Ecommerce Data Challenge

I played the role of a Junior Data Analyst to analyze the dataset of an eCommerce Store based in Ghana.

Objective

In this challenge, you are required to analyze the 6 years data of the company to draw insights to assist the marketing team in their upcoming marketing campaign.

Data Structure

The data contains records of Customers Online Orders from 2015–2020. Each record represents individual customer order including the Order Date, Delivery Date, CustomerID, Customer Age, Customer Gender, Location, Zone, Delivery Type, Product Category, Product Subcategory, Product, Unit Price, Shipping Fee, Order Quantity, Sale Price, Delivery Status, and Rating.

Data Cleaning

The dataset contained 19 columns and 11300 rows. In the Gender column, I changed “M” with “Males” and “F” with “Females” using the “find and replace function. I extracted the “Delivery days” column from the “Order date” and “Delivery date” to know how long it took to deliver an item when it is ordered.

Lastly, I added the “Total Sales” column by multiplying the “Sale Price” by the “Order Quantity”.

Analysis and Insights

The analysis was performed using Microsoft Excel functions, PivotTables, and PivotCharts.

The Total Revenue generated from 2015–2020 was 105,946,079

There was a total of 113,000 Customers who ordered from the Company.

There was a total of 602,834 Orders that were made from 2015–2020

Averagely, it took 10 days for items to be delivered. The minimum delivery days were 2 and the maximum was 20 days.

From 2015–2020, the month of January recorded the highest Total Revenue while June recorded the least. March recorded the highest MoM% change at 12.74%.

The items that were ordered from the company are grouped into 6 product categories. “Phones and Tablet” and “Electronics” recorded the Most Revenue for the company.

Distribution of Customers by Gender.

Looking at the reasons why most orders were returned.

The Visualization Dashboard

Recommendations

1. The Marketing team should target the other locations aside from the top 10 to increase the campaign and advertisement in those areas or better still provide some discount to aid people to order from such locations.

2. The Marketing team should investigate what delays the delivery of items after it has been ordered to reduce the average delivery days.

3. Again, items should be verified/checked carefully before they are delivered to avoid the return of orders after they have been delivered.