Business Statistics: Analysis of Retail Surge Customer Data Report

This report presents a comprehensive statistical analysis of customer data for Retail Surge, an online clothing and shoe retailer. The analysis addresses key business questions, including identifying the most profitable product categories (customized products), determining the highest cost product category, and examining differences in payment methods (PayPal vs. credit card). The report employs various statistical techniques such as bar graphs, independent sample t-tests, and ANOVA to analyze customer attitudes across different user groups (light, medium, and heavy users) and genders. The findings reveal significant differences in customer attitudes based on user group and gender, providing valuable insights for strategic decision-making. The report concludes with a discussion of the results and provides recommendations for Retail Surge to improve its business operations and customer engagement.

Business statistics
Business analytics and statistics
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Table of Contents
1.0 Introduction...........................................................................................................................................3
2.0 Problem definition.................................................................................................................................3
3.0 Analysis and results...............................................................................................................................5
3.1 Which product categories are making the most profit?....................................................................5
3.2 Which product category costs the most (COGS)?..............................................................................6
3.3 Is there a difference in payments methods?.....................................................................................7
3.4 Are there any differences in the user groups on all of the customer attitudes? (6 outcomes)..........7
3.5 Test for the difference in means of user groups between different customer attitudes.................11
3.6 Are there any differences in gender on all of the customer attitudes? (6 outcomes).....................15
4.0 Discussion and recommendation.........................................................................................................20
Reference...................................................................................................................................................21
Appendix...................................................................................................................................................22
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1.0 Introduction
Technological advancement in the field of business coupled by global inflation has made
business world to be a ground of competition. Many companies in various industries which
have not been able to keep up with the competition in the market have been forced to collapse.
For this reason, majority of businesses have strove to embrace various technological
advancements which have made business operations easier. For example in tours and travel
industry, customers are now able to book their tickets online at the comfort of their couches
instead of appearing physically in the offices. Such kind is what technology can do. It also
enables the company to understand the market better by analysing customer centric data. Retail
Surge, an online company, deals in clothes and shoes for both men and women. It has also
been experiencing a down surge in revenues in due to global dynamics in the market. For this
reason, the business collected customer data to be analysed so as to be able to better understand
the market and their customers.
2.0 Problem definition
 The product category is made the most profit
A graph was used to present the products and their profits. This descriptive was employed since
it is visual thus easy to interpret.
 Product category that cost the most
A graph was used to present the products at Retail Surge and their costs. This descriptive was
employed since it is visual thus easy to interpret.
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 Is there a difference in payment methods?
Independent sample t-test was used in this test because there were only two independent
variables that were being compared (PayPal and credit card).
 Are there any differences in the user groups on all of the customer attitudes? (6
outcomes)
Analysis of variance (ANOVA) was used in this test because there were more than two variables
that were being compared. These were light, medium and heavy users customers at Retail Surge.
 Are there any differences in gender on all of the customer attitudes?
Independent sample t-test was used in this test because there were only two independent
variables that were being compared (males and females).
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3.0 Analysis and results
3.1 Which product categories are making the most profit?
Figure 1
It can be observed from the results in the bar graph above that customised products are doing
well in terms of profit. The mean total profit foe customized products was 25 dollars. The
men’s shoes followed with a mean total profit of 16 dollars while the third product was girls’
shoes that made a mean total profit of 7 dollars.
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3.2 Which product category costs the most (COGS)?
Figure 2
It can be observed from the results in the bar graph above that customised products are doing
well in terms of mean cost of goods. The mean cost of goods for customized products was
about 10 dollars. The girls’ shoes followed with a mean cost of goods of 8 dollars while the
third product was women’s shoes that made a mean cost of goods of 5 dollars.
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3.3 Is there a difference in payments methods?
Figure 3
From the two box-plots, it can be observed that the confidence interval for the mean overlap
closely. This indicates that there is no big difference in the two methods of payments.
