Profit Analysis Of ECommerce Platform: A Case Study

Data Set Attributes

In Today’s world, eCommerce becoming very popular in all over the world. In Australia, eCommerce market generates about US $ 11789 Million revenue in 2018 and expected to rise in near future.

eCommerce provides the online platform to the end user to buy the products directly. This service is very useful for the people who are very busy in their work. eCommerce also provides offers to the customers which attract the more people. In eCommerce platform, consumers have more choice in quality, brand, color, price etc.

As the people got interest in the buying through eCommerce platform, it increases the challenges to the service provider. It mainly consist of customer satisfaction and increased competition. For the service provider, it is mandatory to satisfy the customer and compete in the market.

In this case study, we develop the data sets regarding to computer and computer accessories for the 1000 products. We considered the following attributes

1. Product
2. Price

• Shipping Type (Free or Customer Paid)

1. Sale Price
2. Customer Type (New or Existing)
3. Number of customers

• Region (NSW, QLD, VIC, ACT, SA)
• Profit

1. Average Rating
2. Total monthly sale amount
3. Total monthly profit

• Profit Percentage

We define following variables as

Total Monthly sale amount= Sale Price × Number of customers

Total monthly profit= Profit × Number of customers

Profit Percentage= Total monthly profit /

We are interested to know the following things

1. Profit analysis by shipping type, customer type and region wise.
2. Whether there is any association between shipping type and region.

• Whether there is any association between customer type and region.

1. Whether there is any association between shipping type and customer type.
2. Whether the price is significantly different for different shipping type.
3. Whether the sale price is significantly different for different shipping type.

• Whether the profit is significantly different for different shipping type.
• Whether the rating is significantly different for different shipping type.

1. Whether the price is significantly different for different customer type.
2. Whether the sale price is significantly different for different customer type.
3. Whether the profit is significantly different for different customer type.

• Whether the rating is significantly different for different customer type.
• Whether the price is significantly different for different region.
• Whether the sale price is significantly different for different region.

1. Whether the profit is significantly different for different region.

• Whether the rating is significantly different for different region.
• Correlation analysis of variables
• Regression analysis for total monthly sales

Statistical tools and techniques are the necessary for analysis. In the analysis, selection of proper statistical tools and techniques is very important. For profit analysis, we summarised the profit percentage on total sale and profit per item for shipping type, customer type and region. We have used the Chi-square test of association for testing the associations of attributes. We have used independent two sample t-test for the testing the mean price, sale price, profit and rating for shipping type and customer type. We analyzed the mean price, sale price, profit and rating of region using one way ANOVA technique. We test the significance of correlation between two variable. We tried to fit regression model for the monthly profit and monthly sales. 

Profit Analysis:

In profit analysis, we have given the total sales amount, total profit, profit percentage on total sale amount and profit per item. Larger the value of profit percentage on total sale amount and profit per item would be considered better for increasing the profit.

Table 1 gives the profit analysis according to shipping type. Both the shipping type customer paid and free have no much difference in the average profit percentage. Still total monthly sale and profit seems to be large for paid shipping customer than free shipping customer.  

Statistical Tools and Techniques

Table 1: Profit analysis according to shipping type

SHIPPING_TYPE	Total Monthly Sale (Amount)	Total Monthly Profit	Profit Percentage
Customer paid	12543201	8973541	61.73%
Free	11502245	8275595	61.76%

Table 2 represent the profit analysis according to customer type. New customers produces more total monthly sale and profit than existing customer. Also profit earned in case of new customer is more than existing customer.

Table 2: Profit analysis according to customer type

CUSTOMER_TYPE	Total Monthly Sale (Amount)	Total Monthly Profit	Profit Percentage
Existing	11518008	8313334	61.36%
New	12527438	8935802	62.10%

Table 3 represent the profit analysis according to region. ACT region shows less profit percentage than other region. Total monthly sale was significantly large for NSW region and small for VIC and SA region.

Table 3: Profit analysis according to region

REGION	Total Monthly Sale (Amount)	Total Monthly Profit	Profit Percentage
ACT	3948523	2789840	56.7%
NSW	9063314	6460887	62.1%
QLD	5714404	4118342	60.6%
SA	2939639	2132391	65.6%
VIC	2379566	1747676	66.5%

From Table 4, we have observed the summary statistics for profit and rating variable. One can study the properties of variables by observing the summary statistics. For example, as skewness are negative for both the variables suggest that they are negatively skewed.

