The rapid developments in technology and the rise of online stores has proven to be a huge challenge for traditional-based and conventional retail brands globally (Ireland, et al., 2008). The large percentage of customers presently are youthful, trendy and more often too busy or occupied to physically visit a store to make a purchase. The change in customer demographics implies that online shopping is the most suitable for this market base (Hsiao, 2009).
Many of the traditional-based retail brands have however been too slow to switch to online stores. This has given opportunity for new market entries that have filled the void in the online stores unoccupied by the traditional-based retailers (Laudon & Guercio, 2014).
In addition to the developments in technology and rise of online stores, the market has also changed in other ways. The ease in the access to information has also meant that the modern customer is far too dynamic compared to previous periods in the early 2000s going back (Tang, 2014).
A ripple effect of having dynamic consumers in the market has been the risk of fall in demand. The customers may prefer a product now only to change preference after a while (Pappas, 2016). This is especially risky for a store that stocks luxury products, the cost of the goods could be crippling to the business if the demand for the goods fall drastically.
This business report aims at investigating the problems faced by retail store brands are in the process of transitioning to the online platforms. The study will cite the case of Athlete Panda which is a brand of retail stores that deal in products for athletes. The retailer has been operational for the past five years but has been mostly brick and mortar stores only recently venturing into the provision of online purchase services. The study will focus on identifying the challenges faced by Athlete Panda by observing and analyzing data from one of the brand’s retail stores.
Athlete Panda being a traditional-based and conventional business entity, it is faced with challenges in terms of sales, profits and the cost of goods.
This study will be concerned with addressing the following research questions for this business report:
This business report is going to apply the exploratory data analysis to address this research question. The exploratory data analysis applies the use of graphs and tables to display the characteristics of a dataset (Martinez, et al., 2010; Roles, et al., 2016).
To address this research question we will also apply the exploratory data analysis. The exploratory data analysis being a data visualization technique (Theus & Urbanek, 2008), will help into displaying the different costs for the different product categories.
To determine whether there is difference in the payment methods, we will apply the paired t-test method. The paired t-test method is a statistical technique that is used for hypothesis testing (Barbara & Susan, 2014; Hastie, et al., 2009).
To evaluate the existence of difference in store location and sales, we will apply the one-way ANOVA analysis. The one-way ANOVA is a statistical tool that is applied when comparison is being made between an independent variables that is categorical in nature, and a dependent variable that is measured on the interval scale and is normally distributed (Everitt & Skrondal, 2010; O’Neil & Schutt, 2013).
In addressing this research question, we will apply the Kruskal Wallis Test. The Kruskal Wallis Test is a form of the ANOVA test in non-parametric statistics. This test compares variables in the case where the independent variable is nominal and the dependent variable is measured either on the interval scale or the ordinal scale (Corder & Foreman, 2009).
In evaluating this research question, we will also apply the Kruskal Wallis Test. The Kruskal Wallis Test is a form of the ANOVA test in non-parametric statistics. This test compares variables in the case where the independent variable is nominal and the dependent variable is measured either on the interval scale or the ordinal scale (Corder & Foreman, 2009).
For this research question, in the study we will apply multiple regression analysis. Regression analysis is a statistical technique that describes the relationship between variables in the form of an equation (Claeskens & Hjort, 2008; Freedman, 2009; Galit, et al., 2018).
Profit Total above shows a plot of the product category against the average total profit for all the categories. From this plot we observe that the product category making the most profit is customise.Cost of Goods above shows a plot of the product category against the average cost of goods for all the categories. From this plot we observe that the product category with the highest cost is customise.
The results in Table 1: T-Test for PayPal and Credit Card Payments show that the p value < 0.05. We therefore conclude that the payments through PayPal and those through Credit Card are statistically different from each other. ANOVA for Location of Stores and Sales
ANOVA for Location of Stores and Sales above show that the p value < 0.05. We therefore conclude that the sales in the different locations differ. Knowledge of Company shows the p value < 0.05 (0.000 < 0.05). Therefore we conclude that the user groups significantly differ in terms of Knowledge of the Company.
From Table 15: Regression Model Summary the value of the adjusted R Squared = 0.907. This implies the model explains 90.7% of the relationship between the variables, therefore a good fit. Also from Table 16: Regression ANOVA the p value from the sig. column < 0.05, hence making the model a good fit.
The results from Table 17: Regression Coefficients show only the p-value for the Month of the year is greater 0.05. Therefore the Month of The Year is not a significant variable in the relationship with profit total.
The relationship of the variables can hence be described as follows:
From the analysis done in this study, we can conclude that;
The following recommendations can be made following the analysis in this study:
References
Claeskens, G. & Hjort, N. L., 2008. Model Selection and Model Averaging. 1st ed. London: Cambridge University Press.
Corder, G. W. & Foreman, D. I., 2009. Non Parametric Statistics for Non Statisticians. pp. 99-105. 1st ed. Hoboken: John Wiley & Sons.
Everitt, B. S. & Skrondal, A., 2010. Cambridge Dictionary of Statistics. 4th ed. London: Cambridge University Press.
Freedman, D. A., 2009. Statistical Models: Theory and Practice. 1st ed. London: Cambridge University Press.
Galit, S. et al., 2018. Data Mining for Business Analytics. 1st ed. New Delhi: John Wiley & Sons, Inc..
Hsiao, M. H., 2009. Shopping Mode Choice: Physical Store Shopping Versus E-Shopping. Logistics and Transportation Review, 45(1), pp. 86-95.
Ireland, R. D., Hoskisson, R. & Hitt, M., 2008. Understanding Business Strategy: Concepts and Cases. 2nd ed. London: Cengage Learning.
Laudon, K. C. & Guercio, T. C., 2014. E-commerce. Business. Technology. Society. 1st ed. Chicago: Pearson.
Martinez, W. L., Martinez, A. R. & Solka, J., 2010. Exploratory Data Analysis With MATLAB, 2nd Edition. 1 ed. London: CRC/Chapmann & Hall.
O’Neil, C. & Schutt, R., 2013. Doing Data Science. 3rd ed. London: O’Reily.
Pappas, N., 2016. Marketing Strategies Perceived Risks, and Consumer Trust in Online Behaviour. Journal of Retailing and Consumer Services, 29(1), pp. 92-103.
Roles, R., Baeten, Y. & Signer, B., 2016. Interactive and Narrative Data Visualization for Presentation-Based Knowledge Transfer. Communication in Computer and Information Science, 4(6), p. 739.
Tang, D., 2014. Introduction to Strategy Development and Strategy Execution. Flevy, 3(1), pp. 1-5.
Theus, M. & Urbanek, S., 2008. Inteactive Graphics For Data Analysis. 1st ed. Boca Raton: CRC Press.
T, H., Tibshir, R. & Jerome, H., 2009. The Elements of Statistical Learning. 2nd ed. New York: Springer.
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