The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Analyzed point-of-sale data with Power BI and Excel to spot purchasing trends for a retail shop. The insights led to a 15% increase in upselling opportunities and a 10% rise in average transaction value.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
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.
SaaS Runway & Burn Dashboard for Founders
An interactive dashboard that shows founders how long their cash will last. Change hiring, revenue growth or expenses and see runway, burn and cash-out date update instantly. Built as a demo of the financial dashboards I create for early-stage startups.
financialmodelingfinancialforecastingstartupfinanceData VisualizationFinancial PlanningFinancial AnalysisGoogle SheetsMicrosoft ExcelPython
Letting founders drag a hiring slider and watch the cash-out date move is what makes runway real. A spreadsheet gives them a number; this shows the cost of each decision before they make it.
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