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1. Introduction:

The introduction of telecommunication industry has been pretty late in our country

compared to that of the neighboring countries. This is very much attributable to the

political unrest in early 1990s. Though the first telecom company, Citycell, was

introduced ages ago, the device has not become so pervasive until 1997, the year

when the biggest telecom of the country, GrameenPhone (GP) hit the market with its

GSM technology. Since then there was no looking back. The industry grew at such an

incredible rate in just a decade that anyone could hardly imagine. Now there are a

number of players battling so hard for their respective market share and the

consumers as well as the economy benefitted tremendously from this fierce

competition. The following sections elaborate on almost every aspect of the industry,

ranging from how it become so big, what fueled the growth to what are the risks and

opportunities that it currently renders for the players.

1.1 Objectives:

a) Broad Objective: The broad objective of the research is:

“To analyze the relationship between the advertisement expenditure and the number

of subscription in the telecom industry of Bangladesh.”

b) Specific Objectives: Specific objectives of this research are:

o To give a brief overview of the telecom industry.

o To demonstrate the ad spends and changes in the number of subscribers over

time for six major mobile operators of Bangladesh.

o To find out the correlation between the advertisements spend and number of

subscription.

1.2 Methodology:

Research Scope: In this research paper the effectiveness of advertisement

expenditure and the change in the number of subscribers for different operators‟ in

Bangladesh is analyzed and evaluated. The correlation between these two variables is

focused on.

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Research Design: The research is exploratory in nature as there the problem has not

been clearly defined.

Type of Data: The underlying research on the Effectiveness of Advertisement on

Number of Subscribers in the Telecom Industry of Bangladesh is based on secondary

data source and is analyzed to get an insight of the actual situation.

Methods of Data Collection: The data is collected from the Software Media Express

and AdEdge by Sirius Communication, and different articles, journals, and web sites.

1.3 Expected Result:

There is a common concept that if any company increases their advertisements spend

their number of subscribers will surely increase. In this research this assumption has

been verified. This research is to focus on the correlation between advertisement

spend and the increase of the number of subscribers over time. This paper will be

useful to those who want to work on this area.

1.4 Limitations:

Unavailable data: As the idea of people meter software was conceived by

the end of 2006, the related data on advertisement spend of different

mobile operators of Bangladesh before 2007 were unavailable.

Limited access: Data used in this research are monitoring data taken from

the software, as the researcher did not have access to the real data from the

companies.

Lack of experience and time: A research as broad as this needs proper

experience in the researchers‟ side where as a longer period of time is

needed to.

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2. Overview of Mobile Phone Operators of Bangladesh:

The telecommunication sector, specially the mobile phone sector, in Bangladesh is

one of the fastest growing business segments of the country which provide a lot of

value addition to the society with its service & creation of employment opportunities.

At present there are six mobile phone operator in the country with – GrameenPhone,

Banglalink, Robi, Teletalk, Airtel and Citycell. Except the GrameenPhone, none of

the operator can make little profit till now.

Before going to the analysis, there is a simple overview on the companies, operating

in the mobile phone sector in the country. The overviews of the operators are as

follows –

GrameenPhone – Before Grameenphone‟s inception, the phone was for a selected

urbanized few. The cell phone was a luxury: a flouting accessory for the select elite.

The mass could not contemplate mobile telephony as being part of their lives.

Grameenphone started its journey with the Village Phone program: a pioneering

initiative to empower rural women of Bangladesh. The name Grameenphone

translates to “Rural phone”. Starting its operations on March 26, 1997, the

Independence Day of Bangladesh, Grameenphone has come a long way.

Grameenphone pioneered the then breakthrough initiative of mobile to mobile

telephony & became the first & only operator to cover 98% of the country‟s people

with network. Since its inception they have built the largest cellular network in the

country with over 13,000 base stations in more than 7000 locations. Presently, nearly

99% of the country's population is within the coverage area of the Grameenphone

network. They have always been a pioneer in introducing new products & services in

the local market. GP was the first company to introduce GSM technology in

Bangladesh when it launched its services in March 1997. Grameenphone was also the

first operator to introduce the pre-paid service in September 1999. It established the

first 24-hour Call Center, introduced value-added services such as VMS, SMS, fax &

data transmission services, international roaming service, WAP, SMS-based push-pull

services, EDGE, personal ring back tone & many other products & services. The

entire Grameenphone network is also EDGE/GPRS enabled, allowing access to high-

speed Internet & data services from anywhere within the coverage area. There are

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currently nearly 2.6 million EDGE/GPRS users in the Grameenphone network.

