By the definition of book, “A book is a series of pages assembled for easy portability and reading, as well as the composition contained in it”. In everyone life book plays the important role.
Today we can get the bool easily from online store. Book section in every online store is main part of business. Today we purchase the most items online. We can get desired product at home within stipulated time.
eCommerce becoming very raising and popular business in every corner of the world. Online shopping is most popular from the all eCommerce business. eCommerce business increasing exponentially in recent decade bring new challenges to the service provider. Business competition and customer satisfaction are the most important factors in the eCommerce business.
About Data:
For this case study, we have developed the data. We have data regarding the sale of books (1320 books) in the month. We assume that this data is from company International Online Book Store. We considered the following attributes
We defined following variables for the study objectives as
Total Monthly sale amount (in $) = Book Sale Price (in $) × Number of customers
Total monthly profit (in $) = Profit (in $) × Number of customers
Project Problem:
We concentrate on the following
Research Methodology
Statistical tool and techniques are important aspect of data analysis. In literature there are many statistical tools and techniques are available. But use of proper tools and technique is important part of analysis.
For the profit analysis, we presented the total monthly sale amount (in $) and total monthly profit (in $) for shipping type, customer type, region and category. We have presented the descriptive statistics for number of customers for shipping type, customer type, region, and category. We used two sample t test and one way ANOVA for testing the difference between mean number of customers who bought the books for shipping type, customer type, region and category. We studied the correlation between product price, profit, sale price, number of pages and number of customers. We try to fit the regression model for total sale amount. We used Python 3.6.5 and MS-Excel for the data analysis. The sample code are given in appendixes. We used Grus (2015), McKinney (2012), Pedregosa et al. (2011) and Schutt and O’Neil (2013).
Analytical Findings
In this section, we carried the following
Profit Analysis:
For the profit analysis we have given the total monthly sale amount (in $), total monthly profit (in $) and profit percentage for shipping type, customer type, region and category. We referred Berenson et al. (2012), Black (2009), Groebner et al. (2008), Kvanli et al. (2000) and Mendenhall and Sincich (1993). Profit analysis is represented in Table 1.
Table 1: Profit analysis according to for shipping type, customer type, region and category
|
Attributes |
Level |
Total Monthly Sale (in $) |
Total Monthly Profit (in $) |
Profit Percentage |
|
Shipping Type |
FREE |
86870 |
8488 |
9.77% |
|
PAID |
141014 |
13815 |
9.80% |
|
|
Customer Type |
Existing |
42399 |
4159 |
9.81% |
|
New |
185485 |
18144 |
9.78% |
|
|
Region |
SA |
123220 |
12120 |
9.84% |
|
WA |
104664 |
10183 |
9.73% |
|
|
Category |
Comics & Graphic Novels |
32336 |
4049 |
12.52% |
|
Literature & Fiction |
97097 |
9640 |
9.93% |
|
|
Mystery, Thriller & Suspense |
20941 |
2611 |
12.47% |
|
|
Romance |
77510 |
6003 |
7.74% |
|
|
Total |
227884 |
22303 |
9.79% |
From Table 1 we can claim that International Online Book Store earns on average 9.79% profit on each laptop. The profit percentage is not significantly different for shipping type, customer type and region. The profit percentage for Comics & Graphic Novels and Mystery, Thriller & Suspense books is more whereas profit percentage on Romance book is less.
Descriptive statistics for number of customer:
Total monthly sale and profit are proportional to the number of customers. So here we displays the summary statistics for the number of customers who bought the books for shipping type, customer type, region and category. We referred 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). Table 2 displays the size, mean, standard deviation, minimum and maximum of number of customers who bought books.
Table 2: Summary statistics for numbers of customer who bought the books for shipping type, customer type, region and category
|
Attributes |
Level |
Size |
Mean |
Standard Deviation |
Min |
Max |
|
Shipping Type |
FREE |
411 |
5.825 |
2.461 |
1 |
14 |
|
PAID |
909 |
4.287 |
2.154 |
1 |
12 |
|
|
Customer Type |
Existing |
243 |
4.798 |
2.434 |
1 |
11 |
|
New |
1077 |
4.759 |
2.348 |
1 |
14 |
|
|
Region |
SA |
792 |
4.284 |
2.181 |
1 |
12 |
|
WA |
528 |
5.489 |
2.442 |
1 |
14 |
|
|
Category |
Comics & Graphic Novels |
258 |
4.442 |
2.238 |
1 |
13 |
|
Literature & Fiction |
592 |
4.596 |
2.249 |
1 |
13 |
|
|
Mystery, Thriller & Suspense |
164 |
4.445 |
2.117 |
1 |
12 |
|
|
Romance |
306 |
5.539 |
2.633 |
1 |
14 |
|
|
Total |
1320 |
4.766 |
2.363 |
1 |
14 |
From Table2, we observed following
Two Sample t-test:
Here we are interested to know whether there is significant difference between the mean of number of customers who bought the books for shipping type, customer type and region. Our null hypothesis is that there is no significant difference between mean of number of customers for levels of attributes and alternative hypothesis is that there is significant difference between mean of number of customers for levels of attributes. We used two sample independent test assuming unequal variances. Table 3 displays the value of test statistic and p-value of two sample independent test assuming unequal variances.
