---
id: "action-analyze-user-prompts"
type: "action-item"
source_timestamps: ["\\\"§ Understand the prompt", "not just the agent.\\\""]
tags: ["consumer-research", "seo"]
related: ["concept-prompt-driven-optimization", "framework-ai-commerce-adaptation"]
action: "Study common consumer prompt structures in your category to optimize product data for specific mandates."
outcome: "Ensures products surface favorably for the exact parameters users are commanding their agents to optimize for."
speakers: ["Jafar Sabbah", "Oguz A. Acar"]
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-tier2-06-ai-shopping-agents"
sourceUrl: "https://hbr.org/2026/05/research-traditional-marketing-doesnt-work-on-ai-shopping-agents"
sourceTitle: "Research: Traditional Marketing Doesn’t Work on AI Shopping Agents"
---
# Analyze common user prompt structures

**Action:** Study common consumer prompt structures in your category to optimize product data for specific mandates.

**Do this:** Conduct consumer research, analyze query patterns, or partner with AI platforms to understand exactly how customers instruct their agents. Knowing whether users ask for "the best reviewed under $100" versus "the cheapest that ships tomorrow" dictates how you should structure your product data (see [[concept-prompt-driven-optimization]]).

**Expected outcome:** Ensures products surface favorably for the exact parameters users are commanding their agents to optimize for.

**Framework position:** Step 4 of the [[framework-ai-commerce-adaptation|AI-Centric E-Commerce Adaptation Strategy]].

**Related:** [[concept-prompt-driven-optimization]] · [[quote-agent-mandate]] · [[framework-ai-commerce-adaptation]]