Project Background

QuickBite Express is a food delivery startup based in Bengaluru that connects customers with local restaurants and cloud kitchens. Since its start in 2020, the company grew quickly by focusing on fast service and a wide variety of food options. By early 2025, it was a successful platform handling over 91,000 orders every month across several major cities.

However, in June 2025, the company faced a major crisis that threatened its survival. A viral social media scandal regarding food safety violations at partner restaurants broke out at the same time as severe monsoon rains, which caused a week-long delivery outage. These events caused a massive 80.8% drop in orders and destroyed customer trust, leading to extreme delivery delays and high cancellation rates.

Problem Statement

The management of QuickBite Express needed to understand the exact damage caused by this crisis. The main goal was to move past general worries and use data to find exactly where the business was failing. This analysis was required to separate temporary bad luck from deep operational problems.

Management expects the following insights:

Customer Segments: Identify which groups of customers can be won back and which ones need a completely new approach.
Order Patterns: Look at how customer behavior changed from the normal period to the crisis period.
Delivery Performance: Check how delivery times, order cancellations, and late deliveries affected the business.
Restoring Trust: Use customer feedback to understand why trust was lost and how to rebuild it.
Recovery Strategy: Create a clear plan to help leadership spend the recovery budget in the right areas to get the business back on track.

Data Set Overview

The dataset follows a professional star schema to ensure data accuracy and efficient reporting. It connects central transaction records with detailed descriptive tables for a complete view of the business , the data set contains 8 Tables .

Table Name	Description
fact_orders	Stores core transaction details like dates, total amounts, and cancellation status.
fact_order_items	Tracks individual food items within each order, including quantities, unit prices, and discounts.
fact_ratings	Contains customer feedback scores and text used for sentiment analysis.
fact_delivery_performance	Tracks logistics data by comparing expected versus actual delivery times.
dim_customer	Maintains customer profiles including their location and signup information.
dim_restaurant	Lists partner restaurant details, cuisine types, and their active status.
dim_delivery_partner	Stores information on delivery personnel, vehicle types, and ratings.
dim_menu_item	Details specific food items including their categories and individual pricing.

Project Execution Approach

I followed a systematic workflow to ensure data integrity and to deliver a reliable recovery strategy for the management team.

Data Preprocessing (SQL): I loaded raw datasets into SQL to handle missing values and fix data types, ensuring the data was clean and reliable for analysis.
Stakeholder Analysis (SQL): Using complex queries and CTEs, I quantified the exact drop in order volumes and revenue to answer key business questions.
Data Transformation (Power Query): I imported the results into Power BI and used Power Query to further refine the data and ensure correct formatting for reporting.
Data Modeling (Schema Design): I built a star schema by connecting fact tables to dimension tables, creating an efficient structure for fast and accurate data retrieval.