Product Placement Analysis for Good Harvest

This assignment analyzes product sales data for Good Harvest Organics, investigating the relationship between product class and their placement within the store. It utilizes chi-square tests to determine if there's an association between location and sales performance. The analysis reveals insights into top-performing products (vegetables, fruits, dairy) and recommends strategies for investors, such as focusing on high-selling items and strategically placing products based on customer traffic flow.

Running head: HARVEST KITCHEN
Business Analytics – Harvest Kitchen
Name
Course Number
Date
Faculty Name

Running head: HARVEST KITCHEN
Table of Contents
1. Introduction......................................................................................................................................................3
2. Problem Statement...........................................................................................................................................3
Research Questions..............................................................................................................................................4
3. Descriptive Analysis.........................................................................................................................................4
4. Results..............................................................................................................................................................7
4.1 Top/worst selling products.......................................................................................................................7
4.2 Payment methods......................................................................................................................................9
4.3 Sales performance by location..................................................................................................................9
4.4 Sales and gross profits by months...........................................................................................................10
4.5 Sales performance by season..................................................................................................................11
4.6 Correlation between rainfall and profits.................................................................................................11
4.7 Days of the week and total orders...........................................................................................................12
4.8 Chi-Square test: Association between product class and location on the shop........................................12
5. Discussions and Recommendations................................................................................................................14
References..............................................................................................................................................................14

BUSINESS ANALYSIS HARVEST KITCHEN 3
Business Analytics – Harvest Kitchen
1. Introduction
Good harvest investments is an agribusiness organization which grows organic food and offers
delivery of their products’ package to their customers. They are determined in making a better
community by producing and delivering quality products. The agricultural production process is
determined by various factors, hence the need for time, money, water among other resources. Some
natural resources such as rainfall may inconvenience the production process. Therefore, artificial sources
of such resources should be developed to ensure that the agricultural activities continue effectively
throughout the year regardless of the climatic conditions. In some cases, structures such as greenhouses
might be required to elevate the production levels for the plants. All these forms of dedication are
focused on improved production amounts to maximize the available resources. From a business
perspective, the investors should minimize the agricultural inputs and produce maximum outputs. Good
harvest has compiled all these resources for efficient and effective production and supply of their
products (GoodHarvest, 2017).
Despite the production process, it is also important for agribusiness investors to learn the likes and
preference of their customers. Achieving this business principle, they will be able to produce their
products proportionally based on their demands. In cases of underselling products, it is the responsibility
of the investors to inform their potential and existing customers of the existing products and their
benefits (Agrawal & Anil, 2009). For instance, vegetables are very important for their sufficiency in
vitamins, which nourish the body. Enabling the customers to learn how vitamins are important for
improving body’s immunity will improve the products demand hence boosting their sales. Therefore,
performing blinded business might affect the sales and demands negatively. Business indicators are also
important to avoid investing in products that are not selling well in the market. This paper will focus on
answering the hypothetical business question to develop inferences about sales performances and profits
(Dlamini, Kirsten & Masuku, 2014).

BUSINESS ANALYSIS HARVEST KITCHEN 4
2. Problem Statement
Good harvest needs to understand the best and the worst selling products on their investments. This
will help them focus on the best performing specializations. Profit levels assist the investors to quantify
the level of success achieved. Customer shopping behaviours also affect levels of product sales based on
their location in the shop. Some of the customers prefer to shop on the products that are closer to the exit
because they're not interested in walking around the outlet. In addition, products that are located in the
visible area are likely to be bought because of the concept that a customer will see, like and make a
purchase. Therefore, a business needs to understand the relationship between the location of a product
on the shop and their respective sales. Based on purchasing behaviours, it is assumed that time is a
determining factor, hence the need to understand what time of the year the sales are high or low in
consideration with product type to make effective investment plans (Dlamini, Kirsten & Masuku,
2014).
Research Questions
i. What is the top/worst selling products in terms of sales?
ii. Is there a difference in payment methods?
iii. Are there differences in sales performance based on the location of products in the shop?
How are the profits and revenue affected?
iv. Is there a difference in sales and gross profits between months of the year?
v. Is the difference in sales performance between different seasons?
vi. What is a relationship between rainfall and profits?
vii. Is there any relationship between days of the week and total orders?
viii. Is there any difference between product sales and net profit?
3. Descriptive Analysis
Table 1: Descriptive statistics for sales data
Descriptive Statistics – Sales Data
Variable N Range Minimum Maximum Mean Std.
Deviation
Payment
Methods Cash_Total 366 1195 0 1195 404.29 153.643
Credit_Total 366 1407 0 1407 584.80 228.860
House_Account 366 1377 -264 1113 37.39 113.204

