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Project Final: Business Analytics in SAS Studio – Clothing Retail Store & Distribution Company
Elsie Kiboma
Colorado State University Global
22SC-MIS543-1 Enterprise Performance Management
Instructor: Shawn Harrs
07/10/2022.
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Introduction Clothing Retail Store and Distribution Company
The clothing store objective over the years analyzed and kept its data in a Microsoft excel sheet
that has both the Clothing store sales and clothing store orders dataset. The business objective is
to increase sales, while maximizing the current profits in the stores by using direct marketing
promotions.
Business Problem Statement
The clothing store want to increase sales, while maximizing profits using direct marketing.
There are different forms of direct marketing and as defined, according to Abraham and Joseph
(2019) “is a promotional method that involves presenting information about the company, product
or service to the target customer through a variety of media.” (Joseph, 2019, p. 1). It builds strong
relationships with their current customers and increase the customer base through good customer
service. Secondly the company wants to know what marketing promotion boost their business and
how can they predict the future with the current strategy.
Clothing Store Sales and Order Dataset Business Questions.
On analyzing the clothing Store sales, how will the business identify an ideal segment that a
customer responds on regarding clothing store promotion?
Secondly, will the clothing store still maximize profits with the current marketing initiative, or
should it consider looking for other promotions to increase their return?
Clothing Store Order Sales dataset, some customer received discounts on their purchases with
large orders, and some did not, while others had less orders and got discount, will this affect the
customer behavior and buying patterns?
Also, with the Store order dataset how will the Clothing Store use the direct marketing to get more
customers to shop at their stores with the current sale order discount?
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Clothing Store Sales Dataset business question and hypothesis.
The clothing Store Dataset has 15 variables, how will the business generate an ideal segment to
which the customer will respond to?
Figure 1
Customer ID and the number of marketing promotion in the file
Note: 4 PROMOS generated 1749 customers in the clothing store.
There are three different customer segmentation as illustrated in OpenView partner’s blog
prior segmentation, needs-based segmentation, and value-based segmentation. (Nguyen, 2021).
The need-based segmentation focuses on the needs that a customer show for a specific product,
and this verified as mentioned by Nguyen (2021) “primary market research and segments are
demarcated based on those different needs rather than characteristics such as industry or company
size” (Nguyen, 2021, p. 2).
This is demonstrated with the Six most common lifestyle cluster types in the dataset, the
Customer dataset has no record of what kind of marketing promotion name type whether is direct
or online the customer responded to. Figure 1 shows that PROMO 4 had more customer responding
to the marketing strategy.
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Secondly, will clothing store maximize profits with the current marketing initiative, or should
it consider looking for other promotions to increase their return?
Figure 2 shows the different form of marketing strategies that the Clothing store would implement
if they were currently on a direct marketing strategy to maximize sales.
Figure 2
Different form of Direct Marketing
Note: Showing different forms of direct marketing the clothing store is utilizing. Direct Marketing benefits (referenceforbusiness.com)
In today business having a traditional marketing technique alone cannot guarantee a better return of
investments. In the European Journal of Management and Marketing Studies the authors emphasized
that “traditional marketing techniques are not enough alone to convey the message to the market which
is nowadays highly dynamics and competitive” (Safari Valens, 2015-2020, p. 3).
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Interpretation and Analysis of the Clothing Store Sales Dataset
Linear Regression the predictor variable helps to predict the value of the output. To determine
the relationship between the variable’s linear regression “it is used to explain the relationship
between one continuous dependent variable and two or more independent variables” (Solutions,
2021, p. 1).
The Clothing Store Sales predictor variables are FRE, PROMOS, lndaysbetweenpurchases
and inlifeavetimepurchase. The independent variable is the change of the independent variable
which is CustomerID will affect the revenue.
The programing language that will be used to analyze this data set is Python Jupyter Notebooks
and SAS Studio. SAS Studio to plot the descriptive statistics of the variables and Jupyter
Notebooks to measure the relationship between predictor variables and independent variable.
Secondly, will the clothing store can maximize profits with the current marketing initiative, or
should it consider looking for other promotions to increase their return? The current marketing in place
is direct marketing to measure the marketing effectiveness of the clothing store is by how much
revenue the promotion was able to generate.
