01 / Context
The problem and my role
Marketplace data is spread across orders, customers, payments, products, and reviews. A useful report needs consistent definitions and ways to examine the patterns behind a total.
I developed the analytics project around KPI selection, customer and product analysis, time trends, and exportable reporting. The dashboard is a way to explore the analysis and communicate it to a business audience.
Business reviewer
Understand the headline performance and where it varies.
Analyst
Move from an overall pattern into a category, geography, payment, or customer breakdown.
The experience at a glance
- 01Start with a business question
- 02Review the headline measure
- 03Compare relevant breakdowns
- 04Explain the finding and follow-up
02 / Product judgment
The choices that shaped the work
The central question
What does a headline number leave out, and what should someone investigate next?
01Organize the report around business questions
Group headline KPIs with revenue trends, categories, geography, payments, and customer segments.
- Why it matters
- Different breakdowns help distinguish a broad trend from a concentrated pattern.
- The tradeoff
- A visually strong category is a starting point for investigation, not proof of why it performs differently.
02Make the analysis portable
Include CSV and PDF exports alongside the dashboard.
- Why it matters
- People need to review findings, share a report, or check the numbers outside the interface.
- The tradeoff
- The exported view needs enough context for someone who did not explore the dashboard.
03Keep the demonstration available
Use analyzed-data fallback when the optional database is unavailable.
- Why it matters
- A reviewer can still explore the reporting experience.
- The tradeoff
- The portfolio represents a historical dataset and must not be interpreted as a live business feed.
03 / Evidence
Questions the report helps investigate
These are analytical paths supported by the project, rather than claims of an achieved commercial improvement.
| Business question | View to inspect | Interpretation to check |
|---|---|---|
| How does performance change over time? | Revenue and order trends | Compare equivalent periods and check whether volume or order value explains the change. |
| Where is activity concentrated? | Product categories and Brazilian states | Examine the mix and scale before treating a large total as superior performance. |
| How do customers differ? | Customer segments and payment behavior | Review the segment definition and population behind a comparison. |
| What does customer feedback suggest? | Review scores alongside operating measures | Treat the pattern as a question to investigate, not a causal conclusion. |
04 / Evaluation
What I would measure and why
Measures to review or validate next. These describe the evaluation approach, not achieved improvements.
| Measure | What to examine | Why it matters |
|---|---|---|
| Order volume | The number of orders represented by a view. | State the period and order population before comparing results. |
| Revenue and order value | The monetary measures used in the report. | Reconcile the chosen value definition and avoid counting an order repeatedly across related records. |
| Review score | Customer feedback summarized with its response coverage. | Averages need context from the number of reviews and who responded. |
05 / Outcome
Where the work stands
The published project includes KPI summaries, interactive charts, customer and category views, and downloadable reports. It demonstrates an analytical workflow using historical marketplace records; no business uplift is attributed to the project.
What comes next
My takeaway
A useful insight connects a pattern to a decision and makes the uncertainty visible. The chart should help someone ask a better next question.
Based on the AS Intelligence project documentation, dashboard scope, and published historical dataset description.