A Statistical Analysis Of Nike’s Sales And Customer Response

Problem Definition and Business Intelligence Required

Nike is an American multinational company which deals in design, manufacture and sale of athletic footwear, apparel, gear and accessories and other related services. Its profile specifies that it engages in innovation and production of products which serve the athletes of today and the future (“About Nike – Company Profile”, 2018). Keeping in line with this, they engage in an array of community service programs, primary among which are getting children actively engaged in sports and physical activities through their coaching and programs and promoting equality in communities by supporting partners in furthering the agenda of bringing about just that (“Nike Global Community Impact”, 2018).

They have over 70 partners with whom they engage to live up to the commitment of championing equality (“What We Do | Nike Global Community Impact”, 2018). Another cause that Nike has been pursuing is that of sustainable innovation whereby they actively involve sustainability practices in their business process and production with a focus on reducing carbon emissions. Nike maintains that the organization believes that is efforts towards sustainable innovation transcends the act of just ensuring low emissions. Nike has been actively making efforts to innovate in ways which leads to products which use lesser resources and lasts longer, adopting a business model which is circular instead of linear (“Sustainable Innovation”, 2018).

2. Problem definition and business intelligence

The report deals with addressing the following research questions in regard to Nike’s sales and customer response to their services and products.

1. What the top selling product categories are and what the worst selling products are?

The initial exploratory analysis is done using statistical tools of descriptive statistics such as mean and bar charts. This is followed up by inferential statistical approaches of ANOVA to verify the difference in profits among groups labelled by product category and then Tukey’s post hoc test is used to find the order of the groups (Berenson et al., 2012). The level of significance was taken to be 5%. The ANOVA method is used to test for difference between means of more than one group on the basis of the same factor, such as profit in this case or more than one factor in general (Anderson et al., 2016).

1. Does there exist any difference in the sales in terms of payment method by customers?

There are two kinds of payment methods adopted by the customers, namely, credit card and Paypal. It is to be seen if the purchases via one is greater than the other or not. Two sample t-test for means is therefore used to check if any such difference exist or not. The level of significance was taken to be 5%. According to Hair (2015), since only two groups exist, the purchases using credit and by Paypal can be treated as two separate samples and the difference between the sample means can then be done using independent samples t-test.

1. Does user group have any impact on attitude of the customer and how?

Results of the Selected Analytics Methods and Technical Analysis

Users had been divided into 3 groups in the data on the basis of how much they use Nike services and products. The five attitude aspects of satisfaction, brand awareness, preference, purchase intentions and loyalty could then be compared among the groups using five separate one way ANOVA with level of significance taken as 5%.The factors for the five ANOVA are the five behavioral attribute variables.

1. Does gender play a part in the observed attitudes of the customers and how?

The tentative difference between the two genders for the five customer attributes defined in the data are verified using independent sample t-test with level of significance taken to be 5% (Larson-Hall, 2015).

3. Analysis and results

1.What the top selling product categories are and what the worst selling products are?

The measure of how well a product is selling could be taken to be the profit incurred for the specific product category. Using this rationale, the mean profits for each product and the percentage of total profit accounted for by each product is calculated and scrutinized. The following table gives the means for each category.

 Table 1: Case summaries of Profits from Products

Clearly, it is seen that customized products yield maximum mean profit amount whereas boys’ shoes yield minimum or least mean profit amount. The following figure shows the mean profits by product category for Nike as given in the data. However it is seen that maximum proportion of the total profits is accounted for by men’s shoes, then men’s clothing, followed by women’s shoes and customized products with girls’s clothing, shoes having the least contribution to profits.Conducting one way ANOVA over the categories with profit as factor it was seen that customized products had maximum of all the other products whereas boys’ shoes had least. The following tables shows the summary of the ANOVA and Tukey’s post hoc test that gives the result.

Table 2: Summary table of ANOVA comparing profits for product groups 

Table 3: Group Mean profits of Product groups

1.    Does there exist any difference in the sales in terms of payment method by customers?

The mean of the purchases made through Credit cards was found to be $3.0587 through PayPal and $5.1183 through Credit card. A two sample t-test was done to test whether the purchase using Credit card differs from that made through Paypal. It was seen that the conjecture that there is no difference was rejected at 5% in favour of alternative that mean purchase amount through Credit card is higher than that through Paypal.

Discussion of the Results and Recommendations

The following tables gives the summarized results of the t-test:

 Table 4: Summary of mean transactions by Paypal and Credit card 

Table 5: Summary of t-test for difference in Paypal and Credit card transactions 

2. Does user group have any impact on attitude of the customer and how?

The five customer attribute outcomes of brand awareness, satisfaction, and preference for the brand and purchase intention are scrutinized for the three customer groups based on their history of purchase, the groups being labelled as, heavy users, mediums users and low users. Since there are more than two groups, one way ANOVA is carried out for each of the five outcomes being considered as the factor on the basis of which there might be a tentative difference among the groups (Cronk, 2017). The mean brand awareness scores for each group, the mean satisfaction scores, mean preference scores and purchase intention score for each groups are given in the following tables:

Table 6: Customer behaviour scores for each user group

It was seen from the results of the ANOVA that for the factor brand awareness, the difference between the three groups light users, medium users and heavy users was significant. The difference among the three groups in terms of satisfaction, preference and purchase intent were also found to vary significantly. Brand loyalty however was found to not be so significantly different among the groups.

