Business Analytics and Statistics
In this ever changing dynamic environment, it is vital for firms to continuously track the on-going activities of the other businesses so that they come to know about which technology they are adopting to satisfy their customers.
The customer behavior, their perceptions, their pattern of purchase is important to be analyzed by the firms because it will help them to develop the products or services accordingly. With the help of data analytics the forms can discover the required data in regard of sales as well as the product mix for Good Harvest on Sunshine cost which is a small health food shop (Good Harvest, 2017).
This firm produces and distributes the organic food all over the country. This assignment is significant as it helps in attaining an outlook in context of implications of data analytics approach for solving the issues of the business.
Problem Definition and Business Intelligence Required
The major issue that is observed in business firm is that the product of organic food’s sale is reducing and also there is a raise in the cost of goods sold. The company is currently positioned on the initial phase of the life cycle and is entirely emphasized on reducing the cost of products as well as raising the sales and profit for the company.
In this context, the pattern of the sales and product performance can be discovered by analyzing the data set which is related to product mix and sales. To further analyze the data the company adopts to use method of statistical data analysis which includes ANOVA test, descriptive statistics, t-test, and correlation.
The table presented below depicts the data set for the products and sales.
Data set demonstrated appropriately:
From the table illustrated above it can be depicted that data set of product mix shows two ordinal variables that are the category as well as the product class while the data set of sales shows ordinal variables like season, day, month, weekday.
The questions mentioned below will help to overcome the issues going on in the business:
Q1. Identify the top/worst selling products in respect to sales? Is there any difference found in 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 product located in retail shop? And also how this sales performance affects both revenue and profit of the company?
Q4. Identify the differences in their sales performance as based on different seasons?
- How this difference in sales performance will relate to rainfall and profits?
Some Extra questions:
Q5. Show the difference that exists in average sales in different months of the year?
Q6. Show the difference that exists in the gross profit margin between different seasons?
Q7. Show the difference that exists in average sales between different seasons?
Visualize Descriptive Statistics
Descriptive Statistics (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 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)
| Descriptive Statistics | ||||||
| N | Minimum | Maximum | Sum | Mean | Std. Deviation | |
| Day of the Year | 366 | 1 | 366 | 67161 | 183.50 | 105.799 |
| 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 | |||||
Q1. Identify the top/worst selling products in respect to sales? Is there any difference found in payment method?
For solving this question, The Pareto principle will be adopted which demonstrates that 80% of the total impact is arisen from 20% of the reason in a specific event. In context of the product and sales class, the Pareto curve can be illustrated below:
The Pareto curve picture presented above shows 80% of the total sales on the y-axis and the x-axis shows the total products which are the top selling products.
Additionally, the values left on x-axis depict the worst products. Further it can be observed that products which are selling more like vegetables, snacks, etc bring 80% of the toytal sales. The 20% consists of the worst products like freezer, grocery, etc (Mashiqa, et al., 2016).
The payment method is easy to differentiate by the help of 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 |
Regression table presented above illustrate that the value of p for every variable is unalike payment methods is zero. It also depicts that the significance p-value is < 0.05. It shows that there exists a significant variation in the payment methods.
Q2. In different months of a year, is any difference found between sales and gross profits?
For identifying the variation in the gross profit as well as the sales between different months in a particular year the regression analysis will be done:
Table f) Regression analysis (sales in different months)
The regression analysis table which is shown above, it can be signified that p-value that comes to be 0.221 is > 0.05. It indicates that there exist no variations in the sales between various months in a particular year. While on the other side, the ANOVA test helps to analyse the variations in the different months and the gross profits (Snoeck, et al., 2013).
Table g) Regression Analysis (gross profit in different months)
The table which is presented above signifies that there exists a significant difference in the gross profit in various months of a particular year. The cause for it is that the p-value is < 0.05 as it is combined form.
Q3. Identify the difference in sales performance on the basis of where product located in retail shop? And also how this sales performance affects both revenue and profit of the company?
Pareto curve helps to recognize the variation in the sales performance which determines the relationship in the performance and location of the product.
The picture showed above helps to illustrate that there exists a variation in location of the product as well the sales performance. The sales of the products which are located near are high as compared to those located outside the product location (Bezawada & Pauwels, 2013).
Also, the picture presented below depicts the relationship that exists between the location of the product and the profit:
The picture presented above demonstrates that product located in front give more profit when compared to the ones located outside. Further, this analysis gives an insight about the location of the product which has a high influence over the revenue and also the profit.
Q4. Identify the differences in their sales performance as based on different seasons?
