Sales maximization strategy of brand “Gunung Madu”

© 2015 The 7th Indonesia International Conference on Innovation, Entrepreneurship, and Small Business.

The 7th Indonesia International Conference on Innovation, Entrepreneurship, and Small Business (IICIES 2015)

Sales maximization strategy of brand “Gunung Madu”

Evo Sampetua Hariandjaa, Rany Wahyu Larasatib*

aBusiness School Universitas Pelita Harapan, Jl. M.H. Thamrin Boulevard

Tangerang, 15811, Indonesia bResearch Assistant at MBA-ITB Program, Jl. Ganesha 10, Bandung, Indonesia

Abstract

Sugar is emerging as a branded commodity. Few sugar companies turn to packing white sugar as a branded product by presenting such naming and promotional activities. The reason being packed white sugar is giving an assurance of hygiene as compared to loose sugar. But, packaged white sugar is not only produced by sugar companies. Many retailers also create their own private brand and offer more competitive price to consumers. Brand equity of white sugar has led to various type of packaging for general or specialty sugar. Segments are broadly classified into retail, food and beverage, and industries. In Indonesia, sugar for daily consumption must be white sugar and for industrial purposes, refined sugar takes place.

Its nature as commodity product has made sugar a generic product. Labeling and other promotional activities do not change the fact that there is no added value in term of nutrition or any other beneficial content inside the product itself. Few varieties of sugar are being marketed in different sizes, shapes and packing, as well as the availability in traditional markets, convenient stores, department stores, and other retail chains. Therefore, competition in term of pricing has become big concern to producers. Since the benefit would remain the same, customers might prefer the cheaper one.

This research will be limited to analyze the branded packaged-sugar project of Koperasi Gunung Madu. In this paper, there are identification about current business and market issues related to problems faced by Koperasi Gunung Madu during the running of packaged-sugar project. There is identification about potential consumers which will be maintained as committed buyer to the brand.

In the end, this research provides business solution in term of strategy and marketing plan to support the business growth. Several practical implementations are presented as tools and recommendation to be considered by Koperasi Gunung Madu management for turning them into real and hopefully could be a refreshment for the improvement of Gula Gunung Madu brand in the marketplace.

Keywords: marketing plan, branded packaged-sugar, sugar industry

* Corresponding author. Tel.: 62-21-5460901 ext. 1620; fax: 62-21-5460910

E-mail address:[email protected]

2 Evo Sampetua Hariandja, Rany Wahyu Larasati

1. Introduction

1.1. Indonesian Sugar Industry

Sugar industry in Indonesia was on its glory in around 1930s. At the time, there were 179 sugar companies

which the productivity was reaching 14.8% with the rendement (production efficiency) up to 11-13.8%. Export

reached 3 million tons. (Sudana et al., 2000). Indonesia is considered potential to be the world’s sugar producers

due to the support from agro-ecosystem, areas, and labors. In addition, market in Indonesia is predicted to have 4.2

- 4.7 million ton/year of total sugar consumption.

At the beginning, the local sugar industry was only consisted of white sugar. On the other hand, refinery sugar

was still imported. Since 2000s, when the price of raw sugar increased, government of Indonesia gave permission

to the establishment of refined sugar factories which the raw sugar itself is still imported. Since the first time, sugar

industry has been dominated by BUMN (state owned company), which are PTPN and RNI, counted for 10

companies that spread over Java Island and Sumatera. This oligopolistic system has been covered both production

and distribution. The distributions of white sugar in Indonesia are mostly from six key players which are PTPN IX,

PTPN X, PTPN XI, RNI, PT Gunung Madu Plantation (GMP), and Sugar Group Company.

According to Presidential Decree No. 58 of 2004 about Handling of Illegal Imported Sugar Article 1 Act 3,

“Refined sugar as raw production materials for industrial utilization...”. In 2004, there was only 3 (three) players in

refined sugar industry which were able to supply 300,000 – 1,500,000 tons of sugar per year to meet the demand

from food and baverage, and pharmaceutical industry. Then, in 2006 – 2008, the refined sugar factories was

increased to 7 companies with total supply 1.2 – 1.5 million tons per year. In 2009, refined sugar players were

counted 8 companies which helped the supply increased to be around 2 million tons per year.

Total sugar prodution (white sugar and refined sugar) in 2009 and 2010 were 2.6 million ton and 2.9 million ton.

