Sales maximization strategy of brand Gun
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*
a
Business School Universitas Pelita Harapan, Jl. M.H. Thamrin Boulevard
Tangerang, 15811, Indonesia
b
Research 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:evo.hariandja@uph.edu
© 2015 The 7th Indonesia International Conference on Innovation, Entrepreneurship, and Small Business.
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
4
Evo Sampetua Hariandja, Rany Wahyu Larasati
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.
probability
1.0
0.8
0.6
0.4
0.2
0.0
-4
4
-2
0
2
logit
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.
Evo Sampetua Hariandja, Rany Wahyu Larasati
5
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
Pi
Βi
xi
= Logit Model
= Probability
= Coefficient
= 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
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Evo Sampetua Hariandja, Rany Wahyu Larasati
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
Evo Sampetua Hariandja, Rany Wahyu Larasati
7
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
CoefficientStd. Error z-Statistic Prob.
C
AGE
JOB
SPENDING
EDUCATION
-1.476138
0.161499
0.201633
-0.859450
0.502909
0.840444
0.532077
0.279643
0.286868
0.661177
-1.756378
0.303526
0.721038
-2.995982
0.760628
0.0790
0.7615
0.4709
0.0027
0.4469
McFadden R-squared0.036412
S.D. dependent var 0.437143
Akaike info criterion 1.126926
Schwarz criterion
1.184747
Hannan-Quinn criter. 1.149995
Restr. deviance
373.2216
LR statistic
13.58973
Prob(LR statistic) 0.008726
Mean dependent var0.256098
S.E. of regression 0.430962
Sum squared resid 59.99030
Log likelihood
-179.8159
Deviance
359.6319
Restr. log likelihood-186.6108
Avg. log likelihood -0.548219
Obs with Dep=0
Obs with Dep=1
Total obs
244
84
328
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.
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Evo Sampetua Hariandja, Rany Wahyu Larasati
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.
b.
c.
d.
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).
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.
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).
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 officeworkers 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 nonoffice 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
Evo Sampetua Hariandja, Rany Wahyu Larasati
9
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 qualityoriented.
Table 2 Proposed Segmentation
Attribute
Age
Occupation
Social Economic Status
Description
Productive Age
Non-productive Age
Officers
Entrepreneurs
Housewifes
Students
Freelancer
(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.
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Evo Sampetua Hariandja, Rany Wahyu Larasati
Table 3. Proposed Targeting
Demographic
Target Market
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
Well educated (min Senior High School)
Both Office and non-office workers
20-59 years old (productive and non-productive age)
Price Sensitive
Tertiary Terget
M/F
SES C1
Well educated (min Senior High School)
Both office and non-office workers
20-59 years old (productive and non-productive age)
Price Sensitive
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.
and Small Business (IICIES 2015)
Sales maximization strategy of brand “Gunung Madu”
Evo Sampetua Hariandjaa, Rany Wahyu Larasatib*
a
Business School Universitas Pelita Harapan, Jl. M.H. Thamrin Boulevard
Tangerang, 15811, Indonesia
b
Research 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:evo.hariandja@uph.edu
© 2015 The 7th Indonesia International Conference on Innovation, Entrepreneurship, and Small Business.
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
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Evo Sampetua Hariandja, Rany Wahyu Larasati
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.
probability
1.0
0.8
0.6
0.4
0.2
0.0
-4
4
-2
0
2
logit
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.
Evo Sampetua Hariandja, Rany Wahyu Larasati
5
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
Pi
Βi
xi
= Logit Model
= Probability
= Coefficient
= 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
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Evo Sampetua Hariandja, Rany Wahyu Larasati
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
Evo Sampetua Hariandja, Rany Wahyu Larasati
7
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
CoefficientStd. Error z-Statistic Prob.
C
AGE
JOB
SPENDING
EDUCATION
-1.476138
0.161499
0.201633
-0.859450
0.502909
0.840444
0.532077
0.279643
0.286868
0.661177
-1.756378
0.303526
0.721038
-2.995982
0.760628
0.0790
0.7615
0.4709
0.0027
0.4469
McFadden R-squared0.036412
S.D. dependent var 0.437143
Akaike info criterion 1.126926
Schwarz criterion
1.184747
Hannan-Quinn criter. 1.149995
Restr. deviance
373.2216
LR statistic
13.58973
Prob(LR statistic) 0.008726
Mean dependent var0.256098
S.E. of regression 0.430962
Sum squared resid 59.99030
Log likelihood
-179.8159
Deviance
359.6319
Restr. log likelihood-186.6108
Avg. log likelihood -0.548219
Obs with Dep=0
Obs with Dep=1
Total obs
244
84
328
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.
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Evo Sampetua Hariandja, Rany Wahyu Larasati
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.
b.
c.
d.
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).
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.
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).
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 officeworkers 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 nonoffice 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
Evo Sampetua Hariandja, Rany Wahyu Larasati
9
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 qualityoriented.
Table 2 Proposed Segmentation
Attribute
Age
Occupation
Social Economic Status
Description
Productive Age
Non-productive Age
Officers
Entrepreneurs
Housewifes
Students
Freelancer
(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.
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Evo Sampetua Hariandja, Rany Wahyu Larasati
Table 3. Proposed Targeting
Demographic
Target Market
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
Well educated (min Senior High School)
Both Office and non-office workers
20-59 years old (productive and non-productive age)
Price Sensitive
Tertiary Terget
M/F
SES C1
Well educated (min Senior High School)
Both office and non-office workers
20-59 years old (productive and non-productive age)
Price Sensitive
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.