FoodHub Project


Data Analysis Project

Food delivery services have become a huge part of modern life. Almost every house in America have used food delivery service by using apps or calling the restaurants directly. Here, Im using FoodHub, as an example for a delivery app. Let’s assume that FoodHub is a leading food aggregator platform, connects customers with a diverse range of restaurants, offering convenience and culinary variety at their fingertips.

As a data scientist and a project manager, I used the example dataset to optimize the business operations and enhance customer satisfaction. This was done by a data-driven analysis of FoodHub’s extensive order data.

You ready? Im ready …

Customers have been using this app for food delivery on the weekdays and weekends. Interestingly, the orders are significantly higher on the weekends.


So, my goal is to find out more about the customers orders, business, and the food delivery quality, follow along…

As I expected, there were a few elements that stood out from the data.

  • American Cravings: Customers were all about American food! Japanese, Italian, and Chinese were also popular choices.

  • Weekend Deliveries: Weekends were busier than weekdays, with more people ordering food.

  • Delivery Dynamics: Weekday deliveries were slightly faster than weekend deliveries, potentially due to increased driver availability on weekends.

  • Happy Customers: Most customers were pretty happy, with an average rating of 5 stars while using the app.

  • Price vs. Satisfaction: The price didn't seem to affect how happy customers were.

  • New vs. Regular: More new customers tried FoodHub than came back for seconds.

>> these are crucial for the business to improve their overall growth.

The customers showed similar interest when it comes to food cuisines. Both the weekends and weekdays have the American cuisine as the most popular food.

After further analyzation, the data showed great insights!

My Advise:

  • Expand American Options: Partner with more American restaurants to satisfy the craving for classic cuisine.

  • Incentivize Reviews: Offer rewards or discounts to encourage customers to leave reviews. This helps identify areas for improvement and showcase positive experiences.

  • Nurture Repeat Business: Implement loyalty programs or personalized offers to keep customers coming back.

  • Optimize Weekend Operations: Consider hiring additional drivers or adjusting delivery routes to handle the increased demand during weekends.

  • Analyze High-Cost Orders: Identify factors contributing to higher-priced orders and explore opportunities to offer value-added services or promotions.

This data-driven approach ensures the app remains competitive and relevant in the ever-evolving food delivery market.

As a project manager, I’d divide my team to do these 3 tasks:

  • Explore Geographic Variations: Analyze regional preferences to tailor offerings to specific customer demographics. For example, in areas with a large Asian population, consider expanding the selection of Asian cuisines.

  • Evaluate Delivery Partner Performance: Assess the performance of delivery partners to identify areas for improvement and ensure consistent service quality. This can involve tracking delivery times, customer feedback, and overall performance metrics.

  • Monitor Customer Feedback: Continuously track customer feedback through ratings, reviews, and social media to identify emerging trends and address any concerns promptly. This can help identify areas for improvement and maintain a positive customer experience.

By incorporating these additional insights, FoodHub can further refine its strategies and maintain its position as a leading food delivery platform.