This repository contains a comprehensive case study analyzing customer patterns across Brazil using BigQuery and SQL. The insights drawn from the analysis aim to inform strategic decisions and enhance operational efficiency for businesses.
For an in-depth look at the analysis, insights, and recommendations, read the full case study on Medium:
- Significant increase in order volume from October 2016 to November 2017, with clear seasonal trends.
- Brazilian customers primarily place orders in the afternoon, with notable activity in nighttime as well.
- São Paulo has the largest customer base, followed by Rio de Janeiro and Minas Gerais.
- São Paulo leads in both total and average order prices, while other states show varying price levels.
- States like Roraima and Amapá have longer average delivery times, while São Paulo boasts the shortest.
- Sustain growth strategies observed from October 2016 to November 2017.
- Launch targeted marketing campaigns during peak months (May to August).
- Conduct monthly analyses for better planning and resource allocation.
- Investigate anomalies in order volume during specific months.
- Optimize operations to handle fluctuations in order volume.
- Enhance customer engagement to retain and attract customers.
- BigQuery
- SQL
- Data Analysis
- Data Visualization
Feel free to explore the repository, run the code, and gain insights from the analysis!