Profit Analysis with Customer Satisfaction for Nike

1
A Detailed Profit Analysis with Customer
Satisfaction for Nike
Student Name: Student ID:
Unit Name: Unit ID:
Date Due: Professor Name:
Word Limit:

2
Contents
Table of Tables............................................................................................................................................2
Table of Figures...........................................................................................................................................2
1.0 Introduction of the Problem..................................................................................................................4
2.0 Classification of Business Problems of Nike...........................................................................................4
3.0 Results of Analytical and Technical analysis..........................................................................................6
3.1 Customer Characteristic Investigation...............................................................................................6
3.11 Do the customer states of mind demonstrate a differentiation for three user factions..............6
3.12 Do the customer states of mind demonstrate a distinction for two sexes..................................7
3.2 Profit and COGS Analysis...................................................................................................................9
3.21 Which are the best and the worst selling product........................................................................9
3.22 Is there any significant difference in payment methods............................................................12
3.23 How COGS score was related product category........................................................................13
3.24 How the two payment methods are different for product wise payment.................................15
3.25 What is the level of association between COGS and net revenue levels...................................16
3.26 How profit pattern changes on monthly basis...........................................................................16
3.27 How profit pattern changes on sex basis...................................................................................18
4.0 Discussion and Recommendations......................................................................................................20
5.0 References...........................................................................................................................................20
6.0 Appendix..............................................................................................................................................22
Table of Tables
Table 1: Descriptive statistics for different personality traits......................................................................7
Table 2: Sex based descriptive value for customer attributes.....................................................................8
Table 3: Website User Group * Gender * Awareness of Nike Cross Tabulation..........................................9
Table 4: Post Hoc results for customer attributes.....................................................................................23
Table 5: Sex * Product Class Cross tabulation............................................................................................24
Table 6: One-Sample Test for two payment options.................................................................................25
Table 7: One way Descriptive values for usage groups..............................................................................26
Table 8: ANOVA for attributes of customers.............................................................................................27
Table 9: Paired Samples t-Test for payment methods..............................................................27
Table 10: Correlation between Cost of goods and Profit Total..................................................................28
Table 11: Chi-Square Tests for customer attributes..................................................................................28

3
Table of Figures
Figure 1: Gender frequency based on usage group.....................................................................................9
Figure 2: Annual total profit based on product category...........................................................................10
Figure 3: Identification of best and worst product....................................................................................11
Figure 4: Best and worst selling product category.....................................................................................12
Figure 5: Classification tree for entire product segments..........................................................................12
Figure 6: Box Plot for Credit card payment................................................................................................13
Figure 7: Box Plot for Pay Pal payment......................................................................................................14
Figure 8: Classification tree describing entire product segments on COGS...............................................15
Figure 9: Product lines on Pay Pal payment...............................................................................................16
Figure 10: Product lines on Credit Card payment......................................................................................17
Figure 11: Monthly total_ profit distribution bar diagram........................................................................18
Figure 12: Month wise total profit distribution.........................................................................................19
Figure 13: Sex wise profit distribution.......................................................................................................19
Figure 14: Classification tree for profit based on broadly considered sex.................................................20
Figure 15: Average profit product item wise.............................................................................................25

4
1.0 Introduction of the Problem
'Nike' is an overall brand name for sports apparel and it puts a lot of effort to attract all the people
involved in any sort of competitive sporting activity. The organization follows by their main goal
of giving creative sportswear to all competitors around the globe. The witticism of manageability
and excellent modernization has made an incredible brand an incentive for the customers.
Committed research group, scientists have fabricated a model which is controlling Nike to an
exponential development in item goes alongside overall revenues. The professional ecological
approach has added to the brand name and supported constant development at each edge of the
globe. Assortment in product offering, every climate don extras has pulled in customers from the
entire age sections. The financial specialists and partners are keen on new interest in the
organization. The centre administration of the organization has embraced a pilot task to
contemplate late patterns in consumer loyalty and profit examination. The market survey was
directed in two stages, first for customer examination and afterward for organization profit
investigation in light of chosen item extends (Li et al., 2015).
2.0 Classification of Business Problems of Nike
The main period of the work included customer introspection in light of responses of the
customers. The study group collected information on five fields, they were, product preference,
purchase intent awareness about various product lines, satisfaction level, and reliability of the
customers. Customers were separated into three classes in view of their choice of item extends.
Heavy, medium and light utilization class of customers were categorised based on their sex.
Emphasis was given on two decisive questions identified with the gathered information. The
inspection arranged inquiries were as per the following,

