Projectsjanafinal.pdf

Pharmaceutical Sales Strategy: Project to Determine Promotional Strategy of Galvus Using Factor Analysis

By: Sourav Jana IISWBM MBA (evening) Session: 2016-18 Roll No.107/MBA/162008

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Acknowledgement The success and final outcome of this project required a lot of guidance and assistance from many people and I am extremely privileged to have got this all along the completion of my project. All that I have done is only due to such supervision and assistance and I would not forget to thank them. I respect and thank Mr. Chiranjib Ghosh, Regional Business Manager - IT, Novartis India Limited, for providing me an opportunity to do the project work in Novartis and giving me all support and guidance which made me complete the project duly. I am extremely thankful to him for providing such a nice support and guidance, although he had busy schedule managing the corporate affairs. I owe my deep gratitude to my project guide Prof. Manjit Sarkar, IISWBM, who took keen interest on my project work and guided me all along, till the completion of our project work by providing all the necessary information. I would not forget to remember Mr Amrit Karar, Area Business Manager Novartis for his timely support till the completion of my project work. I heartily thank Prof. Chinmoy Jana, HoD - MBA, IISWBM, for his support during this project work. I am thankful to and fortunate enough to get constant encouragement, support and guidance from all Teaching staffs of MBA Evening of IISWBM which helped us in successfully completing our project work

SOURAV JANA

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CHAPTER 1: INTRODUCTION

ABOUT NOVARTIS Novartis International AG is a Swiss multinational pharmaceutical company based in Basel, Switzerland. It is one of the largest pharmaceutical companies by both market cap and sales. Novartis manufactures the drugs clozapine (Clozaril), diclofenac (Voltaren), carbamazepine (Tegretol), valsartan (Diovan), imatinibmesylate (Gleevec/Glivec), ciclosporin (Neoral/Sandimmun), letrozole (Femara), methylphenidate (Ritalin), terbinafine (Lamisil),Vildagliptin (Galvus).

MISSION Our mission is to discover new ways to improve and extend people`s lives.

VISION Our vision is to be a trusted leader in changing the practice of medicine.

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CORPORATE STRUCTURE Novartis AG is a publicly traded Swiss holding company that operates through the Novartis Group. Novartis AG owns, directly or indirectly, all companies worldwide that operate as subsidiaries of the Novartis Group.[ Novartis's businesses of are divided into three operating divisions: Pharmaceuticals, Alcon (eye care) and Sandoz (generics) Novartis operates directly and through dozens of subsidiaries in countries around the world, each of which fall under one of the divisions, and that Novartis categorizes as fulfilling one or more of the following functions: "Holding/Finance: the entity is a holding company and/or performs finance functions for the Group; Sales: the entity performs sales and marketing activities for the Group; Production: the entity performs manufacturing and/or production activities for the Group; and Research: the entity performs research and development activities for the Group." Novartis AG also holds 33.3% of the shares of Roche however, it does not exercise control over Roche. Novartis also owned 24.9% of Idenix Pharmaceuticals prior to its sale to Merck & Co, Inc.Novartis also has two significant license agreements with Genentech, a Roche subsidiary. One agreement is for Lucentis; the other is for Xolair, both of which Novartis markets outside the US. Novartis has established a multi-functional center in Hyderabad, India, in order to offshore several of its R&D, clinical development, medical writing and administrative functions.The global service centere began in 2001 with 17 Sourav Jana-107/MBA/162008

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people; Hyderabad was chosen from a shortlist of 23 cities, including Pune, Chennai and Gurgaon.Thecenter supports the drug major’s operations in the pharmaceuticals (Novartis), eye care (Alcon) and generic drugs segments (Sandoz). This centre covers more than 870,000 square feet large enough to house 8000 people.

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HISTORY OF NOVARTIS In 1996, Ciba-Geigy merged with Sandoz; the pharmaceutical and agrochemical divisions of both companies formed Novartis as an independent entity. Other Ciba-Geigy and Sandoz businesses were sold, or, like Ciba Specialty Chemicals, spun off as independent companies. The Sandoz brand disappeared for three years, but was revived in 2003 when Novartis consolidated its generic drugs businesses into a single subsidiary and named it Sandoz. Novartis divested its agrochemical and genetically modified crops business in 2000 with the spinout of Syngenta in partnership with AstraZeneca, which also divested its agrochemical business. Novartis was created in 1996 from the merger of Ciba-Geigy and Sandoz Laboratories, both Swiss companies with long histories. Ciba-Geigy was formed in 1970 by the merger of J. R. Geigy Ltd (founded in Basel in 1758) and CIBA (founded in Basel in 1859). Combining the histories of the merger partners, the company's effective history spans 250 years

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NOVARTIS STRATEGY: We believe Novartis is well prepared for a world with a growing, aging population and continuously evolving healthcare needs. We have a clear mission, focused strategy and strong culture, all of which we expect will support the creation of value over the long term for our company, our shareholders and society. Our strategy is to use science-based innovation to deliver better patient outcomes in growing areas of healthcare.

