Statistical Analysis Of Honeybee Fruit Company Performance

Research Questions

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.

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.

Product Performance 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.

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

 

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.

H_A: 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.

Payment Methods Analysis

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.

H_A: 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.

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.

H_A: 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. 

Locations Analysis

 

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.

H_A: 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

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.

H_A: 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

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

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