BUSINESS ANALYSIS HARVEST KITCHEN 5
Mastercard_Tota
l
366 399 0 399 22.09 67.823
Visa_Total 366 1407 0 1407 555.85 244.870
Sales Gross_Sales 366 2642 0 2642 1044.97 326.285
Average_Sale 358 53 8 61 18.52 3.985
Net_Sales 366 2370 0 2370 1014.26 313.986
GST_Exclusive 366 2492 0 2492 930.56 303.827
GST_Inclusive 366 271 0 271 114.42 48.723
Profit Total 366 305.95 -33.98 271.97 30.7098 30.05661
Rainfall 365 63 0 63 3.98 9.811
Staff_Cost 366 181 170 351 248.69 52.418
Total_Orders 366 129 0 129 55.54 15.844
Table 2: Descriptive statistics for product data
Descriptive Statistics – Product Data
N Range Minimum Maximum Mean Std. Deviation
Cost of Goods ($) 1034 8573 0 8573 205.22 561.072
Net Profit ($) 1034 8703 0 8703 164.74 482.106
Total Profit 1034 8702.93 .00 8702.93 164.7338 482.10651
Quantity 1034 3768 1 3769 71.90 212.400
Total Sales ($) 1034 17276 0 17276 369.96 1014.719
Weight 209 2913 0 2913 77.30 242.323
Table 3: Gross sales by months
Gross_Sales
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval
for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
January 31 979.65 368.350 66.158 844.54 1114.76 0 1662
February 29 1066.47 237.830 44.164 976.00 1156.94 778 1840
March 31 1063.69 379.880 68.228 924.35 1203.03 0 1722
April 30 1078.77 314.570 57.432 961.31 1196.23 0 1737
May 31 1054.09 336.527 60.442 930.65 1177.53 0 1753

BUSINESS ANALYSIS HARVEST KITCHEN 6
June 30 918.81 223.442 40.795 835.38 1002.25 437 1425
July 31 1004.78 248.080 44.556 913.79 1095.78 503 1449
August 31 1025.26 308.806 55.463 911.99 1138.54 61 1502
September 30 1014.06 302.101 55.156 901.25 1126.86 548 1787
October 31 1056.03 353.343 63.462 926.42 1185.64 0 1591
November 30 1197.64 343.489 62.712 1069.38 1325.90 723 2642
December 31 1082.74 413.527 74.272 931.05 1234.42 0 1864
Total 366 1044.97 326.285 17.055 1011.43 1078.51 0 2642
Gross Sales
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval
for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Summer 91 1042.44 349.184 36.604 969.71 1115.16 0 1864
Autumn 92 1065.37 341.391 35.593 994.67 1136.07 0 1753
Winter 92 983.65 264.131 27.538 928.95 1038.35 61 1502
Spring 91 1088.88 339.446 35.584 1018.18 1159.57 0 2642
Total 366 1044.97 326.285 17.055 1011.43 1078.51 0 2642

BUSINESS ANALYSIS HARVEST KITCHEN 7
Figure 1: Bar plot for average amount by payment method
On average, most customers prefer using credit as their form of payment followed by Visa and
then cash. Based on the sales data, customers paid an average of $584.80, $555.85 and $404.89 using
credit, visa and cash payment methods respectively.
Figure 2: Bar plot of total sales and net profit by product location