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TOP MNC COMPANIES ACROSS THE WORLD:

RESEARCH DISEASE AREAS We focus on discovering and advancing new treatments for serious patient needs. From the inception of a therapeutic through early clinical development, our disease area teams collaborate across scientific disciplines and organizations in support of our mission to improve and extend peoples’ lives. Immuno-oncology Immuno-oncologyOurImmuno-oncology research program aims to treat most human cancers with immune therapy.

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Oncology OncologyOur Oncology group explores mechanisms and potential therapeutics for a wide range of both common and rare cancers. Neuroscience NeuroscienceLearn how our recent advances in model systems and technology help us pursue revolutionary therapies for neurodegenerative, neurodevelopmental and neuropsychiatric conditions. Cardiovascular & Metabolic Diseases Cardiovascular and Metabolic DiseasesConcentrates on dyslipidemia, atherosclerosis and vascular diseases, type 2 diabetes, heart failure, cardiac arrhythmias and associated disorders. Autoimmunity, Transplantation & Inflammatory Disease Autoimmunity, Transplantation & Inflammatory DiseasePioneers novel therapeutic modalities for the treatment of rheumatic and dermatological diseases as well as other auto-immune and inflammatory conditions. Infectious Diseases Infectious DiseasesThe infectious diseases research team searches for novel therapies for major bacterial and viral infections for which current therapies are lacking or suboptimal. Current work focuses on multidrug-resistant, Gramnegative bacterial pathogens, several important respiratory viruses, chronic hepatitis B, and certain virus-associated malignancies.

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Musculoskeletal Diseases Musculoskeletal DiseasesThe musculoskeletal disease research team focuses on discovering therapies for muscle wasting and bone disorders. Ophthalmology OphthalmologyThe ophthalmology team at NIBR is working on new scientific discoveries for eye disorders including age-related macular degeneration. Respiratory Diseases The respiratory research team focuses on the discovery and development of novel therapies for a broad range of respiratory diseases, from common to rare (genetic) diseases such as cystic fibrosis. Tropical Diseases Novartis Institute for tropical diseases The Novartis Institute for Tropical Diseases is a drug discovery research institute dedicated to finding novel treatments for neglected tropical diseases, including malaria, cryptosporidiosis, and three major kinetoplastid diseases – human African trypanosomiasis, Chagas disease, and leishmaniasis.

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NOVARTIS TOP PRODUCTS AVAILABLE IN INDIA

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ABOUT CLASS OF THE PRODUCT DPP4i is a class of drug which lowering blood glucose level. This is a newer class of drug which is better than the previous class of drug i.e. SU.DPP4i is providing some other advantages rather than only lowering the blood glucose like Cardiac safety,Renalsafety,weight reduction ,low risk of hypoglycaemia In this class of Drug few companies are researched their DPP4i Vildagliptin by NOVARTIS Sitagliptin by MSD Linagliptin by Boringheringellham Saxagliptin by Astra Zeneca Gemigliptin by Sanofi Alogliptin by Takeda Pharma Teniligliptin by Mitsubishi & many All of these DPP4 Some of the advantages over others All the innovator companies Promote their DPP4i on their advantages DPP4 inhibitors India India is the second largest diabetic population in the world. GBI Research's analysis revealed that the overall anti-diabetes market in India was worth $680.3m in 2011 and is projected to grow at a CAGR of

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11.3% between 2011 and 2018 to reach $1,446m in 2018. High and growing diabetes population in the country, rising obese and geriatric population, and rapid market adoption of drugs from the classes such as Dipeptidyl Peptidase 4 (DPP-4) inhibitors, Glucagon-Like Peptide (GLP)-1, Sodium-Glucose Transport Proteins (SGLT-1) and others would drive the anti-diabetes market in the forecast period. The R&D product pipeline for DM, dominated by OAD agents, consists of around 200 molecules at various stages of clinical development. Paradoxically, type 2 diabetes therapeutics market, crowded with many generics and branded generics drug products, is being seen as a significant growth opportunity for new patent protected products owing to high prevalence, progressive nature of the disease and considerably high unmet needs. Of overall R&D pipeline molecules, more than 90% are being studied for the treatment of type 2 DM.

