The Influential Factors of Profit: A Case Study on a Healthy Snack in

Current Food Market Base on Minitab

Han Chen

1,*,†

, Yushi Dai

2,*,†

and Muxuan Gao

3,*,†

Department of Industrial and commercial management, Shanghai University, Jiangding District, Shanghai, China

Department of Mathematics & Department of Psychology, New York University, New York, U.S.A.

Department of Statistics and Operations Research & Department of Economics,

University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.

Keywords: Profit, Purchase Intention, Advertisement, Price, Number of Stores, City, Case Study.

Abstract: As there are growing concerns towards food healthiness recently, especially after the outbreak of Covid-19

pandemic, packaged healthy snack is gaining a larger marketing share in the snack market. For this specific

type of snack to better make a profit, the motivation of this study is to explore what factors in the current food

market could affect the profit earned by launching a new packaged healthy snack. To solve the problem, this

study made a case study on SMARTFOOD company. It used a multiple regression model to further investigate

the independent or interactive effects of price, advertising, number of stores, and geographical location on K-

Pack sales. Minitab software was used to build the multiple regression model, calculate relevant data, as well

as conduct best subsets with the selected independent variables. The point of this study is that all except the

geographical location is an important reason for profit growth. This research concludes that price, number of

stores, and advertisement positively affect profit. Also, selling location in the store and city the store is in are

not important factors for deciding the profit. Moreover, the interaction between price and the number of stores

leads to a negative influence on profit. In the case of SMARTFOOD, the company should choose the

combination of 50 cents per package and 3 million advertising costs to receive the highest profit. The

theoretical and marketing implications of the study’s conclusion are then discussed.

1 INTRODUCTION

Making a profit has always been one of the most

crucial things a profit corporation would consider.

First, it is important to first develop a marketing plan

before launching any products to the broad market to

make a profit. Undoubtedly, this should also be

applied to the current food market, specifically the

snack market. Snacking is still commonly seen in all

age levels worldwide for its convenience, affordable

price, and good taste. Nowadays snack market is

different from the time that traditional EDNP

(energy-dense, nutrient-poor) snacks (the so-called

“junk food”) were first introduced. People have

realized that whether snacking is harmful to health is

also important as obesity and possible cancers

unhealthy snacking could have become a more

serious issue lately. Also, with the effect of global

Covid-19 pandemic has brought in recent two years,

people have started to pay even more attention to

health factors of their daily diet, including snacks as

health concerns could result in healthier diet choice,

packaged healthy snack that is often shown in the

form of low-calorie, low-sugar or low-carbon

hydrated. It has great potential in the current food

market, and a good marketing plan can optimize the

potential profit to a great extent. Thus, this research

will narrow down the general food product to directly

aim at this specific category, which is packaged

healthy snacks.

Previous studies are suggesting the attributes

people would consider when choosing what type of

snack to purchase. In particular, Hartmann et al. in

2017 found that, in general, children prefer good-taste

snacks (Hartmann, Monika, et al. 2017). While

Rusmevichientong et al. in 2019 showed that college

students prefer quick and cheap snacks; furthermore,

healthiness and sugar respectively were the most

important snack factors and nutritional ingredients to

consider among college students (Rusmevichientong,

Pimbucha, et al. 2019). In addition, in the general

population of all age levels, Kershaw et al. in 2019

674

Chen, H., Dai, Y. and Gao, M.

The Inﬂuential Factors of Proﬁt: A Case Study on a Healthy Snack in Current Food Market Base on Minitab.

DOI: 10.5220/0011754300003607

In Proceedings of the 1st International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2022), pages 674-681

ISBN: 978-989-758-620-0

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

showed that healthiness and taste are weighed the

most toward food choice (Kershaw, Kiarri, et al.

2019). From these previous studies, it can be

concluded that packaged healthy snacks, along with

the advantages that traditional EDNP snacks possess-

- the convenience, good taste, and affordable price--

could make packaged healthy snacks obtain a great

potential in the current food market against traditional

EDNP snacks. However, fewer previous studies

directly examine the factors in a real-life context that

might influence people’s choice or explore the

marketing plan on this comparably new type of snack.