3.4 Are there any differences in the user groups on all of the
customer attitudes? (6 outcomes)
1.
Descriptives
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Knowledge of the
company
Light
Users
104 2.85 1.711 .168 2.51 3.18 1 6
Medium
Users
204 4.90 1.664 .116 4.67 5.13 2 7
Heavy
Users
284 6.45 .601 .036 6.38 6.52 5 7
Total 592 5.28 1.840 .076 5.14 5.43 1 7
Satisfaction with
the company
Light
Users
104 2.54 1.157 .113 2.31 2.76 1 6
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Medium
Users
204 5.39 1.033 .072 5.25 5.53 2 7
Heavy
Users
284 6.07 .776 .046 5.98 6.16 2 7
Total 592 5.22 1.585 .065 5.09 5.34 1 7
Preference for
Nike
Light
Users
104 2.46 1.507 .148 2.17 2.75 1 6
Medium
Users
204 2.82 1.572 .110 2.61 3.04 1 5
Heavy
Users
284 4.45 1.654 .098 4.26 4.64 1 7
Total 592 3.54 1.826 .075 3.39 3.69 1 7
Purchase Intention
for Nike
Light
Users
100 4.16 1.835 .184 3.80 4.52 1 7
Medium
Users
204 4.04 1.893 .133 3.78 4.30 2 7
Heavy
Users
284 5.01 1.195 .071 4.87 5.15 3 7
Total 588 4.53 1.648 .068 4.40 4.66 1 7
Would recommend
company to a
friend
Light
Users
104 3.46 1.131 .111 3.24 3.68 2 6
Medium
Users
204 4.92 .494 .035 4.85 4.98 4 6
Heavy
Users
284 6.35 .477 .028 6.29 6.40 6 7
Total 592 5.35 1.260 .052 5.25 5.45 2 7
Loyalty for Nike Light
Users
104 3.92 1.499 .147 3.63 4.21 2 6
Medium
Users
204 4.14 1.563 .109 3.92 4.35 2 6
Heavy
Users
284 3.92 1.575 .093 3.73 4.10 2 7
Total 592 3.99 1.559 .064 3.87 4.12 2 7
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
Knowledge of the company Based on Mean 137.679 2 589 .000
Based on Median 43.167 2 589 .000
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Based on Median and with
adjusted df
43.167 2 366.541 .000
Based on trimmed mean 130.226 2 589 .000
Satisfacition with the
company
Based on Mean 34.012 2 589 .000
Based on Median 19.318 2 589 .000
Based on Median and with
adjusted df
19.318 2 543.743 .000
Based on trimmed mean 28.470 2 589 .000
Preference for Nike Based on Mean 3.007 2 589 .050
Based on Median 2.032 2 589 .132
Based on Median and with
adjusted df
2.032 2 534.316 .132
Based on trimmed mean 3.259 2 589 .039
Purchase Intention for Nike Based on Mean 51.499 2 585 .000
Based on Median 48.655 2 585 .000
Based on Median and with
adjusted df
48.655 2 555.807 .000
Based on trimmed mean 49.574 2 585 .000
Would recommend company
to a friend
Based on Mean 155.697 2 589 .000
Based on Median 75.229 2 589 .000
Based on Median and with
adjusted df
75.229 2 489.408 .000
Based on trimmed mean 152.051 2 589 .000
Loyalty for Nike Based on Mean .438 2 589 .645
Based on Median 1.134 2 589 .322
Based on Median and with
adjusted df
1.134 2 466.890 .323
Based on trimmed mean .333 2 589 .717
Table 1
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In the test above, homogeneity of variance has been used to assess Lavene’s test. Lavene’s test uses the F-
test to if the variance between variables is equal (null hypothesis) (Derrick, Toher, & White, 2017).
