Business Analytics Report: Performance Analysis and Strategic Insights

This business analytics report provides a comprehensive analysis of a business's performance, examining key variables such as total sales, cost of goods sold, and net profit. The analysis includes descriptive statistics, revealing skewed data distributions. The report further investigates the performance of different payment methods, products, and sales across various shop locations. It uses ANOVA to compare sales and profit across months and seasons, highlighting significant differences. Regression analysis explores the association between rainfall and both profit and gross sales, revealing no significant relationships. The findings indicate the importance of shop location on profit, and identifies seasonal variations in performance. The report concludes with recommendations for business managers to address fluctuations and improve profitability.

Running head: BUSINESS ANALYTICS
Business Analytics
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BUSINESS ANALYTICS
Table of Contents
Introduction................................................................................................................................2
Results and discussion................................................................................................................2
Descriptive analysis................................................................................................................2
Method of payment performance...........................................................................................3
Products performance.............................................................................................................4
Sales performance by location...............................................................................................4
Business performance in different months.............................................................................6
Business performance in different seasons............................................................................8
Association between rainfall and profit...............................................................................10
Association between rainfall and gross sale.........................................................................11
Conclusion............................................................................................................................12
Recommendation..................................................................................................................12
References................................................................................................................................13
Appendix..................................................................................................................................14
Introduction
Businesses need an understanding of the progress of the business throughout the year.
It should be able to determine whether their sales/profit and cost of goods sold is different in
different seasons and the month of the year. The main challenge of businesses is determining
whether their net sales, profit among other crucial factors are affected by rainfall. Therefore,
this research will use different business analytic approaches to gain insight into the business
performance. The research is designed to understand the business performance. Each
subsection will answer a specific research question.
Results and discussion
Descriptive analysis
Descriptive statistics for the three crucial business variables were computed, and the
results are summarized below.

BUSINESS ANALYTICS
Statistics
Total Sales ($) Cost of Goods ($) Net Profit ($)
N Valid 1034 1034 1034
Missing 0 0 0
Mean 369.96 205.22 164.74
Median 85.15 49.55 35.02
Std. Deviation 1014.719 561.072 482.106
Skewness 8.511 8.325 9.234
Std. Error of Skewness .076 .076 .076
Percentiles 25 32.62 16.10 13.65
75 280.76 162.19 110.46
The average sales total is $369.96 (SD = $1014.72). The median of the total sales is
significantly lower than the average, which implies that the data are very skewed. The
skewness coefficient is 8.511 indicating a case of very skewed data. Notably, all the three
variables (Total Sales ($), Cost of Goods ($), and Net Profit ($)) are very skewed (Skew
Coef. > 3.0). The middle 50% of the total sales is between $32.62 and $280.76. The average
cost of goods sold is $205.22 (SD = $561.07). The middle 50% of the cost of goods sold is
between $16.10 and $162.19. The business earns on average $164.74 (SD =$482.106) net
profit. The middle 50% of the net profit lies between $13.65 and $110.46. The most
significant issue that can be obtained from this is that the average total sales are higher than
the cost of goods sold which indicate that the business is earning a profit.
Method of payment performance
An assessment was carried out to determine whether the different method of payment
adopted rate of performance is the same. This was aimed at determining whether there was a
difference in the method of payment. A paired t-test was carried out for different payment
methods, and the results are as follows.
Paired Samples Test
Paired Differences t df Sig. (2-
tailed)

BUSINESS ANALYTICS
Mean
Std.
Deviation
Std. Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair 1 Cash_Total - Credit_Total -180.512 236.235 12.348 -204.794 -156.229 -14.618 365 .000
Pair 2 Cash_Total - Visa_Total -151.563 249.173 13.024 -177.175 -125.950 -11.637 365 .000
Pair 3 Cash_Total - Mastercard_Total 382.192 170.933 8.935 364.622 399.763 42.776 365 .000
Pair 4 Cash_Total - House_Account 366.899 178.242 9.317 348.577 385.220 39.380 365 .000
Pair 5 Credit_Total - Visa_Total 28.949 89.437 4.675 19.756 38.142 6.192 365 .000
Pair 6 Credit_Total - Mastercard_Total 562.704 236.975 12.387 538.346 587.063 45.427 365 .000
Pair 7 Credit_Total - House_Account 547.410 248.710 13.000 521.846 572.975 42.108 365 .000
Pair 8 Visa_Total - Mastercard_Total 533.755 274.257 14.336 505.564 561.946 37.233 365 .000
Pair 9 Visa_Total - House_Account 518.462 261.812 13.685 491.550 545.373 37.885 365 .000
Pair
10
Mastercard_Total -
House_Account -15.294 133.405 6.973 -29.006 -1.581 -2.193 365 .029
The t-values (365), p < .05 shows that adequate evidence exists to reject the null
hypothesis (Rietveld & Hout, 2017). In summary, the findings indicate that all the method of
payment is performing significantly different.
Products performance
An assessment to determine which product has the highest sale, and the least sales
were performed. For accuracy, the profit yield by each product should be used. The analysis
results indicate that water has the highest average total profit of $884.29 (SD = 1188.119).
The second best performing product is the fruit (M = $530.73, SD =$1,247.33). The worst
performing product or one that has the least total profit is the Juicing with an average of
$3.00, followed by Spices (M = $8.39, SD =$16.33).