Therefore, this paper analyzed and investigated

the core question: what factors in the current food

market could affect the profit earned by launching a

new packaged healthy snack? Since packaged healthy

snack is still a relatively broad category with a large

number of products, the research object was therefore

narrowed down by selecting a specific product K-

Pack, a low-carbon hydrated snack bar that is planned

to be launched to the market by SMARTFOOD

Company, as the case for this research to study. One

way to analyze the profit gained is to compute it by

establishing a mathematical regression model. In this

case, five factors in real-life context were first

assumed that would affect profit, namely price,

selling location in the store, advertising strategy, city

the store is in and the number of stores, and the data

for sales and these five variables of K-Pack were

systematically generated. Meanwhile, the expected

cost for production and distribution of K-Pack was

also generated. Then through conducting multiple

regression models according to best subsets of the

factors and hypothesis testing, this research computed

and compared the profit earned in different conditions

by employing the regression model, deleted the

variables that would lower the accuracy of the model,

then eventually found out the one that can maximize

the profit as the marketing plan of K-Pack. In general,

this work aims to determine the best marketing

scheme for a specific category, “healthy snack” of

packaged snack food, with the possible affecting

factors in the current food market to maximize the

profit gained after the products are launched to the

market. The profit-earned model of the K-pack could

also be generalized to other products in this broad

category, so the contributions made should be of wide

interest.

2 LITERATURE REVIEW

2.1 Profit and Purchase Intention

The ultimate purpose of this paper is to find the

maximum profit. The first thing that should be clear

about is the calculated equation of profit, which is

“profit = total revenue - total cost”. This equation

shows that profit is directly affected by the total

revenue of selling a product and the total cost of

producing that product. Total revenue is calculated by

price multiply quantity, and the total cost is the sum

of various kinds of cost. As shown in many essays

that studied factors that will affect profit, researchers

studied purchase intention instead of directly

studying the profit for the revenue from sales. To

better build the relation between profit and purchase

intention, the definition of purchase intention should

be clarified first. As Mirabi et al. cited in their paper,

purchase intention is a situation where a consumer

tends to buy a certain product in a certain condition.

It can also be understood as the willingness of a

customer to buy something (Mirabi, Akbariyeh, and

Tahmasebifard 2015). This definition shows that

purchase intention can somehow affect the quantity

sold so that further affect the profit. In this way,

studying factors that can affect purchase intention is

comparable to studying factors that will affect profit.

This research will focus on studying how

advertisement strategy, profit, namely price, selling

location in the store, city the store is in, and the

number of stores can directly affect profit or affect

quantity sold then affect the profit earned by sales

(Mirabi, Akbariyeh, and Tahmasebifard 2015, Jahns,

Payne, Whigham, et al. 2014, Ruswanti, Hapsari,

Januarko, and Kusumawati 2019, Dehghani 2015,

Hyun & Kakwani. 2009, Karfakis, Velazco, Moreno

and Covarrubias 2011, Jeronim, et al. 2010).

2.2 Advertisement

Advertisements are nearly everywhere in our daily

life, including online and offline. It is a way of

making others learn the name, brand, and maybe price

of a product with attracting images and words.

According to the research done by Mirabi et al., there

is a significant and positive relationship between

advertising and purchase intention (Mirabi,

Akbariyeh, and Tahmasebifard 2015). Also, in the

paper written by Jahns et al., they mentioned the

underconsumption of fruits and vegetables. Still,

overconsumption of protein foods was reflected in the

relative frequency of food groups advertised in

weekly sales circulars (Jahns, Payne, Whigham, et al.

The Inﬂuential Factors of Proﬁt: A Case Study on a Healthy Snack in Current Food Market Base on Minitab

675

2014). This research showed that the content of

advertisements could affect the food that people

purchase. Moreover, the essay written by Ruswanti et

al. talked about the research question: if organic

vegetables are offered through advertising, sales

promotion, personal selling, and direct marketing,

consumers can buy so that organic consumers expand

and the number increases. They finally got the

conclusion that advertising affects consumer

purchase intentions (Ruswanti, Hapsari, Januarko,

and Kusumawati 2019). Milad Dehghani and Mustafa

Tumer got a similar conclusion in their research that

advertising significantly affected the brand image and

brand equity, both of which factors contributed to a

significant change in purchasing intention (Dehghani

2015). Based on all research reviewed above,

advertising is truly a factor that can affect the

purchase intention of consumers, which will further

influence the quantity sold. According to the above

reviews, the hypothesis could be made as follow:

Ha: Advertisement has a positive effect on

consumers' purchase intention and would positively

influence sales.

