Agricultural Sales Analysis and Trends

This assignment analyzes data from an agricultural company to understand the relationships between various factors like rainfall, sales, and net profit. It investigates if rainfall influences sales and explores the distribution of monthly gross profit. The analysis culminates in recommendations for the management to improve sales performance based on the findings.

Research report 1
Business report
Student Name:
Student number:
Lecturer:

Research report 2
Table of contents
Content Page
1.0 Introduction ……………………………………………………….3
2.0 Problem statement…………………………………………………….3
3.0 Research questions……………………………………………………4
3.1 Top and worst performers…………………………………………….4
3.2 Difference in payment methods ……………………… …………….5
3.3 Difference in sales performance …………………………………….6
3.4 Difference in sales and gross profit………………………………….8
3.5 Test for correlation-sales and rain …………………………….…...13
3.6 Test for correlation-sales and gross profit ………………………….13
3.7 Product category…………………………………………………….14
4.0 Discussion and recommendation …………………………………...15
5.0 Reference……………………… …………………………………...16

Research report 3
1.0 Introduction
New enterprises in the market face a lot of challenges in the market especially if the industry is
dominated by organizations which have been in the business for long (Kotler , 2012) and
(Romano, 2009). Start up business face various problems from competition to low sales volume
due to unpopular company name or products that they sell. Harvest kitchen, a company that deals
in green groceries has also not been spared. It has been facing challenges from competition from
other established businesses to low sales.
2.0 Problem statement
The main challenge to harvest kitchen organization has been inability to close in on leads hence
leading to low sales across the year. Other factors that contributed to the staggering sales levels
were assumed to be rainfall and location of the produced products in their shops. In order to
increase sales, harvest kitchen has decided to conduct an research survey to understand the
various factors that are perceived to be influencing sales and hence gross profit.
3.0 Research questions
The main research questions for this report are;
 What are the top and worst performing products in terms of sales?
 Are there differences in payment methods?
 Are the differences in sales performance based on where the product is located in the
shop? How does this affect both profits and revenue?
 Is there a difference in sales and gross profits between different months of the year?

Research report 4
 Are their differences in sales performance between different seasons?
 How does this relate to rainfall and profits?
3.1 PRODUCTS AT HARVEST KITCHEN IN TERMS OF SALES
Graph of best and worst performing products
Ayurvedic
Bakery
Chocolates & Slices
Dairy
freezer
drinks
dry goods
678.75
10137.55
135.92
9305.82
4477.2
20673.7
8772.81
best performing products
Figure 1
The figure above is a graphical description of average sales for various products being sold at
Harvest Kitchen. It can be observed that the products varied in terms of sales. For example, the
graph shows that chocolates and slices performed poorly compared to the rest of the products. It
had an average annual sale of 135.92 thousand dollars. Another relatively worst performing
product is Ayurdevic which had an average annual sale of 678.75 thousand dollars. The best
performing product in terms of sales is seen to be drinks which had an annual sale of 20637.7

Research report 5
thousand dollars. Other better performing products were bakery products which had a sale of
10137.55 thousand dollars.
3.2 ESTABLISHING THE EXISTENCE DIFFERENCES IN PAYMENT METHODS
Since Harvest Kitchen is a bigger company serving customers from various regions, they have
diversified their modes of payments such that they accept visa and credits. To establish whether
there was any difference in the two payment methods, a paired sample t-test was run to
determine whether there was any significant difference in the two methods of payment. The
report arrived at the decision of using a paired sample t-test because there were only two main
methods of payments hence only two variables (Robert, 2008) and (Rubin, 2002). This
parametric test was also chosen on the assumption that there is normal distribution in these data
otherwise a non-parametric approach would have been employed.
The hypothesis to be tested is therefore as below;
Hypothesis
At 95% confidence level,
H0: There is no significant difference in the two payment methods
Versus
H1: There is a significant difference in the two payment methods
The paired sample t-test results are as seen in the results table below;