Today, Grameenphone is the leading telecommunications service provider in

Bangladesh with more than 36 million subscribers as of December 2011.

Banglalink - Orascom telecom Bangladesh limited ("Banglalink") is fully owned by

Orascom Telecom holding s.a.e, egypt, ("OTH"); the ultimate parent company of the

group is Vimpelcom, the 6th largest mobile phone operator in the world. Banglalink

was acquired by OTH in 2004, & after a complete overhaul & the deployment of a

new GSM network, its telecommunication services were re-launched under the brand

name Banglalink. When it began operations in Bangladesh in February 2005, its

impact was felt immediately: overnight mobile telephony became an affordable option

for customers across a wide range of market segments. Banglalink‟s success was

based on a simple mission: "bringing mobile telephony to the masses" which was the

cornerstone of its strategy. They changed the mobile phone status from luxury to a

necessity & brought mobile telephone to the general people of Bangladesh & made a

place in their hearts. The mobile phone has become the symbol for the positive

change in Bangladesh. This positive change that is quite correctly attributed to

Banglalink, has become their corporate positioning & is translated in their slogan

"making a difference" or "din bodol". "Making a difference" not only in the telecom

industry, but also through its products & services, to the lives of its customers. This

corporate stance of "making a difference" has been reflected in everything Banglalink

does. They attained 1 million subscribers by December 2005 & 3 million subscribers

in October 2006. In less than two years which is by December 2007, they overtook

Aktel to become the second largest operator in Bangladesh with more than 7.1 million

customers. They currently have 20.05 million subscribers as of April 2011,

representing a market share of 27.03%. Growth over the last years have been fuelled

with innovative products & services targeting different market segments, aggressive

improvement of network quality & dedicated customer care, creating an extensive

distribution network across the country, & establishing a strong brand that

emotionally connected customers with Banglalink.

Robi - Robi Axiata Limited is a joint venture company between Axiata Group

Berhad, Malaysia & NTT DOCOMO INC, Japan. It was formerly known as Telekom

Malaysia International (Bangladesh) which commenced operations in Bangladesh in

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1997 with the brand name AKTEL. On 28th March 2010, the service name was

rebranded as „Robi‟ & the company came to be known as Robi Axiata Limited. Robi

is truly a people-oriented brand of Bangladesh. Robi, the people's champion, is there

for the people of Bangladesh, where they want & the way they want. Having the local

tradition at its core, Robi marches ahead with innovation & creativity. To ensure

leading-edge technology, Robi draws from the international expertise of Axiata &

NTT DOCOMO INC. It supports 2G voice, CAMEL Phase II & III & GPRS/EDGE

service with high speed internet connectivity. Its GSM service is based on a robust

network architecture & cutting edge technology such as Intelligent Network (IN),

which provides peace-of-mind solutions in terms of voice clarity, extensive

nationwide network coverage & multiple global partners for international roaming. It

has the widest International Roaming coverage in Bangladesh connecting 600

operators across more than 200 countries. Its customer centric solution includes value

added services (VAS), quality customer care, easy access call centers, digital network

security & flexible tariff rates.

Airtel – Airtel Bangladesh Ltd. is a GSM-based cellular operator in Bangladesh.

airtel is the 6th mobile phone carrier to enter the Bangladesh market, & launched

commercial operations on May 10, 2007. Warid Telecom International LLC, an Abu

Dhabi based consortium, sold a majority 70% stake in the company to India's Bharti

Airtel Limited for US$300 million. Bharti Airtel Limited took management control of

the company & its board, & rebranded the company's services under its own airtel

brand from 20 December 2010. The Bangladesh Telecommunication Regulatory

Commission approved the deal on Jan 4, 2010. In January 2010, Bharti Airtel

Limited, Asia‟s leading integrated telecom services provider, acquired 70% stake in

Warid Telecom, Bangladesh, a subsidiary of the UAE-based Abu Dhabi Group.

Bharti Airtel is making a fresh investment of USD 300 million to rapidly expand the

operations of Warid Telecom & have management & board control of the company.

This is the largest investment in Bangladesh by an Indian company. Dhabi Group

continues as a strategic partner retaining 30% shareholding & has its nominees on the

Board of the Company. The new funding is being utilized for expansion of the

network, both for coverage, capacity, & introduction of innovative products &

services. As a result of this additional investment, the overall investment in the

company will be in the region of USD 1 billion. This is Bharti Airtel‟s second

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operation outside of India. The company launched its mobile services in Sri Lanka in

January 2009 on a state-of-the-art 3.5G network. On July 19, 2007, the company

crossed the 1 million customers mark in the first 70 days of operation. Airtel

Bangladesh had 5.045 million subscribers as of June 2011.

