Key performance, operational, group or at individual level is a noteworthy goal of any organization. In order to appreciate the degree at which organizational objectives have been accomplished and business techniques have been successful, it is important to create a benchmark of an integrated system that can tell at any given time the performance of the business. In addition, any decision made by the management ought to be founded on a decent learning of the current condition of the business, which isn’t conceivable without performance benchmarks. It is for this reason the CEO of the Good Harvest firm, with full knowledge of the critical need to learn from data tasked the data analyst to present him with analysis of the company’s one-year data. Good Harvest is a company dealing in organic farm products. The company is involved in growing and selling directly to their customers through home delivery program.
The major concern of the CEO is in regard to the sales performance of the company. However, it has to be noted that sales performance is a dependant of multiple factors. This report will therefore be presenting the learnings obtained from the analysed data.
The major concern of the CEO is analyzing the sales performance of the organization-this is the main objective. However, to answer this objective, a number of research questions listed below need to answered;
Two datasets are provided for this report. The first dataset consists of 18 variables while the second dataset consists of 12 variables.
Analysis involves a number of statistical techniques. The techniques varies depending on the nature of the problem that needs to be solved.
Using descriptive statistics, we were able to analyse the average sales of all the product classes thereby identifying which of the product class had the highest average sales and which ones had the lowest average sales. The products with highest average sales are regarded the best performing while those with lowest average sales are regarded as worst performing products.
Table 1: Top 10 best-selling products
|
Product class |
Average of Total Sales ($) |
|
Water |
$1,867.08 |
|
Fruit |
$1,048.67 |
|
Vegetable |
$871.51 |
|
Dairy |
$619.12 |
|
Drinks |
$574.31 |
|
Coconut Water |
$514.27 |
|
Bakery |
$432.73 |
|
Fridge |
$354.31 |
|
Dry Goods |
$341.24 |
|
Health products |
$332.88 |
Table 2: Top 10 worst performing products
|
Product class |
Average of Total Sales ($) |
|
Harvest Kitchen |
$45.25 |
|
Chocolates & Slices |
$37.00 |
|
Pastas |
$36.00 |
|
Other |
$33.56 |
|
Stocks Sauces |
$32.50 |
|
Salad Greens |
$25.00 |
|
Snacks |
$20.50 |
|
Spices |
$19.07 |
|
Herbal Teas |
$18.00 |
|
Juicing |
$5.00 |
The top 10 best performing products had averages sales amounting to over $300 while the worst performing products average sales being less than $50. Some of the best performing products include health products, dry goods, water, fruit, and vegetables among others (see table 1 above).
Worst performing products included spices, juicing, herbal teas, pastas and snacks among others (see table 2 above).
In analysis 2, the aim was to identify whether the different payment methods result to significantly different total cash received. Four payment methods are mentioned and the four are; cash, credit, visa and master card payment methods. Since master card and visa card belong to one and the same category of cards, we grouped them together into one category while cash and credit were also grouped together. With this we now had two hypothesis to test;
H0: There is no significant difference in the total cash received between the cash and the credit payment methods
H0: There is significant difference in the total cash received between the cash and the credit payment methods
|
Cash |
Credit |
|
|
Mean |
412.1755 |
604.6356 |
|
Variance |
20811.55 |
42140.48 |
|
Observations |
359 |
354 |
|
Pooled Variance |
31401.02 |
|
|
Hypothesized Mean Difference |
0 |
|
|
df |
711 |
|
|
t Stat |
-14.5002 |
|
|
P(T<=t) one-tail |
3.14E-42 |
|
|
t Critical one-tail |
1.647 |
|
|
P(T<=t) two-tail |
6.27E-42 |
|
|
t Critical two-tail |
1.963306 |
An independent samples t-test was performed comparing the mean total cash received from cash payment method and from credit payment method. The cash payment method (M = 412.18, SD = 144.26, N = 359) received less total cash as compared to the credit payment method ((M = 604.64, SD = 204.28, N = 354), t(711) = -14.50, p < .001, two-tailed.
H0: There is no significant difference in the total cash received between the Visa and the MasterCard payment methods
H0: There is significant difference in the total cash received between the Visa and the MasterCard payment methods.
|
Visa |
MasterCard |
|
|
Mean |
576.3144 |
152.5472 |
|
Variance |
50355.11 |
12000.98 |
|
Observations |
353 |
53 |
|
Pooled Variance |
45418.44 |
|
|
Hypothesized Mean Difference |
0 |
|
|
df |
404 |
|
|
t Stat |
13.49813 |
|
|
P(T<=t) one-tail |
7.81E-35 |
|
|
t Critical one-tail |
1.648634 |
|
|
P(T<=t) two-tail |
1.56E-34 |
|
|
t Critical two-tail |
1.965853 |
An independent samples t-test was performed comparing the mean total cash received from visa card payment method and from master card payment method. The visa card payment method (M = 576.31, SD = 224.40, N = 353) received less total cash as compared to the MasterCard payment method (M = 152.55, SD = 109.55, N = 53) at the .05 level of significance (t = 13.50, df = 404, p < .05, 95% CI for mean difference 365.02 to 485.48).