The business might not maximize profits with direct marketing only, and it should look for other
marketing advertising strategies. In the consumers’ preference and their buying choice Basariya
(2021) “now a days online marketing is trending compared to traditional marketing and it is more costeffective one” (Basariya, 2020, p. 4). Online marketing will get better results compared to traditional
marketing because most users now have internet access in their own homes.
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Graphs and Charts Analysis of the Clothing Store Sales Dataset
Figure 3
Descriptive Analysis of the Clothing Store Sales Dataset Customer ID
Note descriptive Statistics of Customer ID
Figure 4
Descriptive Statistics Mean for the Clothing Store Sales Dataset.
Note: Showing the Means of the Variables
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Figure 5
Descriptive Statistics Histogram Customer ID with lndaysbetweenpurchases
Note: Histogram of the indaysbetweenpurchase vs the Customer ID
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Correlation of the Dependent and independent variables in Clothing Customer dataset
The Clothing Customer Dataset is imported to Python Jupiter Notebook to show the correlation
between customer ID is the dependent variable that is affected by independent variable FRE, Total
number of purchases visits, DAYS number of days the customer has on the file, In days between
purchases and lifetime average time between visits in days. Figure 6 to store the independent and
dependent variable in the data frame df3.
In the Jupiter Notebook importing seaborn as sns to plot the correlation between dependent
variable CustomerID and Independent variable FRE
Figure 6
Defining the data frame storing independent and dependent variables.
Note: Using the sns.regplot to plot the correlation between CustomerID and FRE.
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Predictive Analysis of Clothing Customer dataset using Linear Regression
Linear Regression between the dependent variable CustomerID and Independent variable
FRE as mentioned in the blog “it performs better when there is a continuous relationship between
the inputs and output” (Alam, 2022, p. 2). Figure 7 shows how Training dataset Clothing Store
sales split the dataset into Training and Test.
Figure 7
Clothing Store Dataset linear dataset and the output predictive analysis.
Simple Linear regression displaying the X=independent variables(input) and y=dependent
variable(output).
Note: Displaying the columns or X and Y variables for linear regression.
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Figure 8
Using Sciki-learn to divide the data into training data and test data.
Note: importing the function train_test-split to split data to test 30% and training 70%.
Creating linear regression function then assigning it to model. Displaying the model.fit
function the printing=model.coef_. and model_intercept_. (McCullum, n.d.)
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Figure 9
Importing matplotlib scatterplots using plt.scatter methods.
Note: Plotting the Scatterplots to show the CustomerID predictions using the FRE, Total
number of purchases visits, DAYS number of days the customer has on the file, In days
between purchases and lifetime average time between visits in days
Figure 9 shows no correlation between X and Y variables. This is because as mentioned in
the statology scatterplots with two variables that have no clear patterns show no correlation (Zach,
2021).
The Clothing Store customer availability is not affected by the predictors FRE, Total number
of purchases visits, DAYS number of days the customer has on the file, In days between purchases
and lifetime average time between visits in days.
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Clothing Store Order Dataset business question and hypothesis.
Customer Store Orders dataset had customers who received discounts on their purchases with large
orders, and some did not, while others had less orders and got discount, will this affect the customer
behavior and buying patterns?
It will affect the customer behavior and buying patterns will shift and the business customer
relationship will be altered. Alternative hypothesis, customers behavior and buying patterns will not
be affected.
Secondly Store order dataset, how will the Clothing Store use the direct marketing to get more
customers to shop at their stores with the current sale order discount?
Graphs and Charts Analysis of the Clothing Store Order Dataset
Descriptive Analysis of the Clothing Store Order Dataset Order ID
Figure 10
Note Descriptive Statistics of Clothing Store Order Datasets
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Figure 11
Descriptive Statistics Mean for the Clothing Store Order Dataset.
Note: Mean of Clothing Store Order Dataset.
The Clothing Store Sales dataset shows the cluster types of lifestyles that determine what product
the customer will buy. Culture is one of the factors that will affect the buying patterns of consumers
as mentioned by Dr Durmaz Yakup( 2011) “ learning of cultural properties in the analysis of
consumer behaviour has been an important variable in marketing especially in market
segmentation, target market and product positioning” (Dr Durmaz Yakup, 2011, p. 2).
