Marketing Research quest and ans

NMITD

MARKETING RESEARCH NOTES – MMS – III SEM

Topics covered in the notes• Applications of Marketing Research• Structure of Market Survey or Steps in Marketing Research

Process• Multivariate Techniques

Factor Analysis Cluster Analysis Discriminant Analysis Conjoint Analysis

• Consumer Research

• Format of Report Preparation & Criteria of Report Writing

*Prepared by Dr. M. GOWRI SHANKAR*

Dr. M. Gowri Shankar – Marketing Research Applications (1 | P a g e )

CONCEPT OF MARKETING RESEARCH

• Research is the search for knowledge

• Research is the search for new facts

• Research is the systematic effort to gain knowledge

• Research discover answer to questions through the application of scientific procedures.

• Research is systematic and objective investigation of a subject or problem

• Research is the process ( Quantitative or Qualitative) of systematic gathering, recording, analysing and evaluating the data related to the problems faced by industry/organization/markets/society

• Research helps the management in decision-making related to the various problems faced by industry, organization, markets and society.

• Research helps to provide the information about the stakeholders ( Customers, employees, suppliers) to the management.

• Research helps to reduce the uncertainty existing in the organization.

• Research may be either Quantitative or Qualitative

QUANTITATIVE Vs QUALITATIVE RESEARCH

• Quantitative research is a structured research methodology that seeks to quantify data and typically involves statistical analysis. Quantitative research is called as Problem solving research

• Quantitative Research – It is formally structured. The process of data collection involves extensive use of statistical procedures.

• Quantitative research determine relationships and differences among large samples of target population

• Quantitative research is widely used for the data which requires statistical analysis to measure the various parameters used for the study

• Quantitative research recommends a final course of action.

• Quantitative research helps the top management to derive conclusions for finding solutions to various complex problems

• Ex: data collection methods like survey, observation, experimentation etc were used


Qualitative research is a unstructured exploratory research methodology based on small samples that provides insights & understanding or problem settings. Qualitative research is called as problem identification research

• Qualitative research – it is highly unstructured. It is less formal in nature.

• Qualitative research is applied to understand underlying reasons and motivations

• Qualitative research is non statistical. It uses small number of non representative cases

• Qualitative research is used for formulating the problem or to develop an initial understanding of the problem

• Ex: data collection methods like focus group interview, in-depth interview, projective techniques etc were used

Population – It is the potential set of respondents in a geographical area. It refers not only to people but to all the items that have been chosen for the study.

Ex: proportion of consumers who are loyal to a particular brand of soft drinks

Census – The measurement or examination of every element in the population. Sample – A portion of the elements in a population chosen for direct examination or

measurement. A sub-set of the population under study.

Parameter – It is a characteristic of the target population. Values that describe the characteristics of a population

Ex: Suppose the mean height in inches of all the tenth graders in India is 60 inches. 60 inches is a characteristic of the population. “All tenth graders” can be called as population parameter

Statistic – A statistic is a characteristic or measure of the sample. The sample statistic is used as an estimate of the population parameter

Degrees of freedom – it refers to the amount of information available to estimate population parameters from sample statistics. It refers to the number of values in a sample we can specify freely once we know something about the sample. For ex: there are 7 elements in a sample and the mean of these elements is 16. then it can have a+b+c+d+e+f+g/7 = 16, the degrees of freedom or the number of variables we specify will be n – 1 = 7 – 1 = 6. It is the total number of observations less the number of independent restrictions imposed on the observations. It is the number of independent variates which form the statistic like chi-square, Kolmogorov D etc. It is (n-1) for one dimensional table and (r-1)(c-1) for two dimensional table, where n is the number of dimensions or variables or attributes, r is number of rows & c is the number of columns.


Hypothesis – it represents a researcher’s expectation what is true of the population parameter

Null Hypothesis - H0 – A statement in which no difference or effect is expected. If the null hypothesis is not rejected, no changes will be made. It is a statement of no association

In formulating the ‘null’, usually the words “no”, “not”, or “same” or “independent” will be the part of the stated hypothesis

Ex: we might wish to see whether the mean age of a college was 21 years. The null hypothesis is “The mean age of the college class is not different from 21 years. This is same as the mean µ age of the class is equal to 21 years. The null hypothesis would be H0 : µ = 21

Ex: To know the proportion of consumers who purchased a product before and after an advertising campaign. The null hypothesis would be “There is no difference in the proportion of consumers who purchased cola before and after the magazine advertising campaign. This is same as the two proportions are equal and it is written as H0:p = P1

Ex: To investigate the seat belt usage between standard size car owners and subcompact car owners. The null hypothesis might be written as, “Standard size car owners and subcompact car have the same seat belt usage rate”.

