Why stated importance fails
Asking respondents to rate feature importance produces socially desirable, undifferentiated scores. Everything is "important." The method measures salience, not preference.
Forced trade-offs between realistic bundles reveal preference — rating scales do not.
Insight
Table-stakes trap
Teams that rely on stated importance over-invest in features everyone rates highly and under-invest in differentiators that actually drive choice at realistic price points.
Product teams that rely on stated importance alone often over-invest in table-stakes features and under-invest in differentiators that actually drive choice.
Trade-off design fundamentals
Conjoint and discrete choice methods force trade-offs: bundles of attributes at realistic price points, with experimental design that separates main effects.
3–5 each
Attribute levels
8–12
Tasks per respondent
2+
Holdout concepts
Fantasy bundles respondents would never see in market. Too many attributes causing cognitive overload. Sample that includes non-buyers for a product-only decision. Missing holdout validation concepts. Any one of these produces precise nonsense.
Instrument quality depends on attribute selection, level realism, and sample definition. A conjoint on the wrong sample or with fantasy bundles produces precise nonsense.
When to use each method
Use Van Westendorp and Gabor-Granger when the decision is primarily price-led. Use choice-based conjoint when feature bundles and willingness-to-pay for combinations matter.
Price sensitivity curves from Van Westendorp anchor conjoint price levels.
Insight
Conjoint tells you what; qual tells you why
Adaptive interviewing after conjoint explains the narrative behind trade-offs — especially when a segment chooses unexpectedly or when price sensitivity breaks the model.
Use adaptive interviewing when you need the "why" behind the trade-off — conjoint tells you what they choose; qual tells you what the choice means.
Integrating quant and qual
Our strongest pricing engagements triangulate: conjoint for structure, adaptive interviews for narrative, and behavioral data where available for validation.
Quant structure + qual narrative + behavioral validation = decision-grade pricing.
“A simulator without limits is a liability. A recommendation with limits is an asset.”
Insight
Ship limits with the model
Every deliverable includes segment-level holdout accuracy, confidence bands, and explicit notes on where the model breaks — so leadership knows when to trust the numbers and when to run a follow-up wave.
The deliverable is not a simulator alone — it is a recommendation with explicit limits, confidence grades, and the segments where the model holds versus breaks.
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