INTRODUCTION:
Advertising
is done in order to promote the business, a product, a service, an event, primarily
to increase brand awareness, brand recall contributing to the increase in sales.
Advertising thus, is an essential part of a business’s strategy and usually is allotted
good resources and funding. Advertising helps in building brand identity or brand
image in today’s highly competitive scenario and because of these reason businesses
are strategically using this advertising tool to gather success. There are various
mediums of advertising. Advertising can be done through television, through newspapers
or through internet etc.
This
process is termed as the Advertising Media Selection. Advertising media selection
is the process of selecting the most appropriate and efficient media channel for
an advertising campaign. This media efficiency is calculated by considering the
required coverage, number of exposures required, strength of target market, and
the cost of the media advertising. Media planning also involves buying media space
in the media vehicles and all these steps are time and cost consuming specially
for a small enterprise. Today’s media industry is really dynamic and therefore new
media vehicles like Social media, Digital Advertising and Mobile Advertising are
soon booming apart from the traditional media vehicles. Some main forms of advertising
used in today’s communication industry are Print Advertising (Magazines, Brochures,
Newspapers, Fliers), Broadcast Advertising (Radio, Television), Outdoor Advertising
(Flags, Banners, Building/Fence Wraps, Billboards, Events), and Internet advertising.
Print advertising is one of the most popular mode of advertising because it is capable
of reaching a wide audience and also has a permanent nature. Newspaper or magazines
sell space to thee businesses who wish to showcase their advertisement. The Classifieds
columns of the newspapers are the most searched spaces for advertising and buying.
The cost of advertisement in a newspaper or a magazine depends upon the location
of ad or the size of the ad. The advertisement rate differs from the front page,
full/half page, the quality of the material of paper like plain, coloured, glossy
paper and on the amount of times the advertisement is to be featured over time.
With the development in the audio/visual technology over these years, broadcast
media is booming. Television, radio is most favoured because of its creative nature
and mass media approach. It is favoured because of its eye-catchy nature, attention
grabbing power and because of this nature it is the most expensive media. Outdoor
advertising is continuously on display and has a wider reach to the audience who
might pass every day from that location. Various types of outdoor advertising include
vehicular advertising like on Metro Trains, other public transport, event banners,
hoarding signage, building wraps, construction signage, fence wraps, banners and
flags. The Internet is tone of the most successful and the fastest growing advertising
media. Majority of the small and medium enterprises are spending lavishly on the
internet advertising because this medium has a wide reach being the cheapest options
available. With strong internet penetration and easy good smartphone penetration
around the world it is one of the best medium for advertising.
Over
the past few years, Indian education sector has witnessed the incoming of a parallel
education system. This system is of the coaching institutes or the tutor centres
which has become an essential part of the life of Indian students. Enrolling in
coaching institute has become a common practice since apart from schooling or University
education, coaching gives a more personalized concern on the student’s learning.
Thus, today the private coaching industry in India is one of the fastest booming
areas in the service sector. It is estimated to be around 70 billion USD by ASSOCHAM.
Indian middle-class parents today are spending one-third of their monthly income
on private tuitions of their children either to better them on education front or
to prepare them for competitive exams. There is a very stiff competition between
the coaching institutes in order to get the maximum enrolments done. Major players
in this segment of coaching in Delhi primarily are IMS, Edu-mentor, Brilliant Tutorials,
FIITJEE, Aakash, Narayana, Career Launcher, TIME’s etc. All these coaching institutes
in order to take a larger piece of the share are fighting to gain maximum attention
from the students are their parents. In order to win this eyeball game, they are
focussing die-hard on their advertising planning.
Earlier,
most of the coaching institutes had great reputation through free word-of-mouth,
primarily because they were located in one small district/ area but today, these
institutes want to widen their reach and therefore want strong advertising to reach
every nook and corner of the city and the country. Coaching industry is fastly changing
and growing and firms have grown bigger, both in revenue and geographical spread
terms, they are thus using large advertising outlays to reach their target audience.
Economic Times reports that Career Launcher spends close to Rs 10 crore, IMS claims
it spends about 10% to 12% of its turnover (estimated to be more than Rs 200 crore)
on advertising. Some firms like Career Forum, TIME and IMS have experimented with
TV as well, but the returns were not good enough. Economic Times reports that most
institutes spend on the print and outdoor media. The other medium that they are
increasingly investing towards is online. Since, most of the students in the age
range of 14-28 are net savvy.
Linear
programming being the most prominent optimization technique is applicable for the
solution of real business problems in which the objective function and constraints
appear as linear functions of the decision variables (Kumar and Nandi, 2017). Many
managers take the decision on the basis of their knowledge or forecast but it is
not always true that those decisions give maximum profits. The problem of decision
making can be solved by using the mathematical tool of linear programming model
which is now one of the most powerful tools which all decision makers (managers)
must apply before achieving effective decision. Linear programming has multiple
applications because the constraints can be easily incorporated into the linear
programming model (Miller, 2007). Karagiannis
and Apostolou (2010), see linear programming as a great revolutionary development
which has given mankind the ability to state goals and to take a decision in order
to “best” achieve its goals when in dilemmatic problem.