Table 4: Descriptive statistics for profit and rating variable

	PROFIT	Rating
Mean	969.775	2.83622
Standard Error	17.62128	0.047632
Median	1023.5	2.72
Mode	1333	5
Standard Deviation	557.2338	1.506243
Sample Variance	310509.5	2.268768
Kurtosis	-0.50686	-1.00682
Skewness	-0.40549	-0.22308
Range	2686	5
Minimum	-531	0
Maximum	2155	5
Sum	969775	2836.22
Count	1000	1000

Here we test the

H₀: There is no association between two attributes.

Vs

H₁: There is association between two attributes.

We studied the association between two variables by Chi-square test for association.  We have three pair of attributes for possible association

1. shipping type and region.
2. customer type and region.

• shipping type and customer type.

 

Shipping type and region.

Contingency Table for shipping type and region

Observed Frequencies	REGION
SHIPPING_TYPE	ACT	NSW	QLD	SA	VIC	Grand Total
Customer paid	80	207	124	67	49	527
Free	81	180	113	53	46	473
Grand Total	161	387	237	120	95	1000

Expected Frequencies	REGION
SHIPPING_TYPE	ACT	NSW	QLD	SA	VIC
Customer paid	84.847	203.949	124.899	63.24	50.065
Free	76.153	183.051	112.101	56.76	44.935

P-value= 0.8754

As P-value is larger (>0.05) we fail to reject H₀ i.e. there is no any association between region and shipping type.

Customer type and region.

Contingency Table for customer type and region

Observed Frequencies	REGION
CUSTOMER_TYPE	ACT	NSW	QLD	SA	VIC	Grand Total
Existing	76	185	121	53	45	480
New	85	202	116	67	50	520
Grand Total	161	387	237	120	95	1000

Expected Frequencies	REGION
CUSTOMER_TYPE	ACT	NSW	QLD	SA	VIC
Existing	77.28	185.76	113.76	57.6	45.6
New	83.72	201.24	123.24	62.4	49.4

P-value= 0.7990

As P-value is larger (>0.05) we fail to reject H₀ i.e. there is no any association between region and customer type.

Contingency Table for customer type and shipping type

Observed Frequencies	SHIPPING_TYPE
CUSTOMER_TYPE	Customer paid	Free	Grand Total
Existing	247	233	480
New	280	240	520
Grand Total	527	473	1000

Expected Frequencies	SHIPPING_TYPE
CUSTOMER_TYPE	Customer paid	Free
Existing	252.96	227.04
New	274.04	245.96

P-value= 0.4499

As P-value is larger (>0.05) we fail to reject H₀ i.e. there is no any association between customer type and shipping type.

We test whether there is significant difference between two groups. Here our null and alternative hypothesis are as follows

H₀: There is no significant difference between the two groups (two independent samples).

Vs

H₁: There is significant difference between the two groups (two independent samples)

In shipping type, we have two groups customer paid and free shipping. We run the Two Sample t-test for testing the difference of mean of two groups customer paid and free shipping.

1. Product Price
2. Sale Price

• Number of customers

1. Profit
2. Rating

Table 5a shows the result of two sample t-test by assuming unequal variances for testing the difference of mean of two groups customer paid and free shipping of product price. From Table 5a, we compare the P value=0.409583 > 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean price of products for which customer paid shipping charges and free shipping.

Profit Analysis

Table 5a: t-Test: Two-Sample Assuming Unequal Variances for testing the difference of mean of two groups customer paid and free shipping of product price

	Customer paid	Free
Mean	387.1404	377.7104
Variance	36877.6	28703.86
Observations	527	473
Hypothesized Mean Difference	0
df	998
t Stat	0.824976
P(T<=t) one-tail	0.204791
t Critical one-tail	1.646382
P(T<=t) two-tail	0.409583
t Critical two-tail	1.962344

Table 5b displays the result of two sample t-test by assuming unequal variances for testing the difference of mean of two groups customer paid and free shipping of sale price. From Table 5b, we can see that the P value=0.856732 > 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean sale price of products for which customer paid shipping charges and free shipping.

