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BUS501 Business Analytics And Statistics

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  • Course Code: BUS501
  • University: University of Sunshine Coast
  • Country: Australia

Question:

This assignment is based on fictional data.You are creating a business report for the CEO of Honeybee Fruit. It must be professional in presentation and contain insightful content for them to make business decisions.

Task instructions:

Use the statistical analyses you have learned to answer all of the questions below

This first dataset is labelled “Fruit shop data product mix”:

  • The variables include:
    • Product class
    • Product name
    • Product category
    • Total sales
    • Cost of Goods (COGs)
    • Net profit
    • Location in the shop – front, left, outside front, rear, right
    • Profit total

The second data set is labelled “Fruit shop data sales summary”:

  • The variables include:
    • Month (January, February, March, April, May, June, July, August, September, November, December)
    • Season (Spring, summer, autumn, winter)
    • Gross sales
    • Net sales
    • Cash total payments
    • Credit total payments
    • Total orders
    • Average sale
    • Staff cost
    • Profit total

Answer these main research questions:

  1. What are the best and worst selling products in terms of sales?
  2. Is there a difference in payments methods? (Cash vs Credit)
  3. Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?
  4. Is there a difference in sales and gross profits between different months of the year?
  5. Are their differences in sales performance between different seasons? (Summer, spring, autumn, winter)
 

Answer:

Introduction

Company sales is one of the key performances that shows that the business is either doing well or in some verge to collapse. The CEO of Honeybee Fruit is a concerned person and would want to understand how the company is performing. He is particularly interested in knowing which of the products are doing well and which ones are not doing well in the market. With this in mind, this study therefore sought to analyse the performance of Honeybee Fruit and make recommendations to the CEO regarding how best or worst the company is doing in terms of the profits made, sales, which months of the year the company made more profits, what about the seasons among others.

Problem definition and business intelligence required

The main research questions that this study sought to answer include;

  • What are the best and worst selling products in terms of sales?

This is the first research question that the study sought to answer. To answer this question, there is need to have an idea of how the products perform. Considering sales and product type, we will find the mean sales for each of the products and the rank based on the product with the highest average sales to the product with the lowest average sales. This means that descriptive statistics will be able to answer this research question.

  • Is there a difference in payments methods? (Cash vs Credit)

Different payment methods are normally available for different organizations. This study sought to find out whether any of the payments methods brings in more revenue than the other and if yes, then which payment method is that? To answer the research question, a recommended test is the t-test which compares the average for two groups (Marden, 2000).

  • Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?

This is another question that we sought to tackle in this study. To answer it, we needed to make a comparison between the average sales performances for all the locations and test the hypothesis. Since this question unlike the previous one has more than 2 factors, the t-test used above would not be ideal but rather we would use analysis of variance (ANOVA). ANOVA test is useful when we want to compare the average of more than 2 factors (Wilkinson, 1999)

  • Is there a difference in sales and gross profits between different months of the year?

Just like the above research question, to answer the question as to whether the sales between the various months of the years is different or not, we have to use ANOVA test since there more than two factors for the independent variable (Derrick, Broad, Toher, & White, 2017).

  • Are their differences in sales performance between different seasons? (Summer, spring, autumn, winter)

Again analysis of variance (ANOVA) test would be the most ideal test to be used since the number of season are four and this number is more than 2 factors hence the need to use ANOVA test.

 

Results of the selected analytics methods and technical analysis

This section presents the results of the research questions discussed above. The section also reports on the hypothesis that was tested in each of the research question.

What are the best and worst selling products in terms of sales?

As had been mentioned, answering this research question requires comparing the average sales for the different products and ranking them to see which of the products rank high (best performing) and those that rank low (worst performing). The results are given in table 1 and table 2 below;

In table 1, the top 5 products that had the highest average sales is presented. Water ranks the highest among all the products with an average sales revenue of $1867.08. It is closely followed by sales from the fruits which had an average of $1,048.67. Drinks closes the least of top 5 products that had highest sales with an average sales revenue of $574.31.

