Research Questions and Analytics Methods
Athlete Panda is a small retail store that sells athletic products, mainly clothing and shoes. Nonetheless, the store also sells athletic accessories and sporting equipment. The business has five major sectors: Men’s, Boys, Women’s, Girls and Customization. The common brands that can be found at the store are Under Armour, Converse, Puma, ASICS, Nike, New Balance and Adidas. The company has been operating using the brick and mortar model, with several retail stores across the region for five years. However, the rise of e-commerce pushed the business to start trading online one year ago. Hence, the growing customer base. Customization is popular for online customers from all over the country because it allows them to customise their sneakers depending on use by a click of a button from the comfort of their home. Sneakers can be customised depending on lifestyle, running, football, basketball, gym & training, and skateboarding. Moreover, there are options to customise boys’ and girls’ shoes.
The Chief Executive Officer of Athlete Panda is concerned with the profitability and sustainability of the business. The company operates in a very competitive industry and faces several challenges. The main challenges are cost of goods margins, profit margins and number of sales. This report will analyse the financial data (shop sales) of the company over the past year, as well as customer responses to an online survey. Thereafter, the analysis will indicate how the business can increase sales and how their customers feel about the store.
The challenges stated by the company’s management are the basis of the report’s research questions. Therefore, critically analyse the business of Athlete Panda, the following research questions were used.
- Which product category costs the most?
The report uses this question to clarify which product category incurs the most cost. Hence, the business will be to analyse how the product category affects the profitability of the store.
- Which product categories are making most profit?
One of the main challenges for the company’s management is making good profit. This question will help the research identify which product category brings in more revenue for the business.
- Is there a difference in store location and sales?
It is important too understand how the sales of the business are affected by the location of the store. Generally, a physical store at the front of a busy street is expected to attract more customers, hence should have higher sales. This question also allows the research to analyse whether trading online is beneficial to the business.
- Is there as difference in payments methods?
The company offers its customers two option to pay for their products; cash or credit card. This question allows the research to analyse which payment method is popular among the customers. Hence, the business can plan its finances more effectively.
- Are there any differences in gender on all the customer attitudes?
Results and Conclusions
The online customer survey aimed at identifying how customers feel about the business. In particular, the researched attributes of the customers were knowledge, satisfaction, preference, purchase intention, loyalty and recommendation. This question will enable the researcher to identify whether the gender of a customer affects their attitude towards the company’s products
- Are there any differences in the user groups on all the customer attitudes?
This question is vital in analysing how customer attitudes vary across the different user groups. In other words, does the frequency of visits to the store influence the customer’s attitude towards the business and its products.
The use of contingency tables, also known as cross-tabulation, is often used to provide information about the relationship between two or more variables (Waller, 2010). This analytic method is simple to use and the results allows the researcher to identify patterns, trends and probabilities within data sets. When using large datasets, it becomes a daunting task to infer any insights for business decisions. Hence, cross tabulation creates tables that divided the total dataset into representative subgroup which become easier to interpret. Analysing contingency tables reduces confusion and error while providing profound insights effectively (Hinton, Brownlow and McMurray, 2014).
The report utilised contingency table to analyse research questions one and two. This is because the variables involved are categorical. The Chi-Square test was used to test the statistical significance of the relationship between the variables (Simpson, 1951). If there is no relationship, the variables are independent, and the results of crosstabulation are not dependable for strategic business decisions.
The paired sample t-test also known as dependent t-test is used to compare two means of related objects (Newbold, Carlson and Thorne, 2013). The purpose of this analytical method is to determine whether the mean of two different but related variables are statistically different. The dependent t-test was used to analyse research question four to determine the difference in payment methods.
The independent samples t-test is used to compare the means of two independent variables so as to determine whether they are significantly different. This analytical method was used to determine whether there is any statistically significant difference in gender on each of the six customer attitudes, as stated in research question five.
The one-way analysis of variance (ANOVA) is used to compare the means of more than two independent groups (Albright, 2017). For this test, the grouping variable is divided into more than two levels, and is compared to the dependent variable. This test will be used to determine if there are any significant differences between the variables in research question three and six.
The cross- tabulation indicate that women’s clothing incur the greatest proportion of cost of goods sold (COGS). The chart below indicated how the cost of goods sold is distributed among the product categories.