Research report 6
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 credit 584.8115 366 228.86716 11.96308
visa 555.8443 366 244.88987 12.80060
Table 2
Paired Samples Correlations
N Correlation Sig.
Pair 1 credit & visa 366 .931 .000
Table 3
Paired Samples Test
Paired Differences t df Sig. (2-tailed)
Mean Std.
Deviation
Std. Error
Mean
95% Confidence Interval of
the Difference
Lower Upper
Pair 1 credit - visa 28.96721 89.48246 4.67732 19.76933 38.16510 6.193 365 .000
Table 4
From table 3 above it can be observed that the two variables (visa and credit payment methods)
have a correlation coefficient of .93. This is an indication of a very strong correlation in the two
methods of payments. In the t-test table, the results show that the p-value, .00 is less than the
level of significance which is .05. We are therefore directed to not to accept the null hypothesis
and not to fail to accept the alternative. It is therefore clear that the conclusion is that there is a
significant difference in the two methods of payment.
3.3 LOCATION OF PRODUCTS IN THE SHOP AND SALES PERFORMANCE.
Products arrangement in a shopping space can influence the amount of sales of a given product
through various ways. For example if a product is conspicuously displayed, then it will be able to
attract more customers who will then be tempted to buy it thus increasing its sales. To add on, if

Research report 7
a low moving product is placed next to a fast moving product, there is likelihood that this could
influence the buyer to also consider buying the low moving good out of impulse (Mowlana &
Smith, 2003). In this way, the sales levels of the product whose turnover is low will have been
boosted. The other approach in terms of location that can influence sales of a given product is
whether the product is located near the entrance or in the back end shelves. Products at the
entrance can be spotted very first by the customers thus get stuck into their minds and this can
also prompt impulse buying. Products that are shelved at the back end of a shop can be very
difficult to be spotted by customers especially those in a hurry. In regard to location, Harvest
Kitchen limited wanted to establish whether there has been a variance in sales of various
products according to where they are located in the shop (Mowlana & Smith, 2003). There are
five locations in the shop and therefore five variables. This means that the analysis of variance is
employed in this case to establish whether a significant difference in sales exist due to difference
in location.
The test hypothesis
H0: There is no significant difference in sales levels for products in various locations in shop.
Versus
H1: At least one product’s sales level from one location is different.
ANOVA
Sum of
Squares
df Mean Square F Sig.
location Between Groups 295770218.3 185 1598757.937 4.448 .000

Research report 8
1 64
Within Groups 44931115.37
9
125 359448.923
Total 340701333.7
43
310
location
2
Between Groups 51535594.66
7
185 278570.782 2.754 .000
Within Groups 12643881.98
2
125 101151.056
Total 64179476.65
0
310
location
3
Between Groups 244998995.0
00
11 22272635.90
9
. .
Within Groups .000 0 .
Total 244998995.0
00
11
location
4
Between Groups 177884531.4
50
147 1210098.853 1.389 .139
Within Groups 27873309.50
0
32 871040.922
Total 205757840.9
50
179
Table 2
The analysis of variance results above indicate that the p-value calculated is .00. Comparing this
value with the level of significance which is .05, it is found that the p-value is less. According to
an ANOVA test, if the p-value is less than the level of significance then the test is advised to
reject the null hypothesis and fail to reject the alternative hypothesis. This therefore means that
according to the hypothesis above, the null hypothesis that there is no significant difference in
sales levels for products in various locations in shop is rejected and the alternative upheld. It is
then concluded that At least one product’s sales level from one location is different. If the
research is interested in finding the different items then a further Duncan’s test is recommended.

Research report 9
3.4 IS THERE A DIFFERENCE IN SALES AND GROSS PROFITS BETWEEN DIFFERENT MONTHS OF THE
YEAR?
Different seasons affect sales and hence the gross profits that are realized for the same periods. A
certain month may experience an upsurge of sales levels while another month might experience a
decline in sales levels. These variations are normally due to various factors due to weather. A
favorable weather encourages an increase for example in agricultural products and other products
that go with weather such as umbrellas and trench coats during rainy and cold seasons
respectively. On the other hand during dry seasons there is low production of agricultural
products. This may mean that the supply will go low and demand will rise hence raising sales
volume and hence gross profit. The converse also holds true. Harvest kitchen being a company
that deals with agricultural products is bound to experience lows and highs in terms of sales
volume and hence gross profit across the months. It is for this reason that it carried out an
analysis of variance to ascertain whether there were significant differences in gross profit and
sales across the months.
The test hypothesis is as below;
H0: The mean sales level is generally the same across all the months of the year.
Versus
H1: At least one month is different in terms of sales.
The test’s confidence level is 95%