Alternate Hypothesis – H1 – A statement that some difference or effect is expected. Accepting the alternative hypothesis will lead to changes in opinions or actions. It is a statement of association.

Ex: The formulation of an alternative hypothesis depends on the nature of the situation at hand and may be directional or non directional. If the situation does not call for the direction of the difference, the alternative hypothesis is considered to be two-tailed test. For the mean age example, the alternative hypothesis would be H1: µ≠21. If the alternative hypothesis states a direction, the test is referred as a one-tailed test. Suppose that there is interest in whether the mean age of the class is greater than 21 years, the alternative hypothesis would be written H1 : µ>21. To determine whether the mean age of the class is less than 21 years, the alternative hypothesis would be written as H1: µ <21.

Significance level – It is a level of risk or probability or risk a researcher takes when rejecting the null hypothesis when it is true.

There is always a probabilistic, component involved in the accept-reject decision in testing hypothesis. The criterion that is used for accepting or rejecting a null hypothesis is called the significance level or p-value. This represents the chance that we may be


making a mistake of a certain type. It can also be set as ( 100 minus confidence level desired in the test, divided by 100). For example, if we desire that the confidence level for the test should be 95, then (100 – 95) divided by 100. For example , if we desire that the confidence level for the test should be 95, then (100 – 95)/100 or .05 becomes the significance level.

The p-value represents the probability of concluding(incorrectly) that there is a difference in your samples when no true difference

Confidence interval – it is the range into which the true population parameter will fall, assuming a given level of confidence.

Confidence Level – it is the probability that a confidence interval will include the population parameter


Definition of Marketing Research

Marketing Research is the systematic gathering, recording, analyzing and evaluating the data related to the marketing problems

Importance/applications of MR

The applications of MR can be classified as Problem Identification Research, Problem Solving Research - General & Critical.

Problem Identification Research – is undertaken to help identify problems that are not apparent on the surface and yet exist or are likely to arise in the future.

Types of Problem Identification Research

a. Marketing Potential Research

b. Market Share Research

c. Image Research – It is the attitudes, beliefs, intentions, knowledge and understanding of the consumers towards product, brand and company called as product image, brand image and company image

d. Forecasting Research – involves both quantitative and qualitative techniques of forecasting

Ex of quantitative forecasting techniques include trend analysis, exponential smoothing, moving average method, simple regression, multiple regression. Qualitative forecasting techniques include delphi method, salesforce composite method, consumer panels etc

e. Sales Analysis Research – called as Market analysis. The components of Sales Analysis Research is classified as follows

Components of Sales Analysis Research

Managing the salesforce – recruiting, selecting, training, directing, compensating and evaluating

Defining the sales territories

Allocating the funds for advertising and promotional efforts

Setting the sales quotas

Designing the distribution channels

Deciding the location and size of the plants, new sales offices and warehouses


Determining the strategy for market entry

Problem Solving Research – is undertaken to arrive at a solution. The findings of problem-solving research are used in making decisions that will solve specific marketing problems. The problem solving research is classified based on general & critical.

The applications related to general problem solving research is classified as follows:

Problem Solving Research – General applications

a. Segmentation Research – determine basis of segmentation, establish market potential for various segments, select target markets, create lifestyle profiles and product image characteristics.

b. Product Research – determining product mix, new product development, product innovation, adoption & diffusion, product life cycle, test marketing, brand positioning & repositioning. It creates product equity

c. Pricing Research – determining price mix, importance of price in brand selection, price elasticity of demand and the impact on sales and profits of various levels of price changes. It creates price equity

d. Distribution Research – determining distribution mix, types of distribution, location and design of distribution centres, dealer supply and storage requirements, handling and packing of merchandise, cost analysis of transportation methods, intensity & coverage, channel margin, channel conflicts

Distribution research creates distribution equity through a stronger network of channels which creates value for both the company’s products and the consumers. It helps in designing channels

e. Promotional Research – determining promotional mix, setting optimal promotional budget, measuring brand equity, sales promotion relationship, copy decisions, media research – involves research into viewership of various media, evaluation of advertising effectiveness called as brand tracking

Promotional research creates brand equity through the aggressive promotional programs which enhances the company to build the long term relationships with the customer and brand image.

