---
id: "action-conduct-wtp-experiments"
type: "action-item"
source_timestamps: ["§ Price"]
tags: ["pricing-strategy", "llm-testing"]
related: ["claim-model-idiosyncrasy", "framework-ai-4ps"]
action: "Run experiments across multiple LLMs to monitor how they characterize your brand's pricing and value."
outcome: "Identifies model-specific undervaluations, allowing marketers to tweak contextual cues to correct AI price perception."
speakers: ["David Dubois", "Allison R. Hess", "John Dawson", "Akansh Jaiswal"]
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-new-29-luxury-brands-optimize-for-ai"
sourceUrl: "https://hbr.org/2026/06/llms-misunderstand-luxury-brands-heres-how-to-optimize-your-marketing-strategy-for-ai"
sourceTitle: "LLMs Misunderstand Luxury Brands. Here’s How to Optimize Your Marketing Strategy for AI."
---
# Conduct AI Willingness-to-Pay Experiments

**Action (Price leg of the [[framework-ai-4ps]]):** Run willingness-to-pay experiments across multiple LLMs to monitor how each characterizes your brand's pricing and value.

**Outcome:** Identifies model-specific undervaluations, allowing marketers to tweak contextual cues to correct AI price perception.

**How:** Systematically prompt different AI assistants ([[entity-chatgpt-5-1]], [[entity-claude-sonnet-4-5]], [[entity-gemini-3-pro]]) to evaluate products and see whether they are labeled "premium," "overpriced," or "good value." Because models have idiosyncratic lenses ([[claim-model-idiosyncrasy]]), the same brand may be valued radically differently across systems. If a model labels a brand "overpriced," inject richer, explicit evidence into the brand's digital ecosystem to justify the price point to *that specific* algorithm. Measurement tooling such as [[entity-org-jellyfish]]'s "share of model" can operationalize this monitoring.