This document presents a case study aimed at optimizing retail sales and profitability through the analysis of customer segments, product performance, and sales patterns. Key objectives include identifying high-value customer segments, assessing product profitability, understanding sales trends, and developing actionable marketing strategies. Recommendations highlight the need for personalized marketing campaigns, adjusting discount rates based on sales trends, and focusing on high-margin products.

1. Presentation 2024
Marketing Analysis
Retail Sales Analytic
Business
Analysis

2. Content
01
02
03
04
05
06
Introduction
Overview
Methodology
Key Insights and Findings
Recommendations and
Implementation
Conclusion

3. Overview The primary aim of this case study
is to optimize sales and
profitability by analyzing customer
segments, product performance,
and sales patterns.
01
02
Evaluate the performance of
different product categories and
sub-categories in terms of sales,
quantity sold, and profitability.
03
04
It is necessary to Identify and
segment customers based on
their purchasing behavior,
demographics, and profitability
Analyze sales patterns over time
to identify trends and seasonal
variations. Also, what would the
sales be in the next 20 years
The problem to solve is identifying key
factors that influence sales and
profitability and using this information to
optimize marketing and sales strategies
to boost overall business performance.

4. Objectives
Arowwai
Industries
Identify high-value customer segments and
develop targeted marketing strategies for
them such that it will further boost their
loyalty for the company
Objective 01
Determine the most and least profitable
products to inform inventory and pricing
strategies
Objective 02
Assess the effectiveness of discount
strategies and optimize discount levels to
maximize profitability
Objective 03
Understand sales trends and seasonality
to plan marketing campaigns effectively.
Objective 04
Provide actionable recommendations
for optimizing sales and marketing
strategies based on the analysis

5. Data Collection
Methodology
The Superstore dataset is a popular dataset
used for retail and business analytics,
containing transactional data from a fictional
store.
Data Analysis
The summary status such as the average,
percentage, frequency were used. Also, text
analysis was used for the thematic analysis
part.
Data Analysis Tools
Excel was used for the data entry in
conjunction with Powerbi used for the
visualization purposes , and R was used to
handled the advance part of the analysis

6. Sale
pattern
There was a balanced trend in the
number of times the sales increase and
decrease. The sales fluctuate for the 4yrs
01
Sum of Percent for Decrease (54.23%
increase) trended up while Increase
(77.56% decrease) trended down between
Friday, January 3, 2014 and Friday,
December 29, 2017.
02
The most recent Sum of Percent anomaly
was on Tuesday, December 5, 2017, when
Increase had a high of 13,506.14.
03
Sum of Percent for Increase started
trending up on Friday, December 11,
2015, rising by 972.29% (95.32) in 6 days.
04

7. Sale
pattern
Sum of Percent for Increase and Decrease
diverged the most when the Day was Tue,
when Increase were 71,379.25 higher
than Decrease
01
Sum of Percent for Increase and Decrease
diverged the most when the Month was
Dec, when Increase were 130,105.37
higher than Decrease.
02
There was uptrends and downtrend in the
patterns for each year. Dec in Label
Increase made up 34.90% of Sum of
Percent.
03

8. Quantity Profit
Sales
Expected growths
Identified revue, financial, volume, and
promotional metric growth
Sales
Quantity
Profit
Discount
Discount
0.20
-0.03
0.50
0.20 0.07 0.01
0.50 -0.22
0.07
-0.03
0.01 -0.22

9. 95%
Customer’s
experience
Good experience
The customer’s experience was
measured based on the number
of times they bought goods
from the company. It was
believed that any customer that
bought something (any product)
from the company more than
one time has shared a good
experience with the company
service, and always want to
come back.

10. SEGMENTATION
Introduce the company's main
customer segment.
Data
Region Sum of Sales Sum of Profit
Central 501239.8908 39706.3625
East 678781.24 91522.78
South 391721.905 46749.4303
West 725457.8245 108418.4489
Grand Total 2297200.86 286397.0217
Data
Ship Mode Sum of Sales Sum of Profit
First Class 351428.4229 48969.8399
Same Day 128363.125 15891.7589
Second Class 459193.5694 57446.6354
Standard Class 1358215.743 164088.7875
Grand Total 2297200.86 286397.0217
Data
Segment Sum of Sales Sum of Profit
Consumer 1161401.345 134119.2092
Corporate 706146.3668 91979.134
Home Office 429653.1485 60298.6785
Grand Total 2297200.86 286397.0217
Data
Category Sum of Sales Sum of Profit
Furniture 741999.7953 18451.2728
Office Supplies 719047.032 122490.8008
Technology 836154.033 145454.9481
Grand Total 2297200.86 286397.0217
Data
Sub-Category Sum of Sales Sum of Profit
Accessories 167380.318 41936.6357
Appliances 107532.161 18138.0054
Art 27118.792 6527.787
Binders 203412.733 30221.7633
Bookcases 114879.9963 -3472.556
Chairs 328449.103 26590.1663
Copiers 149528.03 55617.8249
Envelopes 16476.402 6964.1767
Fasteners 3024.28 949.5182
Furnishings 91705.164 13059.1436
Labels 12486.312 5546.254
Machines 189238.631 3384.7569
Paper 78479.206 34053.5693
Phones 330007.054 44515.7306
Storage 223843.608 21278.8264
Supplies 46673.538 -1189.0995
Tables 206965.532 -17725.4811
Grand Total 2297200.86 286397.0217

11. Customer’s experience
Sales
Customer’s purchasing experience Customer’s purchasing experience Customer’s purchasing experience
Customer’s purchasing experience

12. 1st KPI
2nd KPI
3rd KPI
4th KPI
KPIs
Key Performance
Indicators (KPIs) in the
last four (4) years.
Total Sales: $2,297.2K
Average Sales: $230
Total Profit Year:
$286.4K
Total Quantity Sold:
37,873 units
4th KPI
Total Discount: $1.6K

13. Recommen
dation
Adjust discount rates based on sales trends. For
products with high discounts but low sales impact,
reduce discounts and focus on strategic promotions for
high-margin products.
01
Develop personalized marketing campaigns based on
customer segments (e.g., high-value customers or
geographic regions). Use insights from purchasing
behavior to increase customer loyalty and retention
02
Review the product mix to eliminate or improve low-
performing products and invest in high-margin, high-
demand categories.
03
Monitor profit margins closely, focusing on products
where high shipping or operational costs erode
profitability. Investigate opportunities to renegotiate
supplier agreements.
04

14. THANK
YOU
Superstore Data
https://josephdamilare01.github.io
johdam01@gmail.com
+123-8147 -003647
Joseph ADEKUNLE