f. Consumer Research – determining psychological determinants, buying process, buying roles

g. Motivational Research – it is used in marketing to determine why consumers buy one brand or type of product instead of competing alternatives. It helps in designing the product, its package, pricing and advertising. The nature of the motivational research can be classified as :


The consumer does not know the “why” of purchase, the consumers will not tell about their purchase, The consumers may put forward illogical reasons for their purchase

The applications related to Critical Problem Solving Research are classified as :

• Factor analysis, • Cluster Analysis• Conjoint Analysis • Discriminant Analysis

Problem Identification Research and Problem Solving Research go hand in hand, and any marketing research project may combine both types of research


Structure of Survey or Steps in Market Research Process

1) Problem Definition ( Identifying the underlying causes of problem)

2) Development of an approach to problem

3) Research design formulation – it involves the following

a) type of design – exploratory or conclusive

b) Methods of collecting data – primary and secondary

c) Scaling procedures – Measurement Scales, Rating Scales

d) Questionnaire design

e) Sampling Design – Population, Census, Sample, Sample size, Sampling Unit, Sampling Frame, Sampling Method

f) Statistical methods used

4) Field work

5) Data Analysis & Interpretation

6) Report Preparation & Presentation

The four P’s of the Marketing Research Process can be summarized as Problem, Population, Procedures and Presentation

1) Problem Definition ( Identifying the underlying causes of problem)

It involves in identifying the underlying causes of problem.

For ex: the management wants to launch a new product then the management has to consider the following aspects – what are the consumer preferences?, what is the price to be quoted, what is the effectiveness of the advertising?

2) Development of an approach to problem

The approach to the problem are as follows

a. Objective or theoretical – based on secondary data

b. Analytical – identifying set of variables and their relationships (dependent, independent and extraneous)


Independent Variables – are variables that the researcher can control and wishes to manipulate. Independent variables are also called as Predictors which create cause phenomenon

Ex of independent variables – level of advertising, price level, package design, display location, compensation method, investment, interest rates etc

Dependent variables – are the variables that measure the effect of the independent variables on the test units. The test units may include consumers, stores or geographical units.

Dependent variables are called as Criterion which creates effect phenomenon.

Ex of dependent variables – profits, market share, customer satisfaction, sales, performance etc

Extraneous variable – are variables other than the independent variables that affect the response of the test unit. Extraneous variables are those they may have some effect on dependent variable but yet are not independent variables.

Ex of extraneous variables – Store size, geographical location, traffic flow count etc

c. Graphical – provides a visual picture of relationship between variables

d. Research questions like ( ex: whether the customer holds the credit card)

e. Setting hypothesis – tentative statement about relationships between two or more variables

3) Research design formulation – Research design is the outline for the entire research process. It involves the following

a) types of Research design –

i) Exploratory and ii) Conclusive – Descriptive & Causal

b) Methods of collecting data – Primary and Secondary( Library Research)

c) Scaling Procedures

d) Questionnaire design

e) Sampling design

f) Statistical Methods used


Types of Research design

Exploratory research – identifies problems, generate hypothesis and gain insights about particular subject, problem etc. It helps in defining the variables of the research(independent, dependent & extraneous)is commonly unstructured, less formal in nature and that is undertaken to gain background information about general nature of research problem.

Exploratory research is inexpensive and flexible in nature.

Exploratory research helps in initial understanding of the problem or it provides the basis to the researcher to proceed further into the research

Methods of conducting exploratory research – literature survey, expert survey, pilot survey, focus group interview, in-depth interview, projective techniques etc

Conclusive research design – it is designed to assist the decision-maker in determining, selecting and evaluating the final course of action in a given situation.

Types of Conclusive Research – Descriptive & Causal

Descriptive research – is commonly structured, quantitative & formal research. It describes attitudes, perceptions, characteristics, activities and situations of certain groups like employees, customers, suppliers etc. it is used in testing hypothesis. Descriptive research allows the researcher to have considerable background knowledge related to the problem or concern. Results obtained from descriptive studies are conclusive and the results can be used for decision making

Ex: identify the consumer’s buying behavior for that particular product, study the characteristics of consumers, analyse the market potential for a product etc

It is widely used in estimating market share, sales analysis, pricing, advertising, distribution, image studies etc

Types of Descriptive Research – Cross-sectional and Longitudinal

Cross-sectional Descriptive Research – It is a one shot research or one time study. In cross-sectional research, the information is collected from the sample of respondents only once. The study looks at what is occurring at one moment of time.

