Case Study: Bolt Food

Introduction

Bolt Foods is a transportation platform that provides ride-hailing, micromobility, and food delivery services. With over 75 million happy customers across 45+ countries, Bolt Foods has become one of the fastest-growing startups globally.

In this case study, we’ll explore and analyze the dataset related to Bolt Foods’ food delivery service to provide actionable insights.

The Dataset

The dataset contains the following columns:

Created Date: Date and time when the order was created.
Country: The country where the order was placed.
City: The city within the country.
Restaurant ID: Unique identifier for the restaurant.
Restaurant Name: Name of the restaurant.
Order State: Order status (e.g., delivered, canceled).
Cancel Reason: Reason for order cancellation (if applicable).
Cuisine: Type of cuisine (e.g., Italian, Asian).
Platform: The platform used for ordering (e.g., mobile app, website).
Payment Method: How the customer paid for the order.
Card Issuer: The bank or financial institution associated with the payment card.
Products in Order: Number of items ordered.
Order Value (€ Gross): Total order value (including delivery fee).
Delivery Fee: Cost of delivery.
Delivery Time: Time taken for delivery.

Click here to access the dataset

Case Study Objective

The objective of the case study for Bolt Foods is to analyze the food delivery dataset and create an intuitive dashboard using Power BI or Tableau. The dashboard should provide actionable insights, identify areas for improvement, and help enhance Bolt Foods’ food delivery services.

Needed output:

Seasonality Analysis: Analyze order patterns across different months or seasons to determine seasonality. Calculate monthly order counts for each country and visualize any trends. Seasonal peaks or troughs may reveal insights for planning resources, marketing, or promotions.
Predicting Orders in March 2020: Use historical data to predict the number of orders in March 2020 for each country.

Steps:

Aggregate order counts by month for each country.
Apply a time series forecasting method (e.g., ARIMA, exponential smoothing) to predict March 2020 order counts.
Consider external factors (e.g., holidays, events) that might impact order volume & present the predicted order counts in an intuitive dashboard.

Other Valuable Insights:

Analyze cancellation reasons: Identify common reasons for order cancellations (e.g., long delivery time, restaurant unavailability) and propose solutions (e.g., improving delivery logistics, partnering with reliable restaurants).
Explore cuisine preferences: Which cuisines are most popular? Are there opportunities to expand the menu?
Investigate delivery times: Are there delays in specific cities or countries? Optimize delivery routes or partner with more efficient couriers.
Monitor payment methods: Track trends in payment preferences and ensure seamless payment experiences.
Visualize order value distribution: Identify high-value orders and tailor promotions accordingly.

Tools Used

Power BI
Tableau
Excel
Python (Pandas, Matplotlib, Seaborn)

Conclusion

This case study aims to build intuitive and actionable insights/conclusions from the provided data. The ultimate goal is to create a comprehensive dashboard that provides clear and actionable insights to support strategic decision-making.

Bolt Foods can leverage these insights to enhance its food delivery service, optimize operations, and improve customer satisfaction. An intuitive dashboard will empower stakeholders to make informed decisions and drive growth.