BUSINESS ANALYSIS HARVEST KITCHEN 8
Products that were placed outside front had more sales on average than any other on the shop. There
was no much difference between the products placed in front or at the rear side of the shop. Similarly,
there was no difference in sales and net profit for product placed on the left and right side in the shop.
4. Results
4.1 Top/worst selling products
Table 4: Product type by total sales
Mean Standard Deviation Sum
Product Class
Vegetable 871 1226 66233
Fruit 1049 2469 56629
Dairy 619 1474 40858
Drinks 574 1729 33881
Dry Goods 341 604 28666
Snacks & Chocolates 246 481 27076
Water 1867 2542 22403
Bakery 433 884 19038
Fridge 354 389 18065
Freezer 202 421 12552
Personal Products 84 114 8101
Oils & Vinegars 311 422 7770
Grocery 109 108 6959
Meats Smallgoods 177 259 6008
Health products 333 758 5657
Coconut Water 514 563 5656
Household 196 256 4906
Spreads, Sauces, Sweeteners 114 296 3181
Tea Coffee 89 147 2125

BUSINESS ANALYSIS HARVEST KITCHEN 9
Milks non-dairy 225 297 2021
Pasta 114 139 1713
Ayurvedic 226 253 679
Packaging 62 106 498
Tinned Goods 48 33 385
Other 34 26 302
Spices 19 32 266
Stocks Sauces 32 12 194
Chocolates & Slices 37 16 185
Harvest Kitchen 45 24 180
Market 89 97 178
Herbal Teas 18 24 72
Snacks 20 1 41
Pastas 36 . 36
Salad Greens 25 . 25
Juicing 5 . 5
Vegetables and fruits are the best performing products with the highest total sales while salad greens
and juicing are the worst performing.
4.2 Payment methods
H0: There is no difference between the payment methods
Table 5: Payment methods
Report
Cash_Total Credit_Total Visa_Total Mastercard_Total House_Account
Mean 404.29 584.80 555.85 22.09 37.39
N 366 366 366 366 366
Std.
Deviation
153.643 228.860 244.870 67.823 113.204

BUSINESS ANALYSIS HARVEST KITCHEN 10
Based on the means, there is a significant difference between the payment methods used by the
customers. Credit mode of payment is the best performing and MasterCard is the worst.
4.3 Sales performance by location
H0: There is no difference between product location on the shop and sales performance
Table 6: Sales performance by product location
Total Sales ($)
Mean Standard
Deviation
Sum Minimu
m
Maximu
m
Location of product
in shop
Front 573 1431 88777 7 11910
Left 218 428 82052 0 3300
Outside Front 3384 4719 40612 435 17276
Rear 536 1072 96493 4 10814
Right 240 553 74607 2 4236
There is a difference between the location of the products on the shop and the equivalent sales
performance. Produced located on the outside front performs much higher than those located inside the
shop. Similarly, produced placed in front and rear parts of the shop have more sales compared to those
located on the right and left sides.
Table 7: Product location by net profit
Net Profit ($)
Mean Standard Deviation Sum Minimum Maximum
Location of product
in shop
Front 252 694 39,074 0 6301
Left 100 196 37,432 0 1391
Outside Front 1810 2343 21,716 235 8703
Rear 211 388 37,922 2 2853
Right 110 299 34,194 0 2828
Although the products located on the outside front sales more, they perform poorly based on the net
profits. The products placed in front performs best based on profits made followed by those located in
the rear side.

BUSINESS ANALYSIS HARVEST KITCHEN 11
4.4 Sales and gross profits by months
i. H0: There is no difference in means of gloss sales between the months
ANOVA
Gross Sales
Sum of Squares df Mean Square F Sig.
Between Groups 1508892.474 11 137172.043 1.300 .222
Within Groups 37349615.455 354 105507.388
Total 38858507.929 365
The p-value is greater than 0.05, hence concluding that there is no difference in gross sales’ means
by the months of the year at 95% confidence level.
ii. H0: There is no difference between total profit means between months of the year.
ANOVA
Profit Total
Sum of Squares df Mean Square F Sig.
Between Groups 35370.948 11 3215.541 3.867 .000
Within Groups 294370.006 354 831.554
Total 329740.954 365
The p-value is less than 0.05, hence concluding that there is a significant difference in total profit
between months at 95% confidence level (Larose, 2016).
4.5 Sales performance by season
H0: There is no difference between means of sales by season
ANOVA
Gross Sales
Sum of Squares df Mean Square F Sig.
Between Groups 560240.410 3 186746.803 1.765 .153
Within Groups 38298267.520 362 105796.319
Total 38858507.929 365
We reject the null hypothesis because the p-value is greater than 0.05. Therefore, we conclude that
there is no difference between gross sales’ means by seasons of the year (Larose, 2016).