Citycell - Citycell (Pacific Bangladesh Telecom Limited) is Bangladesh‟s & South

Asia‟s pioneering mobile communications company & the only CDMA mobile

operator in the country. Citycell is a customer-driven organization whose mission is to

deliver the latest in advanced telecommunication services to Bangladesh. The

company offers a full array of mobile services for consumers & businesses that are

focused on the unique needs of the Bangladeshi community. Citycell‟s growth

strategy is to integrate superior customer service, highest standards of technology & a

choice of packages at affordable rates. The company operates a 24-hour call centre

with well-trained operators to respond to customer queries. Citycell‟s customer

service is open 7 days a week to ensure customers can access Citycell at any

convenient time.

Teletalk – Teletalk Bangladesh Limited was incorporated on 26 December, 2004 as

a public limited company under the Companies Act, 1994 with an authorized capital

of Tk.20,000,000,000 being the only government sponsored mobile telephone

company in the country. On the same day the Company obtained Certificate of

Commencement of Business. It is the only governmental mobile phone operator of the

country. It is a GSM based state-owned mobile phone company in Bangladesh.

TeleTalk started operating on 29 December 2004. It is a Public Limited Company of

Bangladesh Government, the state-owned telephone operator. TeleTalk provide GPRS

& EDGE internet connectivity & now waiting for the license from Government to

start the 3G which is the latest cellular information service. Teletalk is the first

operator in the country that gave BTTB (now BTCL) incoming facility to its

subscribers. The mission statement of Tele Talk is "Desher Taka Deshey Rakhun"

("Keep your Money in your Country") TeleTalk is the 6th largest mobile phone

operator in Bangladesh with 1.147 million subscribers as up to JUly, 2010.

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3. Analysis & Findings:

3.1 Basic Data Scrutiny:

The data has two main variables – Ad Spending (A), and Number of Subscribers (S).

In the numbers, both are measured in Million units. By number of subscribers the

customers who are using the Sim card of a Mobile Phone Operator in a given time are

concentrated on.

The monthly data spans from May 2007 to 2011 August, i.e. 52 months. There are six

mobile phone operators under consideration – Grameen Phone, Banglalink, Airtel,

Citycell, Robi and Teletalk. The August 2009 entry is missing for Teletalk, and

appropriate missing value corrections were taken during the data analysis.

Number of Subscribers is the variable of interest, also called the response variable or

dependent variable in regression terminology. Ad Spending is the explanatory

variable, also known as the predictor variable in regression. The investigation will be

on whether Number of Subscribers is in any way related to Ad Spending. Intuitively,

it is thought that increase in ad spending will ideally bring in more subscribers.

When the Number of Subscribers is simply plotted across time in the next plot, it is

seen that for all the operators, subscriber base increases in time, which is only natural

to expect, given the steady growth of population and the spread of technology. This

automatically tells a keen observer that time could be another explanatory variable for

this data analysis. And so, Time (T), measure in month units, is also included in the

data. Overall, the trend for Number of Subscribers looks to be a linear growth with

Time. So, the principle of parsimony tells that while performing regression, there is

no worth in including terms of T higher than first order, e.g. T2, T

3, or, for example,

using other models like exponential.

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Next, there is the other variable – Ad Spending. Unlike Number of Subscribers, which

is pretty smooth over time, this has heavy fluctuations, and the variable itself is

distributed quite haphazardly Since linear regression works best with, and often

assumes, an underlying Normal (Gaussian) distribution, a Quantile-Quantile Plot is

made, separately for each operator. The Q-Q plots, and also the histograms, show that

this variable is quite positively skewed (i.e. more mass at the smaller values, and a

long, heavy tail).

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So, in order to standardize it, a log transform on the sample values is applied. The Q-

Q plot with the transformed values show that the data distribution is more normal

now, with only a few extreme values deviating from the diagonal line.

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From now on, whenever the variable Ad Spending is referred to, it would mean the

log-transformed values, not the original ones. A plot of this variable across time for

the various operators is shown next.

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It is seen that the nature of Ad Spending is not smooth over time. The high level of

fluctuations also tells right away that, the effect of Ad Spending on the Number of

Subscribers, if any, would not be very high, since the other one is considerably

smooth. One can also easily spot some patterns, for example, that the trend in

spending by Citycell actually decreased over time, while the trend for Robi increased

at a decently high rate.