Table 5b: t-Test: t-Test: Two-Sample Assuming Unequal Variances for testing the difference of mean of two groups customer paid and free shipping of sale price

	Customer paid	Free
Mean	1355.313	1349.271
Variance	286744.9	272226.6
Observations	527	473
Hypothesized Mean Difference	0
df	991
t Stat	0.180583
P(T<=t) one-tail	0.428366
t Critical one-tail	1.646393
P(T<=t) two-tail	0.856732
t Critical two-tail	1.962361

Table 5c demonstrate the result of two sample t-test by assuming unequal variances for testing the difference of mean of two groups customer paid and free shipping of number of customers. From Table 5c, we can see that the P value=0.174658 > 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean number of customers for which customer paid shipping charges and free shipping.

Table 5c: t-Test: Two-Sample Assuming Unequal Variances for testing the difference of mean of two groups customer paid and free shipping of number of customers

	Customer paid	Free
Mean	17.37002	17.86258
Variance	27.62518	37.39421
Observations	527	473
Hypothesized Mean Difference	0
df	936
t Stat	-1.35842
P(T<=t) one-tail	0.087329
t Critical one-tail	1.646483
P(T<=t) two-tail	0.174658
t Critical two-tail	1.962502

Table 5d shows the result of two sample t-test by assuming unequal variances for testing the difference of mean of two groups customer paid and free shipping of profit. From Table 5d, we can see that the P value=0.923549 > 0.05, so we do not reject null hypothesis i.e. there is no significant difference between the mean profit for which customer paid shipping charges and free shipping.

Table 5d: t-Test: Two-Sample Assuming Unequal Variances for testing the difference of mean of two groups customer paid and free shipping of profit

	Customer paid	Free
Mean	968.1727	971.5603
Variance	313939.3	307339.2
Observations	527	473
Hypothesized Mean Difference	0
df	989
t Stat	-0.09599
P(T<=t) one-tail	0.461774
t Critical one-tail	1.646396
P(T<=t) two-tail	0.923549
t Critical two-tail	1.962366

Table 5e presents the result of two sample t-test by assuming unequal variances for testing the difference of mean of two groups customer paid and free shipping of rating. From Table 5e, we can see that the P value < 0.05, so we reject the null hypothesis i.e. there is significant difference between the mean rating for which customer paid shipping charges and free shipping.  

Table 5e: t-Test: Two-Sample Assuming Unequal Variances for testing the difference of mean of two groups customer paid and free shipping of rating

	Customer paid	Free
Mean	1.996262	3.772072
Variance	1.858913	1.064905
Observations	527	473
Hypothesized Mean Difference	0
df	971
t Stat	-23.3604
P(T<=t) one-tail	1.97E-96
t Critical one-tail	1.646424
P(T<=t) two-tail	3.95E-96
t Critical two-tail	1.96241

In customer type, we have two groups’ new customer and existing customer. We run the Two Sample t-test for testing difference between the new customer’s and existing customer’s mean of

1. Product Price
2. Sale Price

• Number of customers

1. Profit
2. Rating

Shipping Type Analysis

Table 6a shows the result of two sample t-test by assuming unequal variances for mean of product price. From Table 6a, we compare the P value=0.75979 > 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean price of products for the new customer and existing customer.

Table 6a: t-Test: Two-Sample Assuming Unequal Variances for testing the difference of mean of new customer’s and exiting customer’s product price

	Existing Customer	New Customer
Mean	380.8521	384.3673
Variance	32279.71	33724.35
Observations	480	520
Hypothesized Mean Difference	0
df	995
t Stat	-0.30584
P(T<=t) one-tail	0.379895
t Critical one-tail	1.646386
P(T<=t) two-tail	0.75979
t Critical two-tail	1.962351

Table 6b displays the result of two sample t-test by assuming unequal variances for testing the difference of mean of new customer’s and exiting customer’s product price. From Table 6b, we can see that the P value=0.345394> 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean sale price of products for the new customer and existing customer.

Table 6b: Two-Sample Assuming Unequal Variances for testing the difference of mean of new customer’s and exiting customer’s sale price

	Existing Customer	New Customer
Mean	1368.904	1337.271
Variance	287795.1	272108.4
Observations	480	520
Hypothesized Mean Difference	0
df	986
t Stat	0.944013
P(T<=t) one-tail	0.172697
t Critical one-tail	1.6464
P(T<=t) two-tail	0.345394
t Critical two-tail	1.962373

Table 6c demonstrate the result of two sample t-test by assuming unequal variances for testing the difference of mean of new customer’s and exiting customer’s number of customers. From Table 6c, we can see that the P value=0.431685> 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean number of customer for the new customer and existing customer.

