imran841/sales-performance-customer-insights-powerbi

This repository holds the write-up for a Power BI dashboard built as a submission to the EasyTech Talent May 2026 data analysis challenge. The author, Nurudeen O., was given a retail sales dataset and asked to look at it as if working for the shop, with the goal of understanding revenue trends, the effect of discounts, and customer buying habits. The repository itself is mainly documentation that describes the dashboard; the actual Power BI file is the deliverable. The data covers 3,660 transactions between January and November 2024. Each row has a customer ID, a product ID, a product category out of seven options, the original price in Indian rupees, a discount percentage, the final price after discount, the payment method out of five options, and the purchase date. From this the analyst built four headline numbers: total revenue of about 757,000 rupees, the total of 3,660 orders, an average order value of about 207 rupees, and the total amount given away in discounts, around 175,000 rupees. The Power BI report is laid out as two pages. The first page is a sales overview with key performance indicator cards, a monthly revenue trend with a second axis for discounts, a sorted bar chart of revenue by category, and a donut chart of payment methods. The second page focuses on discount analysis and customer behavior, with orders by category, orders by month, a scatter plot of discount percentage versus final price, and a bar chart of the average discount per category. Both pages share slicers for month, category, and payment method, so a viewer can filter all the visuals at once. The findings are summarised in the README. Clothing is the top revenue category, electronics is the lowest, and revenue peaks in April and May then dips in July and August before recovering in October. Payment methods are split fairly evenly across the five options, with credit card slightly ahead. Higher discount percentages line up with lower final prices, suggesting discounts are reducing the take per order without obviously increasing volume. The analyst then turns this into four recommendations. Cap discounts on already strong categories like clothing, books, and home and kitchen at ten to fifteen percent to recover an estimated 40,000 to 60,000 rupees. Run flash sales or loyalty campaigns in July and August to fill the mid-year dip. Boost the electronics category with targeted promotions or warranty offers. Reward credit card and UPI users with cashback to lift order frequency. The README lists the tools and DAX measures used and credits the EasyTech Talent challenge and Easy Technologies Academy.