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3.2 Subscriber Takeover

A behavior of interest would be subscriber takeover – whether a telecom operator can

„take away a part of the subscriber base of another operator, probably through

aggressive advertising or by offering bonus features etc. Also if a potential new

customer of one operator joins another instead, that will be reflected in the reduced

growth rate of the first operator. While the first-hand data of subscribers actually

switching operators is not available, an idea is generated by focusing on the behavior

by comparing the Number of Subscribers data across different operators.

But first, the linear trend over time is to be removed from these numbers. Otherwise,

it is the strongest component of the data, and any other effects are masked. The trend

is removed via a linear regression of Number of Subscribers against Time, and the

residuals are taken for our purpose. The residuals are shown in the next plot.

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<Now, looking at the residual subscriber numbers, it can be observed how they

compare to each other. For this, the pairwise correlations for all the operators are

looked at. In the next plot, the correlation matrix for the six operators is represented.

The color indicates the direction of the association – black if the two operators are

positively correlated, and grey if negatively correlated. The size of the marker denotes

the strength of the association – the larger the magnitude (absolute value) of the

correlation, the larger the marker. There are no diagonal entries, obviously, because

every variable is perfectly correlated with itself, so it is of no interest to plot.

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It is seen that Grameen Phone, Banglalink and Airtel have similar subscriber patterns,

while that of Citycell is rather the opposite of them. Robi and Teletalk do not show

any markedly strong relation with any operators.

3.3 Regression Studies:

In the initial analysis, some basic adjustments were made to the data via

transformations, & also got insights into what kind of models would be suitable for

the subsequent regression analysis. It was also ensured that the variables are

continuous numbers (not categorical or binary etc.) & are not oddly behaved, which

allows to perform simple Linear Regression using Ordinary Least Squares. To

recapitulate, the response variable is Number of Subscribers (S), & explanatory

variables are Time (T) & Ad Spending (A). A is already being log-transformed. It is

also seen that S is significantly (p-value almost equal to 0) dependent on T, linearly,

for every mobile phone operator.

Next, an interesting question is whether the other explanatory variable Ad Spending

(A) also depends on Time (T). Just like Number of Subscribers, a linear regression is

done. The regression coefficient (b), P-values, and significance levels of those values

are shown in the next table for each operator.

Operator Coeff. (b) P-value Significance

Grameen Phone 0.008 0.025 *

Banglalink 0.032 0 *****

Airtel -0.023 0.111

Citycell -0.021 0 *****

Robi 0.044 0 *****

Teletalk -0.004 0.709

The notation for significance is as follows – if the coefficient is significant at 5%

level, i.e. P-value < 0.05, is denoted by *; if it is significant at 0.1% level, i.e. P-value

< 0.001, is denoted by *****; if the coefficient is not significant at all, it is left blank.

When using simple linear regression model where response variable is Number of

Subscribers (S) & Ad Spending (A), the result is:

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For Grameen Phone, the R-

Square is 0.0256, which

means that the correlation

between the two variables is

very low to bring a

significant change in the

subscriber number if ad

spend is increased. Although

the value of the regression

coefficient, 2.32, seems large and positive (a positive coefficient means that increased

ad spending would lead to increasing subscriber numbers), it is actually not

statistically significant at all (P-value 0.257), which means that ad spending and

subscriber number is not associated in fact, and as a result, increased ad spending

would not affect the number of subscribers.

For Banglalink, the R-Square

is 0.2676, which means that

the correlation between the

two variables is moderate, and

there will be change to some

extent in the subscriber

number if ad spend is

increased. The value of the

regression coefficient, 3.009,

seems large and positive (a positive coefficient means that increased ad spending

would lead to increasing subscriber numbers), and here it is actually statistically

significant (P-value 0), which means that ad spending and subscriber number is really

associated. Therefore, increased ad spending can actually have an effect on the

number of subscribers.

For Airtel, the R-Square is 0.000047, which means that the correlation between the

two variables subscriber number and ad spend is too low. And the value of the

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regression coefficient,

0.0047, is also very small (it

is positive, but that is of no

real consequence), and in the

plot the regression line looks

almost horizontal. And

indeed, the regression

coefficient it not statistically

significant at all (P-value

0.962), which means that ad spending and subscriber number is not associated. So,

increased ad spending would not affect the number of subscribers.

