Good Harvest Organic Farm And Markets: Sales Analytics Report

Problem definition and business intelligence required

This report concerns the Good Harvest Organic Farm and Markets. The enterprise grows a range of quality fresh organic produce and sells direct to the local community through its home delivery service. They strive to bring to their clients the greatest selection of the freshest, healthiest, natural & organic grocery products on the market today.

Organic products are those products that are grown under some set standards for the Organic Food Production Act and they are strictly regulated to ensure a chemical-free product (Markandya & Setboonsarng, 2008). Organic food handlers, for instance the Good Harvest Market, are mainly committed to ensure that integrity in the label is maintained.

Good Harvest Organic Farm has just been in the market for barely two years. The CEO is interested in utilizing data to understand how the firm has been performing in terms of sales. This study therefore aimed at understanding how the firm has been performing in the last 2 years and probably advice on the best practises that the company can undertake in order to ensure they remain ahead when it comes to their performance in terms of sales revenues.

Managers as well as the board of directors of companies are normally faced by myriad of challenges. They burn the midnight oil trying to come up with ways in which they can ensure their company remains in the market for the next several years to come. This is not an exception to the CEO of Good Harvest Organic Farm. The company is apparently young and as such a lot of learnings need to be documented and most likely the best practices be used while doing away with bad practices that can make the company not prosper. We are presented with the following questions that needs to be answered;

1. What are the top/worst selling products in terms of sales?
2. Is there a difference in payments methods?
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?

Two datasets are provided for this study. The fist dataset had 18 variables while the second variable had 12 variables. This brings to a total of 30 variables for the two datasets. The portion of the data for the first few observations is shown in the following two figures below;

The first analysis we performed was on trying to figure out among the products that the company is involved in which ones are best performing and which ones are not performing to the expectations (worst performing products)? To check on this, we did a descriptive statistics to see how the various products compare in terms of the average sales. We analysed and filtered the top five products (best performing) and the bottom five products (worst performing). Table 1 below gives the descriptive of the mentioned products.

Selected analytics methods and technical analysis

Table 1: Top 6 best-selling products

Table 2: Top 5 worst-selling products

Tables 1 and 2 present the average total sales for the worst performing and best performing products. Best 5 performing products have sales amounting to an average greater than $500. Six products fall in this category and they are; water, fruit, vegetable, dairy, drinks and coconut water. The worst performing products have sales averaging to less than $30. The products in this category include; salad greens, herbal teas, juicing, spices and snacks.

Another important analysis conducted that applies the inferential statistics is one testing whether there are differences in payment methods. Two hypothesis are performed; the first one tests the difference in total cash received for the credit and cash payment methods.

In the first hypothesis we check whether there is significant difference in total cash received from the cash and credit payment methods. The following hypothesis was answered;

There is no significant difference in the total cash received for the credit and cash payment methods 

There is significant difference in the total cash received for the credit and cash payment methods

Tested at α = 0.05

Results are given in the table below;

Table 3: t-Test: Two-Sample Assuming Equal Variances

The mean total cash received from cash payment is $412.78 while that received from the credit payment method is $604.64. The p-value for the test is 0.000 (both two-tailed and one-tailed), we thus reject the null hypothesis and conclude that cash and credit payment methods are significantly different at 5% level of significance.

The second hypothesis sought to test whether there is a significant difference between the Visa payment method and the MasterCard payment method. The hypothesis tested is as follows;

There is no significant difference in the total cash received for the visa card and master card payment methods 

There is significant difference in the total cash received for the visa card and master card payment methods

Tested at α = 0.05

Results are given in the table below;

Table 4: t-Test: Two-Sample Assuming Equal Variances

The mean total cash received from visa card payment is $576.31 while that received from the master card payment method is $152.55. The p-value for the test is 0.000 (both two-tailed and one-tailed), we thus reject the null hypothesis and conclude that visa card and master card payment methods are significantly different at 5% level of significance.

Results and findings

Analysis 3: Are the differences in sales performance based on where the product is located in the shop?