5
 Do the customer states of mind demonstrate a differentiation for three user factions?
 Do the customer states of mind demonstrate a distinction for two sexes?
Three user class and sex of the users were two ordinal factors. Examination of five customer
identity qualities, which were ordinal in nature, was performed for the user class and their sex.
The proper decision of explanatory technique for correlation was considered as Cross-
Tabulation. The inferential conclusion was drawn from Chi-square test (Mikulić et al., 2017).
Profit analysis was completed in the second stage which included COGS data of one year with
nine distinctive product offerings. Edge of cost of items (COGS) and aggregate profit, alongside
payment modes were examined. As the primary concern of each industry is profit, the
administration chose to centre on the profit figures, related with item classifications. For
definitive conclusion, seven questions were organized as underneath,
 Which are the best and the worst selling product?
 Is there any significant difference in payment methods?
 How COGS score was related product category?
 How the two payment methods are different for product wise payment?
 What is the level of association between COGS and net revenue levels?
 How profit pattern changes on monthly basis?
 How profit pattern changes on sex basis?
The engaging measures for each exploration question were discovered in view of nature of the
information, and proper inferential factual measure was taken to set up the cases of illustrative
qualities. The chi-square test, t-test, one way analysis of variance, correlation was utilized with
the end goal of the examination.

6
3.0 Results of Analytical and Technical analysis
3.1 Customer Characteristic Investigation
3.11 Do the customer states of mind demonstrate a differentiation for three user factions
The quantity of light sportswear customers, medium range customers and overwhelming frill
customers were individually 27, 51 and 72. The normal and standard deviation of the customers
have been given in table 1
Table 1: Descriptive statistics for different personality traits
Theories were set for the inferential investigation. It was speculated that the three customer
bunches did not vary in normal number for five characteristics. As the examination included
association of treatment bunches with ordinal nature with piece factors of scale variable, ANOVA
was the decision for the inferential test. The three customer bunches varied fundamentally in,
mindfulness about Nike items (F = 82.3, p < 0.05), fulfilment level (F = 139.9, p < 0.05),
inclination of items (F = 23.5, p < 0.05), buy intension of items (F = 6.2, p < 0.05). Be that as it

7
may, with a lovely amazement, the sections were similarly faithful to Nike items (F = 0.4, p >
0.05). Tukey HSD test built up the interrelation of customer sections; they varied essentially (p
esteem < 0.05) in mindfulness and fulfilment scores, however likeness was seen for buy intension,
inclination and unwaveringness for 'Nike' items (Table 3 in Appendix).
3.12 Do the customer states of mind demonstrate a distinction for two sexes
The information was gathered for 99 females and 51 guys. The mean and standard deviation for
customer qualities for the two sexual orientations has been given in table 2.
Table 2: Sex based descriptive value for customer attributes
It was guessed that sexual orientation write has no impact on the trait scores of the customers.
ANOVA was picked as the proper device for inferential factual investigation. The outcomes
uncovered factually huge contrast in mindfulness (F = 6.0, p < 0.05) and inclination in item go (F
= 11.4, p < 0.05), and dedication for 'Nike' items (F = 44.3, p < 0.05). The two sexual
orientations were found to have comparable traits when fulfilment (F = 0.3, p > 0.05) and buy
intension (F = 2.3, p > 0.05) scores were investigated.

8
Figure 1: Gender frequency based on usage group
Cross tabulation was done with chi-square test, the difference in attributes between two genders
was found to be non significant (table 11 in appendix). The distribution of users for two sexes
has been provided in table 3.
Table 3: Website User Group * Gender * Awareness of Nike Cross Tabulation
Awareness of Nike: Total
Count
Gender
TotalFemale Male
Website
User
Group
Light
Users 18 9 27
Medium
Users 31 20 51
Heavy
Users 50 22 72
Total 99 51 150

9
3.2 Profit and COGS Analysis
3.21 Which are the best and the worst selling product
The best and most noticeably bad offering item were distinguished as Men's shoes and Girls
garments. Add up to profit for Men's shoes and Girls garments were $ 1446.3 and $ 8.0. The
histogram in figure 1 and box-plot in figure 2 were utilized to pick and affirm the best and most
exceedingly bad offering items.
Figure 2: Annual total profit based on product category

10
Figure 3: Identification of best and worst product
The choice tree examination was utilized for affirmation of the histogram result. Young ladies
apparel was in the not exactly $ 4.00 profit section, though Male shoes shared right around 78%
of profit for the most astounding profit section of $ 6.00.

