This project consists of 6 tables with operational and sales data for a Brazilian e-Commerce company A&B, similar to Amazon. Data loading, exploration, cleaning, analysis and visualization was all conducted in Power BI.

1. 2017-2018 Executive Summary
Revenues, Order Trends and Products
Ximena Bustamante—Data Analyst

2. Introduction
Microsoft Power BI is a powerful tool known for its dynamic visualization capabilities,
yet it offers so much more than that. As an example of its versatile components, I have
completed transformation, exploration, analysis and visualization of the following
dataset within Power BI.
This dataset provides powerful insights into sales and operations for e-Commerce
company A&B (similar to Amazon). The presentation is intended to summarize the
most relevant informational and actionable items for the company’s executives.

3. DATASET
• Tables—Dataset is composed of 6 different tables with multiple variables each:
• Orders dataset: Provide information for each item ordered
• Order items dataset: Information for items within each order and the cost to ship and price
broken out for each item within an order.
• Order payments dataset: Provides information regarding payments made on each order.
Make sure you aggregate total payment for each order to get unique order price.
• Product dataset: Provides information about the product.
• Product category name translated dataset: This dataset is Brazilian, so all categories are in
Portuguese, join category name on this table to get the translated category.
• Order reviews dataset: This table has review information for each order.
• Customers dataset dataset: This dataset has information regarding customer_id, which links
directly to order_id in the orders dataset. However, to get the unique customer id for each
order you need to link to this table.
https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce/discussion/71650

4. KEY INSIGHTS

5. Revenues, Orders and Payment Types
• The data suggests that both
revenues and total orders peaked in
2017 Q4 and 2018 Q1/Q2, then
started to decline right before the
start of Q3.
• This would suggest that sales
may be seasonal and
increasing sales/promotions
during slow seasons may be
suitable.
• It’s important to recognize that
data for Q3 may also be
incomplete.
• Most common form of payment
appears to be credit card.
• Forming partnerships with
credit providers may result
beneficial for the company, as
well as the customers.

6. Revenues and Orders by States
• Data suggests that the top 3 states
with the highest revenue and total
number of orders are SP, RJ and
MG.
• This information would help
A&B make informed decisions
on where to focus their
marketing/sales efforts.

7. Orders and Profits by Categories
• Top 10 most profitable categories are
almost the same as the top 10 most
popular.
• However, there are some minor
differences between the two.
• Toys and telephony are
on the top 10 most
common, but not on the
top 10 most profitable.
• Conversely, garden tools
is among the most
profitable. Thus, it would
be worth devoting more
marketing efforts into
this category.

8. Customer Satisfaction
• This chart shows the relation
between “Average Review Score” and
the total number of orders .
• Highest total number of orders
appears to be in the mid-to-low
average review scores.
• This would motivate managers
to become familiar with the
major customer pain points
based on reviews.

9. DATA PROCESS-ACQUISITION,
PREPARATION AND ANALYSIS

10. Data Acquisition, Preparation and Analysis
Microsoft Power BI
• Data was acquired via Kaggle and downloaded into Excel Files.
• Cleaning, transformation, analysis and visualization of the data was ALL conducted in Power BI.
• Due to the multiple tables of data, a data model was created, and multiple joins were conducted resulting in a final merged
table.

11. Data Acquisition, Preparation and Analysis
Microsoft Power BI
• Within Power Query, multiple tables were joined.
• Data was also clean, standardized and grouped to reduce cardinality.
• Location data types were changes to represent geographical points in order to create maps.
• Given the size of the dataset, unnecessary columns were removed, and new custom columns were added.
• Column distribution, quality and profile tools were used to analyze the content of the columns.

12. Data Analysis and Visualization
Microsoft Power BI
• To analyze and visualize the data, multiple groups, charts, matrix filters and slicers were used.
• Dashboard-wide
filters were
used to exclude
transactions
before 2017.
• Page wide
filters were also
used to meet
the needs of
each visual.
• Categories were
grouped to
minimize
cardinality.
• Slicers were
integrated to
provide the
audience a more
dynamic
experience.

13. CHALLENGES AND COOL
TECHNIQUES

14. Challenges and Cool Techniques
• Multiple tables
• The dataset consisted of 6 main tables that had to be joined in order to analyze and visualize the information.
• Cool Technique
• Power BI provided a very helpful model view that allows the user to outline the relationship between tables.
• High Cardinality
• The dataset consisted of 6 main tables that had to be joined in order to analyze and visualize the information.
• Cool Technique
• Grouping categories is another genius feature within Power BI, along with the column profile which allows you to get
a sense of what data may need to be standardized and/or grouped.
What If I had More Time?
• If I had more time, I would look into repeat customers and some of the categories that may be sold/marketed together
based on consumer trends.

15. THE END!
 Thank you for checking out my project!
 If you have any feedback, comments, ideas, JOB offers, please feel free to
DM me at my LinkedIn @https://www.linkedin.com/in/ximenab/