2.3 Price

In food sales, price is an important factor affecting

profit, and the price is defined in the clearest sense as

the amount of money charged for a product or service

(Hyun & Kakwani. 2009). As Hyun H. Son & Nanak

Kakwani mentioned in a microeconomic theory,

prices rise, buy less; Prices go down, and purchases

go up (Karfakis, Velazco, Moreno and Covarrubias

2011). In addition, Panagiotis Karfakis et al. believed

that price changes have different impacts on different

commodity demands: price changes have less impact

on the demand for daily necessities but a greater

impact on the demand for high-end durable goods

(Jeronim, et al. 2010). According to this definition, it

can be judged that K-pack belongs to high-grade,

durable goods. According to the above reviews, the

hypothesis could be made as follow:

Hb: Price has a negative effect on sales, which

means that if the price is raised, the sales will decrease,

and so will the profit.

2.4 Number of Stores

The number of stores is also an important factor in

profits. Chain stores are defined as the number of

retail stores operated jointly by the ownership and

management of multiple stores or chain stores. A

multi-store or chain store consists of a number of

similar stores owned by a single commercial

company (Raji Srinivasan et al. 2013). According to

Raji Srinivasan et al., the advantages of chain

operation are a brand association, supply association,

sales association, management association, and

image association. The locking technology mainly

refers to the control of each branch by the

headquarters and a means to maintain each chain

store around itself with technology as a link. As a

large chain enterprise, the management of each

branch is out of control, and the coordination degree

between the headquarters and the branch and the

branch is low. A lock ensures that each branch does

not lose control of its administration. So, according to

the reviews, the hypothesis could be made as follow:

Hc: The number of stores positively influences

profit, which means that the more stores selling that

good, the higher profits will be earned.

2.5 City the Store is In

The location of a business is one of the most

important factors in determining potential success.

Businesses need to establish locations that generate

the most traffic. The impact of a business location can

usually be determined immediately. A business needs

to be located in an attractive area, convenient for most

transportation and public transportation. If it were not

in such areas, the owners would have difficulty

making a profit or even paying their daily expenses.

Businesses located in desirable areas will benefit

from exposure and traffic to other community

businesses. In addition, a company headquarters set

up correctly can project a positive image and be more

attractive to potential customers. According to

Business Knowhow, retail companies often study the

size of a market and the number of competitors before

setting up a business. A market that is too small or

competitive will hinder potential sales and profits.

Businesses often choose where they can take

advantage of certain tax benefits. Additional tax

savings can create more positive cash flow for

businesses. Based on reviews, the hypothesis could be

made as follow:

Hd: Location of stores has a positive influence on

profit.

However, in this paper’s experiment, there are

also some deficiencies and overlooked places. This

paper team did not consider residents' income or age,

which determines their purchasing power. As young

people become more aware of health issues, K-Pack

may become more popular in cities with a lower

average age.

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676

3 METHOD

3.1 Research Design

According to the hypothesis and literature review,

this paper will be using the low-carbon hydrated

snack, K-Pack produced by SMARTFOOD Company,

as the case to verify whether the hypothesis is true. In

this particular SMARTFOOD Company case, the

company plans to develop a new product named “K-

Pack” that is low-carbohydrate and looks like a

common candy bar. The company would like to

determine the demand and a comparably optimized

marketing mix for a K-Pack with variables such as

price, advertising strategy, promotion strategy, and so

on, for K-Pack to enter the market better. In general,

this study would be a prediction problem since it aims

to acquire an estimated annual profit by computing

the estimated sale of K-Pack for the first year, how

different variables impact K-Pack sale independently

or interactively, and obtain a potential annual profit

of K-Pack. Therefore, a multiple regression model

should be employed since it is a mathematical

technique to predict an outcome with multiple

response variables. The data for this study is

secondary data originally obtained from the company.

Since Sales decide total revenue, the profit can be

computed by total revenue minus total cost.

3.2 Data Collection

Initially, there is a test market prediction done by the

company suggesting that the initial predicted annual

sale for K-Pack would be 750,000 cases (with 24

packages in each case), with the price of 50 cents per

package, and the projected fee for annual advertising

would be around 3 million dollars.

To confirm the credibility of the prediction, the

company collected the K-Pack sales data at 24

different grocery stores in 4 cities in 4 months, with 6

grocery stores respectively in each city.

In this study, price per package, advertising, and

location in the store is selected as marketing mix

variables. In particular, there are three levels for

variable price: 50 cents, 60 cents, and 70 cents, per

package respectively; two levels for advertising

(plans of $3 million or $3.5 million); two selections

for location in store: in the section of the bakery or

breakfast food. In addition, there is a controlled

variable of 4 cities. The marketing mix variables of

price and location in store vary across grocery stores

within the city, while the variable of advertising

varies across cities.