11
Figure 4: Best and worst selling product category
Figure 5: Classification tree for entire product segments

12
The invalid speculation was developed with accepting equivalent profit for all the item goes. The
cooperation amongst ordinal and scale factors was cross checked by ANOVA and there was
measurably critical (F = 43106.4, P < 0.05) contrast in the product offerings. The ANOVA
between the best and most exceedingly bad offering items uncovered (F = 1686.6, p < 0.05)
noteworthy distinction in total profit. The invalid theory was rejected in light of the outcomes.
The Chi-square test for the classification tree ( χ2=506 .2 , p < 0.05) finished the inferential
examination for measurably huge distinction in the profit add up to for various level of profits.
3.22 Is there any significant difference in payment methods
Two payment techniques, outwardly PayPal and Visa payment were thought about. It was
estimated that payment strategies did not vary in view of the item goes. The normal buy paid
with Pay Pal was $ 3.06 with SD of $ 3.31, and with credit card was $ 5.12 with SD of $ 5.14.
The t-test set up factually huge distinction between the two payment techniques (t = - 6.18, p <
0.05). Normal payment with credit card was higher contrasted with Pay Pal payment technique.
Figure 6: Box Plot for Credit card payment

13
Figure 7: Box Plot for Pay Pal payment
3.23 How COGS score was related product category
To discover the item astute investigation of COGS of the items, classification tree was shaped for
various item classes. Cost of men attire was observed to be the base with cost edge not exactly or
equivalent to $ 1.00 and Men shoes was in the most elevated cost edge section of more
prominent or equivalent to $ 2.70. ANOVA was utilized as a measure of inferential procedure
and the invalid theory, with the presumption of balance of cost of merchandise was rejected in
light of the esteem (F = 435796.65, p < 0.05) of the measurement (Song & Ying, 2015).

14
Figure 8: Classification tree describing entire product segments on COGS

15
3.24 How the two payment methods are different for product wise payment
Box plots were built and altered product offering was observed to be more comfortable with Pay
Pal payment and payment for Men shoe was done overwhelmingly with credit card strategy.
Figure 9: Product lines on Pay Pal payment
It was guessed that the product offerings did not vary in Pay Pal payment technique, ANOVA
was directed with 7 degrees of opportunity. It was discovered that the product offerings
fundamentally vary in Pay Pal payment strategy (F = 819561.63, p < 0.05), and the invalid
speculation was rejected.

16
Figure 10: Product lines on Credit Card payment
3.25 What is the level of association between COGS and net revenue levels
The inter connection amongst COGS and aggregate profit involved intrigue. The two factors
were scale factors, hence connection coefficient was found between them. The positive
relationship (r (364) = 0.65, p < 0.01) for two followed test was factually noteworthy. The
invalid speculation accepting that, the COGS and aggregate profit were uncorrelated was
rejected. High net revenue for lifted cost of items was clear from the estimation of connection
coefficient (Mahdi et al., 2015)
3.26 How profit pattern changes on monthly basis
Month to month profit circulation was an imperative perception from the organization's
perspective. Histogram was utilized to speak to the aggregate month to month profit. Because of
the brand name and advertising procedure the aggregate profit was observed to be equivalent all

17
through every one of the months. An ANOVA was utilized to cross check the claim, and the
presumption of invalid speculation about uniformity in all out deals for every one of the months
(F = 0.7, p > 0.05) persuaded neglected to be rejected.
Figure 11: Monthly total_ profit distribution bar diagram

18
Figure 12: Month wise total profit distribution
3.27 How profit pattern changes on sex basis
The whole informational collection was isolated in four sexual orientation classes, men, ladies,
children and custom. Add up to profit for the expansive item categories was considered. At first a
histogram was developed, which demonstrated that Men segment was acquiring the most
extreme profit and children area was gaining the lowest profit (Haupt et al., 2017).
Figure 13: Sex wise profit distribution