For analysis 3, we sought to verify whether location of the product in the shop really affects or influences the sales performance of a product.

Tested at α = 0.05

Table 5: Analysis of variance (ANOVA) for the total sales versus product location

ANOVA test was performed to test whether there is difference in the means for the different product locations within the shop. The p-value is 0.000 (a value less than 5% significance level), we therefore reject the null hypothesis and come to a conclusion that at least one of the means is significantly different.

In analysis 4, the aim was to test whether month of the year has significant influence on the sales as well as the gross profits of the company. Hypothesis 5 tests the influence of month of the year on total sales while hypothesis 6 tests the influence of month of the year on the gross profits.

Tested at α = 0.05

Table 6: Analysis of variance (ANOVA) for the gross sales versus months

In table 6 above, we can see that the p-value is 0.222 (this value is greater than α = 0.05), the null hypothesis cannot therefore be rejected. In concluding, we can say that the average gross sales are the same for all the months in a year. There is no month that has a significantly different average gross sales.

Tested at α = 0.05

Table 7: Analysis of variance (ANOVA) for the gross profits versus months

In table 8 above, we can see that the p-value is 0.000 (this value is less than α = 0.05), the null hypothesis is therefore rejected. In rejecting the null hypothesis, we conclude that the average gross profit are at least significantly different for one of the months of the year.  .

For the case of analysis 5, the aim was to verify whether the sales performance is different between the four seasons. Does it appear that some seasons make more sales than the others? To test this, we used analysis of variance (ANOVA) test. The hypothesis tested is given below;

Table 8: Analysis of variance (ANOVA) for the net sales versus seasons

In table 8 above, we can see that the p-value is 0.000 (this value is greater than α = 0.05), the null hypothesis is not rejected. In not rejecting the null hypothesis, we conclude that the average gross profit are the same for the different seasons.

The CEO of Good Harvest Organic Farm and Market tasked the data analyst to provide a detailed analysis of the performance of the company based on sales and gross profits made by the company. Particularly, the data analyst was tasked to:

• Identify the best and worst performing products
• Identify whether there is variation in the payment methods
• Identifying where there exists significant differences in sales performance based on where the product is located in the shop
• Identifying whether the sales and gross profits are influenced by the month of the year
• Identifying whether sales performance is influenced by the seasons

Using a one year data collected from the company we analysed the data in attempt to answer the above objectives. Results revealed the best and the worst performing products that the company has. Among the best performing products were water, drinks, coconut water, vegetables and fruits. On the other hand the worst performing products included; salad greens, snacks, spices, herbal teas and juicing products. We also found out that location of the product in the shop has a significant impact on the sales. Month of the as well as the season did not have any significant impact on the sales revenue. However, month of the year was found to influence the gross profits. The CEO has to decide on which products they need to keep; it is clear that some products had very low returns. It is needless for the company to continue having products that bring in little or no income to the company. The manner in which the company displays its products should also be looked at. Results showed that some positions or locations had huge sales returns as compared to others. It would be important for the CEO to come up with a smart display that ensures that products are well visible hence attract potential buyers.

References

Babbie, E. R., 2009. The Practice of Social Research (12th ed.). p. 436–440.

Chacarbaghi, L., 2009. Competitive Advantage: Creating and Sustaining Superior Performance. p. 45.

Derrick, B., Toher, D. & White, P., 2017. How to compare the means of two samples that include paired observations and independent observations. The Quantitative Methods for Psychology, 13(2), p. 120–126.

Markandya, A. & Setboonsarng, S., 2008. Organic Crops or Energy Crops? Options for Rural Development in Cambodia and the Lao People’s Democratic Republic.

Moore, D. S. & McCabe, G. P., 2003. Introduction to the Practice of Statistics. p. 764.

Passemard, C., 2000. Competitive Advantage: Creating and Sustaining Superior Performance. p. 18.

Sheskin, D. J., 2011. Handbook of Parametric and Nonparametric Statistical Procedures.

Trochim, W. M., 2006. Descriptive statistics. Research Methods Knowledge Base.

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order