Figure 1: Pie Chart Cost of Goods Sold (COGS) by Product Category
The results indicate that the product categories that are making most profit for the business are men’s shoes ad women’s clothing. The sum total of these two product categories are higher than any other product category as indicated in the chart below.
Figure 2: Bar Chart of Sum of Profit Total by Product Category
The mean plot results indicate that the mean profit differ among the four store locations. With online stores having the highest mean profit. The ANOVA results, shown in the table below indicate that the mean profit total is significantly different for at least one of the store locations (F3,608 = 733.219, p < 0.001). The Turkey’s HSD post-hoc results show that the mean profit total is not significantly different between middle and back stores, but is significantly different between, the other pairs of store locations.
Figure 3: ANOVA results for Profit Total by Store Location
The paired t-test indicate that payment by cash and payment by credit card are significantly strongly and negatively correlated (r = -0.800, p < 0.001). There was a significant difference in the two payment methods (t611 = -7.112, p < 0.001).
Figure 4: Paired T-Test for Payment Methods
The independent t-test results indicate that there was a significant difference in knowledge of the company between male and female customers (t522.857 = -5.347, p < 0.001).
Figure 5: Independent t-test on Knowledge by Gender
The results indicate that there was no significant difference in satisfaction with the company between female and male customers (t590 = -0.867, p > 0.05).
Figure 6: Independent t-test on Satisfaction by Gender
The independent t-test results indicate that there was a significant difference in preference for the company between male and female customers (t493.730 = -7.223, p < 0.001).
Figure 7: Independent t-test on Preference by Gender
The t-test results indicate that there was no significant difference in purchase intentions for the company’s products between female and male customers (t586 = 2.875, p > 0.05).
Figure 8: Independent t-test on Purchase Intentions by Gender
The t-test results indicate that there was a significant difference in loyalty for the company between male and female customers (t586.589 = -16.471, p < 0.001).
The t-test results indicate that there was no significant difference in whether the customer would recommend the company to a friend between female and male customers (t590 = 1.442, p > 0.05).
The ANOVA results indicate that the knowledge of the company is significantly different for at least one user groups (F2,589 = 35.401, p < 0.001). From the mean plots, the user groups differ a lot in their knowledge of the company.
The results indicate that the satisfaction of the company is significantly different for at least one user groups (F2,589 = 538.032, p < 0.001). From the mean plots, the user groups differ a lot in their satisfaction of the company.
The results indicate that the preference for the company is significantly different for at least one user groups (F2,589 = 89.966, p < 0.001). From the mean plots, the customer user groups differ a lot in their preference for the company.
The results indicate that the purchase intentions for the products is significantly different for at least one user groups (F2,585 = 25.83, p < 0.001). From the mean plots, the user groups differ a lot in their purchase intentions for the company’s products.
The results indicate that the loyalty for the company is not significantly different among the three customer user groups (F2,589 = 1.331, p > 0.05). From the mean plots, the user groups differ a lot in their knowledge of the company.
The results indicate that whether the customer would recommend the company to a friend is significantly different for at least one user groups (F2,589 = 829.181, p < 0.001).
The report aimed at addressing the main challenges faced by Athlete Panda which are profit margins, cost of products sold and sales. The quantitative analysis indicate that the most profitable product categories are men’s shoes and women clothing. However, women clothing has the higher COGS margins. There is significant difference in the payment methods. The customer attributes differed by gender except satisfaction, purchase intentions and recommendations. Lastly, user groups greatly influenced the attitudes of customers except loyalty for the company.
References
Waller, D., 2010. Statistics for business. London: Routledge.
Albright, S. (2017). Business analytics. Boston, MA: Cengage Learning.
Libguides.library.kent.edu. (2018). LibGuides: SPSS Tutorials: One-Way ANOVA. [online] Available at: https://libguides.library.kent.edu/SPSS/OneWayANOVA [Accessed 4 Oct. 2018].
Newbold, P., Carlson, W. and Thorne, B. (2013). Statistics for business and economics. Harlow, Essex: Pearson Education.
Academic.udayton.edu. (2018). Using SPSS for t-Tests. [online] Available at: https://academic.udayton.edu/gregelvers/psy216/spss/ttests.htm [Accessed 4 Oct. 2018].
Simpson, E.H., 1951. The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society. Series B (Methodological), pp.238-241.
Hinton, P., Brownlow, C. and McMurray, I. (2014). SPSS Explained. Hoboken: Taylor and Francis.
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