Many multinational companies, such as Novartis, Eli Lilly, are engaged in setting up strategic marketing and distribution agreements with domestic players to improve their patient base and so the market share. Growing at a double digit year on year growth rate, the Indian anti-diabetics market is very promising and offers lucrative opportunities to both domestic as well as foreign pharmaceutical player

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CHAPTER 2: RESEARCH METHODOLOGY

OBJECTIVE OF THE STUDY Objective : DPP4 inhibitor is a class of drug which use to reduce blood sugar.5 molecules by 5 companies are operating in this market (vildagliptin,sitagliptin,linagliptin,saxagliptin,teneligliptin). A doctor considers 6 below mentioned factors while prescribing a DPP4i  Cardiac Safety  Strong HbA1c Reduction  Renal Safety  Price  Weight reduction  Low risk of Hypoglycemia

By doing factor analysis Novartis wants to determine which 2-3 factors are mainly doctors are seeking while prescribing a DPP4i. Finding 2-3 factor Novartis wants to prepare Strategy to promote and position GALVUS (brand of Vildagliptin)& also build the communication which MR should communicate to the doctors to build the brand GALVUS over other Competitor .

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What is Research ??

Research is a systematic inquiry to describe, explain, predict and control the observed phenomenon. Research involves inductive and deductive methods (Babbie, 1998). Inductive methods analyze the observed phenomenon and identify the general principles, structures, or processes underlying the phenomenon observed; deductive methods verify the hypothesized principles through observations. The purposes are different: one is to develop explanations, and the other is to test the validity of the explanations.

One thing that we have to pay attention to research is that the heart of the research is not on statistics, but the thinking behind the research. How we really want to find out, how we build arguments about ideas and concepts, and what evidence that we can support to persuade people to accept our arguments. Gall, Borg and Gall (1996) proposed four types of knowledge that research contributed to education as follows: 1. Description: Results of research can describe natural or social phenomenon, such as its form, structure, activity, change over time, relationship to other phenomena. The descriptive function of research relies on instrumentation for measurement and observations. The descriptive research results in our understanding of what happened. It sometimes produces statistical information about aspects of education. 2. Prediction: Prediction research is intended to predict a phenomenon that will occur at time Y from information at an earlier time X. In educational research, researchers have been engaged in: Sourav Jana-107/MBA/162008

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o

Acquiring knowledge about factors that predict students' success in school and in the world of work

o

Identifying students who are likely to be unsuccessful so that prevention programs can be instituted.

3. Improvement: This type of research is mainly concerned with the effectiveness of intervention. The research approach include experimental design and evaluation research. 4. Explanation: This type research subsumes the other three: if the researchers are able to explain an educational phenomenon, it means that they can describe, can predict its consequences, and know how to intervene to change those consequences. What are the purposes of research? Patton (1990) pointed out the importance of identifying the purpose in a research process. He classified four types of research based on different purposes: 1. Basic Research: The purpose of this research is to understand and explain, i.e. the research is interested in formulating and testing theoretical construct and propositions that ideally generalize across time and space. This type of research takes the form of a theory that explains the phenomenon under investigation to give its contribution to knowledge. This research is more descriptive in nature exploring what, why and how questions. 2. Applied Research: The purpose of this research is to help people understand the nature of human problems so that human beings can more effectively control their environment. In other words, this type of Sourav Jana-107/MBA/162008

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research pursues potential solutions to human and societal problems. This research is more prescriptive in nature, focusing on how questions. 3. Evaluation Research (summative and formative): Evaluation research studies the processes and outcomes aimed at attempted solution. The purpose of formative research is to improve human intervention within specific conditions, such as activities, time, and groups of people; the purpose of summative evaluation is to judge the effectiveness of a program, policy, or product. 4. Action Research: Action research aims at solving specific problems within a program, organization, or community. Patton (1990) described that design and data collection in action research tend to be more informal, and the people in the situation are directly involved in gathering information and studying themselves. What is the research process? Gall, Borg, and Gall (1996) described the following stages of conducting a research study: 1. Identify a significant research problem: in this stage, find out the research questions that are significant and feasible to study. 2. Prepare a research proposal: a research proposal usually consists of the sections including introductory, literature review, research design, research method, data analysis and protection of human subject section, and timeline. 3. Conduct a pilot study: the purpose is to develop and try out datacollection methods and other procedures. 4. Conduct a main study 5. Prepare a report Sourav Jana-107/MBA/162008

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FACTOR ANALYSIS Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. This technique extracts maximum common variance from all variables and puts them into a common score. As an index of all variables, we can use this score for further analysis. Factor analysis is part of general linear model (GLM) and this method also assumes several assumptions: there is linear relationship, there is no multicollinearity, it includes relevant variables into analysis, and there is true correlation between variables and factors. Several methods are available, but principle component analysis is used most commonly.