The issue of developing and using linear programming models
for the optimal allocation of advertising expenses among available advertising vehicles
has been debated by few scholars. Brown and Warshaw (1965) illustrated how linear
programming model can be modified to accommodate advertising problems of an organisation.
Ching, W et al. (2006) proposed a new advertising model which could formulate optimization
problem of maximising overall sales into linear programming problem and capture
the advertising wear out phenomenon in order to derive the optimal pulsation advertising
strategy. Rajeiyan, et al. (2013) in their study used linear programming for solving
the transportation problem of Services Company and found that the linear programming
can be used to solve the tasks of marketing management, media choice, and media
mix.
Analysing the strong competition in the Indian coaching
industry, it is difficult for a small institute to make its foray into the industry.
Small institutes require an optimum advertising plan within the monetary constraints
of their business in order to make a mark in this competitive industry today. In small coaching institute, most of the decisions are
qualitative decisions.i.e. these decisions are taken by the owners or managers on
the basis of intuition, judgmental approaches. The application of optimization techniques
(model-based decision making) like linear programming models, have little or no
application in such institutes. Hence, this study is an initiative to conduct an
assessment of the application of linear programming in such coaching institutes
to help them in getting an optimum advertising
plan for optimum outreach of customers within the monetary constraints of their business.
LITERATURE REVIEW:
Linear
programming deals with optimization problems. These optimization problems has an
objective to either minimize resources for a fixed level of performance, or to maximize
performance at a fixed level of resources (Wu, 1989).
Linear programming is a mathematical programming tool used for the allocation of
limited resources on the basis of the given criterion of optimality. It is a special
technique used in operation research to achieve best outcome, such as the maximization
of profit or minimization of cost. This technique of Linear Programming is applied
in all areas including retail, agriculture, transportation, raw materials, social
science, education, military etc. In mathematical optimization, one of the most
popular methods to get the best outcome is the simplex method, which was published
by George B. Dantzig in 1947. After that, LP was used by many industries in a wider
context An operation research technique.i.e. simplex method is widely used in finding
solutions to many real business problems (Kumar and Nandi, 2017). Simplex method is used to solve several problems in many
different fields like minimize cost, optimize maximum profit, marketing, agriculture,
human resources and manufacturing decision making etc. Many researchers supported
the use of scientific methods, particularly linear programming in the allocation
of scarce resources play a vital role to the manufacturing to boost the output.
Mula et al. (2005) stated that the most important application of optimization tools
using linear programming is in solving the problems of production. According to
Miller (2007), linear programming can be used in modelling variety of real-life
problems like designing the schedule of airline routes or in designing an inexpensive
diet taking in consideration the daily dietary requirements. Veli and Ulucan (2010)
reported that in aggregate production planning, mixed integer linear programming
plays an important role as it can be helpful in solving the problem of deciding
the quantity and mix of products to be produced or how many employees the firm should
actually retain. Fagoyinbo and Ajibode (2010) proposed linear programming as the
best quantitative approach to decision-making. They used linear programming model
to solve the problems of effective use of resources in staff training by taking
decision variables for the model as junior staff and senior staff and the constraints
was the time available for training. As per them the success of an organization
largely depends on the decision the firm takes and any decision by the manager cannot
be made on the basis of their personal experiences or on the basis of intuition
thus quantitative tools should be used to make appropriate decisions. Imran (2010)
in Pakistan used revised simplex method to maximize the profits generated for the
different products manufactured at a multinational company ICI. The products considered
in the study were polyester, soda ash, paints and chemicals. They found that the
production of the soda ash is contributing most productively. They also revealed
that the company can earn a significant profit by operating on the production forecasts
which is proposed by them. Nabasirye et al. (2011) defined optimization as the selection
of the best alternative out of a large number of possibilities present. They found
that animal feed is a major factor in the overall cost on animal production, and
therefore to maximize profits, they used linear programming tool to minimize the
cost of animal feed. Balogun et al. (2012) analysed that the major problem is in
the judicious and optimal use of limited resources such as capital, raw material
or manpower. They used linear programming for optimal production in Coca-Cola Company.
There were nine decision variables like Coke, Crest soda, Fanta Tonic, Fanta and
Schweppes etc. and the constraint were taken as concentration of the drinks, water
volume, carbon oxide, sugar content. Using simplex, they found out that out of the
nine products, only two contribute most to the profit maximization of the company.