Table 1: Top best-selling products in terms of sales

Rank

Product Class

Average Sales

1

Water

 $   1,867.08

2

Fruit

 $   1,048.67

3

Vegetable

 $       871.51

4

Dairy

 $       619.12

5

Drinks

 $       574.31

 

In table 2, the products were ranked from the bottom in order to check for the worst performing products (Mahdavi , 2012).  The number 1 product among the worst selling products as can be seen in table 2 below is the juicing with only an average sales revenue of $5. Other products that did not perform well in terms of sales are the herbal teas, spices, snacks and salad greens.

Table 2: Bottom 5 worst selling products in terms of sales

Rank

Product Class

Average Sales

1

Juicing

 $       5.00

2

Herbal Teas

 $    18.00

3

Spices

 $    19.07

4

Snacks

 $    20.50

5

Salad Greens

 $    25.00

 
 

Is there a difference in payments methods? (Cash vs Credit)?

This is another important research question that the CEO would want answered. To answer this, we needed to test the following hypothesis;

H0: The average sales revenue from the cash payment does not significantly differ from the sales revenue from the credit payments.

HA: The average sales revenue from the cash payment significantly differ from the sales revenue from the credit payments.

Table 3: Group Statistics

 

Payment method

N

Mean

Std. Deviation

Std. Error Mean

Total amount received

Credit

366

584.81

228.87

11.96

Cash

366

404.29

153.65

8.03

Table 4: Independent Samples Test

 

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

Total amount received

Equal variances assumed

42.885

.000

12.528

730

.000

180.52

14.41

152.23

208.81

Equal variances not assumed

 

 

12.528

638.5

.000

180.52

14.41

152.22

208.81

Looking at the above two tables, it can be seen that the average sales revenue received from cash payments averaged at 404.29 (SD = 153.65) while that from the credit payments averaged at 584.81 (SD = 228.87). The t-test results further showed that the p-value was 0.000 (a value smaller than 5% level of significance) hence resulting to the rejection of the null hypothesis (Nikoli?, Muresan, Feng, & Singer, 2012). Rejecting the null hypothesis implies that we come to a conclusion that the average sales revenue from the cash payment significantly differ from the sales revenue from the credit payments. The amount received from the credit payments is by far higher than what is received from the cash payments.

 

Are the differences in sales performance based on where the product is located in the shop? How does this effect both profits and revenue?

The products were found to be located in five different locations and so to answer this research an ANOVA test was to be performed. The hypothesis tested was;

H0: Average sales of the product does not significantly differ depending on the location of the product.

HA: Average sales of the product significantly differ depending on the location of the product.

Table 5: Descriptive statistics

 

N

Mean

Std. Deviation

Std. Error

95% Confidence Interval for Mean

Lower Bound

Upper Bound

Outside Front

12

3384.37

4719.35

1362.358

385.84

6382.90

Front

155

572.75

1430.66

114.913

345.74

799.76

Rear

180

536.07

1072.15

79.914

378.38

693.77

Right

311

239.89

553.00

31.358

178.19

301.59

Left

376

218.22

427.61

22.053

174.86

261.58

Total

1034

369.96

1014.72

31.556

308.04

431.88

Products located outside front had the highest sales revenue (M = 3384.37, SD = 4719.35) which were almost 6 times that of the second best sales revenue (M = 572.75, SD = 1430.66) which came from products located in front. Products on the left had the lowest average sales revenue at 218.22 (SD = 218.22).

Table 6: ANOVA

Total Sales ($)  

 

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

134299725

4

33574931.26

37.176

.000

Within Groups

929333381

1029

903142.26

 

 

Total

1063633106

1033

 

 

 

The p-value for the ANOVA test is 0.000 (a value smaller than 5% level of significance) hence resulting to the rejection of the null hypothesis. Rejecting the null hypothesis implies that we come to a conclusion that the average sales of the product significantly differ depending on the location of the product (Székely & Rizzo, 2017). Products located outside front had the highest sales revenue (M = 3384.37, SD = 4719.35) which were almost 6 times that of the second best sales revenue (M = 572.75, SD = 1430.66) which came from products located in front. Products on the left had the lowest average sales revenue at 218.22 (SD = 218.22).

