# Michaels reports 2x conversion from Google Gemini search assistant over keyword search *The craft retailer replaced its site search bar with conversational AI and saw conversion rates double, per company data.* By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC. Published 2026-07-25. Canonical: https://www.pops4.com/stash/articles/michaels-2026-07-25t12-2 Subject: Michaels Tags: ai search, conversion rate, site search, conversational commerce, product discovery, craft retail --- Michaels deployed a Google Gemini-powered conversational AI assistant to replace its traditional site search and reported a doubling of conversion rate compared to keyword search, according to Modern Retail. The craft retailer launched the assistant as its primary search interface, letting customers type questions in natural language instead of keywords. The mechanics are straightforward. The assistant interprets conversational queries like "what do I need for a birthday wreath" and returns product recommendations with context. It replaces the standard search box that parsed keywords and returned SKU-level results. Michaels integrated the tool using Google's Vertex AI platform and trained it on its catalog, category structure, and historical search data. The interface sits where the old search bar lived, and the user experience is a chat window that surfaces products mid-conversation. The mechanism behind the **2x conversion lift** is specificity at the point of intent. Keyword search returns matches for words. Conversational AI infers project scope and recommends a bundle. A customer typing "wreath supplies" into keyword search sees hundreds of wreath forms, floral stems, and wire. The same customer asking the AI assistant "what do I need to make a spring wreath" gets a curated list of foam base, faux greenery, ribbon, and glue, with quantities. The friction drops from browse-filter-guess to receive-add-checkout. The AI collapses the consideration phase into a single interaction, and more browsers convert because the cognitive load to purchase falls. The secondary mechanism is capture of ambiguous or exploratory intent. Traditional search punishes vague queries with poor results. A query like "kids craft" returns thousands of SKUs with no ranked relevance. Conversational AI asks clarifying questions: age range, occasion, skill level. It turns a dead-end search into a guided discovery session. Michaels keeps the customer in the funnel instead of losing them to frustration or exit. Every refined question is another chance to route the customer toward checkout. The steal for a small physical-product brand is accessible because the underlying technology has commodified. You do not need Google's enterprise contract. Shopify has a native AI search app called Searchspring or Algolia NeuralSearch. BigCommerce and WooCommerce support similar plugins. A one-person brand installs the app, connects the catalog, and writes a **10-15 prompt examples** that model how customers ask questions about your category. If you sell coffee equipment, the prompts are "what grinder do I need for espresso" and "beginner setup under two hundred dollars." The AI learns the pattern and generalizes. Cost is **$50-$150 per month** depending on SKU count and query volume. You replace the default search bar with the AI widget, run it for **30 days**, and compare conversion rate on search traffic versus the prior period. If the lift is **20 percent or better**, the tool pays for itself in margin. The tactic works best when your product requires assembly, compatibility decisions, or project planning. If you sell modular shelving, the AI can ask room dimensions and usage and recommend the bracket count and board lengths. If you sell hydroponic supplies, it can query grow space and plant type and return a starter kit. The more your product demands education pre-purchase, the larger the conversion gain from conversational search. Michaels sells project-based product where the customer often does not know the full bill of materials. That uncertainty is where conversational AI has the highest leverage. The risk is training quality and hallucination. The AI must be trained on accurate product data and cannot recommend SKUs you do not carry. Most plugins handle this by restricting the AI to your catalog and disabling freeform generation. You feed it product descriptions, categories, and variant data. It matches and ranks but does not invent. Test the assistant with **20 common questions** before you make it the default search. If it returns wrong products or dead links, the conversion rate will fall, not rise. The tool only works when the answers are correct and the products are in stock. Michaels is a **$5 billion retailer** with enterprise resources, but the play is platform-agnostic. A small brand with **100 SKUs** and **$20,000 monthly revenue** can deploy the same conversational search interface in an afternoon using off-the-shelf tools. The lift will vary by category, but the mechanism holds: reduce cognitive load, capture ambiguous intent, guide the customer to a complete solution. The customer who previously bounced after a vague keyword search now converts because the AI asked the clarifying question and assembled the cart. ## The takeaway Conversational AI search cuts browse-to-buy friction by inferring project scope and bundling products mid-query instead of returning keyword matches. --- ## Publisher **Hako Shikin LLC** — Virginia Beach, Virginia. Founded 1997. ASI 217876 · DUNS 18-204-6339. Principal and author: **Jenny Huang Goodman MPA MSc MHSA**. - Author: https://www.huanggoodman.com/about - LLM context: https://www.pops4.com/stash/llms.txt - MCP endpoint, for AI agents: https://mcp.pops4.com/mcp - Client dashboard: https://dashboard.pops4.com/ - Catalogue: 70,000+ products, 200+ brands