# Fast Simon's AI discovery agent hit 22% conversion by running dual search rails *Analysis of 50,000 shoppers shows chatbot discovery outperformed standard search when both ran side-by-side.* By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC. Published 2026-06-05. Canonical: https://www.pops4.com/stash/articles/fast-simon-2026-06-05t21-2 Subject: Fast Simon Tags: ai discovery, conversion rate, product merchandising, chat commerce, dual-engine search, exploratory queries --- Fast Simon ran AI shopper agents alongside traditional search bars across its e-commerce client base and logged a **22%** conversion rate from the AI discovery path, according to an analysis of nearly **50,000** shoppers published by Business Insider Markets. The conversion figure measures shoppers who engaged the AI agent, received a product recommendation, and completed a purchase. The company positioned the result as validation for dual-engine merchandising: keeping the existing search infrastructure while layering a conversational agent that handles vague or exploratory queries. The AI agent worked as a chatbot interface asking clarifying questions—budget, use case, recipient—then returned curated product sets instead of keyword-matched grids. Fast Simon's dataset covered multiple product categories and did not isolate a single vertical, so the **22%** rate represents a blended average across apparel, home goods, and other physical-product catalogs. The company did not publish a control-group conversion rate for traditional search in the same cohort, so the **22%** stands as an absolute figure rather than a lift percentage. The mechanism turns on intent refinement. A shopper typing "gift for dad" into a search bar gets a flood of unranked results. The same query to an AI agent triggers a short dialogue: budget range, interests, occasion. The agent narrows the catalog in real time and surfaces three to five options with explanatory copy. Fast Simon's thesis is that this reduces decision fatigue and shortens the path from browse to cart, particularly for shoppers who arrive without a specific SKU in mind. The dual-engine setup hedges the risk: power users who know exactly what they want still use the search bar, while exploratory traffic routes to the agent. A small physical-product brand can run the same play without Fast Simon's platform budget. Install a chat widget—Tidio, Chatbase, or Landbot—and connect it to a simple decision-tree script. Write five to seven clarifying questions for your top product category: Who is this for? What is the occasion? What is your budget? Each answer branch leads to a short product set of two to four items, written as if a human associate picked them. Host the logic in a Google Sheet or Airtable base and update it weekly based on inventory and margin. Cost is under **$50** per month for the chat tool and zero if you write the script yourself. Point the widget at your homepage hero or collection landing pages where intent is still forming. Track the conversion rate separately in Google Analytics by tagging chat-initiated sessions with a UTM parameter. If the agent converts above your site average, expand the question set and add it to email flows and SMS. The broader pattern is segmentation by query confidence. High-confidence shoppers—those searching by model number or exact product name—do not need an agent. Low-confidence shoppers—those browsing by occasion, recipient, or problem—convert better when a system asks questions and shrinks the choice set. The dual-engine model keeps both paths open and routes traffic based on behavior. Fast Simon's data suggests the trade-off is worth it: even if only a fraction of visitors use the agent, that fraction converts at a rate high enough to justify the added complexity. For a brand running paid traffic, improving conversion on exploratory queries means lower cost-per-acquisition without changing the ad creative or audience targeting. The next move is testing the agent on owned channels first—email and SMS—where traffic is warm and tolerance for a conversational interface is higher. Send a campaign with a single question in the subject line: "What kind of gift are you looking for?" and link to the chat widget instead of a collection page. Measure click-to-purchase and compare it to a standard collection-link control. If the agent outperforms, roll it to the site and allocate a percentage of homepage real estate to the widget. The key is treating the agent as a merchandising tool, not a customer-service add-on, and writing the scripts with the same care as product descriptions. ## The takeaway Dual-engine merchandising—AI agent for exploratory queries, search bar for specific ones—converted at **22%** in Fast Simon's 50,000-shopper dataset. --- ## 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