Today most of the world population preferred the online shopping which results into the exponential growth in eCommerce. eCommerce becoming very raising and popular business in every corner of the world. Online shopping is most popular from the all eCommerce business. In Today’s market we can see the words like Amazon, eBay, etc. which very common in day to day life. Service provider attract the customer by giving offers to the customers.
Growth in the online shopping creates the opportunity and challenges to the service provider. The main challenges in the online shopping is competition and customer total satisfaction.
We have data sets regarding to Cloths (Shirt, Trouser and Track Suit) for the 2000 products. We considered the following attributes
We define following variables for our analysis from the above variables
Total Monthly sale amount (in $) = Sale Price (in $) × Number of customers
Total monthly profit (in $) = Profit (in $) × Number of customers
We are interested to know the following things
Any data analysis is stronger if it is supported by statistical tools and techniques. We summarised the total sale amount and total profit in profit analysis. We carried the chi-squared test for association of attributes (shipping type, customer type, region, brand and material). We analyzed the mean of number of customers for shipping type, customer type, region, brand and material by the two sample t test and one way ANOVA. We also carried correlation analysis. We used Python and MS-Excel for data analysis. We used Grus (2015), McKinney (2012), Pedregosa et al. (2011) and Schutt and O’Neil (2013).
Profit Analysis:
In profit analysis, we have reported the total sales amount, total profit, profit percentage for the shipping type, customer type, region, brand and material. We referred Berenson et al. (2012), Black (2009), Groebner et al. (2008), Kvanli et al. (2000) and Mendenhall and Sincich (1993). Table 1 summarizes the profit analysis according to shipping type, customer type, region, brand and material.
Table 1: Profit analysis according to different attributes
|
Attributes |
Shipping Type |
Sum of Total Sale |
Sum of Total Profit |
Profit % |
|
Shipping Type |
Free |
661525 |
88664 |
13.40% |
|
Paid |
1276094 |
171178 |
13.41% |
|
|
Customer Type |
Existing |
879349 |
117241 |
13.33% |
|
New |
1058270 |
142601 |
13.47% |
|
|
Region |
QLD |
370742 |
49741 |
13.42% |
|
TAS |
204055 |
27225 |
13.34% |
|
|
VIC |
792802 |
106086 |
13.38% |
|
|
WA |
570021 |
76790 |
13.47% |
|
|
Brand |
Adidas |
387014 |
45529 |
11.76% |
|
Arrow |
393305 |
60874 |
15.48% |
|
|
Nike |
371811 |
46476 |
12.50% |
|
|
Polo |
377941 |
52755 |
13.96% |
|
|
Woodland |
407548 |
54209 |
13.30% |
|
|
Material |
Cotton |
709179 |
94216 |
13.29% |
|
Other |
601860 |
81071 |
13.47% |
|
|
Wool |
626580 |
84555 |
13.49% |
|
|
Total |
1937620 |
259842 |
13.41% |
Company getting 13.41% profit overall. There is significant changes in the profit by different attributes (expect brand). In the brand, company gets 15.48% profit in Arrow whereas earned only 11.76 % profit earned on the Adidas. Total sales amount is bigger for paid shipping type. Total sales revenue is comparatively low for existing customer and customers from TAS region.
Descriptive statistics:
We used the well-known books for this section such as Bickel and Doksum (2015), Casella and Berger (2002), DeGroot and Schervish (2012), Hodges Jr and Lehmann (2005), Papoulis (1990), Pillers (2002) and Ross (2014).
Profit is mainly depend on the number of customers who bought the product. In this section, we reported the summary statistics for number of customer for the shipping type, customer type, region, brand and material. In Table 2, we have given sample size, mean, standard deviation, minimum and maximum for number of customers for shipping type, customer type, region, brand and material. We can observed that averagely there is 37.99 customer for each products with standard deviation 6.61.We can observed one interesting thing is that average number of customers for free shipping is more than paid shipping.
