Project Overview
Project Goal: To analyze "Vrinda store" sales data to uncover trends, identify key insights, and make data-driven decisions to improve business performance.
1) Project Objectives:-
Data Collection and Preparation: Gathered sales data from store location and as well as product categories. Clean and format the data to ensure accuracy and consistency.
Data Analysis: Perform exploratory data analysis (EDA) to understand the distribution and patterns in sales data. Identify top-selling products, best-performing States, and seasonal trends.
Visualization and Dashboard Creation: Create interactive dashboards using Excel to visualize sales performance. Include charts, graphs, and tables that summarize key metrics such as revenue, units sold, and average order value.
Insights and Recommendations: Derive actionable insights from the data analysis. Make recommendations for improving sales, optimizing inventory, or targeting specific customer segments.
2) Steps Involved :-
Data Collection and Preparation: Import or input sales data into Excel. Clean data (remove duplicates, handle missing values, format dates, etc.).
Data Analysis: Calculate total revenue, units sold, average order value, etc. Use functions like SUM, AVERAGE, COUNT, IF, VLOOKUP, etc., as needed. Create PivotTables to summarize data by product, store location, or time period.
Visualization and Dashboard Creation: Design the dashboard layout (overview, sales performance, product analysis, etc.). Insert charts (line charts for trends, bar charts for comparisons, pie charts for proportions). Add interactive elements (drop-down menus for filtering, clickable buttons for navigation).
Insights and Recommendations: Analyze trends and patterns in sales data. Identify best-selling products, peak sales periods, underperforming stores, etc. Provide actionable recommendations based on insights derived.
3) Used Functions and Techniques :-
Data Manipulation: SORT, FILTER, SUMIF, COUNTIF, AVERAGEIF, IFERROR, etc.
PivotTables: Summarize and analyze large datasets.
Charts and Graphs: Create visual representations of data trends.
Conditional Formatting: Highlight important trends or anomalies.
Data Validation: Ensure data accuracy with drop-down lists and input restrictions.
Conclusion :-
In conclusion, this project aims to leverage Excel’s capabilities to analyze store sales data effectively. By following the outlined steps and utilizing appropriate functions and techniques, you can gain valuable insights into sales performance and make informed decisions to drive business growth.