Blinkit Grocery Sales Analysis

Dataset Overview

The dataset pertains to sales data from Blinkit, an online grocery delivery service. It includes sales records of various grocery items across multiple outlets.

Outlet Information:

• Type: Data is categorized based on the type of outlet (e.g., supermarket, grocery store).

• Size: Outlets are classified by size.

• Location: Sales data is segmented by different locations where Blinkit operates.

• Year of Establishment: The year when each outlet was established is provided.

Item Details:

• Type: Various types of grocery items are included in the dataset.

• Value: The dataset records the sales value of each item.

• Weight: Item weight data is available.

• Visibility: Data on the visibility of each item, possibly indicating how prominently the item is displayed or featured, is included.

Product Attributes:

• Fat Content: The dataset includes information about the fat content of products (Low or Regular).

All the files related to this project are available at Github.com/nitin6753/Dashboards/tree/main/Blinkit

Summary

Generated a visually appealing and interactive dashboard to present the analysis findings.

Objective:

• Sales Performance Analysis: Evaluate overall sales performance, focusing on total revenue, average sales, number of items sold, and customer ratings.

• Detailed Segmentation: Analyze sales by various attributes such as outlet type, size, location, and item fat content to uncover key insights.

• Customer Insight: Assess customer preferences by examining ratings and their correlation with item sales and outlet characteristics.

Tasks Performed:

• Data Preparation:

  • Conducted data cleaning to ensure consistency and accuracy in the data, including handling missing values and standardizing categories.

  • Calculated key metrics including total sales, average sales, total number of items sold, and average customer rating.

• Segmented Analysis:

  • Analyzed sales performance segmented by fat content using a donut chart to understand the impact on total sales.

  • Evaluated sales by item type using a bar chart to identify top-performing product categories.

  • Compared sales by outlet size and type using stacked column and line charts to identify trends based on establishment characteristics.

  • Investigated the relationship between sales and outlet location using a funnel map to assess geographical distribution.

• Visualization Enhancements:

  • Designed a visually appealing dashboard that highlights key insights through interactive charts and filters.

  • Implemented custom visuals such as donut charts, bar charts, stacked column charts, and funnel maps to effectively communicate findings.

  • Added dynamic slicers to allow users to filter the data by outlet type, item category, location, and year of establishment, providing a tailored view of the data.