Types of factoring: There are different types of methods used to extract the factor from the data set: 1. Principal component analysis: This is the most common method used by researchers. PCA starts extracting the maximum variance and puts them into the first factor. After that, it removes that variance explained by the first factors and then starts extracting maximum variance for the second factor. This process goes to the last factor. 2. Common factor analysis: The second most preferred method by researchers, it extracts the common variance and puts them into factors. This method does not include the unique variance of all variables. This method is used in SEM.

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3. Image factoring: This method is based on correlation matrix. OLS Regression method is used to predict the factor in image factoring. 4. Maximum likelihood method: This method also works on correlation metric but it uses maximum likelihood method to factor. 5. Other methods of factor analysis: Alfa factoring outweighs least squares. Weight square is another regression based method which is used for factoring. Factor loading: Factor loading is basically the correlation coefficient for the variable and factor. Factor loading shows the variance explained by the variable on that particular factor. In the SEM approach, as a rule of thumb, 0.7 or higher factor loading represents that the factor extracts sufficient variance from that variable. Eigenvalues: Eigenvalues is also called characteristic roots. Eigenvalues shows variance explained by that particular factor out of the total variance. From the commonality column, we can know how much variance is explained by the first factor out of the total variance. For example, if our first factor explains 68% variance out of the total, this means that 32% variance will be explained by the other factor. Factor score: The factor score is also called the component score. This score is of all row and columns, which can be used as an index of all variables and can be used for further analysis. We can standardize this score by multiplying a common term. With this factor score, whatever analysis we will do, we will assume that all variables will behave as factor scores and will move. Criteria for determining the number of factors: According to the Kaiser Criterion, Eigenvalues is a good criteria for determining a factor. If Eigenvalues is greater than one, we should consider that a factor and if Eigenvalues is less Sourav Jana-107/MBA/162008

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than one, then we should not consider that a factor. According to the variance extraction rule, it should be more than 0.7. If variance is less than 0.7, then we should not consider that a factor. Rotation method: Rotation method makes it more reliable to understand the output. Eigenvalues do not affect the rotation method, but the rotation method affects the Eigenvalues or percentage of variance extracted. There are a number of rotation methods available: (1) No rotation method, (2) Varimax rotation method, (3) Quartimax rotation method, (4) Direct oblimin rotation method, and (5) Promax rotation method. Each of these can be easily selected in SPSS, and we can compare our variance explained by those particular methods. Assumptions: 1. No outlier: Assume that there are no outliers in data. 2. Adequate sample size: The case must be greater than the factor. 3. No perfect multicollinearity: Factor analysis is an interdependency technique. There should not be perfect multicollinearity between the variables. 4. Homoscedasticity: Since factor analysis is a linear function of measured variables, it does not require homoscedasticity between the variables. 5. Linearity: Factor analysis is also based on linearity assumption. Non-linear variables can also be used. After transfer, however, it changes into linear variable. 6. Interval Data: Interval data are assumed. Key concepts and terms: Exploratory factor analysis: Assumes that any indicator or variable may be associated with any factor. This is the most common factor analysis used by researchers and it is not based on any prior theory. Sourav Jana-107/MBA/162008

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Confirmatory factor analysis (CFA): Used to determine the factor and factor loading of measured variables, and to confirm what is expected on the basic or pre-established theory. CFA assumes that each factor is associated with a specified subset of measured variables. It commonly uses two approaches: 1. The traditional method: Traditional factor method is based on principle factor analysis method rather than common factor analysis. Traditional method allows the researcher to know more about insight factor loading. 2. The SEM approach: CFA is an alternative approach of factor analysis which can be done in SEM. In SEM, we will remove all straight arrows from the latent variable, and add only that arrow which has to observe the variable representing the covariance between every pair of latents. We will also leave the straight arrows error free and disturbance terms to their respective variables. If standardized error term in SEM is less than the absolute value two, then it is assumed good for that factor, and if it is more than two, it means that there is still some unexplained variance which can be explained by factor. Chi-square and a number of other goodness-of-fit indexes are used to test how well the model fits. Collection of Data : Field Work : All Data are primary Data.Data are collected by doing fieldwork .novartis associates are given the below questioners and ask them to filled that by the doctors while they are visiting them. Duration : 5 working day (12th February to 16th February 2018) Location : Kolkata has been divided in to 3 parts (South,Central,North) Sourav Jana-107/MBA/162008