Muazu (2012) used the technique of linear programming to derive the maximum profit
out of production of soft drink for a Nigeria bottling company. He derived two particular
items which should be produced to run on a profitable front. Benedict and Uzochukwu
(2012) recommended use of optimization technique in production firms in Nigeria,
to determine the product mix (such sizes of PVC pipes produced). Linear programming
technique was adopted by the company for optimizing production of different size
of PVC pipes which to lead optimal level. Waheed et al. (2012) concluded from his
work that linear programming can be effectively used by the organizations to eradicate
the problem of the use of scarce resources. In their paper, they used linear programming
to derive an optimum product mix of medicated soaps using R statistical package
to obtain optimal monthly profit level. Vakilifard, Esmalifalak and Behzadpoor (2013)
revealed in their study that the linear programming model can be effectively used
in solving the product-mix problem. Their results showed that the information derived
from the model can be used to enhance production and through this the organization
can use the under-utilized constraints to expand the capacity of over utilized constraints.
Rajeiyan, et al. (2013) in their study used linear programming for solving the transportation
problem of Services Company. Maryam et al. (2013) in their study inferred that linear
programming is very important for quality management decisions and therefore they
are used in all the areas of management like in allocation of resources, production
planning, inventory control and advertisement. In Nigeria, Adebiyi, Amole and Soile
(2014) used linear programming for product-mix optimization. The identified two
out of five products, whose production will be profitable for the company and will
help in optimum firm performance. Kumar et al. (2014) used linear programming to
solve the problem of scheduling of nurses in hospitals. They took the case of scheduling
of nurses for an eight-hour shift. Their study determined the minimum number of
nurses that can be employed so that sufficient number of nurses are available in
the hospital. Ibitoye, Atoyebi, Genevieve and Kadiri (2015) in their work took a
case of a fast food firm. This fast food firm faced challenges in the production
of doughnut, meat pie and chicken pie because the prices of raw materials increased.
In their study, they inferred that the fast food company should discontinue the
production of doughnut and chicken pie and should continue only with the production
of meat pie to run on profits by using linear programming model. Igbinehi et al.
(2015) used linear programming to maximize profit of a soap production company.
They analysed that the company was producing three different types of soaps. They
inferred from their study that the company was spending more on the production of
the coloured soap, but they derive more profit from selling white soap than the
coloured soap. So, they suggested that the company should produce white soaps to
run on profit. Iwok et al. (2016) used simplex algorithm to allocate raw materials
in bakery for their profit maximization. It was observed in the analysis that production
of small loaf is more profitable as compared to the production of big loaf. Hence,
for the bakery to run on profit, it is more important to produce small loafs more
than the big loafs. Woubante G. W. (2017) considered an apparel industrial unit
in Ethiopia as a case study and suggested that the profit of the company can be
improved by 59.84%, by applying linear programming models if customer orders have
to be satisfied. Mitra and Avittathur (2018) presented an application of linear
programming in optimizing the procurement and movement of coal for an Indian coal-fired
thermal power-generating company. Results of their research showed that there was
immense potential not only for significant cost savings but also for reduced logistics
between different coal source–power plant pairs.
Given
the lack of literature on application of model-based decision making for effective
advertising in coaching institutes, this study tries to answer this research gap.
OBJECTIVE
AND RESEARCH METHODOLOGY:
Objective:
The objective of the study is to formulate a linear programming
model that would suggest a viable numbers of advertising methods to be used by a
coaching institute to ensure optimum outreach of customers by the institute
Research
Method:
To
answer the research question, this study uses a descriptive research design. The
study is carried out in two stages, first stage involves use of primary data which
was collected through personal face-to-face interview mode and second stage involves
application of linear programming model on the collected data. Interviews were conducted with the manager of institutes and it involved information
on the advertising practices of the institute under study.
Sampling:
The
coaching institute industry was chosen as the research setting for this study. The
sampling elements or the respondents were the managers and owners of these coaching
institutes. The duration of the interviews ranged from 15 to 30 minutes. Total 50
coaching institutes were included in the sample frame. Convenience sampling was
used for data collection.
Data
analysis tool: Since the purpose of this study was to develop linear programming
model for the collected data from the institute, the authors tried to transform
the data into a linear programming model and solved the model using simplex algorithm
in order to determine the optimal combination of the numbers of advertising methods
which should be adopted by the institute in maximizing its customer reach within
the available scarce resources.
Research Model:
Linear Programming is a mathematical technique for generating
and selecting the optimal or the best solution for a given objective function. Linear
programming technically can be defined as a method of optimizing a linear function
either i.e. maximizing or minimizing for several linear inequalities.
Linear programming model with n decision variables and
m constraints can be itemized in the following form.
Z = c1x1 + c2x2 (Objective
function)
Subject to the linear constraints:
a11 x1+ a12 x2(≤
or≥) b1
a21 x1+ a22 x2(≤
or≥) b2
am1 x1+ am2x2(≤
or≥) bm
x1, x2 ≥ 0.