Table 7: ANOVA table for difference in customer behaviour among user groups

The results of Tukey’s post Hoc test suggested that the factor brand awareness was found to be greatest for heavy users and least for light users. The following table and subsequent figure shows the mean score of brand awareness for the three groups. 

Table 8: Post-Hoc test for ANOVA on Awareness among user groupsThe results of Tukey’s post Hoc test suggested that the factor product satisfaction for Nike products was found to be greatest for heavy users and least for light users. The following table and subsequent figure shows the mean score of product satisfaction for the three groups.

Table 9: Post-Hoc test for ANOVA on Satisfaction scores among user groupsFigure 4: Mean Satisfaction scores against User Groups

The results of Tukey’s post Hoc test suggested that the factor preference for Nike brand and its products was found to be greatest for heavy users and it was least for light users. However although the difference between heavy users vary significantly with those of light users, the difference is not so pronounced between the medium users and heavy users whereas it is pronounced for medium and light users. Consequently, the three groups could grouped into two subsets. The following table and subsequent figure shows the mean score of preference for Nike for the three groups.

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Table 10: Post-Hoc test for ANOVA on preference scores among user groupsThe results of Tukey’s post Hoc test suggested that the factor purchase intent of the customers found to be greatest for heavy users and it was least for light users. However although the difference between heavy users vary significantly with those of light users, the difference is not so pronounced between the light users and medium users or medium users and heavy users. Consequently, the three groups could be grouped into two subsets. The following table and subsequent figure shows the mean purchase intent scores for the three groups.

Table 11: Post-Hoc test for ANOVA on purchase intention scores among user groups1. Does gender play a part in the observed attitudes of the customers and how?

The five customer attribute outcomes of brand awareness, satisfaction, and preference for the brand and purchase intention were then scrutinized for differences with the consumers having been grouped as per their gender. Since there are just two groups, independent two sampled t-tests were carried out for each of the five outcomes on the basis of which there might be a tentative difference between men and women consumers. The following table gives the mean of the outcome scores for men and women separately. 

Table 12: Customer behaviour scores for each Gender 

            Having conducted the t-tests for awareness, satisfaction, preference, purchase intention and loyalty for the two gender based groups, having assumed unequal variance between the two groups, it was seen that the conjecture for no difference for the brand awareness was rejected at 5% level of significance. The alternate conjecture that awareness of the men is greater than that of the women was thus favored as opposed to the null hypothesis. Again, the t-test to test for differences between the two groups in terms of satisfaction having assumed unequal variance in scores for the two groups , was found to fail to reject the hypothesis of no difference which is the null hypothesis at 5% level of confidence.

The test for difference in preference between men and women consumers of Nike products was found to reject the conjecture of no difference or null hypothesis at 5% level of significance, where the variance of the two groups was assumed to be unequal. The test for difference in purchase intent for men and women was found to fail to reject the hypothesis of no difference which is the null hypothesis at 5% level of confidence. Finally the test for no differences in loyalty between the two groups revealed that there is sufficient evidence to support the rejection of the no difference conjecture or null hypothesis, having assumed unequal variances and at 5% level of significance. The following table gives the summarized results for the five t-tests.

Levels of Achievement

 Table 13: Summaries of t-tests for difference in behaviour scores between genders

4. Discussion

            Drawing upon the results of the analysis it could thus be said that the Nike’s customized products seem to have maximum profit amount per item whereas its sales on boy’s shoes constitute lowest profit amount. However, looking at the picture from a sales perspective, profits from men’s apparel and shoes make up for the bulk proportion of the total profits. The least profit is seen to come from the products aimed towards minors and among them it’s particularly low for girl’s shoes and clothing. Hence it could be said that the key consumer section for Nike are adults and more so it is adult males. Although amount of profit is maximum for customized products the overall figures indicate that sales of apparel and shoes far exceed that of customized products making their contribution to profits greater owing to number of items sold rather than the profit per item.

The maximum transactions were found to be made through Credit Card rather than PayPal. Upon looking at the observed customer behaviour attributes or brand awareness, satisfaction, preference and intent of purchase it was seen that consumers who were classed as heavy users had significant higher scores than low users, who were also significantly different from medium users. However brand loyalty was found to be consistent among the three groups where overall loyalty score for all the consumers was found to be 3.99 and standard deviation 1.569.

It is hence suggested that Nike has managed to maintain the loyalty of its consumers across sections of its market. Again the discrepancies in the score among men and women suggested that men constitute the major portion of its consumer base since brand awareness, preference and loyalty was found to be distinctively higher for males than females betraying that men are more likely to opt for Nike products than women. The behavioural score of satisfaction, purchase intent and loyalty was however found to be more or less similar for both the genders.

            Hence Nike would benefit by targeting adult men and focus their attention on men’s clothing and footwear. Also given the fact that women’s apparel come a close second to men’s clothes, it presents good market opportunity for Nike and hence they could focus on bringing about more innovations to target the female consumer population. Given the fact that brand awareness in women are not as pronounced as in men, it could also prove to be beneficial to investing to increase brand awareness among women.

References

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2016). Statistics for business & economics. Nelson Education.

Berenson, M., Levine, D., Szabat, K. A., & Krehbiel, T. C. (2012). Basic business statistics: Concepts and applications.

Cronk, B. C. (2017). How to use SPSS®: A step-by-step guide to analysis and interpretation. Routledge.

Hair Jr, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015). Essentials of business research methods. Routledge.

Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and R. Routledge.

About Nike – Company Profile. (2018). 

Nike Global Community Impact. (2018).

Sustainable Innovation. (2018). 

What We Do | Nike Global Community Impact. (2018).

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