bias | Overestimates inflation, weights increases over decreases (ECB) | Stated WTP runs high absent a real budget constraint | Requires validation against a held-out human study |
| Best for: | Watching direction and speed of perceived inflation | Rough price-band screening early in a pricing project | Deciding a specific price move with a number you can defend to finance |
How much should a buyer trust a simulated elasticity?
Enough to act on, with the limitation stated plainly, not enough to treat as a guarantee for a market that hasn't been tested. The only published fidelity result here is a ratio against a measured human ceiling, not a bare percentage: on the best-performing configuration for a single study, the simulated result reaches a 0.832 rank correlation against the published human result, against a 0.959 correlation between two independent samples of real humans, an 87% ratio to that measured ceiling. Across all 43 studies that passed the paper's design filters, the mean drops to 0.73 (causal fidelity paper).
Two limitations belong next to that number, not below it. First, published studies used for validation can sit inside a model's training data; the replication protocol holds out entire studies rather than samples drawn from within a study specifically to catch that failure mode, but the risk is not eliminated by naming it. Second, a confidence interval produced by a simulated experiment covers the effect within that simulated population and design; it is a validation result on past studies, not a guarantee that it transfers to a market that has never been tested. The current full set of tested markets and methods is public on the leaderboard, which is the right place to check before assuming a given category has been validated.
Sentiment trackers and stated-preference studies will keep their place for watching direction and screening rough price bands. But the decision a senior buyer actually has to defend this year, how demand responds to a specific dollar move on a specific SKU, needs a randomized experiment analyzed with a discrete choice model, not another mood read. Start by writing down the exact price change under consideration and the elasticity you'd need to see before approving it, then check the methods and validation hub for how that kind of test is structured before running it. If it's easier to talk through the specific decision first, book time.