Nowadays, sugar is considered as strategic commodity product due to the consumption in all segments of societies.

The national demand of sugar is expected to reach 5.7 million ton in 2014. For that reason, government of

Indonesia encourage the realization of Program Swasembada Gula (self-supporting). Target production from

existing plant is 3.571 million ton and the rest 2.129 million ton will be generated from plant expansion and

establishment of new plants. (Gamal Nasir, Direktur Jendral Perkebunan)

1.2. PT Gunung Madu Plantation

PT GMP was established in October 20th, 1975 as pioneer of sugar cane plantation and factory outside Java

Island, especially in Lampung. It is located at Desa Gunung Batin, Central Lampung – around 90 km in the north

side of Bandar Lampung. At the beginning, PT GMP was PMA – a foreign investment company. In 1976, a modest

sugar factory was established with 4,000 TCD (Total Cane per Day) of capacity. Nowadays, the gross area area of

PT GMP is 35,000 ha with net cane area 25,000 ha. The rest of 11,000 ha is utilized for roads, conservation areas,

factory area, office buildings, and housings.

Below is the history of sugar cane production of PT GMP. According to the picture below, the trend of total

production is considered increased from 1978 to 2008. It has been slowing down until 2011 due to weather

uncertainty, as well as level of rainfall frequency and other reasons.

Evo Sampetua Hariandja, Rany Wahyu Larasati 3

Figure 1 Total Production of PT GMP

Source: PT GMP

1.3. Branded Packaged-Sugar Project

Actually, PT GMP has been well-known among distributors and it runs Business to Business system for the

product distribution. All white sugar produced by PT GMP are distributed directly to large distributors in tons.

According to interview session with The Head of Technical Engineering Department, as well as Chairman of

Koperasi Gunung Madu, PT GMP does not face any difficulty to market its sugar. Societies, especially in

Lampung, has known that the product from PT GMP is qualified, for both traditional and modern market. But,

several people in PT GMP felt that the company needs to build such image among end-user in order to maintain

sustainability and loyalty. As one on the biggest white sugar producers in Indonesia, sugar from PT GMP is not

that visible in retail market (among end-users) because it has been packed and labeled with new private brands

depend on where it be sold at. Then, as the presence of “Gulaku” which has been successfully catched consumer’s

attention and brought the new wave of branded sugar to the market, this product transforms to a new icon in sugar

industry. “Gulaku” is made by Sugar Group Company which operates at the same province with PT GMP, even

the plantation areas between the two companies are very close. Players in branded sugar are still a few in the

market, especially in Lampung. Most of packed sugar in retail market is private brands represent the store name

through naming and labeling. Therefore, there was an idea to launch an own branded sugar in order to increase the

presence of company brand as well. PT GMP refuse to hand in the project to market the small package of branded

sugar due to some risk. Finally, Koperasi Gunung Madu tried to embody the project and started the first packing

process of branded sugar for retail market on August 22nd, 2011 with 1 kilogram package.

2. Literature Review

2.1. Binary Logit Model

The Logit Model is one of the alternatives of Linear Probability Model (LPM) after several problems plague, such

as non-normality of disturbance, heteroscedastic of disturbance, possibility of Y lying outside 0-1 range and the

lower R2 values. The Logit model is used to model a relationship between a dependent variable Y and one or more


probability

independent variables X. The dependent variable, Y, is a discrete variable that represents a choice, or category,

from a set of mutually exclusive choices or categories.†

For this case, the problem that may be occurred is the possibility of Y or the value of customer loyalty lying

outside 0-1 range.

Unlike the normal distribution, the mean and variance of the Binomial distribution are not independent. The mean

is denoted by P and the variance is denoted by P*(1-P)/n, where n is the number of observations, and P is the

probability of the event occurring in any one ‘trial’. (Tranmer)

As a proportion response, the use of logit model transformation is to link the dependent variable to the explanatory

variables.

Figure 2 Logit model graphs

According to the graph above, with logit model, independent variables (X) have result the dependent variable (Y)

between 0 to 1. The result of the model will be appeared on the graph as 0 < y < 1. The natural log of the odds of

an event equal the natural log of the probability of the event occurring divided by the probability of the event not

occurring: {odds(event)} = ln{prob(event)/prob(nonevent)}. The logit transformation defined with:

logit(p) = log {p/(1-p)}

The formula for the logistic curve relates to the independent variable.

