Executive Summary

Running advertising in e-Commerce means making quick decisions and quick decisions need clear data. That's exactly why we built this dashboard.
The goal was simple: bring all our advertising performance data into one place so the team could actually understand what is working and what isn't, without spending half their week just gathering numbers.

The dashboard pulls together: 

• Ads’ spend
• PPC performance
• Sales figures
• Product-level data
• Keyword Analysis
• Top Performing
• Weekly Trends
  

Instead of digging through multiple reports, the team can now open one screen and immediately see how campaigns are performing across products and marketplaces.

The Problem We Were Solving:
Before, the advertising team was drowning in manual work. 
Data lived in different places 

• Amazon Ads reports
• Sales exports
• Internal spreadsheets
  
  

Someone had to stitch it all together every single week. That process alone ate up three to four hours, and by the time the analysis was done, the insights were already a little stale.

There was also a visibility problem at the management level. It was genuinely hard to answer basic questions like -

• Which products are actually profitable?
• Which campaigns are burning through budget without results?
• Where should we be putting more money?

Without a central view, those answers required digging, and decisions got delayed as a result.

How We Built It
The project brought together a few different teams.

• The data analyst handled the
  • Power BI model,
  • Built out the visualizations, and
  • Calculated the core metrics like ACOS, CPC, CTR, and so on. 
• The advertising team defined what they actually needed to see day-to-day.
• E-commerce operations contributed the product and inventory data and management shaped the overall reporting structure.
  
  

Before a single chart was built, we spent time understanding the data itself. The trickiest part was linking advertising data to sales data at the product level accurately  because if that connection is off, every metric downstream is wrong.

What the Dashboard Shows
The finished dashboard is organized into several sections that each answer a specific question.

The Weekly Overview: Table gives an at-a-glance summary of PPC spend, orders, sales, total revenue, and how each of those compares to the previous week. You can spot a trend in seconds rather than building a pivot table.

Top-Performing Products: There's also a section specifically for the ones generating strong sales relative to what's being spent on them. These are the products worth scaling.
 
Bottom-Performers Section: automatically surfaces products with high ACOS and weak conversion, so the team knows exactly where to investigate or pull back.

The Spend-Vs-Sales Chart: tracks whether increasing ad spend is actually translating into more revenue a question that sounds obvious but was surprisingly hard to answer before. 

Campaign-Level Analysis: breaks things down further, showing which campaigns are driving clicks efficiently and which ones are converting those clicks into real sales.


 
The Metrics That Matter
The whole dashboard is built around a handful of KPIs that are used to make decisions:

• ACOS (Advertising Cost of Sales) - how efficiently ad spend is turning into revenue
• CPC (Cost Per Click) - what each click is actually costing
• CTR (Click-Through Rate) - how compelling the ads are to shoppers
• PPC Orders and Sales - the direct output of advertising activity
• Total Orders - the bigger picture of product performance

The Real-World Impact
The time savings alone made this worthwhile. What used to take three to four hours every week now takes minutes. That's roughly ten to twelve hours saved every month, time that can go towards optimizing campaigns instead of just reporting on them.

But the bigger win is strategic. The team discovered products with high sales and low ad spend had real scaling potential. 
They identified campaigns where clicks weren't converting, leading to smarter bidding. And they caught high-ACOS products early, before they quietly drained the budget.

Where It Goes Next
The dashboard is already a core part of how the advertising team operates, but there's more it could do. 

The next phase could include 

• Real-time data via Amazon's SP-API
• Profit tracking that factors in fees and costs
• Keyword-level analysis
• Automated alerts when ACOS crosses a threshold. 
  

Longer term, predictive analytics could help forecast sales and plan budgets more proactively.

Bottom Line
This project is a good example of what happens when you stop treating reporting as an afterthought. By centralizing the data and presenting it clearly, the team went from reactive to proactive catching problems earlier, scaling winners faster, and making better decisions overall. The dashboard didn't just save time; it changed how the team thinks about advertising.