qba final project

7/31/2019 qba final project

1/25

USING MULTIPLE REGRESSIONS MODEL to ANALYZE

REVENUE DECREMENT on PT. ADXN

Andrew Richard Togatorop 1140003462

BINUS BUSINESS SCHOOL

MASTER of MANAGEMENT in STRATEGIC MARKETING

BINUS UNIVERSITY

JAKARTA

2011


2/25




A Project submitted to the Graduated Faculty

In Partial Fulfillment of the Requirements

For Master of Management Degree

Major: Strategic Marketing

BINUS UNIVERSITY


3/25




Supervisor:

Ir. Muhril Ardiansyah, M.Sc, Ph.D

25 July 2011


4/25

TABLE OF CONTENTS

TABLE OF CONTENTS

LIST of TABLES

LIST of APPENDICES/LIST OF FIGURES

CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

1.2 PROBLEM STATEMENT

1.3 LEARNING OBJECTIVE

1.4 SCOPE of THE STUDY

CHAPTER 2: LITERATURE REVIEW

2.1 THEORITICAL FOUNDATION

2.2 RESEARCH PRACTICE

CHAPTER 3: RESEARCH METHODOLOGY

CHAPTER 4: RESULTS and DISCUSSIONS

CHAPTER 5: SUMMARY

REFFERENCES

APPENDIX


5/25

CHAPTER I. INTRODUCTION

1.1 BACKGROUND

PT. ADXN is one of largest display Ad Network Company in SEA and become

pioneer in Indonesia since 2007. Until now Admax has been partnership with hundreds

of local publishers and running almost all the campaigns from major brands or

advertisers from Indonesia market.

ADXN business models basically rely on the advance technology that could

connect all the publishers to become a large network that could represent most of the

online activities in Indonesia. The network clustered based on the content and audience

profile into channels which become ADXN products. Advertiser could buy the

channels based on Cost per Mil (CPM) or Cost per Click (CPC).

Through a single platform ADXNs advertisers could access multiple websites,

target the audience that they desired and manage their campaign performance

effectively. ADXN really concerned on clients campaigns performance since this the

only key value that makes differences with other conventional Internet Advertising

which often doesnt care about it.


6/25

In 2009 ADXN Indonesia succeed to grow the company in terms revenue and

number of human resources as seen on chart 1. Variety of products also make huge

impact on the uptrend progress, here is the product list:

1. ADXN Channels as the main product of the company.

2. Yahoo! (Official Reseller).

3. MSN (Official Reseller).

4. Friendster (Official Reseller). One of the fast growing social networking

sites in the world before the Facebook era.


7/25

Chart 1.1 Organizational Structures

1.2 PROBLEM STATEMENT


8/25

The real turning point started when ADXN enter their fourth fiscal year. ADXN

revenue started having a downtrend each month and management realizes there were

several factors that cause the hassle:

1. Turnover number increase. Some of the ADXN employees were

resigned and move to other online company.

2. Friendster has lost their value because Facebook started to grow even

bigger than expected.

3. ADXN ended the contract as Yahoo official reseller because Yahoo will

open their office in Jakarta.

4. Solely depend with ADXN channels for the product which most of the

client still unfamiliar with the concept.

5. Competitors start to penetrate ID market, each with their unique selling

point.

This research will only evaluate the internal factors from ADXN therefore external

factor such as competitor would not included.


9/25

1.3 LEARNING OBJECTIVE

Expected result from the research is to understand which factors that really

impact the campaign revenue performance. From there the management will have the

knowledge that could help them to determined next step for the company.

Since internet still showing never ending growth in terms of penetration and

innovations, in the future the research result could help other online start up company

to solve similar issue with ADXN case.

1.4 SCOPE of THE STUDY

The research will only highlight factors that came from ADXN Indonesia start

from human resources, products, and sales performance. The collected data will only

reflect from the last 30 months revenue report. The outcome of this research is focusing

on how to capitalize each factor and should not be valid as reference for forecasting

company performance.


10/25

CHAPTER II. LITERATURE REVIEW

2.1 THEORITICAL FOUNDATION

2.1.1 Internet Industry

Internet has become a very common term in public society. It already

part of people lives, they could access it at school, working, home, mobile, caf,

and many other places. Refer to www.internetworldstats.com table tells that

internet penetration in 2011 has reached 30.20% of world population, see Table

1.

