Marketing AI Agent
Helping Amazon sellers optimize their marketing and promotion budget and performance through AI-powered insights and automated actions.
Marketing Management Today is Overly Complex
- 1
Understanding performance is complex — Sellers have to navigate back and forth across different areas of the platform to piece together how they're performing. There's no single place to get a clear picture.
- 2
Over-reliance on third-party software and consultants — Sellers pay for costly external tools and consultants to piece together insights that should be available natively in Seller Central.
- 3
Limited access to benchmarking data — Sellers can't compare their marketing spend and performance against competitors in their category.
- 4
No clear path to optimization — Without a consolidated view of performance, sellers struggle to know where to invest their budget and which promotions are actually driving ROI.
How might we help Amazon sellers optimize their marketing spend to maximize ROI?
The goal was to build an AI agent that would turn fragmented, complex marketing data into a clear, actionable experience.
Make past performance easy to understand — sellers can clearly see how their marketing has performed.
Enable competitive benchmarking — sellers can compare their performance against industry competitors.
Extract actionable insights — complex data distilled into clear, digestible next steps.
Recommend next best actions — sellers understand exactly what steps to take to optimize their marketing.
Sketching the solution space
I started by sketching wireframes to explore two fundamentally different interface models: a dashboard-based approach that surfaced insights and recommendations in a structured view, and a conversational chat model where sellers could interact with the agent directly. A third dimension we explored was levels of autonomy — how much the agent should act on its own versus always requiring seller approval.
We pressure-tested these directions through continuous seller interviews and stakeholder feedback sessions.
Testing with sellers uncovered three critical needs
Conversation and dynamic data visualization — Sellers needed more than a static dashboard. Conversation is the necessary medium for fully understanding performance data — the agent uses dialogue and dynamic visualizations to surface insights, and sellers can ask follow-up questions to dig into the areas they care about most.
Explainability is non-negotiable — Without transparency into the reasoning behind each suggestion, sellers wouldn't trust the system no matter how accurate it was.
Approval and control matter most — Sellers were hesitant about AI acting autonomously. Reviewing and approving before any action was essential to building trust.
Designed around trust — from insight to action
Three core features work together: conversation enables dialogue, transparency ensures understanding, approval maintains agency.
Conversational Interface
The conversational interface is designed for deep dive. Sellers can go back and forth with the agent, asking follow-up questions and drilling into the areas they have the most questions about — doing as much or as little analysis as they want, at their own pace.
Transparent Recommendations
Recommendations include the suggested settings and inputs for each promotion type, and the agent explains the details of how each input was decided.
Seller Approval & Control
The agent proposes, but sellers always review and approve before action. This maintains control and builds the trust necessary for long-term adoption.
See the agent in action
A walkthrough of the end-to-end seller experience — from reviewing insights to approving actions.
In beta — early signals are strong
The Marketing AI Agent is currently in beta launch with early adopter sellers. Early feedback shows sellers appreciate the conversational experience and feel confident reviewing recommended actions.
We're tracking action adoption rate, approval rates, and marketing ROI improvement as the primary success metrics going forward.