• Posted on February 10, 2021
• Unique Submission Team

Business Analytics and Statistics

Introduction

In today’s business environment, it is significant for firms to use the best and efficient technologies in order to satisfy the customer and provide the best experience. The data analytics helps the firm in understanding the customer buying behaviour, their pattern & requirements and most importantly their needs. This paper is also based on data analysis in order to explore the data about sales and product mix for the small retail food shop named Good Harvest on Sunshine Coast. This retail food shop i.e., Good Harvest deals in producing and distributing organic food products across the country (Good Harvest, 2017). In addition, Good Harvest operates its business efficiently as it has 1 retail store, 1 old store warehouse and 6 team staff and 1 distributing van. At the same time, this food shop also operates its business in different areas i.e., wholesale system, Harvest kitchen and retail system of the company. However, this assignment will help in developing insight related to the implication of data analytics approach for determining the solutions for the business problem.

Problem Definition and Business Intelligence Required

The key business problem identified is that company is facing a sales decline of organic food products as well as an increase in the cost of goods. A company is in starting phase of the business life cycle and also it is focused on increasing the sales & profit and decline in the cost of products. In concern to it, the data set is prepared and analyzed for understanding and determining the product and sales performance pattern (Newbold, et al., 2012). This data analytics enables the firm to develop insight for which it uses statistical data analysis method which involves different test such as ANOVA test, t-test, correlation and descriptive statistics. However, the below-stated tables represent the product and sales data set for doing proper analysis:

Table 1: Product Data

Table 2: Sales Data

The above table demonstrates the sales and product data in which product data shows two ordinal variables i.e., product class and category whereas sales data indicates also ordinal variables such as day, weekday, season and month.

For overcoming the business problem, the data analysis will be conducted in which below stated questions will be answered respectively:

Q1. Identify the top/worst selling products in respect to sales? Is there any difference found in the payment method?

Q2. In different months of a year, is any difference found between sales and gross profits?

Q3. Identify the difference in sales performance on the basis of where the product is located in the retail shop? And also how this sales performance affects both revenue and profit of the company?

Q4. Identify the differences in their sales performance based on different seasons?

• How this difference in sales performance will relate to rainfall and profits?

Some Extra questions:

Q5. What is the difference in average sales in each month of the year?

Q6. What is the difference in the gross profit margin of different seasons?

Q7. What is the difference in average sales of different seasons?

Visualize Descriptive Statistics

Descriptive Statistics (Product Data Set)

Table 3: (Product data)

Descriptive Statistics
	N	Minimum	Maximum	Sum	Mean	Std. Deviation
Product Class (number)	1034	1	30	15464	14.96	8.515
Quantity	1034	1	3769	74348	71.90	212.400
Weight	209	0	2913	16156	77.30	242.323
Total Sales ($)	1034	0	17276	382540	369.96	1014.719
Cost of Goods ($)	1034	0	8573	212203	205.22	561.072
Net Profit ($)	1034	0	8703	170338	164.74	482.106
Location of product in a shop	1034	1	5	3218	3.11	1.526
Total Profit	1034	.00	8702.93	1.70E5	1.6473E2	482.10651
Valid N (list-wise)	209

Descriptive Statistics (Sales Data Set)

Table 4: (Sales data)

Descriptive Statistics
	N	Minimum	Maximum	Sum	Mean	Std. Deviation
Day of the Year	366	1	366	67161	183.50	105.799
The month of the year	366	1	12	2384	6.51	3.456
Season of the year	366	1	4	915	2.50	1.117
GST Inclusive	366	0	271	41876	114.42	48.723
GST Exclusive	366	0	2492	340583	930.56	303.827
Gross Sales	366	0	2642	382460	1044.97	326.285
Net Sales	366	0	2370	371220	1014.26	313.986
Cash Total	366	0	1195	147969	404.29	153.643
Credit Total	366	0	1407	214036	584.80	228.860
Visa Total	366	0	1407	203441	555.85	244.870
MasterCard Total	366	0	399	8086	22.09	67.823
House Account	366	-264	1113	13684	37.39	113.204
Total Orders	366	0	129	20327	55.54	15.844
Average Sale	358	8	61	6631	18.52	3.985
Staff Cost	366	170	351	91022	248.69	52.418
Weekday	366	1	7	1463	4.00	1.998
Rainfall	365	0	63	1452	3.98	9.811
Profit Total	366	-33.98	271.97	1.12E4	30.7098	30.05661
Valid N (list-wise)	357

Discussion and analysis

Q1. Identify the top/worst selling products in respect to sales? Is there any difference found in the payment method?

In this answer to the question, the Pareto principle is used which states that on an average of 80% impacts occurs due to 20% of the cause in a particular event. The Pareto curve can be plotted in association with the given class of the product and sales (Gibbons & Chakraborti, 2011).

Figure 1: Pareto Curve

Additionally, the given Plotted curve specifies that most of the value (80%) of total sales on the y-axis is intersecting at the x-axis that is showing the top-selling of the total products. In addition, the other remaining values on the x-axis are the worst part. From the above analysis, it can understand that the most selling products are vegetables, dairy, fruit, dry goods, drinks, snacks and chocolates, water and bakery products, which are helpful to make 80% of the total sales. The remaining products are worst selling products and provide only 20% contribution in sales of the form such as fridge, spices, grocery, freezer, Ayurvedic, etc.

With the help of a t-test, it will be easy to differentiate the payment method:

Table 5: t-test

One-Sample Test
	Test Value = 0
	t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
Cash Total	50.340	365	.000	404.287	388.49	420.08
Credit Total	48.885	365	.000	584.798	561.27	608.32
Visa Total	43.427	365	.000	555.849	530.68	581.02
MasterCard Total	6.232	365	.000	22.094	15.12	29.07
House Account	6.318	365	.000	37.388	25.75	49.02

The above regression analysis table indicates that the p-value for each variable is zero in different payment system methods and also this shows that the significance value of p is less than 0.05 (p<0.05). So, it can be easily stated that there is a significant difference in the payment methods.

