How I Built an End-to-End Sales Analytics Dashboard in Excel for an E-commerce Company I recently completed an end-to-end sales analytics project using Microsoft Excel, transforming raw e-commerce sales data into an interactive dashboard that provides meaningful business insights. In this project, I explored the data, performed analysis, identified key sales trends, and built an interactive dashboard to help businesses make data-driven decisions. I documented the entire process, including the approach, tools, analysis, and dashboard development.

Read the full project walkthrough on Medium: https://medium.com/@Refino/how-i-built-an-end-to-end-sales-analytics-dashboard-in-excel-for-an-e-commerce-company-e8692d81f354

Excellent work! Turning raw data into actionable business insights is a valuable skill.

PROJECT Analyzed a telecom customer churn dataset of 7,043 customers and built five Excel deliverables: a churn count chart, a monthly charges histogram, a gender-based churn comparison, a tenure vs monthly charges heatmap, and sorted and filtered dataset views.

PROBLEMS SOLVED • Identified that saving the raw file as .csv was causing data loss, then moved all work to an .xlsx workbook to protect multi-sheet analysis • Investigated blank Total Charges values and traced them to new customers with 0 months of tenure who had not yet been billed • Turned hundreds of unique tenure and charge values into readable bands (12-month tenure groups, 20-unit charge groups) so a heatmap could be built • Used pivot tables to compare churn counts across categories instead of counting manually

RESULTS • Overall churn: 1,869 of 7,043 customers (about 26.5%) • Gender: churn was nearly equal, 26.9% for female and 26.2% for male customers • Tenure: 999 of the 1,869 churned customers (about 53%) left within their first 12 months • Heatmap: churn was most concentrated among short-tenure customers paying mid-to-high monthly charges • Delivered formatted charts with titles, axis labels, data labels and distinct colors, plus a video walkthrough

SKILLS: Excel, Pivot Tables, Conditional Formatting, Data Cleaning, Data Visualization, Data Interpretation, Churn Analysis