Harvest Kitchen Sales and Profit Analysis

This assignment analyzes Harvest Kitchen's business performance focusing on correlations between sales, rainfall, and monthly gross profits. It uses Pearson correlation to assess relationships and presents findings through tables and a graphical representation of monthly gross profit distribution. The analysis concludes that rainfall doesn't significantly influence sales, and profits are relatively stable across months. Recommendations include implementing robust sales and marketing strategies to boost revenue.

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

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1.0 Introduction
New entrepreneurs undergo myriad of challenges before establishing themselves in the market.
So to remain afloat in the market, a well laid strategy is important (Kotler , 2012) and (Romano,
2009). However, the strategy will be responsive to the business’ challenges if it’s all based on
customer feedback and performance of their products in the market. The challenges include
competition from well established businesses within the industry to aspects such as weather and
unfavorable economy (Mowlana & Smith, 2003) and (March, 2009). For this reason, it is
therefore important that businesses identify their strong points and capitalize on them and their
weak points to improve on them.
2.0 Problem statement
Harvest kitchen is a generally new organization dealing in fruits and vegetables. Just like any
other new ventures, they have had to deal with challenges facing new businesses in the market
and within its industry. The main problems identified so far by Harvest kitchen organization is
low sales. It is perceived that this is as a result of low closes on leads. The other problem is cost
of goods. Sometimes due to unfavorable market conditions their fruit and vegetables are only
able to fetch small prices hence small profit margins. It is against this background that the
management of Harvest Kitchen decided to conduct a survey on their organization to identify
their weak points so as to strategize on how to turn the situation around.
3.0 Research questions

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Some of the main research questions that Harvest Kitchen sought to get answers to so as they can
turn their fortunes in the market include the following;
 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?
 Are their differences in sales performance between different seasons?
 How does this relate to rainfall and profits?
3.1 SALES LEVELS FOR PRODUCTS AT HARVEST KITCHEN.
Graph showing level of sales for different products
Ayurvedic
Bakery
Chocolates & Slices
Dairy
freezer
drinks
dry goods
678.75
10137.55
135.92
9305.82
4477.2
20673.7
8772.81
Graph of Sales levels
Series1
Figure 1

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An analysis of sales performance for various products at Harvest Kitchen is as shown in the
graph above. The graph shows that the best performing product in terms of sales in the business
was drinks. As can be observed, drinks fetched for the organization 20673.7 thousand dollars in a
year. This was highest compared to the other products. The other business within Harvest that
performed relatively well was the bakery business. It fetched the organization an amount
equivalent to 10,137.55 thousand dollars in a year. The worst performing products in terms of
sales was chocolate slices. The business was only able to get 135.92 thousand dollars while also
getting low sales in Ayuderic which earned the businesses 678.75 thousand dollars.
3.2 Testing whether there is a significant difference in payment techniques (Visa and
Credit)
In business transactions such as buying and selling, there are various methods of making
payments. Payment can be made through cash, credit, money gram, visa and MasterCard. The
diversified methods of payment make it convenient for various customers from different
geographical regions to be able to make payments with ease. In regard to this, Harvest Kitchen
has diversified its payment methods so as to be able to accommodate more customers without
locking out potential customers due to their available methods of payment. The business
provides two main methods of payments; credit and visa. A number of their payments have been
done through visa as well as credit. To establish whether there was a significant difference
between the two methods of payments. A paired sample t-test was used to establish the existence
of the significant difference. The test hypothesis was as below;

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Hypothesis
At 0.05 level of significance,
H0: There is no significant difference between payments through credit and visa.
Versus
H1: There is significant difference between payments through credit and visa.
The test results were as below
Paired Samples Statistics
Mean N Std.
Deviation
Std. Error
Mean
Pair
1
credi
t
584.811
5
366 228.86716 11.96308
visa 555.844
3
366 244.88987 12.80060
Table 2
Paired Samples Correlations
N Correlatio
n
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 credit - 28.9672 89.48246 4.67732 19.76933 38.16510 6.19 365 .000

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1 visa 1 3
Table 4
Table 2, 3 and 4 shows the results of the test for the difference in payment methods. Table 2
shows a correlation coefficient of .93. This is a strong correlation since it is almost equal to 1. To
add on the correlation is in the positive direction. This means that there is a strong relationship
between the two variables or methods of payments. The t-test from table 4 indicates a p-value
of .00. If this value is compared to the level of significance which is .05, then we are directed to
fail to accept the null hypothesis and accept the alternative. The conclusion is that there is
significant difference between payments through credit and visa.
3.3 Does location of items in a shop influence their buying?
Research has shown that arrangement of items in a shop can really influence the amount of the
product being bought. For example, products that are conspicuously displayed at the entrance
will attract more customers than those that have been displayed at the shelves in the backend of
the shop. To add on, items put together or close to fast moving consumer goods are likely to be
bought more due to the influence of the fast moving goods. The idea here is the impulse effect
that they induce on shoppers who tend to buy just because they have seen the product and not
because they needed the product. Harvest Kitchen has 5 locations. They wanted to establish
whether the 5 locations influenced sales in any way. For this reason an analysis of variance was
conducted to establish whether there was a mean difference in the sales amounts for the
locations. An analysis of variance was found appropriate since the variables under test are more
than two (5 locations).
The test hypothesis
H0: There is no significant difference in the sales of products from the five locations.