From the above results, it is evident that the profits and the revenues are likely to be affected I the same manner the sales have been affected. This is because profits and revenues are generated based on the sales. So if sales are affected then most likely the profits and revenues will not be spared.

 

Is there a difference in sales and gross profits between different months of the year?

There are 12 months in a year so to answer this research an ANOVA test was to be performed. The hypothesis tested was;

H0: Average sales of the product does not significantly differ depending on the month of the year.

HA: Average sales of the product significantly differ depending on the month of the year.

The results of the test are given below;

Table 7: Descriptive statistics

 

N

Mean

Std. Deviation

Std. Error

95% Confidence Interval for Mean

Lower Bound

Upper Bound

January

31

979.65

368.350

66.158

844.54

1114.76

February

29

1066.47

237.830

44.164

976.00

1156.94

March

31

1063.69

379.880

68.228

924.35

1203.03

April

30

1078.77

314.570

57.432

961.31

1196.23

May

31

1054.09

336.527

60.442

930.65

1177.53

June

30

918.81

223.442

40.795

835.38

1002.25

July

31

1004.78

248.080

44.556

913.79

1095.78

August

31

1025.26

308.806

55.463

911.99

1138.54

September

30

1014.06

302.101

55.156

901.25

1126.86

October

31

1056.03

353.343

63.462

926.42

1185.64

November

30

1197.64

343.489

62.712

1069.38

1325.90

December

31

1082.74

413.527

74.272

931.05

1234.42

Total

366

1044.97

326.285

17.055

1011.43

1078.51

The month of November had the highest sales recorded (M = 1197.64, SD = 343.49) while the month with the lowest sales was the month of June (M = 918.81, SD = 223.44).

Table 8: ANOVA Table

 

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

1508892.474

11

137172.043

1.300

.222

Within Groups

37349615.455

354

105507.388

 

 

Total

38858507.929

365

 

 

 

The p-value for the ANOVA test is 0.222 (a value bigger than 5% level of significance) hence resulting to the non-rejection of the null hypothesis. Failure to reject the null hypothesis implies that we come to a conclusion that the average sales of the products does not significantly differ depending on the month of the year. 

 

Figure 1: Mean plot for gross sales versus month

Also tested in this section is whether the average gross profits differ among the months of the year. The tested hypothesis is given as follows

H0: Average gross profits of the product does not significantly differ depending on the month of the year.

HA: Average gross profits of the product significantly differ depending on the month of the year.

ANOVA test used at 5% level of significance. The results are as follows;

Table 9: Descriptive Statistics

 

N

Mean

Std. Deviation

Std. Error

95% Confidence Interval for Mean

Lower Bound

Upper Bound

January

31

33.0187

41.52022

7.45725

17.7890

48.2484

February

29

23.4317

18.62369

3.45833

16.3477

30.5158

March

31

19.3377

16.22981

2.91496

13.3846

25.2909

April

30

19.6233

12.79108

2.33532

14.8471

24.3996

May

31

20.2316

20.39691

3.66339

12.7500

27.7133

June

30

19.3213

15.05624

2.74888

13.6992

24.9434

July

31

28.8258

17.17982

3.08559

22.5242

35.1274

August

31

34.4823

20.72196

3.72177

26.8814

42.0831

September

30

43.0557

35.75048

6.52711

29.7062

56.4051

October

31

46.2616

38.93676

6.99325

31.9795

60.5437

November

30

43.2477

49.52855

9.04263

24.7534

61.7419

December

31

37.2877

29.33486

5.26870

26.5276

48.0479

Total

366

30.7098

30.05661

1.57108

27.6202

33.7993

 

The month of October had the highest gross profits recorded (M = 46.26, SD = 38.94) while the month with the lowest sales was the month of March (M = 19.34, SD = 16.23).

