Tools used in this project


About this project


Xpress Tech is a tech startup based in Lagos, Nigeria. The startup launched as a ride-hailing company in 2019, before diversifying into Logistics and Food delivery in 2020 after a ban on commercial motorcycles by the Government of Lagos state. Xpress Tech took advantage of this exemption and announced it was diversifying into package delivery using their existing motorcycles and riders.

In the same month, Xpress Tech was reported to have partnered with Chicken Republic on food deliveries while it worked on launching its platform. Later in 2020, Xpress Tech would launch Xshop, its livery service by partnering with restaurants to deliver food to its users on their platform.


I have been employed as a Supply Chain Analyst to provide insight into food deliveries based on customer preferences.


The dataset contains customer survey data in CSV format


This is a large dataset and thus, I divided it into 3 categories;

  1. Customer's Biodata:
  • Age
  • Gender
  • Marital Status
  • Occupation
  • Monthly income
  • Educational qualifications
  • Family size
  • Latitude
  • Longitude
  • Pin code
  1. Customer's Order preference
  • Order Medium (Preference 1): Direct call, Food delivery apps, Walk-in and Web browser
  • Order Medium (Preference 2): Direct call, walk-in and Web browser
  • Meal (Preference 1): Breakfast, Dinner, Lunch and Snacks
  • Meal (Preference 2): Dinner, Snacks and Lunch
  • Food (Preference 1): Bakery items, Non-Veg foods, Sweets and Veg foods
  • Food (Preference 2): Bakery items, Ice cream, Sweets and Veg foods
  1. Customer's Feedback: There were over 25 customer feedback, below are some of them;
  • Ease and Convenient
  • Time-Saving
  • More restaurant choices
  • Easy payment option
  • Good food quality
  • Good Tracking system
  • Long delivery time
  • Maximum waiting time
  • Freshness
  • Good taste etc.


Manipulation and Analysis

1. Age;

I categorized the age of the customers into 4 ranges; 17 - 19 (Teenager), 20 -25 (Youth), 26 - 30 (Young Adult) and >31 (Adult) using the IFS function in Excel. This made analysis and visualization easier, I could answer certain business questions like; the most patronized age range, the gender split in each age range and the total number of customers in each age group.


2. Customer Feedback

Due to the large number of feedback, visualization of each feedback could be difficult and time-consuming. The feedback had responses like; agree, strongly agree, disagree, strongly disagree, neutral, important, slightly important, not important, very important etc. Using the COUNTIF function, I counted the responses based on the above arguments.


After Data manipulation, the data was imported into Power BI, and I visualized the data based on the 3 categories (Biodata, Order Preferences and Feedback). Interact with my dashboard here.


Valuable insights can be gotten from this survey like; the age range of the most patronized customers, occupation, pay range, most preferred meals, the most preferred medium of order etc.

358 customers, 92% of the total customers chose food delivery apps as their first preference of medium of order which means Xpress Tech is on the right track.


Consumers always value businesses that are open to criticism and provide a platform to share their experiences, concerns, and grievances. One of the best mediums to collect valuable data from your customers and use them to enhance their experience is with the help of Customer surveys.

I highly recommend that the information from this survey be harnessed and used to provide optimum customer satisfaction and promote a positive customer experience.

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