BUSINESS ANALYTICS
Sales performance by location
Are the different locations in the shop having the same sales? The Analysis of
Variance (ANOVA) was carried to test whether the net sales were statistically different on
different shop location. The summary is as tabulated below.
ANOVA Table
Sum of Squares df Mean Square F Sig.
Total Sales ($) *
Location of product
in shop
Between Groups (Combined) 134299725.024 4 33574931.256 37.176 .000
Within Groups 929333380.817 1029 903142.255
Total 1063633105.841 1033
The evidence shows that the claim that the average sale of different location is not
different should be rejected (F = 4, 1029) = 37.176, p-value < .05). In short, we can claim that
at least one of the location average sales total is significantly different. A 95% error bar
(confidence interval) was plotted to assess which location had different average sales. In
accordance with Keller, (2014) this has the same results to that of post hoc analysis.

BUSINESS ANALYTICS
The error bar (95% confidence interval) of the Front, outside front, and rear overlap,
indicating that their sales are not significantly different (Farnsworth, 2016). The right and left
location confidence interval do not overlap with the other location error bar, implying their
average is significantly different.
It is important to determine whether the profit earned from different shop location
differs. This will help determine whether the difference in the sales significantly influences
the profit earned.
ANOVA Table
Sum of Squares df Mean Square F Sig.
Net Profit ($) * Location of
product in shop
Between Groups (Combined) 36561758.739 4 9140439.685 46.211 .000
Within Groups 203534122.514 1029 197797.981
Total 240095881.253 1033

BUSINESS ANALYTICS
The at least one locations net profit is different (F (4, 1029) = 46.211 p < .05). The
error bar is as displayed below.
The net profit for the outside front and front are not statistically different. The left and
right makes the least profit. This implies that the location of the shop determines the amount
of profit earned.
Business performance in different months
An assessment was carried out to determine whether the average sales and profit of
the enterprise differ in different months of the year. The claim, in this case, is that the sale
and profit are equal throughout the year. The analysis is as illustrated in the ANOVA table
below.
ANOVA Table
Sum of Squares df Mean Square F Sig.
Average_Sale * Month Between Groups (Combined) 335.651 11 30.514 1.979 .030

BUSINESS ANALYTICS
of the year Within Groups 5333.831 346 15.416
Total 5669.483 357
ANOVA Table
Sum of Squares df Mean Square F Sig.
Profit Total * Month
of the year
Between Groups (Combined) 35370.948 11 3215.541 3.867 .000
Within Groups 294370.006 354 831.554
Total 329740.954 365
At least one of the average sale and profits is significantly different in different
months of the year since both p-values < .05. Therefore, there is a need to determine which
month is not performing consistently with the other months. An error bar was portrayed to
determine which months were not consistent.
The chart shows that the average of the gross sales for June is significantly different
from that of November since their 95% confidence intervals do not overlap (Farnsworth,
2016).

BUSINESS ANALYTICS
The average profit total for April and June are significantly different from those yields
in August, September, and October. It should be noted that although the gross sale in April is
high, the total profit obtained is low.
Business performance in different seasons
It is important to assess whether the average gross sale was significantly different in
the four seasons. The test was conducted, and the results were as follows.
ANOVA Table
Sum of Squares df Mean Square F Sig.
Gross_Sales *
Season of the year
Between Groups (Combined) 560240.410 3 186746.803 1.765 .153
Within Groups 38298267.520 362 105796.319
Total 38858507.929 365
Evidence points that all the seasons average gross sales were not significantly
different (F (3, 362) = 1.765, p-value = 0.153) (Keller, 2014). This implies that the seasons of
the year do not affect the gross sale of the shop. In particular, the average gross sales are still
the same in different seasons of the year.

BUSINESS ANALYTICS
The confidence interval of all the seasons overlaps, supporting the evidence that the
average gross sales in different seasons are not significantly different (Farnsworth, 2016).
However, the assessment of whether the average profit total differs in the four seasons
show otherwise.
ANOVA Table
Sum of Squares df Mean Square F Sig.
Profit Total *
Season of the year
Between Groups (Combined) 28591.757 3 9530.586 11.456 .000
Within Groups 301149.197 362 831.904
Total 329740.954 365
The finding is that at least one of the season has a different average profit total (F (3,
362) = 11.456, p-value < .05).

BUSINESS ANALYTICS
The chart shows that the Autumn has the least average profit totals different from the
rest of the four seasons. The average profit for the summer and spring were not significantly
different, but Winter had a significantly different average to that of spring.
Association between rainfall and profit
A regression model was fitted to determine whether there is an association between
rainfall and the profit total the organization makes. The summary of the model is as follows.
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .008a .000 -.003 30.13165
a. Predictors: (Constant), Rainfall
b. Dependent Variable: Profit Total
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 19.078 1 19.078 .021 .885b
Residual 329573.612 363 907.916
Total 329592.689 364

BUSINESS ANALYTICS
a. Dependent Variable: Profit Total
b. Predictors: (Constant), Rainfall
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 30.650 1.702 18.007 .000
Rainfall .023 .161 .008 .145 .885
a. Dependent Variable: Profit Total
The model is not significant to be used to predict the profit total earned by the shop
using the rainfall (F (1, 363) = 0.021, p-value = 0.885) (Keller, 2014). The findings indicate
that there is no significant association between the predictor (rainfall) and the dependent
variable (profit total).
Association between rainfall and gross sale
It was important to determine whether the gross sales were associated with the
rainfall. The analysis results are as summarized below.
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .033a .001 -.002 326.981
a. Predictors: (Constant), Rainfall
b. Dependent Variable: Gross_Sales
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 43060.065 1 43060.065 .403 .526b
Residual 38810647.758 363 106916.385
Total 38853707.823 364
a. Dependent Variable: Gross_Sales
b. Predictors: (Constant), Rainfall