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Field worker : 6 Novartis Associates collected the data 2 associates in each part of Kolkata Data collection : All 6 associates are trained about the objective and motive of the research before filed work and ask them to collect 10 data each from most DPP4i prescriber of their territory

METHODOLOGY : In

order to determine which 2-3 factors are mainly doctors are seeking while

prescribing a DPP4i. Questionnaire has been distributed to doctors in Kolkata Sampling process is probability sampling Step 1 : Clustering the entire Kolkata into 3 parts (north,central,south) Step 2: select the DPP4i prescriber from each cluster (stratified ) Step 3 : randomly select 20 sample from each cluster’s each strata

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Sampling has been done with MULTISTAGE Sampling process. In the questionnaire all the response are collected all the Data are in Interval Scale with all the collected data Factor Analysis has been done by using SPSS

RUNNING THE ANALYSIS Access the main dialog box (Figure 1) by using the Analyze DataReduction Factor … menu path. Simply select the variables you want to include in the analysis (remember to exclude any variables that were identified as problematic during the data screening) and transferthemtotheboxlabelledVariablesbyclickingon

.

There are several options available, the first of which can be accessed by clicking on

to access the dialog box in Figure 2. The Coefficients option

produces the R-matrix, and the Significance levels option will produce a matrix indicating the significance value of each correlationintheR-

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matrix.YoucanalsoaskfortheDeterminantofthismatrixandthisoption is vital for testing for multicollinearity or singularity. The determinant of the R-matrix should be greater than 0.00001; if it is less than this value then look through the correlation matrix for variables that correlate very highly (R > .8) and consider eliminating one of the variables (or more depending on the extent of the problem) before proceeding. The choice of which of the two variables to eliminate will be fairly arbitrary and finding multicollinearity in the data should raise questions about the choice of items within your questionnaire.KMO and Bartlett’s test of sphericityproduces the KaiserMeyer-Olkin measure of sampling adequacy and Bartlett’s test. The value of KMO should be greater than 0.5 if the sample is adequate. Factor Extraction on SPSS Click on

to access the extraction dialog box . There are several ways

to conduct factor analysis and the choice of method depends on many things (see Field, 2005). For our purposes we will use principal component analysis, which strictly speaking isn’t factor Sourav Jana-107/MBA/162008

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analysis;however,thetwoproceduresoftenyieldsimilarresults(seeField,2005, 15.3.3). TheDisplayboxhastwooptions:todisplaytheUnrotatedfactorsolutionandaScr

eeplot.The screeplotwasdescribedearlierandisausefulwayofestablishinghowmanyfacto rsshouldbe retainedinananalysis.Theunrotatedfactorsolutionisusefulinassessingtheimp rovementof interpretation due to rotation. If the rotated solution is little better than the unrotated solution thenitispossiblethataninappropriate(orlessoptimal)rotationmethodhasbeen use The Extract box provides options pertaining to the retention of factors. You have the choice of eitherselectingfactorswitheigenvaluesgreaterthanauserspecifiedvalueorretainingafixed number of factors. For the Eigenvalues over option the default is Kaiser’s recommendation of eigenvalues over 1. It is probably best to run a primary analysis with the Eigenvalues over 1 option selected, select a scree plot, and compare the results. If looking at the scree plot and the eigenvalues over 1 lead you to retain the same number of factors then continue with the analysis and be happy. If the two criteria give different results then examine the communalities and decide for Sourav Jana-107/MBA/162008

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yourself which of the two criteria to believe. If you decide to use the scree plot then you may want to redo the analysis specifying the number of factors to extract. The number of factors to be extracted can be specified by selecting Number of factors and then typing the appropriate number in the space provided . Rotation The interpretability of factors can be improved through rotation. Rotation maximizes the loading of each variable on one of the extracted factors whilst minimizing the loading on all other factors. Rotation works through changing the absolute values of the variables whilst keeping their differential values constant. Click on to access the dialog box in Figure 4. Varimax,quartimaxandequamaxareorthogonalrotationswhereasdirectoblim inandpromax areobliquerotations.Theexactchoiceofrotationdependslargelyonwhether or not you think that the underlying factors should be related. If you expect the factors to be independent then you should choose one of the orthogonalrotations . If, however, there are theoretical grounds for supposing that your factors might correlate then directobliminshouldbeselected.Forthisexample,chooseanorthogonalrotatio n. The dialog box also has options for displaying the Rotated solution. The rotated solution is displayed by default and is essential for interpreting the final rotated analysis

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Scores The factor scoresdialog box can be accessed byclicking in the main dialog box. This option allows you to save factor scores for each subject in the

data editor. SPSS creates anew column for each factor extracted and then places the factor score for each subject within that column. These scores can then be used for further analysis, or simply to identify groups of subjects who score highly on particular factors. There are three methods of obtaining these scores,allofwhichweredescribedinsections15.2.3.and15.5.3.ofField(2005).