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Versus
H1: At least sales from one location are different.
ANOVA
Sum of
Squares
df Mean Square F Sig.
location
1
Between Groups 295770218.3
64
185 1598757.937 4.448 .000
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 5
An analysis of variance is used to establish whether there is a difference in means of more than
two variables. For decision on hypothesis to be reached, the p-value computed is compared to the
value of the level of significance. If the p-value is less than the level of significance then the null
hypothesis is rejected and the alternative accepted. From table 5 above, it can be observed that

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the p-values (.00) are generally less than the level of significance which is .05. This therefore
means that the null hypothesis is not accepted. The conclusion made is therefore that the sales
from one location are different. In order to determine the location or locations whose sales are
significantly different, then further tests such as Duncan’s tests are recommended.
3.4 Are sales and gross profit different among the months?
Among the factors that normally affect sales are seasons. There are economic seasons and
climate. Favorable climate increases production especially in when it comes to agricultural
products. Bad climate on the other hand leads to low production of agricultural products. Harvest
Kitchen grows fruits and vegetables which are also affected by weather across the year. This
means that their production and hence will always vary sales. What is not evident is the extent to
which these factors affect the sales and hence the gross profits. To establish whether there is any
difference in sales and gross profits in the months of the year due to various factors, an analysis
of variance was employed to establish the same.
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%

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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 .

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Total 2655303.367 29
Table 6
The table above shows the results of the analysis of variance. From the p-values computed above
(0.00), it can be seen that they are less than the level of significance which is .05. The ANOVA
test directs that if the p-value is less than the level of significance then the null hypothesis is
rejected and the alternative accepted. The converse is also true. Since the p-value are less than
the level of significance in general (.00 <.05), we fail to accept the null hypothesis and therefore
fail to reject the alternative. It is therefore concluded at least one month is different in terms of
sales.
TEST FOR GROSS PROFIT DIFFERENCE BETWEEN THE MONTHS
Hypothesis
H0: There is no significant difference in gross profit 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,

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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

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oct_gp Between Groups 42678.595 28 1524.236 1.087 .590
Within Groups 2803.541 2 1401.771
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 7
The table above shows the results of the analysis of variance. From the p-values computed above
(0.00), it can be seen that they are less than the level of significance which is .05. The ANOVA
test directs that if the p-value is less than the level of significance then the null hypothesis is
rejected and the alternative accepted. The converse is also true. Since the p-value are less than
the level of significance in general (.38 >.05), we fail to reject the null hypothesis and therefore
fail to accept the alternative. The conclusion therefore is that there is no significant difference in
gross profit across the 12 months of the year.
3.5 IS THERE ASSOCIATION BETWEEN SALES AND RAINFALL?
In order to establish whether there was a relationship between sales and rainfall, the research
study employed the use of Pearson correlation. In this test the correlation coefficient is used to
determine the extent of relationship. A perfect positive correlation has a correlation coefficient of
1 while a perfect negative correlation has a correlation coefficient of -1.
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

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Table 8
It can be observed that the correlation coefficient is .06. This is an indication that the correlation
between the two variables is not that strong though it is positive.
3.6 TESTS FOR CORRELATION BETWEEN SALES PERFORMANCE AND PROFITS
In order to establish whether there was a relationship between sales and rainfall, the research
study employed the use of Pearson correlation. In this test the correlation coefficient is used to
determine the extent of relationship. A perfect positive correlation has a correlation coefficient of
1 while a perfect negative correlation has a correlation coefficient of -1.
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 9
It can be observed that the correlation coefficient is .02. This is an indication that the correlation
between the two variables is not that strong though it is positive.
3.7 DISTRIBUTION OF MONTHLY GROSS PROFIT
Graph of monthly gross profit.

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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 2
The graph above shows that the gross profits realized by Harvest Kitchen across the 12 months
are almost normally distributed. It can also be observed that there was no big variance in the
amounts of gross profit in the months of the year.
4.0 Discussion and recommendation
From the analysis of the Harvest Kitchen business, there were no relationship found between
rainfall and the amounts of sales that were made for the whole year. This therefore indicates that
the business should not rely on rainfall when projecting about their sales. To add on, it was found
that the gross profits across the months were generally equal, that is there was no significant
difference in the gross profits across the months. The research report therefore recommends that

Research report 16
the business embark on a robust sales and marketing strategies to increase sales and hence gross
profits from month to month.
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.

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