Customer Data Descriptive Analysis

I have a full export of our recent customer records and want to understand what the numbers are really saying. The assignment is straightforward: perform a descriptive analysis that tells the story behind our customers—who they are, how they interact with us, and where the main revenue drivers sit.

To get there, I’ll hand over the raw dataset along with basic field explanations. You’ll clean anything that needs it, run the classic descriptive metrics (counts, means, medians, standard deviations, frequency tables), and then translate those findings into clear, decision-ready visuals and narrative commentary. Think summary tables, pivot-style breakouts, and a concise dashboard or slide deck that lets my leadership grasp the insights at a glance.

I don’t mind whether you work in Python with pandas and seaborn, R with tidyverse and ggplot2, or Excel/Power BI—just pick the stack you’re fastest in and document the process so I can replicate or extend it later.

Deliverables must include:
• A tidy version of the dataset used for analysis
• The code or workbook that produces every figure and table
• A short report (PDF or deck) that walks through key findings, insights, and recommended next questions

Clean structure, reproducible work, and crystal-clear visuals will be the acceptance criteria.