Furthermore, the company has decided to employ

an outsource to take charge of the production and

distribution of this “K-Pack” product, which would

have a relatively fixed predicted cost of $1 million per

year.

For the data collected, price in cents is set as P,

advertising as A, location in store as L, store volume

in $00,000 as V, and city index as C. A and L are

originally treated as dummy variables since they are

not continuous; however, P is also treated as a dummy

variable as three levels were set for it previously.

Since there are sale data for four months

respectively, this study computed the average sale of

S1, S2, S3, and S4 and set it as a new variable in the

dataset. This study is going to apply the software

Minitab and R Language to analyze the data.

3.3 Measurements

Before starting establishing the multiple regression

model, the perfectly correlated variables should be

ruled out from the model to have a more precise

model. Therefore, a correlation matrix of all the

variables is created by applying the software Minitab.

According to the correlation matrix (Table 1), it

shows that the correlation between C (city index) and

A(advertising) is high, which makes sense since the

design of the study assigned City 1 and City 2 with an

advertising level of 0 ($3 million), City 3 and City 4

Table 1: Correlations.

Sales P A L V

P -0.330272 (0.1150)

A 0.589698 (0.0024) 0.00000

(

1.0000

)

L 0.169984 (0.4271) 0.00000

(

1.0000

)

0.00000

(

1.0000

)

V 0.221090 (0.2992) 0.343783

(

0.1000

)

-0.174488

(

0.4148

)

0.022759

(

0.9159

)

C 0.650386 (0.0006) 0.00000

(1.0000)

0.894427

(<0.0001)

0.00000

(1.0000)

0.057677

(0.7889）

Note: Cell Content: Pearson Correlation (P-value); P = Price, A = Advertising, L= Location in Store, V = Store Volume, C = City

The Inﬂuential Factors of Proﬁt: A Case Study on a Healthy Snack in Current Food Market Base on Minitab

677

with advertising level of 1 ($3.5 million). In this case,

the city index should be deleted from the regression

model as a predictor.

3.4 Hypothesis Testing

Besides, hypothesis tests regarding two aspects of the

regression are to be conducted: whether the

relationships between the dependent variable Sales

and each predictor variables (A, L, P, V) are

significant, as well as whether the entire regression

model is significant. These two aspects will be shown

by P-value (and t-value) and F-value in the result of

the regression model. Therefore, the hypothesis test

would be:

a. For P-value (and t-value):

H0 (null hypothesis): the coefficients of the

predictor variables are 0, respectively

H1(alternative hypothesis): respectively, the

coefficients of each predictor variable is not 0

b. For F-value:

H0 (null hypothesis): all the regression

coefficients of the regression are 0

H1(alternative hypothesis): at least one of the

coefficients is not 0

In this part, the significant level is set to be 0.1.

3.5 The First Model

3.5.1 Data Analysis

This study would employ Minitab to establish the

multiple regression model, using the average sale of

S1~S4 as the dependent variable, and price in cents

(P), advertising (A), location in store (L), store

volume in $00,000 (V) as independent variables.

3.5.2 Results

Regression Equation 1:

S = -168.8 + 271.3Pa +234.2Pb + 8.718V –

6.460Pa*V

+47.39A + 3.29L (1)

According to the above results of the regression

model in Minitab using the average sale of S1~S4 as

dependent variable and price in cents (P), advertising

(A), location in store (L), store volume in $00,000 (V)

as independent variables, the F-value is 7.84 with a

P-value of 0.0003. In addition, the P-values of all the

predictor variables except L is less than 0.1; for

variable L, the P-value is 0.76.

Since the F-value for the whole model is 7.84 with

a P-value of 0.0003, which is less than the

significance level 0.1, therefore rejecting H0, which

means that at least one of the coefficients is not 0, and

there is a significant relationship between Sales and

the selected predictor variables. Furthermore, as the

P-values of all the predictor variables except L is less

than 0.1, the null hypothesis for these variables can

be rejected, and the coefficients of all predictor

variables except L are not 0. While the P-value is 0.76

for variable L, which is larger than 0.1; thus, the null

hypothesis for L cannot be rejected, so the

relationship between sales and L is insignificant. In

this case, L should be dropped as a predictor from the

multiple regression model to better predict sales.

Table 2: Analysis of Variance.