The probability of Y needs to stay between 0-1 interval even the dependent variables are increasing or decreasing.

Logit model, as the common Cumulative Distribution Function (CDF) is preferred to be implemented.

1.0

0.8

0.6

0.4

0.2

0.0

logit

-4 -2 0 2

4


Furthermore, proportions and probabilities are different from continuous variables in a number of ways. It should

be bounded by 0 and 1, whereas in theory continuous variables can take any value between plus or minus infinity.

This means that normality for a proportion cannot be assumed, and recognizing that proportions have a binomial

distribution is a must.

Logit Model is represented below:

Where:

Li = Logit Model

Pi = Probability

Βi = Coefficient

xi = Variable

2.2. The Logit Model for Ungrouped or Individual Data ‡

Some general observation are in order before conducting and interpreting The Logit Model for Ungrouped or

Individual Data. Here are the lists:

1. Since the using of maximum likelihood method, which is generally a large sample method, the estimated

standard errors are asymptotic.

2. Instead of using the t statistic to evaluate the statistical significance of a coefficient, this research also use

(standard normal) Z statistic. So inference are based on the normal distribution rules. Recall that if the sample

size is reasonably large, the t distribution converges to the normal distribution.

3. The conventional measure of goodness of fit, R2, is not particulary meaningful in binary regressand models.

Measures similar to R2, called pseudo R2, are available. Another comparative simple measure of goodness of fit

is the count R2, which is defined as:

However, in binary regressand model, goodness of fit is of secondary importance. What matters are the expected

signs of the regression coefficient and the statistical and or practical significance.

4. To test the null hypothesis that all the slope coefficient are simultaneously equal to zero, the equivalent of the F

test in the linear regression model is the likelihood ratio (LR) statistic. Given the null hypothesis, the LR

statistic follows the χ2, distribution with df equal to the number of explanatory variables.

2.3. Dummy Variable

In linear regression model, there are two types of varible which are dependent (Y) and independent variable (X).

Usually, the independent variables are such quantitative. But, in regression analysis, dependent variable is oftenly


affected not only by the quantitative variable, such as income, output, teperature, etc, but also affected by

qualitative variables, such as religion, nationality, etc.

3. Methodology

There research is the quantitative methodology which contains correlational statistic. Correlational statistic is used

to discover relationship between certain attributes or aspects of a situation. In this research, correlational statistic will be done by using Binary Logit Model to explore the relationship between consumer profile and consumer

loyalty in order to find the new potential targeted customers for strategic improvement.

3.1. Potential Consumers Analysis

Dummies

Below are the list of dummies which are used in the equation:

1) Loyalty = 1, if consumers are committed buyers

Loyalty = 0, if consumers are not committed buyers (switchers or satisfied buyers)

The dependent variable is divided into 1 and 0 based in the categories of consumers whose the commitment as

loyal consumers are affected by price. Consumers who are included as committed buyers are those who are less

likely to switch in to other brands though other brands are cheaper; which are represented by the answer of 4

and 5 (4=agree; 5=very agree). The non-committed buyers (switchers and satisfied buyers) are those who

answer the statement of “price-sensitivity” by 1, 2, or 3 on the questionnaires (1=very disagree; 2=disagree;

3=not sure); which means that they are kind of price sensitive buyers.

In this section, customer loyalty is focused on analyzing factors that affect the most to consumer’s price

sensitivity. Therefore, the output will be profiles about potential consumers who are not price sensitive and

more concerning to quality. The outputs will be used to generate recommendation in order to maximize the

market share, as well as sales.

Consideration of representing committed buyers by 1 is that the profiles of committed buyers are necessary to

design the appropriate and strategic marketing mix. Age, job, total monthly spending, and educational

background might affect the price-sensitivity of a person.

2) Age = 1, if consumers age is ranged between 20-49 years old

Age= 0, if consumers age is ranged between 49-59 years old

Consumers are divided into two groups. First, consumers whose age is ranged from 20-49 years old. This group

represents consumers in productive age. Second, consumers whose age is ranged from 40-59 years old. This

group represents consumers at non-productive age.

