Overview

During the Black Friday weekend of 2025, Amazon’s AI‑driven shopping assistant Rufus generated a dramatic lift in conversion. Sessions where shoppers interacted with the chatbot saw purchase completions double compared with the prior month’s average, signaling that AI‑mediated guidance is becoming a core part of the buying journey. Sellers who want their products to appear in these high‑value interactions must adapt their listings, reviews, and metadata now.

Key Points

• Purchase completions doubled — Sessions that engaged Rufus recorded a 100 % increase in completed orders versus the 30‑day trailing average, far outpacing overall marketplace growth.
• 75 % day‑over‑day surge on Black Friday — Rufus‑assisted visits grew purchases by 75 % on the peak day, while non‑assistant sessions managed only a 35 % rise.
• Five‑to‑one performance gap — The uplift generated by Rufus was five times higher than the 20 % lift observed in sessions without the assistant.
• Reduced cart abandonment — Real‑time answers and side‑by‑side product comparisons cut hesitation, leading to noticeably lower abandonment rates in AI‑driven sessions.
• Content‑driven recommendations — Rufus pulls data from listings, reviews, and Q&A, meaning richer product information directly translates into higher visibility and sales.

How Rufus Changed Shopping Behavior

1. Instant data synthesis — When a shopper asks, “Is this blender dishwasher‑safe?” Rufus scans the product description, bullet points, and Q&A, then delivers a concise answer in seconds. A buyer who would otherwise open three separate tabs now receives the needed detail instantly, accelerating the decision.
2. Side‑by‑side comparison — A user comparing two cordless vacuums can request a feature matrix. Rufus extracts specifications from both listings and presents a table that highlights battery life, suction power, and price, eliminating the manual research step. This rapid comparison often tips the purchase toward the product with the clearer, more complete data.
3. — If a shopper indicates a preference for “budget‑friendly kitchen gadgets,” Rufus filters the catalog using price, rating, and relevance signals, then suggests a shortlist. A shopper who might have scrolled through dozens of items now receives a curated set, increasing the likelihood of a click‑through and checkout.