Figure 12 shows
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Figure 12
Clothing Store Order Dataset data and Column Chart.
Note: The Count of CustomerID, with the Count of discounts offered with sum of orders.
As much as culture affect the customer buying behaviors, also the number of products bought by the
customer is related to the amount of discount offered. ERNSH customer has the highest discount of
34 with the sum of orders of 348912.
Figure 9 shows no correlation of how Sales and Order can be affected by the PROMO done in the
clothing store. Therefore, one of the reasons to have a promotion marketing mix either direct or online
marketing is to generate sales and profits. Also mentioned by “sales promotion affects the decisionmaking and purchasing stages of the buying process directly that is affective in the long run since it
leads to increased sales and profits” (Hamidi, 2015, p. 1).
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Recommendation and Conclusion
The Clothing Store Sales Dataset illustrates the different kind of Promotion that the store
utilizes to maximize sales performance. As per the information the data does not show the element
of marketing used either direct or online. Figure 2 shows the different kind of direct marketing that
the store would be utilizing in their stores. The independent variables that would affect the
dependable variable CustomerID are the types of promotions, how often would the customer shop
be using the variable number of days the customer has on the file and the in between purchases
between visits. Figure 9 shows there is no correlation that either of these variables will affect the
customer's purchases.
The “main characteristics of sales promotions are that they offer better value for money, and
they try to cause responses immediately” (Hamidi, 2015, p. 3). The Clothing Store would better
utilize online marketing as well because it reaches several customers in a shorter time and its cost
effective.
Lastly, customer behavior is affected as stated by Hamidi (2015) “cultural, social and
religious, personal and psychological” (Hamidi, 2015, p. 3). The Clothing Store Sales dataset show
the different cluster type of lifestyle that customers shop product for examples, mid-life success,
country home families, movers and shakers, Home sweet home and Upper crust. The buying
process as stated Hamidi (2015) “begins with need recognition or problem awareness, the customer
first recognizes a problem or need or attracted to an advertisement” (Hamidi, 2015, p. 4).
The Clothing Store is aware of the kind of customers shop in their stores, but they would expand
their sales when they in corporate online marketing.
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References
Alam, B. (2022, January 09). Linear Regression Algorithm Using Python. Retrieved from hands
on cloud: https://hands-on.cloud/linear-regression-algorithm-using-python/
Basariya, S. R. (2020, October). Consumers Preference and Their Buying Choice. ResearchGate,
p.
file:///C:/Users/kiboe/OneDrive/Documents/Data%20Science%20First%20Quarter/Enter
prise%20Performance%20Management/Project/Traditional%20Marketing.pdf.
Dr Durmaz Yakup, D. C. (2011, March). The Impact of Cultural Factors on the Consumers
Buying Behaviours Examined through an Impirical Study. International Journal of
Business and Social Science,, p. 6.
Hamidi, M. F. (2015). Analyzing the Influence of Sales Promotion on Customer Purchasing
Behaviour. International Journal of Economics and Management Sciences, p. 6.
Joseph, V. A. (2019, January 06). An Empirical study on Direct Marketing as the Most Effective
Form of Marketing in the Digitazed Marketing Environment. International Journal of
Research Science and Managment, p. 7.
McCullum, N. (n.d.). nickmcculumm. Retrieved from Machine Learning_Linear Regression
Python: https://nickmccullum.com/python-machine-learning/linear-regression-python/
Nguyen, T. A. (2021, may 13). Customer Segmentation: A Step by Step Guide For Growth.
Retrieved from open view partner: https://openviewpartners.com/blog/customersegmentation/
Safari Valens, A. M. (2015-2020). Assessment of the Practices of Direct Marketing Tools on
Customer Awareness and Business Performance: A case of Konka Group Company LTD.
European Journal of Management and Marketing Studies, p. 26.
Solutions, c. D. (2021). Statistic Solution. Retrieved from What is logistic solutions:
https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/whatis-logistic-regression/
Zach, B. (2021, March 31). Statology Statistics Simplified. Retrieved from Statology:
https://www.statology.org/no-correlation-examples/