Introduction:
The Olist Store Analysis project aims to analyze customer purchasing patterns and payment statistics on an E-commerce platform, Olist. This project covers several key performance indicators (KPIs) such as weekday vs weekend sales, payment statistics, delivery time, and customer behavior. The analysis is based on nine CSV files, which are cleaned and manipulated to extract valuable insights.
KPI 1: Weekday Vs Weekend (order_purchase_timestamp) Payment Statistics
The analysis of payment statistics based on weekday vs. weekend provides an understanding of the buying behavior of customers. This KPI answers questions like, which day of the week has the highest sales? How many customers prefer to pay through online modes? The analysis of this KPI can help Olist to improve their weekend sales and plan promotions accordingly.
KPI 2: Number of Orders with review score 5 and payment type as credit card
This KPI analyses the number of orders with a review score of 5 and payment type as credit card. This helps in understanding customer satisfaction levels and payment preferences. Olist can use this information to identify satisfied customers and encourage them to make repeat purchases.
KPI 3: Average number of days taken for order_delivered_customer_date for pet_shop
This KPI analyzes the average number of days taken for order_delivered_customer_date for pet_shop. It helps Olist in identifying areas where they can improve their delivery time and maintain customer satisfaction.
KPI 4: Average price and payment values from customers of sao paulo city
The analysis of average price and payment values from customers of Sao Paulo city helps in understanding the spending patterns of customers in this region. It also helps Olist in identifying high-value customers and creating targeted marketing campaigns.
KPI 5: Relationship between shipping days (order_delivered_customer_date - order_purchase_timestamp) Vs review scores
This KPI analyzes the relationship between shipping days and review scores. It helps in understanding the impact of delivery time on customer satisfaction levels. Olist can use this information to optimize their logistics and improve their delivery time.
Conclusion:
The Olist Store Analysis project provides valuable insights into customer behavior and payment statistics. The analysis of these KPIs helps Olist in identifying areas of improvement and creating targeted marketing campaigns. As a data analyst, I have used Excel and Power BI to clean and manipulate the dataset and create meaningful visualizations. This project serves as a great example of how data analysis can help businesses make informed decisions.
3. Project Analysis
Data Storage through
multiple CSV files
Inefficient
information
Lack of analytics
insights to make
business decisions
Reduce data
storage
Create efficient
Query and analysis
Empower data
driven decision
making capability
RAW DATA
CLEAN DATA
Database
Normalizatio
n
ETL Process
Optimization
Analytics
Insights
Create a
normalized
relation as a
central data
to collect
files in a
Workbook
Conduct
data
manipulation
and data
cleaning
with Excel
and MySql
Generate
analytical insights
through an
interactive
dashboard via
Power BI, Tableau
4. Working On Raw
Dataset Details
Import
Files
Treatment Of
Repetitive
columns
Remove
Duplicates
Manage
Relation among
dataset
Prepare Data
For Analysis
9 Flat CSV files:
1. Customers
2. Geolocation
3. Order Items
4. Order Payment
5. Order Review
6. Orders
7 Products
8. Sellers
9. Category file
The geolocation
dataset has
underlying
“duplicates” which
drop through the
Excel function to
remove duplicates.
After cleaning,
removing duplicates,
merging, appending,
and managing
relations calculate
some measures and
KPIs for Visualization
In the Geolocation
file and Customer
file there are
repetitive columns
like city, state, and
Zip code merge
them.
With the help of
power pivot use a
data model to
manage the
relationship
between all files
with their primary
keys in Excel
5. Manage Relation
Between Datasets
By combining all primary keys and foreign keys, a relationship has been established among the
various datasets.
6. KPI 1st
Weekday Vs Weekend (order_purchase_timestamp) Payment Statistics
• Baleto- 2.4 Million earning
• Credit Card- 9.5 Million, which is
the highest earning between
both weekdays and Weekends.
• Debit Card & Voucher- They
hold a small portion of it.
Payment Type
Weekdays
• Highest transaction placed on
Monday which is 16875
• Lowest transaction placed on
Saturday which is 11379
14768
16875
11379 12425
15470
16695 16274
Total
Total
Weekend
Payment Type
• Baleto- 0.5 Million earning
• Credit Card- 3.0 Million, which is
the highest earning between
both weekdays and Weekends.
• Debit Card & Voucher- They
hold a small portion of it.
Day Wise Transactions
Total Transaction = 103886
7. KPI 2nd
Number of Orders with a review score of 5 and payment type as a credit card
.
The credit card payment
method has consistently
shown the highest payment
amount across all reviews,
with the top payment
received in five reviews.
As indicated in the chart above, it can be observed that
Review 5 reflects a payment value of 59k, denoting the
highest payment amount among all reviews.
As per the table provided, the payment amount of
7003205.48 reflects the highest value and has been made
through the credit card payment method. Additionally, the
corresponding review score for this payment is 5.
8. 8
KPI 3rd & KPI4th
The average number of days taken for order_delivered_customer_date for pet_shop &
Average price and payment values from customers of sao paulo city
According to the chart displayed, the average payment value for the city of Sao Paulo is
154.100.
154.1003804
120.653739
0
20
40
60
80
100
120
140
160
180
Total
Average Price andPayment Values for Customers in
Sao Paulo City
Average of payment_value
Average of price
Average Delivery Time For
Pet Shop
As illustrated in the chart, the average payment value for Sao Paulo city is 154.100.
9. 9
KPI 5th
Relationship between shipping days
(order_delivered_customer_date - order_purchase_timestamp) Vs
review scores.
o Both the line chart and the multiple cards indicate that a review score of 5 is
associated with a longer shipping duration as compared to the other review scores.