Table 6c: Two-Sample Assuming Unequal Variances for testing the difference of mean of new customer’s and exiting customer’s number of customer

	Existing Customer	New Customer
Mean	17.45625	17.73846
Variance	30.20267	34.20892
Observations	480	520
Hypothesized Mean Difference	0
df	998
t Stat	-0.78663
P(T<=t) one-tail	0.215842
t Critical one-tail	1.646382
P(T<=t) two-tail	0.431685
t Critical two-tail	1.962344

Table 6d presents the result of two sample t-test by assuming unequal variances for testing the difference of mean of new customer’s and exiting customer’s profit. From Table 6d, we can see that the P value=0.319876> 0.05, so we fail to reject null hypothesis i.e. there is no significant difference between the mean profit for the new customer and existing customer.

Table 6d: Two-Sample Assuming Unequal Variances for testing the difference of mean of new customer’s and exiting customer’s profit

	Existing Customer	New Customer
Mean	988.0521	952.9038
Variance	321110	300730.3
Observations	480	520
Hypothesized Mean Difference	0
df	985
t Stat	0.995215
P(T<=t) one-tail	0.159938
t Critical one-tail	1.646402
P(T<=t) two-tail	0.319876
t Critical two-tail	1.962375

Table 6e presents the result of two sample t-test by assuming unequal variances for testing the difference of mean of new customer’s and exiting customer’s rating. From Table 6e, we can see that the P value < 0.05, so we reject null hypothesis i.e. there is significant difference between the mean rating for the new customer and existing customer.

Table 6e: Two-Sample Assuming Unequal Variances for testing the difference of mean of new customer’s and exiting customer’s rating

	Existing Customer	New Customer
Mean	1.875688	3.722865
Variance	1.770462	1.092094
Observations	480	520
Hypothesized Mean Difference	0
df	908
t Stat	-24.2784
P(T<=t) one-tail	4.8E-101
t Critical one-tail	1.646534
P(T<=t) two-tail	9.7E-101
t Critical two-tail	1.96258

In this section, we test whether the mean of different region is not significantly different. We run the one way ANOVA for testing whether the different region have different mean or not for

1. Product Price
2. Sale Price

• Number of customers

1. Profit
2. Rating

Customer Type Analysis

Table 7 represent the descriptive statistics for the Product Price, Sale Price, Number of customers, Profit and Rating for different region

Table 7: Summary statistics for the Product Price, Sale Price, Number of customers, Profit and Rating for different region.

Variable	REGION	N	Mean	Median	StDev	Min	Max	Q1	Q3
Product PRICE	ACT	161	392.1	321	184.7	102	789	258	540
NSW	387	385.34	321	178.16	102	854	258	540
QLD	237	379.7	321	185.7	102	987	254	456
SA	120	368.2	321	168	102	956	255	456
VIC	95	381.4	312	198.8	102	937	254	582
SALES Price	ACT	161	1311.4	1456	517.7	258	2365	1023	1674.5
NSW	387	1358.6	1456	531.3	258	2365	1023	1789
QLD	237	1361.9	1569	546.1	258	2365	1023	1789
SA	120	1356.4	1456	514.9	258	2365	1023	1789
VIC	95	1368.4	1550	517.9	258	2365	1065	1695
Number of Customers	ACT	161	18.199	17	6.432	9	40	15	19
NSW	387	17.214	17	4.719	9	43	16	19
QLD	237	17.515	18	6.013	9	43	15	19
SA	120	18.033	18	6.099	9	43	15.25	19
VIC	95	17.853	18	6.459	9	43	15	20
PROFIT	ACT	161	919.3	1000	558.3	-531	1946	689	1337.5
NSW	387	973.3	1000	554.8	-531	2153	625	1420
QLD	237	982.1	1154	589.9	-420	2145	617	1408
SA	120	988.2	1023	532.3	-198	2151	654	1349
VIC	95	987	1000	517.3	-96	2155	696	1345
Rating	ACT	161	2.917	2.73	1.43	0.02	5	2.245	4.42
NSW	387	2.8197	2.75	1.5273	0	5	2.12	4.33
QLD	237	2.84	2.73	1.55	0	5	2.11	4.345
SA	120	2.8	2.6	1.514	0.05	5	2.065	4.248
VIC	95	2.801	2.67	1.451	0.01	5	2.13	4.16

Table 8a shows the output of the one way ANOVA for testing whether the mean product price for different region is significantly different from each other or not. As the P-value = 0.853 > 0.05, we fail to reject null hypothesis that there is no significant difference between the mean product price for different region. Output also gives the 95% confidence interval for mean price of different region.