19
The grouping tree was utilized with the end goal of affirmation. It was watched that child class
product offering had minimum profit run with profit not exactly or equivalent to $ 4.00 and Men
extend had the most profit with profit more prominent than $ 7.00. The chi-square test was
utilized as an inferential test and it affirmed with, p < 0.05 for four sections of profit.
Subsequently, Men class sports extras were procuring the most aggregate profit (Terrien et al.,
2017).
Figure 14: Classification tree for profit based on broadly considered sex

20
Cross tabulation was conducted between the gender category and product category. Men’s
clothing was less sold product than men’s shoes; women preferred clothing more than shoes.
Boy’s shoes were the most sold segment in kid’s section (table 4 in appendix).
4.0 Discussion and Recommendations
The above examinations were gathered together and some critical components were indicated out
the CEO of Nike. Men's product offerings were sold more than some other items, the deal and
profit figures were the most astounding for a similar product offering. Visa was viewed as the real
method of payment from Men shoe, and men were observed to be more pulled in by the
advertising methodologies of the organization. This was recognizable from the consciousness of
men about the product offerings (Lyu & Han, 2017).
It was recommended that assortment in female product offering was required; kids area should be
redone with new items. As a method of payment, Pay Pal should be all the more appealingly
showcased with additional rebate coupons. Promoting target ought to be the female segment;
particularly moms ought to be the focal point of the publicizing group (Terrien et al., 2017).
5.0 References
“Nike Official Site.” NIKE, Inc.- Inspiration and Innovation for Every Athlete in the World.,
Retrieved from www.nike.com/au/en_gb/.
Haupt, J., Stripling, E., Baesens, B., & Lessmann, S. (2017). Profit-maximizing scorecard
development.
Li, J., Wong, J. Y., Leung, P. W. Y., Yip, W. T., & Tian, X. (2015). CASE SYNOPSIS FOR.
Lyu, S. O., & Han, J. H. (2017). Assessing preferences for mega sports event travel products: a
choice experimental approach. Current Issues in Tourism, 20(7), 740-758.

21
Mahdi, H. A. A., Abbas, M., Mazar, T. I., & George, S. A. (2015). A Comparative Analysis of
Strategies and Business Models of Nike, Inc. and Adidas Group with special reference to
Competitive Advantage in the context of a Dynamic and Competitive
Environment. International Journal of Business Management and Economic
Research, 6(3), 167-177.
Mikulić, J., Prebežac, D., Šerić, M., & Krešić, D. (2017). Campsite choice and the camping
tourism experience: Investigating decisive campsite attributes using relevance-
determinance analysis. Tourism management, 59, 226-233.
Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and
prediction. Shanghai archives of psychiatry, 27(2), 130.
Terrien, M., Scelles, N., Morrow, S., Maltese, L., & Durand, C. (2017). The win/profit
maximization debate: strategic adaptation as the answer?. Sport, Business and
Management: An International Journal, 7(2), 121-140.

22
6.0 Appendix
Table 4: Post Hoc results for customer attributes

23
Table 5: Sex * Product Class Cross tabulation
Product Class
Total
Men’s
shoes
Men’s
clothing
Women’s
shoes
Women’s
clothing customize
boys
shoes
girls
shoes
girls
clothing
Sex Men 91 78 0 0 0 0 0 0 169
Women 0 0 13 116 0 0 0 0 129
Kid 0 0 0 0 0 51 2 2 55
Custo
m
0 0 0 0 13 0 0 0 13
Total 91 78 13 116 13 51 2 2 366

24
Figure 15: Average profit product item wise
Table 6: One-Sample Test for two payment options
Test Value = 0
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Total
purchases
paid with
Pay Pal
17.662 365 .000 3.05874 2.7182 3.3993
Total
purchases
paid with
Credit
card
19.049 365 .000 5.11831 4.5899 5.6467