For Citycell, the R-Square is

0.1855, which means that the

correlation between the two

variables is moderate, and

there will be change to some

extent in the subscriber

number if ad spend is

increased. The value of the

regression coefficient, -

0.196, is also moderate, and

noticeably, negative (a negative coefficient means that increased ad spending would

actually lead to fewer subscriber numbers). The value is actually statistically

significant (P-value 0.001), which means that ad spending and subscriber number is

indeed negatively associated, and as a result, increased ad spending can have a

negative effect on the number of subscribers.

For Robi, the R-Square is 0.3622, which means that the correlation between the two

variables is moderate, & there will be change to some extent in the subscriber number

if ad spend is increased. The value of the regression coefficient, 1.43, seems large

and positive (a positive coefficient means that increased ad spending would lead to

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increasing subscriber

numbers), & here too it is

statistically significant (P-

value 0), which means that ad

spending & subscriber number

is in fact associated. Hence,

increased ad spending can

actually have a positive effect

on the number of subscribers.

For Teletalk, the R-Square is

0.0399, which means that the

correlation between the two

variables is quite low, and

there might not be a

significant change in the

subscriber number if ad spend

is increased. The value of the

regression coefficient, -

0.0249, is also low. It is

negative (a negative coefficient means that increased ad spending would actually lead

to fewer subscriber numbers), but with such a small magnitude, the sign is probably

not important. As one can expect, the regression coefficient it not statistically

significant either (P-value 0.162), which means that ad spending and subscriber

number is not associated. So, increased ad spending would not actually affect the

number of subscribers.

As it appears, not all operators‟ ad spend significantly depend on time. Referring back

to the plot & discussion in the first section (section 3.1), it can be seen that how the

patterns of different operators differ. And checking association is important because,

if both T & A as explanatory variables are kept in the regression model, this means

that these variables are significantly correlated themselves. And therefore, the

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regression results will be very different when T is kept in the model versus when it is

not.

From the objective, the initial regression model was to regress Number of Subscribers

on Ad Spending. In notations, the model could be concisely expressed as

S ~ A

or via the more standard linear model notation,

S = c + bA

Where c is baseline constant, and b is the regression coefficient. To recall, a P-value

is the probability of the regression coefficient b being statistically significant (i.e.

significantly different from 0) under the null hypothesis, which is that S is not

associated with A.

But seeing the trend of S over time, the model has been extended to include Time as

well. So, the bigger model can be written either way as

S ~ A + T

S = c + bA + dT

Where d is the regression coefficient corresponding to the variable T. It has been seen

that d is always significant, since Number of Subscribers strongly depends on Time.

But the more important factor is that, since in some cases T is associated with A itself,

that indicates that the behavior of b under the two different models could be very

different.

And that brings to the tricky territory of Causality, which postulates about the possible

underlying mechanisms that lead to a data. It is demonstrated with two different

hypothetical situations:

(i) Consider the simple situation when, if the operator increases spending towards

advertisement, that directly has an effect on the public perception, and as a result the

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number of subscribers increase. This is the case where A is indeed a causal factor

behind S. And of course, time T keeps on increasing in the background.

(ii) As explained in the first section (section 3.1), it is only natural to expect that

subscriber size would increase over time, given various factors such as the steady

growth of population and the spread of technology. There are also similar factors,

such as increasing cost, which can lead to required expense for a fixed amount of

service increase over time – that is, S also depends on T. However, it is assumed that

S doesn‟t actually depend on A, that is, increased ad spending does not really bring in

extra subscribers. In here, T is the real causal factor behind both S and A, and the

similar pattern that will be seen between those two is not because A is controlling S.

Can it be really distinguishable between the two situations from our data? Not really,

and that is the disadvantage of doing such exploratory analyses compared to a

properly designed experiment; but that is something nearly impossible in most

situations, including here.

In the first case, the direct model S ~ A is the proper one, while in the second case,

where T is a causal factor, S ~ A + T would be the correct model to consider. Since it

is not known which is the real one, the best bet is to fit both. Therefore, in the next

table, regression results are shown – coefficient, P-value and significance – for both

the models, for each operator.

