# Michaels doubled conversion with a Google Gemini AI assistant versus traditional site search *The crafts retailer replaced its standard search bar with a conversational assistant and saw conversion rate climb 2x.* 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-2026-07-25t09-1 Subject: Michaels Tags: ai search, conversion rate, site experience, product discovery, chatbot, ecommerce --- Michaels deployed a Google Gemini-powered AI assistant on its e-commerce site and reported a **doubling** of conversion rate compared to its traditional search function, according to Modern Retail. The crafts retailer replaced the standard keyword search bar with a conversational interface that interprets natural-language queries and suggests products in context. The assistant does not merely return SKU matches. It processes loose intent—"supplies for my daughter's birthday piñata"—and assembles a curated list with product images, quantities, and add-on suggestions. The system runs on Google's Gemini model, fine-tuned on Michaels' catalog and transaction history, and surfaces responses in under two seconds. The company did not disclose absolute revenue figures or the time window measured, but executives confirmed the metric in public remarks tied to the rollout. The mechanism works because physical-product buyers often lack the vocabulary vendors use. A shopper hunting for scrapbook adhesive may type "sticky stuff for photos" or "glue that won't yellow." Traditional search engines tokenize those phrases and return sparse or irrelevant results, forcing the visitor to refine queries or abandon the funnel. The AI assistant parses intent, maps it to attributes in the product graph—archival quality, pH neutral, photo-safe—and returns the correct adhesive category without requiring the shopper to know the term "acid-free tape runner." Each successful match shortens time-to-cart and raises the probability of checkout. The second driver is context expansion. When a customer asks for "everything I need to make candles," the assistant generates a kit list: wax, wicks, fragrance oil, pouring pot, thermometer, molds. Traditional search would require six separate queries. The assistant bundles them in one interaction, increasing average order value and reducing the cognitive load that causes cart abandonment. Michaels benefits from higher units per transaction; the shopper benefits from not forgetting the thermometer. A small physical-product brand can run the same play without a Google partnership. Install an AI chat widget on your product page—tools like Chatbase, CustomGPT, or Tidio's Lyro cost **$20 to $80 per month** and connect to your Shopify or WooCommerce catalog via API. Feed the model your product descriptions, FAQs, and a dozen sample customer questions. Write a two-sentence prompt: "You are a helpful assistant for [Brand]. When a customer asks a question, recommend specific products from our catalog and explain why they fit the need." Deploy the widget in the same screen real estate as your search bar. Track conversion rate for visitors who use the assistant versus those who use standard search or browse by category. If the assistant outperforms by **20 percent or more** after two weeks, make it the default and demote the legacy search to a fallback link. Keep the training current. Every month, export your top twenty support tickets and five-star product reviews, then paste them into the assistant's knowledge base. The model learns the language your customers actually use—"doesn't scratch my vinyl," "arrived fast for my event"—and mirrors it back in recommendations. Budget ninety minutes per month for this refresh. The return is a search experience that converts intent into cart without requiring the visitor to guess your taxonomy. Michaels proved the concept at tens of millions in annual search traffic. You prove it at three hundred visitors a week. The playbook scales down clean: replace friction with conversation, measure the lift, and feed the system real customer language until it sells like your best employee. ## The takeaway AI assistants convert product intent faster than keyword search by interpreting loose language and bundling multi-item needs in one interaction. --- ## 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