Table 10: ANOVA Table

Profit Total  

 

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

35370.948

11

3215.541

3.867

.000

Within Groups

294370.006

354

831.554

 

 

Total

329740.954

365

 

 

 

 

The p-value for the ANOVA test is 0.000 (a value smaller than 5% level of significance) hence resulting to the rejection of the null hypothesis. Rejecting the null hypothesis implies that we come to a conclusion that the average gross profits of the product significantly differ depending on the month of the year. Products sold in the month of October had the highest sales revenue (M = 46.26, SD = 38.94) while the month with the lowest sales was the month of March (M = 19.34, SD = 16.23).

 

Figure 2: Mean plot for gross profits versus month

Are their differences in sales performance between different seasons?

There are 4 seasons in a year so to answer this research an ANOVA test was to be performed. The hypothesis tested was;

H0: Average sales of the product does not significantly differ depending on the season of the year.

HA: Average sales of the product significantly differ depending on the season of the year.

The results of the test are given below;

Table 12: Descriptive statistics

 

N

Mean

Std. Deviation

Summer

91

1042.44

349.18

Autumn

92

1065.37

341.39

Winter

92

983.65

264.13

Spring

91

1088.88

339.45

Total

366

1044.97

326.29

 

Spring had the highest gross sales recorded (M = 1088.88, SD = 339.45) while the season with the lowest sales was the winter (M = 983.65, SD = 264.13).

Table 13: ANOVA table

Gross_Sales  

 

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

560240.410

3

186746.803

1.765

.153

Within Groups

38298267.520

362

105796.319

 

 

Total

38858507.929

365

 

 

 

 

The p-value for the ANOVA test is 0.153 (a value bigger than 5% level of significance) hence resulting to the non-rejection of the null hypothesis (Cohen, Cohen, West, & Aiken, 2002). Failure to reject the null hypothesis implies that we come to a conclusion that the average sales of the product does not significantly differ depending on the season of the year.

 

Figure 3: Mean plot for gross sales versus season of the year

Discussion of the results and recommendations

This study sought to analyse the performance of the company focussing on the sales revenue and the gross profits. The study began by looking at the best and the worst selling products. To identify the best and the worst selling products, the average sales of each and every product was determined and the products ranked based on the average with the products having the highest sales revenue ranking first. Some of the best-selling products included water, fruits, vegetables, dairy products and the drinks. On the other hand, the worst performing products included salad greens, snacks, spices, herbal teas and juicing products. Some of the factors that were found to influence the performance of the business (either in terms of sales or the profits) include;

  • Location of the product in the shop (sales)
  • Month of the year (profits)
  • Payment method

Recommendations

Considering the above findings. The following recommendations are made to the company.

  • The company to find out why the month of the year does not influence the sales but influences the profits. The reason could be there are high costs of goods in some months than the others hence the management needs to identify which months have high cost of goods and which ones have low cost of goods and know how to budget in order to maximize on profits.
  • Work on proper way of ensuring the products are well displayed in locations where they can attract customers for subsequent close in sales.
  • Ensure that there are flexible payment methods that would make the customers pay with ease.
  • Product promotions and advertisement of the worse selling products
 

References

Derrick, B., Broad, A., Toher, D., & White, P. (2017). The impact of an extreme observation in a paired samples design. metodološki zvezki - Advances in Methodology and Statistics, 14(2), 1-17.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press, 5(6), 31-39.

Mahdavi , D. B. (2012). The Misleading Value of Measured Correlation. Wilmott, 1(3), 64–73. doi:10.1002/wilm.10167

Marden, J. I. (2000). Hypothesis Testing: From p Values to Bayes Factors. Journal of the American Statistical Association, 95(452), 1316. doi:10.2307/2669779

Nikoli?, D., Muresan, R. C., Feng, W., & Singer, W. (2012). Scaled correlation analysis: a better way to compute a cross-correlogram. European Journal of Neuroscience, 5(4), 1–21. doi:10.1111/j.1460-9568.2011.07987.x

Székely, G. J., & Rizzo, B. N. (2017). Measuring and testing independence by correlation of distances. Annals of Statistics, 35(6), 2769–2794. doi:10.1214/009053607000000505

Wilkinson, L. (1999). Statistical Methods in Psychology Journals; Guidelines and Explanations. American Psychologist, 5(8), 594–604. doi:10.1037/0003-066X.54.8.594

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