Our Journey to Becoming Data Analysts-Project 1

Progress is a natural result of staying in the process of doing anything – Thomas Sterner, The Practicing Mind.

After a wonderful internship experience at Side Hustle, we have realized that active practice is the only way to make meaningful contributions with our knowledge and to ultimately get better at what we do.

On this note, we took our journey on Data Analytics with Side Hustle to another level as 'Bootcampers'. Ouulalaah!! (as we say often)

Here you meet a wonderful team; Portfolio Data Analytics Team 15.

We call ourselves 'The DataloverZ'.

Briefing of The Task

We had finally settled into our various teams and as expected, we all received our tasks. We were eager and happily jumped at it. Little did we know that our hands would soon get dirty just as Garfield (CEO, Side Hustle) emphatically said during one of the onboard meeting sessions. We were to choose a product in Nigeria or used by Nigerians, work on the product's Sales Insight using Microsoft Excel and Power BI after scraping, cleaning and Visualizing the data.

When We Got to Work

The search for the data was quite challenging (did we say quite?!), but eventually we got secondary sales data from Francis Supermart in Lagos, Nigeria. The data set consisted of 4990 rows and 13 columns. We sought to provide insights that would help the CEO of Francis Supermart understand better the financial status of his various outlets as he was beginning to have plans for expansion.

Then, we also sought to determine the level of sales according to Product Fat Content and location factors and the influence of those factors on sales generally. The dataset consists of several product types which were considered in the analysis done on Microsoft Excel. We took into consideration the generally known fact that Bread is widely consumed in Nigeria and decided to narrow down our analysis on Microsoft Power BI to Bread as the only product type to be considered.

The Process

The data was scrapped from Git Hub and Data Cleaning was performed by the team members on Excel by removing duplicates, sorting out missing values, removing unnecessary columns, and so on. An Excel dashboard was created by the team members to create appropriate visualization for our data. We moved forward to load our data on Power Query in Microsoft Power BI, then made the necessary adjustments before finally loading it on Power BI Desktop.

Insights from Excel Analysis

• Total Sales was 30.4 million Naira.
• A total of 4990 products were sold at an average price of 392 Naira.
• There was a significant difference in the Total Sales between low-fat products and normal fat products with a difference of 7.2 million Naira and a 10.1 million Naira difference between normal fat and low-fat products.
• Supermarket type 1 amassed a total of 70% of the total sales with a total of 21.2 million Naira while supermarket type 3 amassed a total of 19% at 5.8 million Naira with supermarket type 2 and grocery store having a combined 11% at a total of 3.3 million Naira in sales.
• Medium-sized supermarkets had a total of 12.5 Million Naira in sales as compared to that of small-sized supermarkets with a difference of 5.2 million Naira in sales and a further 4.9 million Naira difference between small size supermarkets and high size supermarkets.
• The difference in Sales between the top 2 products which are fruits & vegetables and Snacks foods was 9,358 Naira. The top 5 products amassed a total of 18 million Naira.

Total Sales by Supermarket Location Type

Insights From Power Bi

• The total sales revenue was 835.61K Naira while the average product price is approximately 400 Naira.
• The medium supermarket size has the highest number of sales with 0.35m as compared to high and small sizes.
• Low-fat content product had the highest number of sales with 53% of the total sales.
• The product shelf visibility impacted sales positively, i.e. the higher the visibility the higher the sales.
• The lower the weight of the product the higher the visibility and also the higher the sales.
• The outlet location type of clusters 2 and 3 did not have an effect on the sales since there is no significant difference between the sales of the Two outlets.
• For medium-sized outlets, sales in cluster 3 were higher than sales in cluster 1.
• For small-sized outlets, there was an insignificant difference of 50,000 Naira in sales of clusters 1 and 2 with an average product price of 420 Naira.
• After comparing the total sales of clusters 2 and 3, the difference in sales was approximately 25,000 Naira with cluster 3 having the highest number of products sold.

Total Sales by Supermarket Opening

Recommendation

1. Mr. Francis can consider expanding but is advised not to embark on it immediately as the sales trend is still being studied closely.
2. Bread as a product in the Medium sized stores in the Cluster 3 location is doing very well. therefore, it is recommended that the supply of bread is increased.
3. The financial welfare of the Supermarket is good with a general revenue of about 30 million naira.

Click on the link below for more information about the analysis.

drive.google.com/drive/folders/1B1IP81NcJUc..

How it's Been For Us So Far as A Team

This first week was nothing short of hectic. We had to learn on the job and try our very best, we distributed tasks amongst ourselves, got on calls, really really long ones so we could help one another out, and so on. Ultimately, we are glad about the experience we have gained so far. We've become friends and it's just exciting knowing that we are in this for another 5 weeks, not alone but together.

We hereby encourage you all to watch this space for the next 5 weeks as we would be updating our blog weekly.

Till next week Hashnoders!