This dashboard brings those questions into one Power BI report. It uses a historical consumer-complaints dataset containing more than 75,000 records, with views across products, geography and time.

Start with the questions

I built the dashboard to make these measures explorable together. Someone looking at one product can inspect its volume and response outcomes without rebuilding the analysis from a spreadsheet each time.

The repository includes the Power BI file, the source CSV and a dashboard image. That makes the analytical artifact available to inspect, rather than leaving the project at a screenshot alone.

Counts need context

A product with the most complaints is not automatically the worst-performing product. It may also have the most customers. The complaint dataset alone does not provide every denominator needed to make that comparison.

Rates need equally careful reading. A timely-response percentage and a dispute percentage answer different questions; neither is a substitute for understanding the underlying cases. Filters change the population being examined, so the selected product and period are part of the interpretation.

This is why I would present the report as a way to locate patterns worth investigating. It does not establish the cause of a complaint or prove that an operational intervention worked.

What I would add next

The next useful addition would be a short metric dictionary beside the report: each measure’s numerator, denominator, exclusions and treatment of missing values. I would also make the dataset’s coverage period and refresh status more prominent.

Those additions would help another person reproduce the reading of a chart. For an analytical portfolio project, that is a stronger improvement than adding more visuals to the same page.

The published result is an interactive report on historical data. No real-time refresh or measured business impact is claimed here.