A comprehensive Business Intelligence solution that transforms historical sales data into actionable strategies and 15-day revenue forecasts.
Sales Performance Overview
Predictive Sales Forecasting
Businesses often struggle to identify hidden sales patterns and profit leakages within large datasets. The objective of this project was to create a dashboard that not only visualizes past performance—analyzing $1.6M in sales—but also prepares the business for future trends through time-series forecasting.
Power BI: Primary tool used for building the comprehensive BI dashboard and dark-themed UI.
Time Series Forecasting: Utilized for predicting sales trends to assist in inventory planning.
Interactive Filters: Implemented for Region and Category deep-dives.
Data Context: Analyzed an open-source retail dataset (22,000+ records) from 2019-2020.
ETL & KPI Development: Transformed raw data into executive-level metrics, ensuring data integrity and calculating KPIs like Average Shipping Time.
Predictive Analytics: Developed a 15-day sales forecast using time-series analysis to bridge the gap between descriptive and predictive reporting.
Design Strategy: Leveraged a clean, professional dark-themed layout optimized for stakeholder scannability and regional deep-dives.
High-Level Performance: Analyzed a total of $1.6M in Sales and $175K in Profit.
Geographic Strengths: Identified California as the leading state with $0.34M in sales.
Segment Trends: The Consumer Segment drives 48% of the business volume.
Payment Preferences: Cash on Delivery (COD) is the most preferred method at 43%.
Category Leaders: Office Supplies lead in overall volume ($0.64M), while Phones and Chairs drive the most sub-category revenue.
Email: [mraza28067@gmail.com]
WhatsApp: [+92-336-1890356]
An interactive Business Intelligence solution designed to track seasonal trends, regional performance, and product profitability to guide strategic inventory and pricing decisions.
E-commerce BI Dashboard Preview
E-commerce businesses often face profit volatility and struggle to align quantity sold with actual profit margins. This project was developed to identify seasonal fluctuations—such as the dip in profitability during May—and to provide a clear view of regional leaders and payment preferences for smarter resource allocation.
Power BI: Primary tool for data modeling and interactive visualization.
Calculated Measures: Developed custom KPIs including Profit and Average Order Value (AOV).
Data Slicers: Implemented dynamic filters for Quarters and States to allow for granular analysis.
Data Context: Analyzed an open-source E-commerce dataset containing transactional records (Sales, Profit, Quantity, and Payment Modes) across various regions.
ETL & Modeling: Established structured table relationships and cleaned raw records in Power BI to ensure data integrity.
Advanced Analytics: Developed custom DAX measures for key metrics like Profit Margins and Average Order Value (AOV).
Visual Storytelling: Applied diverse charting techniques (Donut, Line/Area, and Geographic Maps) to highlight regional trends and performance.
Profit Volatility: Identified a significant seasonal gap, with a peak profit of $10.3K in November contrasted against a loss of -$3.7K in May.
Regional Powerhouses: Maharashtra ($102K) and Madhya Pradesh ($87K) were identified as the top revenue-contributing states.
Profitability Gap: While Clothing accounted for 63% of quantity sold, high-margin products like Printers ($8.6K profit) and Bookcases ($6.5K profit) were identified as the true profitability drivers.
Consumer Trust: Data revealed that 44% of customers prefer Cash on Delivery, highlighting a specific trust pattern compared to the 21% using UPI.
Email: [mraza28067@gmail.com]
WhatsApp: [+92-336-1890356]
An interactive analytical solution that transforms raw transaction data into actionable insights to optimize operational decisions and understand customer behavior.
Coffee Shop Sales Performance Dashboard
Coffee shop businesses often struggle to interpret high-volume transaction data to identify peak hours and underperforming segments. This project aims to bridge that gap by building a dynamic dashboard that visualizes sales performance, footfall trends, and customer behavior metrics to support data-driven business decisions.
Microsoft Excel: Main platform for the interactive dashboard and visualization.
Power Query Editor: Used for robust data connection and automated cleaning.
Power Pivot: Leveraged to enhance the data modeling workflow.
Excel Developer Tools: Utilized for advanced interactivity and dashboard features.
Data Context: Analyzed a publicly available retail dataset containing over 149K transaction records, including sales figures, timestamps, and product categories.
Data Cleaning (ETL): Utilized Power Query Editor to connect to raw data, remove unnecessary white spaces, and handle data transformations to ensure accuracy.
Data Modeling: Leveraged Power Pivot to create structured table relationships and manage complex data calculations efficiently.
Visualization & Design: Built a dynamic visual layer using Line, Bar, and Clustered Column charts to identify seasonal trends and high-growth product categories.
Financial Health: Achieved $698K in Total Sales with a total footfall of 149K customers.
Customer Spending: Identified an Average Bill per Person of $4.69 and an average of 1.44 orders per person.
Peak Operations: Pinpointed the 8 AM – 10 AM morning rush as the peak order time.
Product Performance: Barista Espresso emerged as the top-selling product, while Coffee & Tea was the highest revenue category.
Upselling Opportunity: Identified that Saturday is the lowest sales day and average orders are below expectations, suggesting a need for targeted upselling strategies.
Email: [mraza28067@gmail.com]
WhatsApp: [+92-336-1890356]
This interactive dashboard transforms raw transactional data into actionable business insights to drive operational efficiency and growth.
365 Coffee Cafe Dashboard Preview
The cafe sector generates massive amounts of raw transactional data that often remains underutilized. The objective of this project was to analyze sales patterns, identify peak operational hours, and evaluate product category performance to turn business data into decision-ready visuals.
Microsoft Excel: Primary tool for data processing and visualization.
Pivot Tables & KPI Metrics: Used for data summarization and performance tracking.
Data Context: Analyzed a publicly sourced Open-Source Cafe Sector dataset containing 20,000+ raw transaction records, including sales timestamps, product categories, and store locations.
Data Cleaning (ETL): Performed end-to-end data preparation in Microsoft Excel, including handling missing values, standardizing formats, and structuring data for pivot-ready analysis.
Analytical Approach: Leveraged Time-Series Forecasting to pinpoint peak operational windows (7:00 AM – 10:00 AM), utilized CAGR modeling to quantify a 15.31% sustainable monthly growth trend, and executed Multidimensional Segmentation to isolate high-velocity product categories like Coffee and Bakery, which drive 85% of total volume.
Visualization & Design: Developed a stakeholder-centric interactive dashboard focusing on trend scannability and high-impact KPIs.
Sustainable Growth: The business exhibits a positive upward trend with a 15.31% CAGR-based monthly growth rate.
Operational Optimization: Identified 7:00 AM to 10:00 AM as the peak activity window, representing the highest transaction volume.
Core Revenue Drivers: Coffee, Tea, and Bakery categories account for approximately 126,661 transactions (~85%).
Targeted Growth Opportunity: Analysis revealed that the Branded category is underperforming in the Tipu Sultan Area, contributing only 15.93% of total transactions.
Email: [mraza28067@gmail.com]
WhatsApp: [+92-336-1890356]