Analysis of Retail Surge's Product Categories: Profit, COGS, Payment Methods, and Customer Attitudes | Desklib

Business Statistics
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Table of Contents
List of tables....................................................................................................................................2
Introduction......................................................................................................................................3
Problem definition and business intelligence required....................................................................3
Results of the selected analytics methods and technical analysis....................................................4
Which product categories are making the most profit?...............................................................4
Which product category costs the most (COGS)?.......................................................................6
Is there a difference in payments methods?.................................................................................7
Are there any differences in the user groups on all of the customer attitudes?...........................8
Are there any differences in gender on all of the customer attitudes?.........................................9
Discussion of the results and recommendations............................................................................10
Recommendations..........................................................................................................................10
References......................................................................................................................................11
Appendix........................................................................................................................................12

List of tables
Table 1: Descriptive Statistics.........................................................................................................5
Table 2: Test of Homogeneity of Variances....................................................................................5
Table 3: ANOVA.............................................................................................................................5
Table 4: Descriptive Statistics.........................................................................................................6
Table 5: ANOVA.............................................................................................................................7
Table 6: t-Test: Two-Sample Assuming Equal Variances..............................................................7
Table 7: Chi-Square test of association (user group and customer attitudes)..................................8
Table 8: Chi-Square test of association (gender and customer attitudes)........................................9

Introduction
This report is about an online retail company called, Retail Surge. The company has its
business divided into several areas including Boy’s, Men’s, Girl’s, Women’s and
Customisation. The company’s product range includes clothing, shoes, sporting equipment
and accessories. This report seeks to analyse and understand the product categories that
generate more income to the company. It also sought to understand the product categories
that had the largest cost of goods. Lastly, the study looked at the association between
gender/website user groups and customer attitudes.
Problem definition and business intelligence required
This study sought to answer the following research questions.
 Which product categories are making the most profit?
To answer this research question, analysis of variance (ANOVA) was employed
(Hinkelmann & Kempthorne, 2008). ANOVA is used to analyse variation in the means of
groups that are more than 2. Since the product categories were more than 2, ANOVA was
the most ideal test to be used.
 Which product category costs the most (COGS)?
Again to answer this research question, analysis of variance (ANOVA) was employed
(Hinkelmann & Kempthorne, 2008). ANOVA is used to analyse variation in the means of
groups that are more than 2 (Gelman, 2005). Since the product categories were more than
2, ANOVA was the most ideal test to be used.
 Is there a difference in payments methods?
Answering this research question required the use of t-test is that test that helps compare

the means of two groups (Sawilowsky, 2005). Since there are only two groups (PayPal ad
Credit Card), t-test became the most ideal test.
 Are there any differences in the user groups on all of the customer attitudes?
To answer this research question, Chi-Square test of association was used. Chi-Square
test of association helps to identify whether there exists any kind of
relationship/association between two categorical/nominal variables (Bagdonavicius &
Nikulin, 2011). The research question to be tested involved two variables with nominal
data values hence Chi-Square was the most ideal test.
 Are there any differences in gender on all of the customer attitudes? (6 outcomes)
This is the last research question that the study sought to answer. Just like the immediate
previous question, this research question was answered by performing a Chi-Square test
of association. The research question to be tested involved two variables with nominal
data values hence Chi-Square was the most ideal test.
Results of the selected analytics methods and technical analysis
Which product categories are making the most profit?
For this section, the study sought to test the following hypothesis.
H0: There is no significant difference in the average profit for the different product categories
HA: There is significant difference in the average profit for the different product categories for at
least one of the product categories
This was tested at 5% level of significance. To test this, analysis of variance (ANOVA) was
used.

Table 1: Descriptive Statistics
N Mean Std.
Deviatio
n
Std.
Error
95% Confidence Interval for
Mean
Lower Bound Upper Bound
Men’s shoes 91 15.8934 .40738 .04270 15.8086 15.9782
Men’s clothing 78 6.0000 .00000 .00000 6.0000 6.0000
Women’s shoes 13 6.5000 .00000 .00000 6.5000 6.5000
Women’s clothing 348 4.2000 .00000 .00000 4.2000 4.2000
Customize 27 25.0000 .00000 .00000 25.0000 25.0000
Boy’s shoes 51 3.3000 .00000 .00000 3.3000 3.3000
Girl’s shoes 2 7.0000 .00000 .00000 7.0000 7.0000
Girl’s clothing 2 4.0000 .00000 .00000 4.0000 4.0000
Total 612 7.0681 5.64691 .22826 6.6199 7.5164
From the descriptive table above, it can be seen that the product with the highest profit to be the
customized items (M = 25.00, SD = 0.00). The product with the least profit was the boy’s shoes
(M = 3.30, SD = 0.00).
Table 2: Test of Homogeneity of Variances
Profit Total
Levene Statistic df1 df2 Sig.
16.253 7 604 .000
Before running the ANOVA, we checked for the homogeneity of variances. Levene’s test of
homogeneity showed that the variances are not homogenous (not equal).
Table 3: ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups 19468.353 7 2781.193 112468.919 .000
Within Groups 14.936 604 .025
Total 19483.289 611