A total complaint count is a starting point. It does not tell you which products generate the complaints, whether responses arrive on time, or how often customers dispute the outcome.
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
| Question | Report view |
|---|---|
| Where is the workload concentrated? | Complaint counts by product, issue and geography. |
| Are responses timely? | Timely-response and in-progress indicators. |
| What happens after a response? | Dispute and resolution measures. |
| Is the pattern changing? | Monthly views and filters. |
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.