Table 8a: Output of the one way ANOVA for testing whether the mean product price for different region

One-way ANOVA: PRICE versus REGION

Analysis of Variance for PRICE   

Source     DF        SS        MS        F        P

REGION      4     44614     11154     0.34    0.853

Error     995  32923389     33089

Total     999  32968004

Individual 95% CIs For Mean

Based on Pooled StDev

Level       N      Mean     StDev  ——+———+———+———+

ACT       161     392.1     184.7              (———-*———-)

NSW       387     385.3     178.2               (——*——)

QLD       237     379.7     185.7           (——–*——–)

SA        120     368.2     168.0  (————*————)

VIC        95     381.4     198.8      (————–*————-)

                                   ——+———+———+———+

Pooled StDev =    181.9                350       375       400       425

Table 8b represents the output of the one way ANOVA for testing whether the mean sale price for different region is significantly different from each other or not. As the P-value = 0.88 > 0.05, we fail to reject null hypothesis that there is no significant difference between the mean sale price for different region. Output also gives the 95% confidence interval for mean sale price of different region.

Table 8b: Output of the one way ANOVA for testing whether the mean sale price for different region

Analysis of Variance for SALES   

Source     DF        SS        MS        F        P

REGION      4    333084     83271     0.30    0.880

Error     995 278994796    280397

Total     999 279327880

Individual 95% CIs For Mean

Based on Pooled StDev

Level       N      Mean     StDev  —–+———+———+———+-

ACT       161    1311.4     517.7   (———-*———–)

NSW       387    1358.6     531.3              (——*——-)

QLD       237    1361.9     546.1            (———*——–)

SA        120    1356.4     514.9       (————-*————)

VIC        95    1368.4     517.9       (————–*—————)

                                   —–+———+———+———+-

Pooled StDev =    529.5              1260      1330      1400      1470

Table 8c demonstrates the output of the one way ANOVA for testing whether the mean number of customers for different region is significantly different from each other or not. As the P-value = 0.342 > 0.05, we fail to reject null hypothesis that there is no significant difference between the mean number of customers for different region. We can look at the 95% confidence interval for mean number of customers of different region.

Region Analysis

Table 8c: Output of the one way ANOVA for testing whether the mean number of customers for different region

Analysis of Variance for NO_OF_CU

Source     DF        SS        MS        F        P

REGION      4     145.6      36.4     1.13    0.342

Error     995   32095.8      32.3

Total     999   32241.4

Individual 95% CIs For Mean

Based on Pooled StDev

Level       N      Mean     StDev  —+———+———+———+—

ACT       161    18.199     6.432            (————*————)

NSW       387    17.214     4.719   (——-*——-)

QLD       237    17.515     6.013     (———*———-)

SA        120    18.033     6.099        (————–*————-)

VIC        95    17.853     6.459    (—————*—————)

                                   —+———+———+———+—

Pooled StDev =    5.680            16.80     17.50     18.20     18.9

Table 8d reveals the output of the one way ANOVA for testing whether the mean profit for different region is significantly different from each other or not. As the P-value = 0.795 > 0.05, we fail to reject null hypothesis that there is no significant difference between the mean profit for different region. We can observe the 95% confidence interval for mean profit of different region.

Table 8d: Output of the one way ANOVA for testing whether the mean profit for different region

Analysis of Variance for PROFIT  

Source     DF        SS        MS        F        P

REGION      4    520860    130215     0.42    0.795

Error     995 309678180    311234

Total     999 310199040

 Individual 95% CIs For Mean

Based on Pooled StDev

Level       N      Mean     StDev  ——+———+———+———+

ACT       161     919.3     558.3  (———-*———-)

NSW       387     973.3     554.8             (——*——)

QLD       237     982.1     589.9            (——–*——–)

SA        120     988.2     532.3         (————*———–)

VIC        95     987.0     517.3       (————-*————-)

                                   ——+———+———+———+

Pooled StDev =    557.9                880       960      1040      1120

Table 8e shows the output of the one way ANOVA for testing whether the mean rating for different region is significantly different from each other or not. As the P-value = 0.959 > 0.05, we fail to reject null hypothesis that there is no significant difference between the mean rating for different region. We can observe the 95% confidence interval for mean rating of different region.

