Amazon Store Command View

Drone view of your sales footprint.

This page turns your Amazon shipment export into a store cockpit: top-line revenue, order momentum, product mix, and exactly where your demand is concentrated. You can also load a fresh CSV later from the same page.

Load one or more CSVs

Download a ready-made marketplace report

Use the attached sample report to explore the dashboard.

Download sample report Loaded reports: 1 file | sales

Primary market -

Top SKU -

Coverage -

Executive Insights

A quick read on concentration, sales behavior, and product dependence.

-

Store Footprint

How wide your delivery reach is and where most demand comes from.

Product Mix

Revenue split by SKU based on the loaded report.

- Top share

Daily Revenue Pulse

Revenue by shipment date to spot bursts and soft patches.

Weekday Pattern

Which shipping days are pulling the most revenue.

State Ranking

Revenue concentration across destination states.

Top Cities

Highest-volume delivery hotspots in the current file.

City	State	Units

Location Growth Over Time

Which cities are growing, how fast, and over what period.

Monthly Scorecard

Month-by-month revenue, order count, units, and average order value.

Month	Revenue	Orders	Units	AOV

Revenue Quality

A quick read on basket depth, concentration, and monetization quality.

Behind the scenes

How I Built This

An inside look at building an interactive analytics product experience using AI-assisted development workflows.

Process Notes

Project Goal

“I created this dashboard as a user-friendly demo to show how I can use AI tools to turn business data into an interactive product experience.”

Challenges Faced

Tokens were exhausted too fast in the early development phase. To counter this, I researched optimization techniques and pivoted to smart prompting strategies. This allowed subsequent logic changes and layout adjustments to execute smoothly with minimal footprint.

Key Takeaway

“This project shows how I use AI as a product-building partner - not just to generate code, but to move faster from idea to working prototype.”

My Role

The responsibilities I intentionally kept on my side while using AI as a build partner.

Defined the product idea and use case
Structured the dashboard experience
Prepared demo-friendly sample data
Used Codex to build and iterate the prototype
Tested the upload flow, charts, and dashboard states
Deployed the project on Vercel

Tools Used

The stack that supported the build.

Codex ChatGPT HTML CSS JavaScript Vercel CSV Data

01

Define Goal

Defined the target user dashboard goals and layout requirements.

02

Sample Data

Created anonymized, clean sample data representing business transactions.

03

Codex Build

Built the core architecture and first rendering version using Codex.

04

UI Iteration

Refined user interface, spacing, typography, and polished chart readability.

05

Data Flows

Integrated native CSV file uploading and automated sample report download.

06

Deployment

Published the optimized, fast production build directly using Vercel.

AI Helped With

The areas where AI accelerated the work.

Code generation and boilerplate setup
CSV parsing logic algorithms
Rapid visual UI iterations
Debugging tricky layout mismatches
Structured deployment step guidance

I Owned

The decisions and quality checks I kept accountable for.

End-to-end product strategic thinking
Core UX framing and flow decisions
Artistic visual direction and branding colors
Prompt sequence architecture strategy
Final code quality assurance review