Where
‘Z’
is the objective function of LP model that represent the measure of performance
which can be either profit, or cost or reverence etc.
x1
and x2 are decision variables.
c1,
c2, --------cn are parameters that represent per unit profit
(or cost) of decision variables x1, x2, -------, xn
to the value of the objective function.
a11,
a12 ---------------, am, am2, -------- amn represent
the amount of resource consumed per unit of the decision variables.
The
bi represents the total availability of the ith resource.
The use of the simplex method to solve a linear programming problem
requires satisfaction of the following two properties.
a)
All the constraints
should be expressed as equations by adding slack or surplus or artificial variables.
b)
The right-hand side
of each constraint should be made of non-negative (if not). This is done by multiplying
both sides of the resulting constraints by -1.
RESULTS:
Data Analysis:
Table
1 shows the summary of data related to advertising methods and expenses of coaching
institutes operating in Delhi. Average of all 50 coaching institutes values were
used for further linear programming model formulation.
Table
1: Summary of data collected from coaching institutes
|
Total advertising cost (monthly)
|
10,000
|
|
Average cost of one pamphlet
advertising programme (monthly)
|
1850
|
|
Minimum number of pamphlet advertising
programme (monthly)
|
1
|
|
Average number of customer outreach
by one pamphlet advertising programme (monthly)
|
13
|
|
Average cost of one online advertising
programme (monthly)
|
2000
|
|
Minimum number of online advertising
programme (monthly)
|
1
|
|
Average number of customer outreach
by one online advertising programme (monthly)
|
100
|
Proposed Linear
Programming Model
After
collecting the data from the coaching institutes, the proposed linear programming
model was constructed.
The
objective function would be:
Maximize
Z = 13x1 + 100 x2
Where
Z = total customer outreach which can be achieved by the
coaching institute by using various advertisement
methods
13
and 100 represent customer outreach by using pamphlet and online advertising method
respectively
x1,
x2 = the set of unknowns to be determined, i.e. number of pamphlets advertisement and number of online
advertisements respectively
The constraints would be:
1850 x1 + 2000x2 ≤ 10000 (cost
constraint)
x1 ≥ 1
x2 ≥ 1
Where
1,850 represent the cost of one package of pamphlet advertisement expenditure and 2000 represent one
package of online advertising expenditure.
10,000 represent total advertisement expenditure available with the institute
Solution
of the LPP by using simplex method
Maximize
Z = 13x1 + 100 x2
The constraints would be:
1850 x1 + 2000x2 ≤ 10,000 (cost
constraint)
x1 ≥ 1
x2 ≥ 1
Applying
simplex method on the above equations we get the following equations and tables.
Maximize
Z = 13x1 + 100 x2 + 0 S1 + 0 S2 + 0
S3 – M A1 – M A2
The constraints would be:
1850 x1 + 2000x2 + 0 S1
= 10,000 (cost constraint)
x1 - S2 + A1 = 1
x2 - S3 + A2 = 1
Based
on the optimum result derived from the model, the study indicates that the number
of pamphlets advertisement (x1) should be 1 and the number of online
advertisement (x2) should be 4. This will ensure optimum outreach of
customers by the institute.
CONCLUSION:
The study clearly brings forth the importance of linear
programming models in determining optimal advertising mix. The objective of this
study was to apply the linear programming techniques in the effective use of resources
by educational coaching institutes to ensure optimum outreach of customers. The
analysis was carried out using simplex method on the data gathered from the institutes. This study helps coaching
institutes to understand the best way of making decisions using quantitative models
to determine its optimal advertising-mixes that can maximize its customer reach
subject to the scarce resources it has. It is evident from the interviews that advertisement
decisions in such coaching institutes are generally qualitative decisions like subjective
estimation, intuition and trial and error. Thus, the results of this paper would
make these institutes to shift to model-based decision-making style. The paper strongly recommends to the management of these
institutes that whenever there is a need to make an advertising decision then they
can use these results to achieve their aim by maximising their customer outreach.
Studies have supported the fact that model-based
decision making is important for its accuracy and objectivity. But such decision-making
approach was not widely used in educational coaching industry. The study also provides
a deep understanding and insight of the applications of linear programming models
in educational coaching institutes and how to apply such models in practical and
real-world experience. The study has been
limited to only those coaching institutes
which have one or two offices thus it might
not be appropriate to generalise the findings to other coaching institutes. Further
research in this area needs to be done by taking a greater number of coaching institutes
to get more valuable solutions. Thus, the methodological approach for the obtaining
optimal number of advertising method presented in this paper could be extended to
all other types of coaching institutes. The findings from this paper will add academic
value in the context of expanding knowledge in relation to the linear programming
use in advertising and will also contribute in filling gaps within the existing
literature related to this topic.
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