Table 8e: Output of the one way ANOVA for testing whether the mean rating for different region

One-way ANOVA: Average Rating versus REGION

 

Analysis of Variance for Average Rating

Source     DF        SS        MS        F        P

REGION      4      1.44      0.36     0.16    0.959

Error     995   2265.06      2.28

Total     999   2266.50

                                   Individual 95% CIs For Mean

                                   Based on Pooled StDev

Level       N      Mean     StDev  ——+———+———+———+

ACT       161     2.917     1.430            (———–*———–)

NSW       387     2.820     1.527           (——-*——-)

QLD       237     2.840     1.550          (———*———)

SA        120     2.800     1.514     (————*————-)

VIC        95     2.801     1.451   (————–*————–)

                                   ——+———+———+———+

Pooled StDev =    1.509                2.60      2.80      3.00      3.20

Correlation Analysis:

In the following Table 9, we represent the Pearson’s correlation coefficient and P-value of its significance for Product Price, Sale Price, Number of customers, Profit and Rating.

Table 9: Pearson’s correlation coefficient and P-value of its significance for Product Price, Sale Price, Number of customers, Profit and Rating

	Product Price	Sale Price	Number of customers	Profit
Sale Price	0.011
	0.73
Number of customers	0.058	0.079
	0.066	0.012
Profit	-0.316	0.945	0.056
	0	0	0.075
Rating	0.158	-0.31	0.016	-0.346
	0	0	0.618	0

From Table 9, we can say that following pair has significant correlation

1. Product price and Profit
2. Product price and Rating

• Sale price and Number of customers

1. Sale price and Profit
2. Sale price and Rating
3. Profit and Rating

We considered the monthly profit as a response variable and monthly sale as a predictor variable. Table 10 displays the output of simple liner regression on monthly profit by monthly sales. We observed that R² =89% which pretty high suggest that model fitting is good. We can predict the monthly profit by observing the monthly sales using regression equation.

Table 10: Regression analysis of monthly profit by monthly sales

The regression equation is

Monthly_Profit = – 3625 + 0.868 Monthly_Sale

Predictor        Coef     SE Coef          T        P

Constant      -3624.6       263.6     -13.75    0.000

Monthly_     0.868094    0.009670      89.77    0.000

S = 3928        R-Sq = 89.0%     R-Sq(adj) = 89.0

Analysis of Variance

Source            DF          SS          MS         F        P

Regression         1 1.24337E+11 1.24337E+11   8058.92    0.000

Residual Error   998 15397640608    15428498

Total            999 1.39735E+1

1. From profit analysis, there is very low monthly sales for VIC and SA region compare to the others. So company has to pay attention for more marketability for this region.
2. Average rating of customers who got delivery free is higher than those who got the delivery by paying so company need to plan to provide the items

• Average rating of new customers is higher than existing customer suggest that we need to plan to satisfies our existing customers to avoid the shifting to any other company for service.

1. As the VIC and SA region have less monthly sale it gives you less profit, so company should focus on attract the customers for purchasing the costly gadget. Company can appoint the marketing executive for marketing the costly gadgets. (As number of customers are not significantly different for the regions)
2. Company can increase the staff of shipping department so that more customer gets free delivery which highly impact on rating.    

• Company must give some offer to the existing customers such as price cut, free shipping etc. so that it impact on the rating.

Conclusions

From the profit analysis, we can say that shipping type and customer type does not show any significant changes in the profit whereas different region have different have different profit.  From two sample t-test for shipping type and customer type, average rating shows significant difference between the groups whereas other variables as product price, sale price, number of customers and profit does not show any significance difference between the groups. There is no any significant difference between the different region for product price, sale price, number of customers, profit and rating. Correlation analysis suggest that there is significant correlation between Product price and Profit, Product price and Rating, Sale price and Number of customers, Sale price and Profit, Sale price and Rating, Profit and Rating. Regression analysis shows that we can predict the monthly profit by observing the monthly sales. They are positively correlates

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