Options Click on

in the main dialog box. By default SPSS will list variables in the

order in which they are entered into the data editor. Although this format is often convenient, when interpreting factors it can be useful to list Sourav Jana-107/MBA/162008

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variables by size. By selecting Sorted by size, SPSS will order the variables by their factor loadings. There is also the option to Suppress absolute values less than a specified value (by default 0.1). This option ensures that factor

for assisting in interpretation; however, it can be helpful to increase the default value of 0.1to either 0.4 or a value reflecting the expected value of a significant factor loading given the sample size (see Field section 15.3.6.2.). For this example set the value at0.4.

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CHAPTER 3: DATA ANALYSIS AND INTERPRETATION

Data Collected Cardiac safety

HbA1c reduction 7 1 6 4 1 6 5 6 3 2 6 2 7 4 1 6 5 7 2 3 1 5 2 4 6 3 4 3 4 2 6 5 5 3 4 2 1 3

Renal safety 3 3 2 5 2 3 3 4 4 6 4 3 2 6 3 4 3 3 4 5 3 4 2 6 5 5 4 7 6 3 2 7 3 2 2 6 3 5

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6 2 7 4 2 6 6 7 2 2 7 1 6 4 2 6 6 7 3 3 2 5 1 4 4 4 7 2 3 2 7 5 4 2 3 2 3 1

Pricing

Weight reduction 4 4 4 6 3 4 3 4 3 6 3 4 4 5 2 3 3 4 3 6 3 4 5 6 2 6 2 6 7 4 6 6 5 5 2 4 6 4

2 5 1 2 6 2 4 1 6 7 2 5 1 3 6 3 3 1 6 4 5 2 4 4 1 4 2 4 2 7 5 6 6 1 2 3 2 2

Low Risk of Hypoglycemia 4 4 3 5 2 4 3 4 3 6 3 4 3 6 4 4 4 4 3 6 3 4 4 7 4 7 5 3 7 2 3 6 6 3 1 7 5 5 Page 30

7 6 6 3 5 6 3 2 3 6 7 5 2 2 6 7 3 5 4 6 2 3

3 3 6 2 7 3 2 7 2 4 2 6 3 6 5 6 5 3 5 4 6 5

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6 3 2 2 6 5 4 5 2 5 6 6 3 3 7 5 1 6 4 6 2 3

3 4 6 7 2 5 3 1 7 4 2 3 2 2 4 4 4 3 6 3 6 6

5 4 4 6 2 7 2 4 2 7 5 4 1 1 5 6 2 3 2 3 7 4

2 6 4 1 6 2 6 5 4 3 2 5 2 5 7 5 4 4 5 4 6 6

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SPSS OUTPUT FACTOR /VARIABLES Cardiacsafety HbA1creduction Renalsafety Pricing WeightreductionLowhypo /MISSING LISTWISE /ANALYSIS Cardiacsafety HbA1creduction Renalsafety Pricing WeightreductionLowhypo /PRINT INITIAL CORRELATION DET KMO EXTRACTION ROTATION /FORMAT SORT /PLOT EIGEN /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX /METHOD=CORRELATION.

Factor Analysis Notes Output Created

03-MAR-2018 11:30:37

Comments

Input

Active Dataset

DataSet0

Filter

<none>

Weight

<none>

Split File

<none>

N of Rows in Working Data File

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MISSING=EXCLUDE: UserDefinition of Missing

defined missing values are treated as missing.

Missing Value Handling

LISTWISE: Statistics are Cases Used

based on cases with no missing values for any variable used. FACTOR /VARIABLES Cardiacsafety HbA1creduction Renalsafety Pricing WeightreductionLowhypo /MISSING LISTWISE /ANALYSIS Cardiacsafety HbA1creduction Renalsafety Pricing WeightreductionLowhypo /PRINT INITIAL CORRELATION DET KMO

Syntax

EXTRACTION ROTATION /FORMAT SORT /PLOT EIGEN /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX

/METHOD=CORRELATION.