The third analysis was conducted to check whether differences in sales performance exists depending on where the product is located in the shop. The hypothesis tested is given below;
H0: There is no significant difference in the mean total sales between the different product locations in the shop
H0: There is significant difference in the mean total sales between the different product locations in the shop.
|
Total Sales ($) |
|||||
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
Between Groups |
134299725 |
4 |
33574931 |
37.176 |
.000 |
|
Within Groups |
929333381 |
1029 |
903142 |
||
|
Total |
1063633106 |
1033 |
A one-way analysis of variance (ANOVA) was conducted to compare the mean total sales depending on the product location within the shop. Table 5 presents the results of the ANOVA test and as can be seen, the null hypothesis is rejected at 5% level of significance (p-value < 0.05). By rejecting the null hypothesis it means that the mean total sales is significantly different for the different product locations within the shop.
How does the average sales and gross profits compare for the different months of the year? This is the question we sought to answer for this analysis. The hypothesis tested is given below ;
H0: There is no significant difference in the mean total sales between the different months of the year.
H0: There is significant difference in the mean total sales between the different months of the year.
|
Net Sales |
|||||
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
Between Groups |
1399993 |
11 |
127272 |
1.303 |
.221 |
|
Within Groups |
34584296 |
354 |
97696 |
||
|
Total |
35984290 |
365 |
A one-way analysis of variance (ANOVA) was conducted to compare the mean net sales based on the month of the year. Table 6 presents the results of the ANOVA test and as can be seen, the null hypothesis is not rejected at 5% level of significance (p-value > 0.05). By failing to reject the null hypothesis it means that the mean net sales is not significantly different for the different months of the year.
H0: There is no significant difference in the mean gross profits between the different months of the year.
H0: There is significant difference in the mean gross profits between the different months of the year.
Table 7: Analysis of variance (ANOVA) for the gross profits versus months
|
Profit Total |
|||||
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
Between Groups |
35371 |
11 |
3216 |
3.867 |
.000 |
|
Within Groups |
294370 |
354 |
832 |
||
|
Total |
329741 |
365 |
A one-way analysis of variance (ANOVA) was conducted to compare the mean profit totals based on the month of the year. Table 7 presents the results of the ANOVA test and as can be seen, the null hypothesis is rejected at 5% level of significance (p-value > 0.05). By rejecting the null hypothesis it means that the mean profit totals is significantly different for the different months of the year.
The last analysis done was analysis 5 that sought to investigate whether seasons have a significant effect on the sales performance. The hypothesis tested is;
Hypothesis 7:
H0: There is no significant difference in the mean net sales between the different seasons.
H0: There is significant difference in the mean net sales between the different seasons.
Table 8: Analysis of variance (ANOVA) for the net sales versus seasons
|
Net Sales |
|||||
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
Between Groups |
487761 |
3 |
162587 |
1.658 |
.176 |
|
Within Groups |
35496529 |
362 |
98057 |
||
|
Total |
35984290 |
365 |
A one-way analysis of variance (ANOVA) was conducted to compare the mean net sales based on the seasons. Table 8 presents the results of the ANOVA test and as can be seen, the null hypothesis is rejected at 5% level of significance (p-value > 0.05). By rejecting the null hypothesis it means that the mean profit totals is significantly different for the different seasons.
A number of interesting findings were reported. First, there was huge differences in sales revenues generated by the different product class. While some generated more than $500 some struggled to generate even $50. Some of the product class that generated more than $500 include vegetables, water, fruits, dairy products and coconut water. The products that struggled to raise $50 include juicing, spice, herbal teas, snacks, stock sauces, pastas, chocolate & slices and harvest kitchen.
It is recommended that the products that have sales amounting to less than $50 should just be done away with by the company.
Month of the year was found to have impact on gross profits made but not on the sales; this implies that the cost of production or may be operating costs could be higher in some months than other months. It is this that might have resulted to some months having less profits as compared to others. It is therefore important for the management to find out what could be causing this.
References
Couldry, N. & Turow, J., 2014. Advertising, Big Data, and the Clearance of the Public Realm: Marketers’ New Approaches to the Content Subsidy. International Journal of Communication, Volume 8, p. 1710–1726.
Dedi?, N. & Stanier, C., 2017. Towards Differentiating Business Intelligence, Big Data, Data Analytics and Knowledge Discovery. Springer International Publishing, p. 285.
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.
Kantardzic, M., 2003. Data Mining: Concepts, Models, Methods, and Algorithms.
Kimble, C. & Milolidakis, G., 2015. Big Data and Business Intelligence: Debunking the Myths. Global Business and Organizational Excellence, 35(1), p. 23–34.
Reichman, O. J., Jones, M. B. & Schildhauer, M. P., 2011. Challenges and Opportunities of Open Data in Ecology. 331 (6018), p. 703–5.
Segaran, T. & Hammerbacher, J., 2009. Beautiful Data: The Stories Behind Elegant Data Solutions. p. 27.
Siegel, E., 2013. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
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