BUSINESS ANALYSIS HARVEST KITCHEN 12
4.6 Correlation test between rainfall and profits
Figure 3: Scatter plot for Rainfall amount by total profit
Correlations
Rainfall Profit Total
Rainfall Pearson Correlation 1 .008
Sig. (2-tailed) .885
N 365 365
The p-value is greater than the significance level, hence concluding that there is no relationship
between rainfall amount and the total profits made in the business. Pearson correlation coefficient
indicates that there is very weak (close to no) [correlation] between profits and rainfall (Larose, 2016).
4.7 Days of the week and total orders
H0: There is no difference in means of total orders by days of the week
ANOVA
Total Orders
Sum of Squares df Mean Square F Sig.
Between Groups 14863.759 6 2477.293 11.585 .000
Within Groups 76765.205 359 213.831
Total 91628.964 365

BUSINESS ANALYSIS HARVEST KITCHEN 13
The p-value for the ANOVA test is less than the significance level (0.05), hence concluding that
there is enough evidence to conclude that totals orders are different by days of the week (Poonia,
2011).
4.8 Chi-Square test: Association between product class and location on the shop
H0: There is no association between product class and their location on the shop.
Product Class * Location of product in shop Cross tabulation
Count
Location of product in shop Total
Front Left Outside
Front
Rear Right
Product
Class
Ayurvedic 3 0 0 0 0 3
Bakery 44 0 0 0 0 44
Chocolates & Slices 5 0 0 0 0 5
Coconut Water 11 0 0 0 0 11
Dairy 0 0 0 66 0 66
Drinks 57 0 0 2 0 59
Dry Goods 0 84 0 0 0 84
Freezer 0 0 0 0 62 62
Fridge 0 0 0 51 0 51
Fruit 0 0 7 0 47 54
Grocery 0 50 0 0 14 64
Harvest Kitchen 4 0 0 0 0 4
Health products 17 0 0 0 0 17
Herbal Teas 0 0 0 0 4 4
Household 2 23 0 0 0 25
Juicing 0 0 0 0 1 1
Market 0 0 0 0 2 2
Meats Smallgoods 0 0 0 0 34 34
Milks non dairy 0 9 0 0 0 9
Oils & Vinegars 0 25 0 0 0 25
Other 0 9 0 0 0 9
Packaging 0 8 0 0 0 8
Pasta 0 15 0 0 0 15
Pastas 0 1 0 0 0 1
Personal Products 0 0 0 0 96 96
Salad Greens 0 0 0 1 0 1

BUSINESS ANALYSIS HARVEST KITCHEN 14
Snacks 0 1 0 0 1 2
Snacks & Chocolates 0 110 0 0 0 110
Spices 0 0 0 0 14 14
Spreads, Sauces,
Sweeteners
0 3 0 0 25 28
Stocks Sauces 0 6 0 0 0 6
Tea Coffee 0 24 0 0 0 24
Tinned Goods 0 8 0 0 0 8
Vegetable 0 0 5 60 11 76
Water 12 0 0 0 0 12
Total 155 376 12 180 311 1034
Chi-Square Tests
Value df Asymptotic Significance (2-sided)
Pearson Chi-Square 2957.360a 136 .000
Likelihood Ratio 2572.370 136 .000
N of Valid Cases 1034
a. 115 cells (65.7%) have expected count less than 5. The minimum expected count is .01.
The p-value is less than the significance level hence concluding that there is an association between
the product class and their locations on the shop (Poonia, 2011).
5. Discussions and Recommendations
The investors should focus on the best performing products such as vegetables, fruits and dairy to
ensure that they make more profits. They should also sensitize their customers on the worst performing
products to check if they improve their sales (Auld, Jones & Thilmany, 2008). Payment methods
available in the shop should be maintained to better customer services. Based on the performance of the
products based on their location in the shop, the investors should perform a thorough analysis to which
products should be placed outside front. Probably, they might perform should be placed outside to
sensitive the customers. Further analysis should be conducted to understand the specific months that
make more profits so that the investors can take a business advantage.