Regression analysis conducted below helps to show the variation in sales performance:
Table h) Regression Analysis (sales performance in different seasons)
The regression table presented above depicts that the value of p which comes out to be 0.153 is > the significant value and also that there is no significant variation in sales performance.
- How it is associated with the profits and rainfall?
Table i) Correlation Analysis (rainfall and profit)
The table above clearly shows that there exist no relation in rainfall and the profits because there exists no correlation in between the rainfall and the profits. Further, the table shows that correlation between these two is a positive correlation which is 0.008 yet not significant. At the same time, there is no impact on the profits when a change is exercised on the rainfall (Ahn, et al., 2012).
Extra questions:
Q5. Show the difference that exists in average sales in different months of the year?
Regression analysis is adopted as it helps to analyze the variation in the different season’s average sales.
Table j) Regression Analysis (average sales of different months)
From the analysis done above, it can be summarized that value of p that is 0.03 is < 0.05 (Polanczyk, et al., 2014).
Q6. Show the difference that exists in the gross profit margin between different seasons?
The regression analysis helps to discover the variation that exists in the different season’s gross profit margin.
Table k) Regression analysis (gross profit in different seasons)
The table demonstrated above depicts that the p-value’s level of significance is < 0.05, this shows all the sets of data of the gross profit. It illustrates that there exist variation in the gross profit margin because of change in seasons (Stanley, et al., 2013).
Q7. Show the difference that exists in average sales between different seasons?
To examine the variation in different season’s average sales, ANNOVA test is been adopted under the regression analysis.
Table l) Regression Analysis (sales of different seasons)
From the analysis done above, it can be stated that the value of p which comes out to be 0.176 is > 0.05. It signifies that there is no variation in the different season’s average sales (González-Rodríguez, et al., 2012).
The results achieved from the above study shows that top quality organic food are sold by Good Harvest Shop to its end users. The products which have a high demand in the market by the customers are the vegetables, dry snack, water, dairy products, fruits, bakery items and chocolate.
At the same time, it is also recognized that there are different types of payment modes adopted by the company for its customers so that it becomes much easy for them to buy the needed products.
At the time of analysis it was identified that there are few of the products which are been located on the front area in the shop and these are the products which are giving high profits and revenue to the company and also gave a rise to its sales.
There are times like during the different seasons and different months of a particular year the company is not observing any variation in the product’s sale. This demonstrates that there is no variation in rainfall as well as the profit of the company. Additionally, there is variation in the profit and revenue of the company in different months of a particular year.
From the above discussion the recommendation is that the company Good Harvest should try to target more consumers that will in return help the company to increase its market [presence or share. Company can do this by focusing on the different locations in the market area where the demand for the company’s product is high.
Another suggestion for the company is that the company should adopt various payment methods such as credit card, which will bring a effect on the sales of the company.
The company should develop an innovative marketing strategy that will help the company in increasing its profit level as well as the sales. The company can offer various attractive offers, discounts and other things to promote its products which are not much popular among the customers as compared to those which are having high demand and are top selling products.
While on the other hand, the company can also shift its products which are having fewer sales on the front side so that it can be viewed by the potential customers and increase the sale of the company.
Ahn, T., Suh, Y. I., Lee, J. K., & Pedersen, P. M. (2012). Sport fans and their teams’ redesigned logos: An examination of the moderating effect of team identification on attitude and purchase intention of team-logoed merchandise. Journal of Sport Management, 27(1), 11-23.
Bezawada, R., & Pauwels, K. (2013). What is special about marketing organic products? How organic assortment, price, and promotions drive retailer performance. Journal of Marketing, 77(1), 31-51.
González-Rodríguez, G., Colubi, A., & Gil, M. Á. (2012). Fuzzy data treated as functional data: A one-way ANOVA test approach. Computational Statistics & Data Analysis, 56(4), 943-955.
Good Harvest (2017) Retrieved from: http://www.goodharvest.com.au
Mashiqa, V. A., Mokoaleli-Mokoteli, T., & Alagidede, P. (2016). Complementarity of fundamental and technical analysis of JSE-listed stocks: an empirical appraisal. Studies in Economics and Econometrics, 40(1), 21-40.
Polanczyk, G. V., Willcutt, E. G., Salum, G. A., Kieling, C., & Rohde, L. A. (2014). ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. International journal of epidemiology, 43(2), 434-442.
Snoeck, D., Lacote, R., Kéli, J., Doumbia, A., Chapuset, T., Jagoret, P., & Gohet, É. (2013). Association of hevea with other tree crops can be more profitable than hevea monocrop during first 12 years. Industrial Crops and Products, 43, 578-586.
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