WORLD INTERNET USAGE AND POPULATION STATISTICS

31-Mar-11

World Regions

PopulationInternet

Users

Internet

Users Penetration Growth Users %

( 2011 Est.)Dec. 31, 2000 Latest Data

(%Population)

2000-2011 of Table

Africa 1,037,524,058 4,514,400 118,609,620 11.40% 2527.40% 5.70%

Asia 3,879,740,877 114,304,000 922,329,554 23.80% 706.90% 44.00%

Europe 816,426,346 105,096,093 476,213,935 58.30% 353.10% 22.70%

Middle East 216,258,843 3,284,800 68,553,666 31.70% 1987.00% 3.30%

North America 347,394,870 108,096,800 272,066,000 78.30% 151.70% 13.00%

Latin America /Carib. 597,283,165 18,068,919 215,939,400 36.20% 1037.40% 10.30%

Oceania / Australia 35,426,995 7,620,480 21,293,830 60.10% 179.40% 1.00%

WORLD TOTAL 6,930,055,154 360,985,492 2,095,006,005 30.20% 480.40% 100.00%

Table 2.1. Internet World Usage and Population
http://www.internetworldstats.com/http://www.internetworldstats.com/


11/25

The most simple definition of Internet was mentioned on Vurro,

Marcella essay that It is a virtual global place, were people globally come

together to interact and exchange information (Vurro, 2009). Internet has

released the distance boundaries for people to connect each other. Since the

introduction of internet and lately web 2.0, there are millions active and non

active websites.

2.1.2 Internet Advertising

The essence of Internet Advertising is an advertising medium that could

provide a flow of information available to consumers on demand enabled

through technological devices in interactive and personalized environment

(Vurro, 2009). Online Ad Networks is a part of Internet Advertising tools that

could aggregate traffic that was previously too difficult buy or undesirable

because of too much website inventory wastage or reluctant to have an

advertisement (DeSilva. Phillip. 2008).

Ad networks Company has the technology that could manage inventory

from many websites or publishers to be used by advertiser to perform their

campaign. The technology will cluster publishers invetories based on targeting

segmentation mostly based on content and user demographic.

2.1.3 Online Advertising Business Model


12/25

Ad network often positioned themselves as Media partner for Advertiser

while in Publisher perspective they become the advertisers because part of the

revenue contribution. Chart 2 could explain furthermore about the work flow.

The essential of Ad network business model is the inventory acquisition

strategy; representation, direct acquisition with profit share or direct acquisition

via arbitrage.

There were three ways for Advertisers to purchase Ad networks services

(DeSilva. Phillip. 2008):

CPM (Cost per thousand impressions)

CPC (Cost Per Click)

CPA (Cost Per Action)


13/25

Chart 2.1 Ad Network Work Flow.

2.1.4 Ad Network Business

Most of Ad network were considered as start up company with

minimum resources and infrastructure. Maintain revenue level above average

has always become every business goal, but each business on different factors

to achieve their goal. Ad Network Company also relies on some fundamental

factors to maintain revenue level as mentioned below.

- Sales order


14/25

- Sales person

- Average sales value

- Product

2.2 RESEARCH PRACTICE

2.2.1 Past Research

The research model will use Multiple Regressions. Noor, Gusti (2008)

stated that this model was suitable for identifying correlation between

dependent variable and more than one independent variable. The formula is:

Yi = 0 + 1X1i + 2X2i + i

Yi = Dependent Variable

0= Y intercept

1=slope of Y with variable Xi

2=slope of Y with variable X2

i= random error in Y for observation i

CHAPTER III. RESEARCH METHODOLOGY


15/25

3.1 RESEARCH DESCRIPTION

This research used secondary data that has been extracted from monthly

revenue report of PT. ADXN for the last 30 months. The last 6 weeks raw data

has been through of variable selection, defined the research hypothesis, and

learnt more about theoretical foundation. Next phase the data will go through

quantitative analysis process with Multiple Regressions model to examine the

variable correlation and test the hypothesis which leads to conclusions and

recommendations.

3.2 FRAMEWORK

Research processes were breakdown into some phase as mentioned on

the figure with below explanation:

1. Data extraction. In this case the researcher used secondary data that has

been extracted from PT. ADXN internal system.

2. Problem Finding. The data was taken from year 2009 up to 2011 and it

shown there is the declining trend in terms of revenue.

3. Identifying Variables. Number of sales person, number of proposal, and

number of sales value were the chosen variables.


16/25

4. Defined the hypothesis. This is the key points because it will lead the

research to the finding the conclusions. Fault hypothesis will mislead the

entire process of research.

5. Data examination using Multiple Regressions. The analysis using multiple

regressions will form in two ways, using Microsoft Excel and manual

calculation.

6. Hypothesis Testing. This phase will involve two type of test which are F

test and T test.

7. Conclusions and Recommendation. Finally the final phase where finally

reach the conclusions of the research and share recommendation to solve

the problem.


17/25

Chart 3.1 Research Framework

3.3 POPULATION


18/25

The secondary data was taken form the internal record systems of PT.

