FNP Sales Analysis Dashboard – Excel Developed an interactive Sales Analysis Dashboard in Microsoft Excel to analyze order performance, revenue trends, customer spending, product performance, and delivery efficiency. The dashboard provides a comprehensive view of sales data through dynamic KPIs, charts, filters, and time-based analysis. Key Features & Analysis

Sales Performance KPIs: Tracked 1,000 total orders, ₹35.20 lakh total revenue, average customer spending of ₹3,520.98, and average order-delivery time of 5.53 days.

Revenue by Occasion: Analyzed revenue generated across occasions such as Anniversary, Birthday, Diwali, Holi, Raksha Bandhan, and Valentine's Day.

Revenue by Category: Compared sales performance across product categories including Cakes, Colors, Mugs, Plants, Raksha Bandhan products, Soft Toys, and Sweets.

Revenue by Hour: Examined order-time patterns to identify hours with higher revenue generation.

Monthly Revenue Analysis: Analyzed revenue trends across different months to identify seasonal fluctuations and peak-performing periods.

Top 5 Products: Identified the highest-revenue-generating products, including Deserunt Box, Dolores Gift, Harum Pack, Magnam Set, and Quia Gift.

Top 10 Cities: Compared order volumes across cities such as Bharara, Bidhanagar, Bilaspur, Dhanbad, Dibrugarh, Gujratkal, Haridwar, Imphal, Kavali, and North Dumdum.

Interactive Filters: Added filters for Order Date, Delivery Date, and Occasion, allowing users to dynamically explore specific periods and sales segments.

Dashboard Design: Created a user-friendly layout with KPI cards, charts, slicers/filters, and interactive visualizations for quick business insights. Tools & Skills Microsoft Excel | Data Analysis | Data Visualization | Dashboard Development | Pivot Tables/Charts | Interactive Filters Project Objective The primary objective of this project was to transform raw sales data into an interactive Excel dashboard that enables users to monitor sales performance, identify revenue trends, understand customer purchasing patterns, and evaluate product and geographical performance for better business decision-making.

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The brief-writing piece is interesting — curious how you handle the validation step. AI-generated language tends to sound confident even when the underlying data is sparse or the trend is ambiguous. One pattern that helps: have the model output a confidence signal alongside the...