3) Job = 1, if consumers are non-office workers (entrepreneurs, housewife, students, others)

Job = 0, if consumers are office workers (office workers/staffs, government employee)

4) Spending = 1, if consumers are categorized at SES B and A

Spending = 0, if consumers are categorized at middle to lower SES (C1, C2, D, E)

5) Education = 1, if consumer’s educational background is at least Senior High School or higher


Education = 0, if consumer’s educational background is Junior High School or lower

According to YLKI, consumers who are concern to product quality must be having enough knowledge to

understand the importance of quality standard in their purchased goods. Therefore, consumers who has senior

high school as their minimum educational background and those who has university degree will be represented

by 1. The less educated will be represented by 0.

4. Results and Analysis

Table 1 Eviews 7 Output – Consumer Profiles

Dependent Variable: LOYALTY

Method: ML - Binary Logit (Quadratic hill climbing)

Date: 08/14/12 Time: 22:51

Sample: 1 328

Included observations: 328

Convergence achieved after 4 iterations

Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob.

C -1.476138 0.840444 -1.756378 0.0790

AGE 0.161499 0.532077 0.303526 0.7615

JOB 0.201633 0.279643 0.721038 0.4709

SPENDING -0.859450 0.286868 -2.995982 0.0027

EDUCATION 0.502909 0.661177 0.760628 0.4469 McFadden R-squared 0.036412 Mean dependent var 0.256098

S.D. dependent var 0.437143 S.E. of regression 0.430962

Akaike info criterion 1.126926 Sum squared resid 59.99030

Schwarz criterion 1.184747 Log likelihood -179.8159

Hannan-Quinn criter. 1.149995 Deviance 359.6319

Restr. deviance 373.2216 Restr. log likelihood -186.6108

LR statistic 13.58973 Avg. log likelihood -0.548219

Prob(LR statistic) 0.008726

Obs with Dep=0 244 Total obs 328

Obs with Dep=1 84

From the EViews 7 output on Table 1 above, the constant and coefficients from each variables are known, which

are age, job, spending, and education. The equation will be as follows:

Interpretation from the output Table 1:

1) McFadden R2 value is 0.036412. This means that age, job, spending, and education represent and explain only

3.6412% of the whole factors that may affect the consumer loyalty in term of price-sensitivity. Meanwhile,

there are another 96.3588% of factors which are not being analyzed in this research.


2) From the estimated Likelihood Ratio (LR) statistic, the four variables are statistically significant at 0.008726

percent level. Since 5% of significant level has been used, the four variables are statistically significant. All

regressors have high impact on Loyalty in term of price sensitivity, as the LR Statistic is 13.58983.

3) The meaning of each coefficient values of age, job, spending, and education will be interpreted below:

a. Since the coefficient value of Spending is -0.859450, it becomes the first most influencing factor to the

price sensitivity of consumers. If the Spending is increased by 1 percentage point, the logit decreased by

0.859450, holding other variables constant. Taking anti-log of -0.859450 (e-0.859450), the result is 0.4234.

This means that 42.34% of committed buyers are those who are categorized in SES B and A (high class).

b. Since the coefficient value of Education is 0.502909, it becomes the second most influencing factors to the

price sensitivity of consumers. If the Education is increased by 1 percentage point, the logit increased by

0.502909, holding other variables constant. Taking anti-log of 0.502909 (e0.502909), the result is 1.6535.

This means that the committed buyers are 1.6535 times more from consumers who have Senior High

School degree or higher as their educational background.

c. Since the coefficient value of Job is 0.201633, it is on the third place of factors which influence the

consumer price sensitivity. If the Job is increased by 1 percentage point, the logit increased by 0.201633,

holding other variables constant. Taking anti-log of 0.201633 (e0.201633), the result is 1.2234. This means

that the committed buyers are 1.2234 time more from non-office workers (entrepreneurs, housewife,

students, and others).

d. Since the coefficient value of Age is 0.161499, Age becomes the least influencing factor to the price

sensitivity of consumers. If the age rate increased by 1 percentage point, the logit increased by 0.161499,

holding other variables constant. Taking anti-log of 0.161499 (e0.161499), the result is 1.175. This means that

committed buyers are 1.175 time more owned by consumers on productive age than the non-productive

ones.

5. Discussion

5.1. Segmentation

According to Table 2 below, segmentation is consisted of two parts, by demographic and psychographic.

Demographic-based is including age, occupation, SES, and educational background. The psychographic side is

considering the consumer buying influence, whether by price or quality. Age is divided into two groups, the

productive and non-productive age. Consumers in productive age representing productive and active people. They

are fresh in mind, open to new information, and impulsive; as well as both money maker and more spender

compared to the non-productive ones.