Source DF Adj SS Adj MS F-Value P-Value

Regression 7 29304.1 4186.30 7.84 0.0003

Erro

16 8540.4 533.77

Total 23 37844.5

Table 3: Model Summary.

S R-sq R-sq(adj)

23.1035 77.43% 67.56%

Table 4: Coefficient.

Ter

Coefficient SE Coefficient 90%CI T-Value P-Value

Constant -168.8 121.4

(

-380.8, 43.3

)

-1.39 0.1836

Pa 271.3 161.2 (-10.2, 552.8) 1.68 0.1118

Pb 234.2 142.8

(

-15.2, 483.6

)

1.64 0.1206

V 8.718 2.657 (4.079,13.357) 3.28 0.0047

Pa*V -6.46 3.421

(

-12.434, -0.487

)

-1.89 0.0773

Pb*V -5.969 2.986 (-11.183, -0.756) -2.00 0.0629

A 47.39 10.16

(

29.65, 65.12

)

4.66 0.0003

L 3.29 10.57 (-15.17, 21.74) 0.31 0.7600

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Table 5: Analysis of Variance.

Source DF Adj SS Adj MS F-Value P-Value

Regression 6 29252.6 4875.43 9.65 0.0001

Error 17 8591.9 505.41

Total 23 37844.5

Table 6: Model Summary.

S R-sq R-sq(adj)

22.4813 77.30% 69.28%

Table 7: Coefficient.

Term Coef SE Coel 90% CI T-Value P-Value

Constant -182.6 110.0 (-373.9, 8.7) -1.66 0.1151

Pa 293.8 140.3 (49.8, 537.8) 2.09 0.0515

Pb 250.6 129.2 (25.9, 475.4) 1.94 0.0692

V 9.054 2.362 (4.944, 13.163) 3.83 0.0013

Pa*V -6.938 2.974 (-12.112, -1.765) -2.33 0.0322

Pb*V -6.319 2.692 (-11.002, -1.635) -2.35 0.0313

A 46.934 9.785 (29.912, 63.955) 4.80 0.0002

Table 8: VIF of The Full Model.

case study $ Pa case study $ Pb case study $ V case study $ Pa*V case study $ Pb*V case study $ A

207.581815 176.127611 7.993862 213.881729 206.824452 1.136584

Table 9: VIF of group1.

case study $ Pa case study $ Pb case study $ V case study $ A

1.338306 1.522422 1.199931 1.036533

3.6 The Second Model

3.6.1 Data Analysis

In this study, Minitab was used to establish a new

multiple regression model. All S1-S4 were taken as

sales variables to establish A regression model. Store

Location (L) was excluded according to the

prediction, and the current independent variables

were Cent Price (P), Advertising (A), and Store

Number (V).

3.6.2 Results

From the results of this model (Table 5), this time, all

the predictors have a p-value less than 0.1. In addition,

the R-sq(adjusted) value obtained this time is 69.28%.

Regression Equation2:

S_Aver= -182.6 + 293.8 Pa + 9.054 V - 6.938 Pa*V - 6.319

Pb*V + 46.934 A

(2)

To further study the regression model, this study

always calculates VIF to ensure that there is no multi-

collinearity problem, so this study also calculates the

VIF of the optimal model obtained. As shown in

Table 8, the VIF of Pa, PB, Pa*V, and PB *V are all

around 200, with very large data. But this makes

sense because Pa star V is calculated in terms of Pa,

and Pa has a bigger effect on the result than V, and

Pb star V can be interpreted in the same way. This

study calculates two VIFs, Group1 including Pa, Pb,

V and, and Group2 including Pa * V, Pb * V, V, A.

The results (Table 9 and Table 10) show that there is

no multi-collinearity problem in these two groups. So

Pa and Pa * V, Pb, and Pb * V caused by the multi-

collinearity problem can be ignored, and it is better to

accept the regression results of this model.

4 DISCUSSION

After ruling out L from predictors, P-value for all

predictor variables this time is less than 0.1, which

means that they are all significant predictors for Sales.

Furthermore, the new R-sq(adjusted) value is larger

than before (67.56%), so dropping L provides a better

fit to the Sales data. The result of examining the best

The Inﬂuential Factors of Proﬁt: A Case Study on a Healthy Snack in Current Food Market Base on Minitab

679

Table 10: VIF of group2.

case study $ Pa*V case study $ Pb*V case study $ V case study $ A

1.375217 1.753186 1.379099 1.036364

Table 11: Best Subsets Regression.