A side note is that, if it is recalled that the residuals (R) from the previous section

(section 3.2) which were obtained by fitting S on T, it can be realized that fitting the

second model S ~ A + T is the same as working with the residuals via the first model,

which would now become R ~ A. That is because, S = c + bA + dT is equivalent to

R = S – dT = c + bA

In the following table, the regression R2 obtained in the first graph, i.e. fitting S ~ A,

is compared with the other model, S ~ A + T. These are the first two columns of the

table. It is to be noted that the R2 in the second model is much higher, which justifies

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the addition of the Time variable in the regression, and is consistent with the earlier

finding that S is strongly correlated with T. Also, in the last column, the R2

corresponding to fitting R ~ A is presented, which shows the goodness of fit of the b

coefficient in the S ~ A + T model. It can be seen that their behavior is quite different

in the two situations. It will be shown later that the b coefficients themselves are also

quite different.

Operator R2 in S ~ A R

2 in S ~ A + T R

2 in R ~ A

Grameen Phone 0.0257 0.9643 0.4018

Banglalink 0.2676 0.9730 0.1561

Airtel 0.00005 0.7390 0.1379

Citycell 0.1857 0.5026 0.0071

Robi 0.3622 0.9510 0.0071

Teletalk 0.0395 0.7576 0.0907

Now the regression results for both the models are plotted. The first graph presents

the data points (via a scatterplot) and the regression line from the model S ~ A. The

second graph involves the model S ~ A + T, and plots the data points – residuals (R)

versus A – as well as the fitted regression line.

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Now in the following table, the regression results for both the models are presented.

S ~ A S ~ A + T

Operator Coeff. (b) P-value Signif. Coeff. (b) P-value Signif.

Grameen Phone 2.322 0.257 -2.243 0 *****

Banglalink 3.009 0 ***** -0.411 0.017 *

Airtel 0.005 0.962 0.141 0.009 *

Citycell -0.196 0.001 * 0.027 0.657

Robi 1.437 0 ***** -0.045 0.647

Teletalk -0.025 0.162 -0.019 0.034 *

The point to notice from the above table is that the operators who had significant

A ~ T association in the previous section (section 3.2), had their findings drastically

switched between the two models: Grameen Phone is only significant in the second

model, whereas Banglalink, Citycell and Robi show much stronger association in the

first model. The interpretation is open to scope, including the decision on which

model to apply in reality.

(i) In the first case of Grameen Phone, it can be said that taking away the common

factor T has helped bring out the true relationship between S and A in in the data, and

a strong linear relationship between R and A can be seen from the plot.

(ii) Similar, statistically significant associations, between R and A can be seen in case

of Airtel and Teletalk as well, albeit less strong than Grameen Phone.

(iii) On the other hand, Citycell and Robi have strong associations in the original data,

but that goes away when the effect of time is removed.

(iv) The case of Banglalink is rather interesting – while a strong positive association

in the original data is seen, a remaining significantly negative association between R

and A is also seen when Time is adjusted for. In this case, the second model might

actually be the true one, with the real causal effect of A on S coming out after

adjusting for the common covariate T.

,

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Another side note: Since the relationship between S and A is not that straightforward

as that of between S and T, we tried fitting higher order regression models to the data,

e.g. R ~ A + A2. But it turns out that the higher order polynomial models do not

provide any better fit, and so they were not included in the analysis presented.

The highlight of the above regression findings would be that, most of the significant

regression coefficients (4 out of 7) are in fact negative! That is quite contrary to the

intuition that the subscriber base should increase if more money is spent on

advertisement. So, irrespective of which model is true, it might be more prudent not to

go overboard with ad spending but concentrate on other factors such as better call

quality, better coverage, lower call rates, etc.

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4. Recommendations & Conclusion:

4.1 Recommendations:

As the assumption that the more money spent on advertisement increases the

number of subscriber too is proved to be wrong, it might be more prudent not

to go overboard with ad spending.

This might even be an indication that the cell phone market is being saturated,

so, providers must introduce more value added services, schemes along with

better network coverage, better signal quality for the current subscribers.

Teletalk, Citycell must come up with new innovation effectively use their

advertisement.

4.2 Conclusion:

This research proved one assumption of a regular marketer to be incorrect. It was a

common assumption that the more presence in the media – electronic or other, the

more the number of subscribers will increase. So it is learnt from this research that

spending a huge amount is poorly designed marketing campaigns may not make the

number of subscribers go up but it can even be negative to some extent.

The mobile telecommunication of Bangladesh is very competitive & the rivals are

facing huge competition in the market every day. To compete with the changing

customers need and want they need to give more new tariff plans, value added

services, better network coverage, and better signal quality and provide interesting

schemes to retain subscribers to switch to a different operator. The mobile companies

are changing their strategies day to day to survive in this ever changing market to stay

in the positive tide and make the profit they desire.