Occupation is divided into two groups, the office and non-office workers. Those who are included in office-worker

groups comes from public officers, private-company officers, and state-owned company officers. The office-

workers are having more fixed income per month than the non-office workers ones. Non-office workers includes entrepreneurs, housewives, and university students. Income per month affects consumers price-sensitivity. The

smaller their fixed income, the more price sensitive they are because the fixed income influences consumer’s total

spending per month. It means that consumer’s SES is affected by income per month. Entrepreneurs and other non-

office workers do not have fixed income per month. It can be much higher than the fixed income that officers can

earn or less.

SES is representing certain characters of consumers. According to YLKI, consumers at middle to high segments

are having higher buying power and more selective. They have more alternatives than those at lower SES. Good

knowledge about product quality and influences from surroundings differ consumers from middle to high social

economic status with the lower ones. Consumer SES represents their price sensitivity toward products. Those who


are at the lower SES will be more price sensitive than those who are at the higher SES ones due to their limited

monthly spending budget.

Educational background will affect consumer’s way of thinking toward products. The more well-educated

consumers, the more selective they are. It is easier to bring the message about product quality to educated

consumers than those who are not. Therefore, it will affect consumer loyalty toward the brand. Eventhough,

previous research presented by YLKI has stated that in general, consumers do not exactly understand the quality

standards of packed-sugars. At the end, there are two tendencies in purchasing decision, price-sensitive or quality-

oriented.

Table 2 Proposed Segmentation

Attribute Description

Age Productive Age Non-productive Age

Occupation

Officers Entrepreneurs Housewifes Students Freelancer

Social Economic Status

(based on total monthly spending – AC Nielsen 2010) SES A : > IDR 3,000,000 (high) SES B : IDR 2,000,001 – 3,000,000 (middle to high) SES C1 : IDR 1,500,001 – 2,000,000 (middle to low) SES C2 : IDR 1,000,001 – 1,500,000 SES D : IDR 700,001 – 1,000,000 SES E : < 700,000

Educational Background < Senior High School ≥ Senior High School

Psychographic Price Sensitive Quality Oriented

5.2. Targeting

Potential target markets are divided into three categories, primary, secondary, and tertiary target. The three

different targeting priorities are formed for sales maximization purpose. Primary target is considering the most

potential consumers, not only to generate sales, but also committed buyers. Secondary target is the second

prioritized group of target consumers. Strategies to catch primary target can be mixed with the needs of secondary

target, as long as it does not interrupt the product positioning in the market. Tertiary target is possibly helpful to

contribute small percentage of sales.


Table 3. Proposed Targeting

6. Conclusion

Determining the market segment and target market are the first most important elements before a company designs

further marketing plans and implementation. Marketing mix which may be created should be according to what

segment of consumer that a firm targeted to. Therefore, the Binary Logit Model above can be used to identify the

appropriate target market based on certain dependent factor which become a basis of marketing objective, such as

identifying the potential target market for being a committed buyers. At the end, researcher can modify the

attributes based on the importance of such attributes to research development.

References

Christensen, Ronald (1990). Log-Linear Models. Springer-Verlag. New York, New York)

Direktorat Jenderal Industri Agro dan Kimia. Department Perindustrian. Jakarta. 2009. Gujarati, Damodar N. – Porter, Dawn C. 2009. Basic Econometrics. Fifth Edition. New York, USA :Mc Graw-Hill

Indonesian Consumer Foundation (YLKI) in “Anticipating SNI* GKP*: Problems and Solutions Toward Sugar Quality Improvement” (2009)

KPPU (Komite Pengawas Persaingan Usaha). Position Paper. KPPU Terhadap Kebijakan Dalam Industri Gula.

Target Market Demographic Psychographic

Primary Target

M/F

SES B to A

Well educated (min Senior High School)

Non-office workers (Housewife, entreprenuer, freelancers, students)

On productive age (20-49 years old)

Quality Oriented (Value

for money)

Secondary Target

M/F

SES B to A


Both Office and non-office workers

20-59 years old (productive and non-productive age)

Price Sensitive

Tertiary Terget

M/F

SES C1


Both office and non-office workers

20-59 years old (productive and non-productive age)

Price Sensitive

Sales maximization strategy of brand “Gunung Madu”

Documents

Transcript of Sales maximization strategy of brand “Gunung Madu”