ADXN. It recorded monthly sales data from January 2009 up to June 2011, in

total there 30 month (see appendix 1). The captured data was list as below:

1. Sales persons

2. Date

3. Clients

4. Sales order

5. Average sales amount

6. Revenue

3.4 SAMPLE

Multiple regressions model need at least 30 samples data to be consider

valid. Therefore number of sample will reflect the number of population. The

sample data only used number of sales person, sales order, and average sales

value to be process on the research, see table.

The graphic shows that revenue declining and this research will find the

solutions to keep the trend downward in the future.


19/25

MonthSales

personSalesOrder

AverageSales Value

Revenue

Jan-09 3 1 228,690,039 228,690,039

Feb-09 5 2 129,111,875 258,223,750

Mar-09 6 3 148,528,642 323,619,800

Apr-09 6 3 311,506,671 623,013,342

May-09 6 1 45,472,000 45,472,000

Jun-09 6 4 143,749,657 574,998,627

Jul-09 6 2 176,523,839 353,047,678

Aug-09 6 2 118,869,483 237,738,965

Sep-09 6 2 158,634,283 317,268,567

Oct-09 6 1 93,251,352 93,251,352

Nov-09 6 4 112,139,703 448,558,812

Dec-09 6 7 119,946,647 839,626,528

Jan-10 4 6 141,103,324 846,619,944

Feb-10 3 4 199,497,113 797,988,452

Mar-10 3 7 118,984,650 832,892,552

Apr-10 2 12 151,575,112 1,818,901,348

May-10 3 8 159,125,993 1,273,007,944

Jun-10 3 11 344,704,463 3,791,749,097

Jul-10 3 13 184,086,314 2,393,122,078

Aug-10 4 12 133,033,157 1,596,397,880

Sep-10 3 16 162,084,341 2,593,349,457

Oct-10 3 8 97,523,852 780,190,815

Nov-10 3 4 151,597,010 606,388,038

Dec-10 3 4 57,604,967 345,629,800

Jan-11 3 6 153,685,255 922,111,532

Feb-11 3 6 63,333,339 380,000,032Mar-11 2 5 87,500,005 350,000,018

Apr-11 2 5 96,884,240 484,421,200

May-11 1 9 61,666,689 555,000,204

Jun-11 3 9 97,280,512 875,524,606

Jul-11 2 4 113,685,188 454,740,750


20/25

Table 3.1 Data Sample

Graphic 3.1 Data Sample Trend

3.5 RESEARCH MODEL

3.5.1 Variable

Based on the table 1, the sample data contained dependent variable

which is revenue and independent variables where the correlation needs to be

proven in this research. These are the independent variables:


21/25

1. Sales person. Ad network business really relies on their sales force.

In this research will see the correlation in terms of quantity rather

than quality.

2. Sales Order. Number of sales order that contributes by sales person

every month.

3. Average Sales Value. It shows average numbers of sales that

happened each month.

3.5.2 Hypothesis

This research was meant to find the significant correlation between

dependent variable and independent variables. These are the hypothesis

statements:

1. H0: There is no correlation between overall independent

variables (sales person, sales order, and average sales value) with

revenue performance in PT. ADXN.

H1: There is correlation between overall independent variables (sales

person, sales order, and average sales value) with revenue

performance in PT. ADXN.


22/25

2. H0: There is no correlation from each independent variable

(sales person, sales order, and average sales value) with revenue


H1: There is correlation from each independent variable (sales

person, sales order, and average sales value) with revenue


3.5.3 Multiple Regressions

In order to understand the correlation between the dependent variable

and independent variables, therefore the analysis will use the multiple

regressions model as follows:

R = b0 + b1 SP + b2 SO+ b3ASV

R = Revenue

SP = number of sales person

SO = number of sales order

ASV = average of sales value


23/25

3.5.4 Hypothesis Testing

1. F Test.

This test objective to determine whether there is a significant

relationship between the dependent variables and set of independent

variables. Below is the hypothesis model to be used:

Ho: b1=b2=b3=0

Ha: b1 or b2 or b3 0

1

==

kn

SSEk

SSR

MSE

MSRF

Figure 3.1 F-Test Graphic

2. T test


24/25

The objective for this test is to shows if there is linear relationship

from each independent variable with dependent variables. Below is

the hypothesis model to be used:

Ho: bi = 0 (no linear relationship)

Ha: bi 0 (linear relationship exist)

jb

j

S

bt

0= (df = n k 1)

Figure 3.2 T-Test Graphic

3.6 PROGRAM for RESEARCH

Research calculation will be conduct through Microsoft Excel 2007

with Analysis Toolpak add-ins. The reason to use this program because of its

environment friendly and widely found in business settings.


25/25

CHAPTER IV. RESULT and DISCUSSION

4.1 DESCRIPTIVE STATISTICS

4.2 INFERENCE STATISTICS

4.3 DISCUSSION

CHAPTER V. CONCLUSIONS and RECOMMENDATION

5.1 CONCLUSIONS

5.2 RECOMMENDATION

qba final project

Documents