Q2. In different months of a year, is any difference found between sales and gross profits?

For determining the difference in the sales and gross profit between different months of a year, regression analysis is done:

Table 6: Regression analysis (sales in different months)

In the above regression analysis table, it can be stated easily that the p-value is 0.221 which is greater than zero i.e., (p>0.05) and this shows that there is no difference in the sales in every month of a year.

On the other hand, the ANOVA test is also conducted for analysing the difference in the gross profit and different months.

Table 7: Regression Analysis (gross profit in different months)

The above table demonstrated that there is a significant difference in gross profit in different months of a year and this is because the p-value of gross profit in different months is less than 0.05 because it is a combined form (Bodie, 2013). So, there is a significant difference.

Q3. Identify the difference in sales performance on the basis of where the product is located in the retail shop? And also how this sales performance affects both revenue and profit of the company?

For identifying the difference in the sales performance, the Pareto curve will help in determining the relationship between the performance and product location in the retail shop.

Figure 2: Pareto curve (sales performance & product location)

The above-stated table helped in depicting that there is a difference in the sales performance and product location. The products which are located rear place those products sales are high in comparison to the products which are displayed backside or outside the product location of the store.

Similarly, the below graph also shows the relationship between the profit and product location on the shop:

Figure 3: Pareto curve (total profit & product location)

The above-stated graph stated that front located products are providing more profit to the company in comparison to the product located at an outside location in the shop (Montgomery, 2010). However, this analysis helped in understanding that product location in the shop also has a high influence over the profit and revenue.

Q4. Identify the differences in their sales performance based on different seasons?

For determining the difference in sales performance on the basis of different seasons, that regression analysis will be conducted:

Table 8: Regression Analysis (sales performance in different seasons)

The above regression analysis states that the p-value is 0.153 which is greater than the significant value and this depicts that there is no significant difference in the sales performance in different seasons.

• How this difference in sales performance will relate to rainfall and profits?

Table 9: Correlation Analysis (rainfall and profit)

Profits and Rainfall are not related significantly due to the lack of correlation between the Profits and Rainfall.  On the basis of the below table, it can understand that the correlation between the Profits and Rainfall is a positive correlation that is 0.008 but it is not significant. If the change in the Rainfall will be done, it will not affect the Profits, so it can be said that Rainfall is not related to the Profits.

Extra questions:

Q5. What is the difference in average sales in each month of the year?

In this, the regression analysis is used for analyzing the difference in the gross profit of different seasons.

Table 10: Regression Analysis (average sales of different months)

From this analysis, it can be interpreted that the p-value is 0.03 which is less than 0.05 and this indicates that every month there is a different sales margin in a year.

Q6. What is the difference in the gross profit margin of different seasons?

Similarly, regression analysis is used for determining the difference in the gross profit margin in different seasons.

Table 11: Regression analysis (gross profit in different seasons)

The above regression table shows that the significance level of p-value computed is less than 0.05, this represents all data set groups of the gross profit (Weltman & Whiteside, 2010). This indicates that there are differences in gross profit margin because of changes in seasons.

Q7. What is the difference in average sales of different seasons?

For analyzing the difference in the average sales of different seasons, the ANNOVA test under regression analysis is conducted.

Table 12: Regression Analysis (sales of different seasons)

From this analysis, it can be observed that p-value if 0.176 and that is greater than the significant value of p i.e., 0.05. This indicates that there is no difference in the average sales of different seasons.

Result and recommendations

From the above study, results are discussed that Good Harvest Retail Shop sells top organic foods to its customers. There are some top products that have high demand and sales in the market like dairy products, dry snacks, vegetables, fruits, chocolate, water and bakery products. In addition, it is also identified that companies have different payment modes through which it becomes easier for the customer to do the purchase required food products. During the analysis, it is also found that there are some products which are located in the front or rear location of the store are providing the company with the high profit and revenue and this leads to increase in the sales of the goods. In different seasons and months of a year, the company is not facing any difference in the sales of products and this indicates that there is no difference in the rainfall and in the company’s profit. But at the same time, the company is facing differences in their profit and revenue in different months of a year.

From this discussion, it can be recommended to Good Harvest Company that they need to increase their market share or segment by targeting more customers in different locations by selling the most demanded products. Moreover, the company also needs to adopt different payment system ways through which sales can be increased like the use of a credit card system. For increasing sales and profit, there is a need to develop a marketing strategy such as providing discounts and offers on the products which are sold less in comparison to the top products which are demanded high. On the other hand, the company should keep its less selling products at the front side with top selling products in order to increase the sales and profit as well as there is need for that company concentrate on the monthly sales ratio.

References

Bodie, Z. (2013). Investments. McGraw-Hill.

Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference. In International encyclopedia of statistical science (pp. 977-979). Springer Berlin Heidelberg.

Good Harvest (2017) Retrieved from:  http://www.goodharvest.com.au

Montgomery, D. C. (2010). A modern framework for achieving enterprise excellence. International Journal of Lean Six Sigma, 1(1), 56-65.

Newbold, P., Carlson, W., & Thorne, B. (2012). Statistics for business and economics. Pearson.

Weiers, R. M. (2010). Introduction to business statistics. Cengage Learning.

Weltman, D., & Whiteside, M. (2010). Comparing the effectiveness of traditional and active learning methods in business statistics: Convergence to the mean. Journal of Statistics Education, 18(1), 1-13.