Table 2: Summary statistics for number of customers
|
Attributes |
Level |
Size |
Mean |
SD |
Min |
Max |
|
Shipping Type |
Free |
636 |
41.09 |
6.69 |
24 |
59 |
|
Paid |
1364 |
36.54 |
6.06 |
15 |
56 |
|
|
Customer Type |
Existing |
908 |
37.97 |
6.47 |
15 |
57 |
|
New |
1092 |
38.00 |
6.73 |
19 |
59 |
|
|
Region |
QLD |
400 |
36.64 |
6.11 |
22 |
56 |
|
TAS |
216 |
37.32 |
6.63 |
19 |
55 |
|
|
VIC |
779 |
39.58 |
6.53 |
20 |
59 |
|
|
WA |
605 |
37.07 |
6.63 |
15 |
58 |
|
|
Brand |
Adidas |
402 |
37.96 |
6.50 |
19 |
55 |
|
Arrow |
401 |
38.01 |
6.88 |
20 |
59 |
|
|
Nike |
384 |
38.28 |
6.83 |
15 |
56 |
|
|
Polo |
391 |
37.81 |
6.45 |
21 |
56 |
|
|
Woodland |
422 |
37.90 |
6.43 |
22 |
56 |
|
|
Material |
Cotton |
665 |
38.08 |
6.72 |
15 |
59 |
|
Other |
662 |
37.91 |
6.33 |
21 |
56 |
|
|
Wool |
673 |
37.98 |
6.78 |
22 |
58 |
|
|
Total |
2000 |
37.99 |
6.61 |
15 |
59 |
In this section we test whether there is any association between attributes or not. Following Table 3 shows the chi-square statistic and p-value for chi-square test of testing association. In this test we have null hypothesis that there is no significant association between two attributes and alternative hypothesis is there is significant association between two attributes. We test the association between following pair of attributes
Table 3: Chi-squared test for association
|
Pairs of attributes |
Chi-Square Statistic |
P-Value |
|
shipping type and customer type. |
0.005 |
0.943 |
|
shipping type and region. |
0.022 |
0.999 |
|
shipping type and brand. |
2.417 |
0.660 |
|
shipping type and material. |
8.489 |
0.014 |
|
customer type and region. |
1.586 |
0.663 |
|
customer type and brand. |
3.550 |
0.470 |
|
customer type and material. |
2.125 |
0.346 |
|
region and brand. |
5.803 |
0.926 |
|
region and material. |
10.034 |
0.123 |
|
brand and material. |
6.133 |
0.632 |
We observed that only sipping type and material have significant association at 5% level of significance whereas all other pairs are not associated.
We used independent two sample t test for testing the difference between mean numbers of customer for shipping type (free and paid) and customer type (new and existing) . For shipping type, we test the null hypothesis that the mean numbers of customer who got free shipping is same as mean number of customers who got paid shipping. For customer type, we test the null hypothesis that the mean numbers of customer who are new is same as existing customers. Following Table 4, shows the result of two sample t test for shipping type and customer type. Table 4 includes test statistics, degrees of freedom and p value.
|
Attributes |
Levels |
Test Statistic |
P value |
|
Shipping Type |
Free and Paid |
14.58 |
0.000 |
|
Customer Type |
New and Existing |
-0.13 |
0.899 |
From Table 4, we can see that P-value of shipping type is less than 5% suggest that there is significant difference between the mean numbers of customer who got free shipping and got paid shipping. As if the shipping is free, customer got interested in buying the item than the items for which shipping is paid. There is no significant difference between mean numbers of new customer and existing customer.
In this section we test whether there is any significant difference between means of different level of
We used one way ANOVA for this purpose. Here our null hypothesis that the different levels has same mean and alternative hypothesis is that at least one level has different mean. Following Table 5 shows the output of one way ANOVA for region, brand and material
|
Attributes |
Level |
F Statistic |
P Value |
|
Region |
QLD, TAS, VIC and WA |
26.13 |
0.000 |
|
Brand |
Adidas, Arrow, Nike, Polo and Woodland |
0.28 |
0.889 |
|
Material |
Cotton, Wool and other |
0.12 |
0.891 |
From Table 5, we conclude that there is significant difference between mean numbers of customer in different region. We can see that VIC has most numbers of customer compared to the other region. We claim that there is no significant difference between mean numbers of customer for brand and material.
Correlation Analysis:
In this section we calculate the correlation coefficient for studying the association between the variables like Product Price, Profit and Numbers of customer. Table 6 represents the Pearson’s correlation matrix for the Product Price, Profit and Numbers of customer.