25
Table 7: One way Descriptive values for usage groups
N Mean
Std.
Deviatio
n
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Awareness
of Nike
Light
Users 27 2.81 1.711 .329 2.14 3.49 1 6
Medium
Users 51 4.90 1.676 .235 4.43 5.37 2 7
Heavy
Users 72 6.46 .604 .071 6.32 6.60 5 7
Total 150 5.27 1.857 .152 4.97 5.57 1 7
Satisfaction
with Nike
Light
Users 27 2.52 1.156 .222 2.06 2.98 1 6
Medium
Users 51 5.39 1.041 .146 5.10 5.68 2 7
Heavy
Users 72 6.07 .775 .091 5.89 6.25 2 7
Total 150 5.20 1.601 .131 4.94 5.46 1 7
Preference
for Nike
Light
Users 27 2.41 1.526 .294 1.80 3.01 1 6
Medium
Users 51 2.82 1.584 .222 2.38 3.27 1 5
Heavy
Users 72 4.46 1.652 .195 4.07 4.85 1 7
Total 150 3.53 1.834 .150 3.24 3.83 1 7
Purchase
Intention for
Nike
Light
Users 26 4.23 1.861 .365 3.48 4.98 1 7
Medium
Users 51 4.04 1.907 .267 3.50 4.58 2 7
Heavy
Users 72 5.01 1.193 .141 4.73 5.29 3 7
Total 149 4.54 1.646 .135 4.28 4.81 1 7
Loyalty for
Nike
Light
Users 27 3.85 1.537 .296 3.24 4.46 2 6
Medium
Users 51 4.14 1.575 .221 3.69 4.58 2 6
Heavy
Users 72 3.94 1.591 .188 3.57 4.32 2 7
Total 150 3.99 1.569 .128 3.74 4.25 2 7

26
Table 8: ANOVA for attributes of customers
Sum of
Squares df
Mean
Square F Sig.
Awareness
of Nike
Between
Groups 271.334 2 135.667 82.253 .000
Within
Groups 242.459 147 1.649
Total 513.793 149
Satisfactio
n with Nike
Between
Groups 250.450 2 125.225 139.932 .000
Within
Groups 131.550 147 .895
Total 382.000 149
Preference
for Nike
Between
Groups 121.528 2 60.764 23.518 .000
Within
Groups 379.805 147 2.584
Total 501.333 149
Purchase
Intention
for Nike
Between
Groups 31.443 2 15.722 6.212 .003
Within
Groups 369.523 146 2.531
Total 400.966 148
Loyalty for
Nike
Between
Groups 1.769 2 .884 .356 .701
Within
Groups 365.224 147 2.485
Total 366.993 149
Table 9: Paired Samples t-Test for payment methods
Paired Differences
t df Sig. (2-
tailed)Mean Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair 1
Total
purchases
paid with
Pay pal -
Total
purchases
paid with
Credit
card
-2.05956 6.37706 .33333 -2.71506 -1.40407 -6.179 365 .000

27
Table 10: Correlation between Cost of goods and Profit Total
Cost of
Goods
($)
Profit
Total
Cost of
Goods
($)
Pearson
Correlation 1 .646
Sig. (1-
tailed) .000
N 366 366
Profit
Total
Pearson
Correlation .646 1
Sig. (1-
tailed) .000
N 366 366
Table 11: Chi-Square Tests for customer attributes
Awareness of Nike Value df
Asymp.
Sig. (2-
sided)
Exact
Sig. (2-
sided)
Exact
Sig. (1-
sided)
a little
familiar
Pearson
Chi-Square .640c 1 .424
Continuity
Correction 0.000 1 1.000
Likelihood
Ratio .980 1 .322
Fisher's
Exact Test 1.000 .625
Linear-by-
Linear
Association
.600 1 .439
N of Valid
Cases 16
somewhat
familiar
Pearson
Chi-Square .000e 1 1.000
Continuity
Correction 0.000 1 1.000
Likelihood
Ratio 0.000 1 1.000
Fisher's
Exact Test 1.000 .745

28
Linear-by-
Linear
Association
0.000 1 1.000
N of Valid
Cases 12
moderately
familiar
Pearson
Chi-Square 2.127f 2 .345
Likelihood
Ratio 2.883 2 .237
Linear-by-
Linear
Association
.774 1 .379
N of Valid
Cases 11
mostly
familiar
Pearson
Chi-Square 10.558g 2 .005
Likelihood
Ratio 11.567 2 .003
Linear-by-
Linear
Association
9.923 1 .002
N of Valid
Cases 57
extremely
familiar
Pearson
Chi-Square 3.197h 1 .074
Continuity
Correction 1.707 1 .191
Likelihood
Ratio 3.279 1 .070
Fisher's
Exact Test .146 .096
Linear-by-
Linear
Association
3.121 1 .077
N of Valid
Cases 42
Total
Pearson
Chi-Square 1.004a 2 .605
Likelihood
Ratio .997 2 .607
Linear-by-
Linear
Association
.274 1 .601

29
N of Valid
Cases 150