Marketing Insight Report

HNG internship data analysis task 1

Introduction

This dataset is a simulation of data collected in a superstore. It has 13 columns and 9994 rows. Dataset appears to be clean as no null values are detected. The purpose of this review is to extract relevant statistics from the data which provides the store a measure of performance in terms of sales, revenue, profit and other important parameters.

Observations

This dataset has no null values in any column and the datatypes per column are as follows:

Summary statistics

Deliverable Insights

1. Top selling products

   The category with the highest amount of money made is technology with a total of $836,154.0330.

   

The category with the highest quantity of products sold is Office supplies with 22,906 items sold.

The category that generated the highest amount of profit is technology while furniture sales generated the least amount of profit.

User Trends

1. All items were shipped to locations within the United states.

2. Most popular shipping destination state-California(2001 deliveries)

3. Most popular shipping destination city-New York City

4. Most customers fall into the consumer segment-(5191 customers) with the least amount of customers in the home office segment

5. Most customers(5968) opted for standard class shipping and the least selected same day delivery.

Conclusion

From my observations I was able to conclude that:

1. The superstore should invest more in technology product offerings as it is the most profitable category.

2. Marketing towards consumers should be intensified as they are the largest customer base and thus have the potential to purchase more.

Potential areas for further analysis:

1. Peak selling periods per category / sub-category.

2. Number of orders per customer.

3. Traffic source data : the dataset does not provide any information regarding where or how customers find the store.