Resources

Processor Time

00:00:04.39

Elapsed Time

00:00:05.99

Maximum Memory Required

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5544 (5.414K) bytes

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[DataSet0] Correlation Matrix

Cardiacsafety

a

HbA1creduction

Renalsafety

Pricing

Cardiacsafety

1.000

-.064

.771

-.081

HbA1creduction

-.064

1.000

-.062

.140

.771

-.062

1.000

-.291

Pricing

-.081

.140

-.291

1.000

Weightreduction

-.193

.061

-.247

.163

Lowhypo

-.089

.619

-.014

.300

Renalsafety Correlation

Correlation Matrix

a

Weightreduction Cardiacsafety

Lowhypo -.193

-.089

.061

.619

-.247

-.014

.163

.300

Weightreduction

1.000

-.077

Lowhypo

-.077

1.000

HbA1creduction Renalsafety Correlation Pricing

a. Determinant = .163

SPSS Output 1: shows an abridged version of the R-matrix. The top half of this table contains the Pearson correlation coefficient between all pairs of questions whereas the bottom half contains the one-tailed significance of these coefficients. We can use this correlation matrix to check the pattern of Sourav Jana-107/MBA/162008

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relationships. First, scan the significance values and look for any variable for which the majority of values are greater than 0.05. Then scan the correlation coefficients themselves and look for any greater than 0.9. If any are found then you should be aware that a problem could arise because of singularity in the data: check the determinant of the correlation matrix and, if necessary, eliminate one of the two variables causing the problem. The determinant is listed at the bottom of the matrix . For these data its value is 0.163 (which is which is greater than the necessary value of 0.00001. Therefore, multicollinearity is not a problem for these data. To sum up, all questions in the SAQ correlate fairly well and none of the correlation coefficients are particularly large; therefore, there is no need to consider eliminating any questions at this stage. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Approx. Chi-Square Bartlett's Test of Sphericity

Df Sig.

.748 101.760 15 .000

SPSS Output 2: shows several very important parts of the output: the Kaiser-Meyer-Olkin measureofsamplingadequacyandBartlett'stestofsphericity.TheKMOstatisticvaries between 0 and 1. A value indicates that the sum of partial correlations is large relative to the sum of correlations, indicating diffusion in the pattern of correlations (hence, factor analysis is likely to be inappropriate). A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis should yield distinct and reliable factors. Kaiser (1974) recommends accepting values greater than 0.5 as acceptable (values below this should lead you to either collect more data or rethink which variables to include). Furthermore, values between 0.5 and 0.7 aremediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great and valuesabove 0.9 are superb For these data the value is 0.748, which falls into the range of being goog: so,

we should be confident that factor analysis is appropriate for thesedata. Sourav Jana-107/MBA/162008

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Bartlett's measure tests the null hypothesis that the original correlation matrix is an identity matrix.ForfactoranalysistoworkweneedsomerelationshipsbetweenvariablesandiftheR- matrix were an identity matrix then all correlation coefficients would be zero. Therefore, we want this test to be significant (i.e. have a significance value less than 0.05). A significant test tells us that the Rmatrix is not an identity matrix; therefore, there are some relationships between the variables we hope to include in the analysis. For these data, Bartlett's test is highly significant (p < 0.001), and therefore factor analysis isappropriate.

Communalities Initial

Extraction

Cardiacsafety

1.000

.752

HbA1creduction

1.000

.714

Renalsafety

1.000

.854

Pricing

1.000

.318

Weightreduction

1.000

.233

Lowhypo

1.000

.819

Extraction Method: Principal Component Analysis.

Sourav Jana-107/MBA/162008

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Component Matrix

a

Component 1

2

Renalsafety

-.814

.437

Cardiacsafety

-.762

.415

Pricing

.523

.211

Weightreduction

.397

-.275

Lowhypo

.448

.786

HbA1creduction

.443

.720

Extraction Method: Principal Component Analysis.

a

a. 2 components extracted.

This output also shows the component matrix before rotation. This matrix contains the loadings of each variable onto each factor. By default SPSS displays all loadings; however, we requested that all loadings less than 0.7 be suppressed in the output and so there are blank spaces for many of the loadings. This matrix is not particularly important for interpretation. At this stage SPSS has extracted two factors. Factor analysis is an exploratory tool and so it should be used to guide the researcher to make various decisions: you shouldn't leave the computer to

Sourav Jana-107/MBA/162008

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make them. One important decision is the number of factors to extract. By Kaiser's criterion we should extract two factors and this is what SPSS has done. However, this criterion is accurate when there are less than 30 variables and communalities after extraction are greater than 0.7 or when the sample size exceeds 250 and the average communality is greater than 0.6. The communalities are shown

in

SPSS

Output

4,

and

two

exceed

0.7.,

because

the

research

Kaiser'scriteriongivesrecommendationsformuchsmallersamples.Wecanalsousethescreeplot,

into which

we asked SPSS to produce. The scree plot is shown below with a thunderbolt indicating the point of inflexion on the curve. This curve is difficult to interpret because the curve begins to tail off after three factors, but there is another drop after four factors before a stable plateau is reached. Therefore, we could probably justify retaining either two or four factors. Given the large sample, it is probably safe to assume Kaiser's criterion; however, you could rerun the analysis specifying that SPSS extract only two factors & compare the results.