Table 3: Two sample independent test for shipping type, customer type and region
|
Attributes |
Levels |
Test Statistic |
p-value |
|
Shipping Type |
Free and Paid |
10.92 |
0.000 |
|
Customer Type |
New and Existing |
0.23 |
0.817 |
|
Region |
WA and SA |
-9.16 |
0.000 |
From Table 3 we observed the following
One way ANOVA:
By using one way ANOVA, we test whether the mean number of customer who bought the books for different category is significantly different or not. Here we test following null and alternative hypothesis as
Null Hypothesis: There is no significant difference between the mean of number of customer who bought the books of different category.
Alternative Hypothesis: There is significant difference between the mean of number of customer who bought the books of different category.
Table 4 shows the value of F statistic and p-value for one way ANOVA.
Table 4: Output of one way ANOVA for Category
|
Attributes |
Level |
F Statistic |
P Value |
|
Category |
Comics & Graphic Novels, Mystery, Thriller & Suspense, Romance and Literature & Fiction |
15.03 |
0.000 |
We observed that there is significant difference between the mean of number of customer who bought the books of different category. Mean number of customers who bought the romance category book is significantly more than other category.
Correlation Analysis:
Here we studied the correlation between product price, Sale price, profit and number of customers. Table 5 represents the correlation coefficient between product price, Sale price, profit and number of customers.
Table 5: Pearson’s correlation coefficient for Product Price, Sale Price, Profit and Numbers of customers
|
Product Price |
Sale Price |
Profit |
Numbers of customer |
|
|
Product Price |
1 |
0.990 |
0.026 |
0.118 |
|
Sale Price |
0.990 |
1 |
0.166 |
0.117 |
|
Profit |
0.026 |
0.166 |
1 |
0.006 |
|
Numbers of customer |
0.118 |
0.117 |
0.006 |
1 |
From Table 5, we observed that
Regression analysis:
We used simple linear regression model for predicting the monthly sale using number of customers who bought the books as predictor variable. Table 6 represents the F Statistics, P value, R2 and regression coefficients of simple linear regression.
Table 7: Output of Regression Analysis
|
F Statistic |
5981.55 |
|
P Value |
0.000 |
|
R2 |
0.819 |
|
Intercept |
-12.264 |
|
Slope |
38.797 |
We observed that P Value =0.000 suggests that there is significant relationship between total monthly sale and number of customers who bought the books. We also observed R2 as 0.819 suggests that model fitting is very good and adequate. We fitted the following straight line as
Total sale (in $) = -12.264 + 38.797 × Number of Customers
Recommendations to the company
From the data analysis, we observed that
An implementation plan based on the recommendations you have provided
Conclusions
From Profit analysis, claim that International Online Book Store earns on average 9.79% profit on each laptop. The profit percentage is not significantly different for shipping type, customer type and region. The profit percentage for Comics & Graphic Novels and Mystery, Thriller & Suspense books is more whereas profit percentage on Romance book is less. We observed that International Online Book Store get averagely 4.766 customers for each book. Mean number of customers who bought the books at free shipping is more than mean number of customers who bought books by paid shipping. Mean number of new customers is less than mean number of existing customers. Mean number of customers from SA region is less than mean number of customers from WA region. Mean number of customers demanding Romance books is more than mean number of customers demanding other category books.
We observed the following there is significant difference in mean number of customers who bought the books at free shipping and who bought at paid shipping. We observed that there is no significant difference in mean number of new customers and existing customers who bought the books. We claimed that there is significant difference in mean number of customers from WA and SA region. We observed that there is significant difference between the mean of number of customer who bought the books of different category. Mean number of customers who bought the romance category book is significantly more than other category. Profit is positively correlated with number of customers but correlation is very low. Regression analysis suggests that there is significant relationship between total monthly sale and number of customers who bought the books.
We have also provided recommendations and plan for 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.
Lee, G.G. and Lin, H.F., 2005. Customer perceptions of e-service quality in online shopping. International Journal of Retail & Distribution Management, 33(2), pp.161-176.
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, C., 2002. Mathematical statistics: Basic ideas and selected topics.
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.”.
Wolfinbarger, M. and Gilly, M.C., 2001. Shopping online for freedom, control, and fun. California Management Review, 43(2), pp.34-55.
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