Number

of prediction

R-sq

(adj)

R-sq

(pred)

Mallows’

S Pa Pb Pa*V Pb*V A L V

1 34.8 31.8 22.4 26.2 33.50 X

2 45.6 40.4 28.7 20.6 31.31 X X

3 58.8 52.7 38.8 13.2 27.90 X X X

4 70.9 64.8 52.3 6.6 24.06 X X X X

5 73.0 65.5 50.6 7.1 23.82 X X X X X

6 77.3 69.3 55.0 6.1 22.48 X X X X X X

7 77.4 67.6 48.6 8.0 23.10 X X X X X X X

sets among all predictor variables (Table 11) shows

the same conclusion that the second model is the best

model to predict Sales.

Sales = -182.6 + 293Pa + 250.6Pb + 9.054V - 6.938 Pa*V -

6.319Pb*V + 46.934A

(3)

Profit = total revenue - variable cost - fixed cost ($1million)

- advertising cost

(4)

Total revenue = predicted monthly sales * 12 (12 months in a

year) * 24 (24 packages per case) * price/100 (convert price

per package by cent to dollar) * 70% (assume that 70% of retail

price is revenue to the manufacturer)

(5)

Variable cost = predicted monthly sales * 12 (12 months in a

year) * 1($1 variable cost per case)

(6)

Equation (3), the equation of the model, shows

that price, number of stores, and advertisement

positively influence the quantity sold. Also, the

former study shows that selling location in the store

and city the store is in are not important factors for

deciding the number of sales and then further decide

the profit. Moreover, the interaction between price

and the number of stores leads to a negative influence

on profit. Bringing (5) and (6) into (4), the result

shows that the predicted monthly quantity of sales has

an overall positive influence on profit and is

combined with the second model. The conclusion is

that price, the number of stores and advertisements

have a positive influence on profit as well. This

conclusion shows that Ha and Hc are supported, but

Hb and Hd are rejected.

In this research, the conclusion on factors of

advertisement and number of stores are the same as

the conclusion mentioned in the literature review part.

Still, the result of price and location of stores are

different from what was said in the literature review.

However, the conclusions on how price can affect

profit are not fully different. The result also shows

that interaction between price and the number of

stores leads to a negative influence on profit.

In the case of SMARTFOOD, there are two main

limitations. Firstly, the sample size is quite small, so

that the result may not be that precise. Data offered

by the company only includes 24 observations got

from the test market. For future study, more case

studies suitable for the research topic and has a larger

sample size can be done. Secondly, there may be

some factors that can also affect the profit but are not

considered in this research. For future studies, more

other possible factors can be tested.

5 CONCLUSION

In summary, under the circumstances that people

started to pay more attention to health issues of diets,

this paper conducted a study that aims at packaged

healthy snacks. To explore what are the factors in the

current food market that could affect the profit earned

by launching a new packaged healthy snack, this

paper went through the effect of purchase intention,

advertising strategy, price, number of stores, and the

city the store is in on the profit by reviewing previous

relevant studies and established a multiple regression

model of a specific packaged healthy snack, K-Pack,

of SMARTFOOD Company. Through the case study,

this paper has discovered that price, the volume of

stores, and advertising positively affect the profit

gained. In addition, the variables of price and volume

of stores have interaction, and the new variables

created by their interaction have a negative effect on

profit.

After this experiment, it can be concluded that

companies should pay more attention to price,

advertising and the number of stores. Reasonable

prices can attract more customers' attention, and

advertising and the number of stores are equally

important. The combination of these three factors can

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maximize a company's profits. Macroeconomic

theories are used to study prices, advertising, and the

number of stores. Advertising has a positive impact

on consumers' purchase intention and will have a

positive impact on sales. This article covers another

theory in macroeconomics: that the price of good

moves in the opposite direction of the quantity

demanded. That is, the higher the price, the less

demand.

In this research, this paper also studied how the

interaction between price and volume of stores can

affect the profit earned, which somehow influences

how price itself can affect profit so that the paper gets

a different conclusion compared with the literature

review. This offers a new perspective for companies

that when deciding the price, they should consider

price and volume of stores in which products will be

sold both separately and together so that they can

make a better marketing decision and earn more

profit. In the future, further research is needed when

the Covid-19 pandemic comes to its end to find out

that whether the conclusion mentioned in this

research is suitable for any social background.

Moreover, it is better to do some further research

testing whether the conclusion is suited for products

with a relatively higher price and products of other

areas beyond the food market.

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