Research report 10
ANOVA
Sum of
Squares
df Mean
Square
F Sig.
Net sales
January
Between Groups 3678107.097 30 122603.570 . .
Within Groups .000 0 .
Total 3678107.097 30
Net sales
February
Between Groups 1492938.000 28 53319.214 . .
Within Groups .000 0 .
Total 1492938.000 28
Net sales March Between Groups 4187028.774 30 139567.626 . .
Within Groups .000 0 .
Total 4187028.774 30
Net sales April Between Groups 2786878.800 29 96099.269 . .
Within Groups .000 0 .
Total 2786878.800 29
Net sales May Between Groups 3317298.839 30 110576.628 . .
Within Groups .000 0 .
Total 3317298.839 30
Net sales June Between Groups 1418345.467 29 48908.464 . .
Within Groups .000 0 .
Total 1418345.467 29
Net sales July Between Groups 1765256.194 30 58841.873 . .
Within Groups .000 0 .
Total 1765256.194 30
Net sales Aug. Between Groups 2698581.935 30 89952.731 . .
Within Groups .000 0 .
Total 2698581.935 30
Net sales Sep Between Groups 2248828.000 29 77545.793 . .
Within Groups .000 0 .
Total 2248828.000 29
Net sales Oct Between Groups 3395575.419 30 113185.847 . .
Within Groups .000 0 .
Total 3395575.419 30
Net sales Nov Between Groups 2655303.367 29 91562.185 . .
Within Groups .000 0 .

Research report 11
Total 2655303.367 29
Table 3
The analysis of variance results above indicate that the p-value calculated is .00. Comparing this
value with the level of significance which is .05, it is found that the p-value is less. According to
an ANOVA test, if the p-value is less than the level of significance then the test is advised to
reject the null hypothesis and fail to reject the alternative hypothesis (Richler, 2012) and (Lucas,
2009). This therefore means that according to the hypothesis above, the null hypothesis that there
is no significant difference in sales levels for products across the 12 months of the year is
rejected and the alternative upheld. It is then concluded that At least one month’s product’s sales
level is different. If the research is interested in finding the different items then a further
Duncan’s test is recommended.
TEST FOR GROSS PROFIT DIFFERENCE BETWEEN THE MONTHS
Hypothesis
H0: The gross profit is almost the same across the 12 months of the year.
Versus
H1: At least one month is different in terms of gross profit.
In this hypothesis, 95% confidence level has been applied.
The ANOVA results are tabulated as below,

Research report 12
ANOVA
Sum of
Squares
df Mean
Square
F Sig.
jan_gp Between Groups 50905.200 28 1818.043 4.474 .199
Within Groups 812.659 2 406.329
Total 51717.859 30
feb_gp Between Groups 7919.956 27 293.332 .164 .980
Within Groups 1791.610 1 1791.610
Total 9711.566 28
march_g
p
Between Groups 7419.298 28 264.975 1.097 .586
Within Groups 482.904 2 241.452
Total 7902.202 30
Apr gp Between Groups 3216.693 27 119.137 .156 .995
Within Groups 1528.049 2 764.024
Total 4744.742 29
may_gp Between Groups 12456.400 28 444.871 36.134 .027
Within Groups 24.623 2 12.312
Total 12481.023 30
june_gp Between Groups 6554.626 27 242.764 25.033 .039
Within Groups 19.395 2 9.698
Total 6574.022 29
july_gp Between Groups 8586.214 28 306.651 2.287 .350
Within Groups 268.174 2 134.087
Total 8854.388 30
aug_gp Between Groups 12119.709 28 432.847 1.136 .574
Within Groups 762.284 2 381.142
Total 12881.994 30
sept_gp Between Groups 22640.467 27 838.536 .116 .999
Within Groups 14424.341 2 7212.171
Total 37064.809 29
oct_gp Between Groups 42678.595 28 1524.236 1.087 .590
Within Groups 2803.541 2 1401.771