Table 6: Pearson’s correlation coefficient a for Product Price, Profit and Numbers of customer
|
Product Price |
Profit |
Number of customers |
|
|
Product Price |
1 |
0.43 |
0.001 |
|
Profit |
0.43 |
1 |
-0.003 |
|
Number of customers |
0.001 |
-0.003 |
1 |
From the correlation analysis, we can say that product price and numbers of customer moderate positive correlation where as there is negligible correlation (zero correlation) between product price and numbers of customer, profit and numbers of customer.
We used simple linear regression model for predicting the monthly sale using number of customers as predictor variable. Table 7 represents the F Statistics, P value, R2 and regression coefficients of simple linear regression.
Table 8: Output of Regression Analysis
|
F Statistic |
7462.109 |
|
P Value |
0.000 |
|
R2 |
0.789 |
|
Intercept |
-3.535 |
|
Slope |
25.596 |
We observed that P Value =0.000 suggests that there is significant relationship between total monthly sale and number of customers who bought the laptops. We also observed R2 as 0.789 suggests that model fitting is good and adequate. We fitted the following straight line as
Total sale (in $) = -3.535 + 25.596 × Number of Customers
Recommendations to the company
Conclusions
Company getting 13.41% profit overall. There is no significant changes in the profit by different attributes (expect brand). In the brand, company gets 15.48% profit in Arrow whereas earned only 11.76 % profit earned on the Adidas. Total sales amount is bigger for paid shipping type. Total sales revenue is comparatively low for existing customers and customers from TAS region.
We can observed that averagely there is 37.99 customers for each products with standard deviation 6.61.We can observed one interesting thing is that average number of customers for free shipping is more than paid shipping.
We observed that only shipping type and material have significant association at 5% level of significance whereas all other pairs are not associated.
There is significant difference between the mean numbers of customer who got free shipping and got paid shipping. As if the shipping is free, customer got interested in buying the item than the items for which shipping is paid. There is no significant difference between mean number of new customers and existing customers.
There is significant difference between mean numbers of customer in different region. We can see that VIC has most numbers of customer compared to the other region. We claim that there is no significant difference between mean number of customer for brand and material. We observed that there is significant relationship between number of customers and total sale. We have also provided recommendation and plan to the company.
List of References
Berenson, M., Levine, D., Szabat, K.A. and Krehbiel, T.C., (2012). Basic business statistics: Concepts and applications. Pearson higher education AU.
Bickel, P.J. and Doksum, K.A., (2015). Mathematical statistics: basic ideas and selected topics, volume I (Vol. 117). CRC Press.
Black, K., (2009). Business statistics: Contemporary decision making. John Wiley & Sons.
Casella, G. and Berger, R.L., (2002). Statistical inference (Vol. 2). Pacific Grove, CA: Duxbury.
DeGroot, M.H. and Schervish, M.J., (2012). Probability and statistics. Pearson Education.
Groebner, D.F., Shannon, P.W., Fry, P.C. and Smith, K.D., (2008). Business statistics. Pearson Education.
Grus, J., (2015). Data science from scratch: first principles with python. ” O’Reilly Media, Inc.”.
Hodges Jr, J.L. and Lehmann, E.L., (2005). Basic concepts of probability and statistics. Society for Industrial and Applied Mathematics.
Kvanli, A.H., Pavur, R.J. and Guynes, C.S., (2000). Introduction to business statistics. Cincinnati, OH: South-Western.
McKinney, W., (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. ” O’Reilly Media, Inc.”.
Mendenhall, W. and Sincich, T., (1993). A second course in business statistics: Regression analysis. San Francisco: Dellen.
Papoulis, A., (1990). Probability & statistics (Vol. 2). Englewood Cliffs: Prentice-Hall.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), pp.2825-2830.
Pillers Dobler, Carolyn. “Mathematical statistics: Basic ideas and selected topics.” (2002): 332-332.
Ross, S.M., (2014). Introduction to probability and statistics for engineers and scientists. Academic Press.
Schutt, R. and O’Neil, C., (2013). Doing data science: Straight talk from the frontline. ” O’Reilly Media, Inc.”
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