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Last Prime Day, ad spend surged 283% compared to the two weeks before the event, but revenue grew only 183% over the same period (CIQ). Brands spent more and got less in return.
But not every brand ended the event in the same position. A divide emerged between the brands still running the old playbook and the early adopters of agentic retail. Share incrementality results.
And the stakes are higher this year as Rufus is influencing more sales.
Those who got ahead last year will likely dominate this Prime Day, too. Fortunately, for those who didn’t, it isn't too late to get on board with agentic retail — if they start now.
Agentic retail isn’t something you simply switch on. AI agents need time to learn a brand's context (retailer rules, category trends, content gaps, and brand goals) before they can monitor, recommend, and execute at the level Prime Day demands.
Brands are approaching agentic retail as if it were new software (purchase it, plug it in, expect immediate results). Agentic retail requires a contextual foundation before agents can act. There is a higher lift, but its a much higher reward. You’re not just getting data. You’re getting outcomes.
Anyone who's used ChatGPT or any LLM knows the output is only as good as the context you give it. Agents are no different. The more time they spend learning your business, the sharper their recommendations get.
AI agents surveybring macro context (economic shifts, pricing trends, buying behavior), retailer context (Amazon suppressing listings with fewer than three images, Walmart ranking shorter titles higher), and category context (trending products, top-searched claims, competitor moves). What takes time for these agents to learn is determining correlation and causality between signals and outcomes, and then applying that to brand specific goals: on top (sales, share, profit) and how you operate
Before agents can start acting autonomously, they need human oversight and input to review early recommendations, correct misreads, and refine the guardrails within which they operate. That back-and-forth improves the agent until the team trusts it to execute on its own.
To make this all happen, a brand needs to work with a solution that provides a Forward Deployed Engineer that wires internal systems together and generates outcomes that align to your workflow.
An agent learns from every action it takes and the feedback you provide, informing its next recommendation, and the one after that. A brand with well-trained agents walks into Prime Day with months of compounding intelligence behind every decision.
A traditional media agency using rules-based automation runs roughly 100 optimizations per day. An agent runs 4,000-plus. But those 4,000 optimizations are only as good as the context behind them, which is why the brands that invested in providing that insight months in advance of big sales events like Prime Day will have a fundamentally different outcome than those that didn't.
While a competitor is rushing to manually update their top SKUs the week before Prime Day, a brand running trained agents is already optimizing across its full catalog.
Last year's Prime Day proved that spending more doesn't guarantee earning more.
Agentic retail gives CPG brands a way to operate at the speed Prime Day demands. But that advantage doesn't reach its full potential when brands flip the “on” switch the week before.
The brands that start now will spend Prime Day executing across their full catalog with agents that already have the context needed to make an impact.