Research report 13
Total 45482.136 30
nov_gp Between Groups 68636.751 27 2542.102 2.032 .383
Within Groups 2502.487 2 1251.244
Total 71139.239 29
Table 4
The analysis of variance results above indicate that the p-values calculated is .00. Comparing this
value with the level of significance which is .05, it is found that the p-values are more.
According to an ANOVA test, if the p-value is more than the level of significance then the test is
advised to accept the null hypothesis and fail to accept the alternative hypothesis. This therefore
means that according to the hypothesis above, the null hypothesis that there is no significant
difference in gross profit levels for products across the 12 months of the year is accepted and the
alternative rejected. It is then concluded that gross profit is almost the same across the 12 months
of the year.
3.5 IS THERE ASSOCIATION BETWEEN SALES AND RAINFALL?
To establish whether there was a correlation between rainfall and sales, a Pearson correlation test
was run to determine whether any kind of relationship existed between the two variables. The
results were as tabulated below;
Correlations
SALES RAINFAL
L
SALES Pearson Correlation 1 .057
Sig. (2-tailed) .273
N 366 366
RAINFAL
L
Pearson Correlation .057 1
Sig. (2-tailed) .273
N 366 366
Table 5

Research report 14
The results of the Pearson correlation are as shown in table 5 above. The coefficient is .057.
Since a strong perfect correlation is 1 or -1, it can therefore be concluded that there is a weak
correlation between rainfall and sales amount. It is positive though weak relationship.
3.6 TESTS FOR CORRELATION BETWEEN SALES PERFORMANCE AND PROFITS
To establish whether there was a correlation between rainfall and sales, a Pearson correlation test
was run to determine whether any kind of relationship existed between the two variables. The
results were as tabulated below;
Correlations
SALES Net
profit
SALES Pearson Correlation 1 .017
Sig. (2-tailed) .745
N 366 366
Net
profit
Pearson Correlation .017 1
Sig. (2-tailed) .745
N 366 1034
Table 6
The results of the Pearson correlation are as shown in table 6 above. The coefficient is .017.
Since a strong perfect correlation is 1 or -1, it can therefore be concluded that there is a weak
correlation between net profit and sales amount. It is positive though weak relationship.

Research report 15
3.7 DISTRIBUTION OF MONTHLY GROSS PROFIT
Graph of monthly gross profit.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
0.00
2000.00
4000.00
6000.00
8000.00
10000.00
12000.00
14000.00
16000.00
MONTHLY GROSS PROFIT
Figure 1
It can be observed that the gross profit is normally distributed across the year. This is indicated
by the graph which assumes a bell-shape but with a relatively flat top. It can also be observed
that the mean gross profit for the months has little variance.
4.0 DISCUSSION AND RECOMMENDATION
This report has come up with various recommendations and conclusions based on analysis
results. The analysis revealed that there is no correlation between rainfall and sales.t therefore it

Research report 16
is advisable for the company to not project their production and hence sales on rainfall since it
deals with agricultural products. The management should therefore concentrate on other factors
influencing sales and not rainfall. The report also found that there was no significant difference
in the levels of gross profit across the months. This is an indication that there is no improvement
of gross profit from month to the next month. The report therefore recommends that the
management should come up with approaches such as sales and marketing so as to close leads on
business opportunities.

Research report 17
Bibliography
Kotler , P. (2012). Marketing Management: Analysis, Planning, Implementation and Control,.
Englewood Cliffs, NJ.: Prentice-Hall.
March, R. (2009). Tourism marketing myopia'', Tourism Management. (Vol. 15).
Mowlana, H., & Smith, G. (2003). Marketing in a global context: the case of frequent traveler
programs. Journal of Travel Research, 33, 20-27.
Romano, C. (2009). Research strategies for small business: a case study. International Small
Business Journal, 7, 35-43.
Lucas, H. C. (2009). Methodological issuesin information systems survey research. .
Richler, J. (2012). Behavior research methods.
Robert , G. (2008). "Two-Sided Confidence Intervals for the Single Proportion: Comparison of
Seven Methods," Statistics in Medicine, .
Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd Survey Methods & Sampling
Techniques 4 Ed.). New York.