Knowledge company has a critical value of 0.00 < 0.05. The null hypothesis is rejected thus it is
concluded that the variance is significantly different across the variables. Another variable is ‘satisfaction
with company”. It has a critical value of 0.00 < 0.05. The null hypothesis is rejected thus it is concluded
that the variance is significantly different across the variables. However, ‘loyalty for nike’ and other items
have critical values of that are greater than 0.05. This forces the research to fail to reject the null
hypothesis (Leigh, 2008) and conclude that there is equality of variance across these two groups in terms
of customer attitudes.
ANOVA
Sum of Squares df Mean Square F Sig.
Knowledge of the company Between Groups 1034.437 2 517.218 315.401 .000
Within Groups 965.888 589 1.640
Total 2000.324 591
Satisfaction with the
company
Between Groups 959.259 2 479.630 538.032 .000
Within Groups 525.065 589 .891
Total 1484.324 591
Preference for Nike Between Groups 461.224 2 230.612 89.966 .000
Within Groups 1509.803 589 2.563
Total 1971.027 591
Purchase Intention for Nike Between Groups 129.379 2 64.690 25.830 .000
Within Groups 1465.070 585 2.504
Total 1594.449 587
Would recommend company
to a friend
Between Groups 692.399 2 346.199 829.181 .000
Within Groups 245.919 589 .418
Total 938.318 591
Loyalty for Nike Between Groups 6.460 2 3.230 1.331 .265
Within Groups 1429.513 589 2.427
Total 1435.973 591
Table 2
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3.5 Test for the difference in means of user groups between
different customer attitudes
a. Attitude 1: Knowledge of the company
H0 : μ1=μ2=μ3
Versus
H1 : At least one μ is different
Alpha value = 0.05
The p-value calculated (0.00) is less compared to the alpha value (0.05). This means that null
hypothesis is rejected thus at least one mean is different.
b. Attitude 2: Satisfaction with the company
H0 : μ1=μ2=μ3
Versus
H1 : At least one μ is different
Alpha value = 0.05
The p-value calculated (0.00) is less compared to the alpha value (0.05). This means that null
hypothesis is rejected thus at least one mean is different (Howell, 2007).
c. Attitude 3: Preference for Nike
H0 : μ1=μ2=μ3
Versus
H1 : At least one μ is different
Alpha value = 0.05
The p-value calculated (0.00) is less compared to the alpha value (0.05). This means that null
hypothesis is rejected thus at least one mean is different.
d. Attitude 4: Purchase intention for Nike
H0 : μ1=μ2=μ3
Versus
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H1 : At least one μ is different
Alpha value = 0.05
The p-value calculated (0.00) is less compared to the alpha value (0.05). This means that null
hypothesis is rejected thus at least one mean is different (Hinkelmann & Kempthorne, 2010).
e. Attitude 5: Would recommend company to a friend?
H0 : μ1=μ2=μ3
Versus
H1 : At least one μ is different
Alpha value = 0.05
The p-value calculated (0.00) is less compared to the alpha value (0.05). This means that null
hypothesis is rejected thus at least one mean is different.
f. Attitude 6: Loyalty for Nike
H0 : μ1=μ2=μ3
Versus
H1 : At least one μ is different
Alpha value = 0.05
The p-value calculated (0.27) is less compared to the alpha value (0.05). This means that null
hypothesis is accepted thus the means are equal across the three groups (Gelman, 2005). .
Multiple Comparisons
Bonferroni
Dependent Variable
(I) Webiste User
Group
(J) Webiste User
Group
Mean
Difference
(I-J)
Std.
Error Sig.
95% Confidence
Interval
Lower
Bound
Upper
Bound
Knowledge of the
company
Light Users Medium Users -2.056* .154 .000 -2.43 -1.69
Heavy Users -3.605* .147 .000 -3.96 -3.25
Medium Users Light Users 2.056* .154 .000 1.69 2.43
Heavy Users -1.549* .118 .000 -1.83 -1.27
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