While everyone debates agentic shopping, Amazon’s Rufus is racking up sales

This data matters because Amazon has been making bold claims about Rufus that the industry struggled to verify. On the company’s Q3 2025 earnings call, CEO Andy Jassy reported that Rufus had reached 250 million active customers, with monthly users up 140% year-over-year and interactions up 210%. Customers who used Rufus during a shopping trip were 60% more likely to complete a purchase, he said. And the headline number: Rufus was on track to deliver over $10bn in incremental annualized sales.

The claims got people talking, mostly because nobody could figure out what the numbers actually meant. What defines a Rufus ‘user’? Does ‘sales’ mean Amazon’s net revenue or gross merchandise value? What baseline are we comparing against? Without answers, many industry observers filed the numbers away as corporate PR and moved on. Sensor Tower’s independent research picks up where those questions left off – and suggests Jassy’s claims, while imprecise, may have directionally undersold what’s actually happening.

What Amazon got right

In a more recent interview with The Information, Jassy doubled down on Rufus as Amazon’s answer to third-party AI shopping agents. Physical retail still has advantages in discovery, he acknowledged – the ability to walk in, ask questions, refine those questions, have somebody point you to different things. Jassy argued that Rufus addresses this gap within the digital experience.

On competitors like OpenAI building shopping capabilities, he was diplomatic but pointed: many third-party agents don’t have your buying history, your preferences, or accurate information about pricing and products.

He’s not wrong, as it pertains to on-site shopping assistants. But notice what Amazon did not do with Rufus: add recipe planning, lifestyle content or other features of questionable relevance to the core shopping mission. Amazon recognizes that people come to its platform to shop. The assistant stays focused on that job.

This restraint sets Rufus apart compared with some other retailer AI experiments I’ve observed. Instead of making assistants genuinely useful alternatives to search, unnecessary features get bolted on. The result is bloated tools that don’t actually help shoppers accomplish their primary goal. Amazon avoided that trap by keeping Rufus tightly scoped to what shoppers actually need: finding products, comparing options, answering questions, building confidence to purchase.

The funnel insight that matters

Beyond headline conversion numbers, Sensor Tower identified 10 distinct shopper behavior patterns that reveal when and why shoppers invoke the assistant.

For example…

Search Assistant (~28% of sessions, ~35% conversion): Most common pattern. Shopper does multiple searches, can’t find the right product, turns to Rufus to get unstuck.

Cart Reconsiderer (~7% of sessions, ~50% conversion): Shopper has items in cart, hesitates, invokes Rufus to overcome objections and confirm fit. Late-funnel rescue.

The insight here is that AI-enabled shopping is not one funnel. The ‘Early Decider’ asks Rufus a quick question at the start, gets confidence and converts at 85%. The ‘Research Conversationalist’ engages deeply throughout 150-minute sessions of heavy product comparison, converting at 54%.

Retailers who get this right will design for these patterns, not just adding a chatbot and hoping for the best. ‘Search Assistant’ type behavior needs different support than ‘Cart Reconsiderers.’

A necessary caveat

Before anyone cites ‘3.5x conversion lift’ as proof that AI assistants cause purchases, some methodological caution is warranted.

Rufus usage could correlate with higher intent rather than cause higher conversion. Heavy Amazon shoppers – those most likely to convert regardless – may also be most likely to use Rufus. The research defines ‘Rufus sessions’ as any session with even a brief interaction, which could inflate gaps. And the methodology relies on modeled panel data that may be revised.

This data shows that AI-assisted journeys are tightly linked to high conversion and likely improve outcomes for at least some shopper segments. Consider it a strong directional signal, not a causal proof point.

The next KPI to watch

If Rufus is genuinely improving product understanding and purchase confidence – not just accelerating decisions shoppers would have made anyway – we should see evidence in post-purchase outcomes. Lower return rates. Fewer cancellations. Reduced wrong-item complaints. Fewer exchanges. Especially in high-research categories where the assistant guides complex decisions.

This is the measurement that will prove whether AI actually makes shopping better – or faster. Retailers and brands should start demanding this data split: AI-assisted versus non-assisted post-purchase outcomes. Until we see that analysis, conversion lift remains an incomplete picture.

What this means for retail media

If assistants become primary decision aids, the monetizable unit shifts from search result placement to inclusion in the assistant’s answers. Amazon has already introduced sponsored ad units within Rufus and I’ll be reporting soon on how those early placements are performing for brand advertisers.

But that’s a topic for a future column. The more immediate point is this: we don’t need fully autonomous shopping agents to see meaningful disruption. Rufus isn’t making purchases on anyone’s behalf. It’s not a true ‘agent’ in the technical sense. But it’s already reshaping how a significant share of Amazon shopping sessions unfold – and capturing a disproportionate share of actual purchases.

The disruption starts with question-answering, comparison and confidence-building. It starts when shoppers invoke an assistant to get unstuck, validate a choice or navigate complex research. It starts well before anyone hands their wallet to an algorithm.

Amazon can afford to play the long game. Other retailers watching these numbers should recognize they don’t have that luxury. Everyone’s waiting for the agentic future. Don’t miss what has already changed.

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