Sourav Jana-107/MBA/162008

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.

Total Variance Explained Component

Initial Eigenvalues

Extraction Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

1

2.071

34.512

34.512

2.071

34.512

2

1.619

26.984

61.496

1.619

26.984

3

.954

15.906

77.403

4

.825

13.755

91.158

5

.357

5.957

97.115

6

.173

2.885

100.000

Sourav Jana-107/MBA/162008

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Total Variance Explained Component

Extraction Sums of

Rotation Sums of Squared Loadings

Squared Loadings Cumulative %

Total

% of Variance

Cumulative %

1

34.512

1.936

32.271

32.271

2

61.496

1.754

29.225

61.496

3 4 5 6

Extraction Method: Principal Component Analysis.

Rotated Component Matrix

a

Component 1

2

Renalsafety

.921

-.078

Cardiacsafety

.865

-.068

-.783

-.014

Lowhypo

.053

.903

HbA1creduction

.022

.845

-.323

.762

Weightreduction

Pricing

Sourav Jana-107/MBA/162008

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Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a

a. Rotation converged in 3 iterations.

Component Transformation Matrix component

1

2

1

-.838

.546

2

.546

.838

Sourav Jana-107/MBA/162008

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Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Interpret Factors : In the rotated factor matrix factor1has high coefficients for Renal safety(0.921),CardiacSafety(0.865) &negative coefficient for Weight Reduction(-0.783),therefore factor may be labelled as Safety Factors. Note : negative coefficient for variable leads to a positive interpretation that weight reduction is important

Factor2is highly related with LowHypo (0.903),HbA1c reduction (0.845),Pricing (0.762),therefore factor may be labell Efficacy Factors.

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CHAPTER 4: Result & Recommendation:

Result of the analysis is there are two major factors Safety Factor& Efficacy Factor doctors are considering while prescribing a DPP4i(Gliptin). These two factors consists of 3 attributes each which are correlated with each other . Strategy of marketing should be based on these two factors  1 executive should meet the doctors twice in a month and in 1st visit should communicate about safety factor & in 2nd visit should communicate about efficacy factor .  LBL(promotional papers) should be categorise in two category(safety & efficacy)  Ipad commutation model should have two parts according to visit no.(1 or 2) of the month executive will open that part from Ipad to communicate.  In one quarter in a territory minimum 2 doctors meet should arrange & the topic should be 1 attribute from safety factor (cardiac safety,RenalSafety,Weight Reduction) other attribute from efficacy factor(Low hypo,HbA1c Reduction)  Pricing is correlated with efficacy factor.As Galvus has strong Data, Scientific studies to prove the Strong efficacy over other brands so the pricing reduction strategy should be avoided

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 Galvus have a strong Data, Scientific studies to prove the Safety of Galvus Executive should communicate that also.  Galvus is considered as more efficacious than safety parameter,but out if the analysis we found both safety and efficacy is important .only taking leverage of efficacy will not help to win the battle against competitor Novartis have to proactively communicate the Galvus Have also Safety Data & Studies

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CHAPTER 5 : ANNEXURE QUESTIONNAIRE NAME : Speciality : Year of Practise:

I only prescribe DPP4i which have Cardiac Safety trials? 1□

2□

3□

4□

5□

6□

7□

It is important DDP4 offers Strong HbA1C Reduction 1□

2□

3□

4□

5□

6□

7□

I prefer DPP4i which have Renal Safety trials? 1□

2□

3□

4□

5□

6□

7□

Price Does matter while prescribing any DPP4i 1□

2□

3□

4□

5□

6□

7□

Weight reduction plays an important role for choosing DPP4i 1□

2□

3□

4□

5□

6□

7□

Approval from ADA does matter while prescribing DPP4i 1□

2□

3□

4□

5□

6□

7□

DPPi should have Low risk of Hypoglycaemia 1□

2□

3□

4□

Sourav Jana-107/MBA/162008

5□

6□

7□

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