Ex: A study assessing the relationship between the emissions from chemical industry and resultant respiratory, health of the residents, found that emissions from chemical industry have adverse effects on the health of the residents.


Longitudinal Descriptive Research – It involves collecting the information from the respondents repeatedly over a period of time. This study is continuous, whereby the same respondents are questioned or observed at predetermined time intervals over a designated time frame. Their responses are collected and after an analysis, conclusions are drawn.

Ex: Procter & Gamble introduces a new pain reliever and contacts the same group of 500 users each month over a 6 month period to determine their level of satisfaction with the product and then analyses the obtained data.

Ex: Ex: A panel of dealers may be contacted by the company to know the type of consumers, purchasing choices, location of the store etc. The company contacts the dealers frequently to know about consumer preferences

Ex: A panel of executives may be contacted by the top management to assess the effectiveness of training programs

Data collection methods used in conducting descriptive research: Survey, observation & experimentation

Causal research – determines the cause and effect relationships of various variables involved in the research

Ex: Suppose the management of a company wants to know the extent to which advertising creates (or causes) revenue for a company, they can go for causal research. This information will enable them to decide how much money required to allocate towards in advertising the company.

Ex: causal relationship between advertising (Independent variable) and sales (dependent variable)

Ex: if the company wants to find out the impact of advertising on sales, the company assess what percentage of advertising increases the sales .

The study of dependent, independent and extraneous variables are the part of causal research

b) Methods of collecting data – Primary and Secondary- Library Research

c) Scaling procedures

A scale is a level of measurement. It is the assignment of number to objects (Ex: Consumers). It reflects the quantity of the attributes that the object possess characteristics(ex: preference to brands).

Types of Scaling procedures – Measurement scales, Rating scales


Types of measurement scales – Nominal, Ordinal, Interval and Ratio scales

Types of Rating scales – Likert scale, Semantic Differential scale and Stapel scale

Measurement Scales

Nominal scale – is one in which numbers are used as labels or tags to categorise various objects/persons

Ex: assigning 1 to male and 2 to female or numbers to cricket players.

Descriptive Statistics : Simple Percentage method, Mode etc

Inferential : Chi Square test, Binomial test etc

Ordinal scale – It is used to rank the attributes of the product like price, quality, consistency, durability etc by the respondent. It asks the respondents to rate career opportunities, brands as Excellent, good, or fair.

Descriptive Statistics : Percentile, Median etc

Inferential : Rank Order Correlation, Chi Square test, etc

Interval scale – to measure the attitude of respondents on a scale of 1 – 5 or 1 – 7 . It is used to rate satisfaction level like job satisfaction, customer satisfaction etc

Ex : 1= highly unfavourable, 2= unfavourable, 3= undecided 4=favourable, 5=strongly favourable

Descriptive Statistics : Average, Range, Mean, Standard deviation etc

Inferential : Z,t,F tests, Regression, Correlation, ANOVA etc

Ratio scale – combines all the properties of Nominal, Ordinal and Interval scales. It is used to measure length, height, weight, age, income etc. all arithmetic calculations are possible in this scale.

Descriptive Statistics : Average, Range, Mean, Standard deviation etc

Inferential : Z,t,F tests, Regression, Correlation, ANOVA etc

Itemized Rating scales – provides the respondents with a scale that has a number or brief description associated with each category. The respondents are required to select the specified category that best describes the object being rated.

The itemized rating scales are classified as Likert Scale, Semantic Differential Scale, Stapel Scale


Likert Scale – A measurement scale with five response categories( Strongly disagree to Strongly agree) which requires the respondents to indicate a degree of agreement or disagreement with each of the series of statements related to the stimulus objects.

Ex: Job Satisfaction is monetary

1 = strongly disagree, 2= disagree, 3 =neutral, 4 = agree, 5= strongly agree.

Semantic Differential Scale – A seven point rating scale with endpoints associated with bipolar labels that have semantic meaning

Ex: Powerful -------weak, Pleasant -------Unpleasant, Complex ----------Simple etc

Stapel Scale - A unipolar rating scale for measuring attitudes of a single adjective numbered from -5 to +5 without neutral zero.

Ex: the higher the number , the more accurately term describe the object (+5), the more inaccurate shows (-5)

e) Questionnaire design

Questionnaire design specifies the information and type of information required.

Ex of questionnaire design

Structured questions – questions that prespecify the set of response alternatives. They are closed end questions ( multiple choice, dichotomous (yes/no)

Unstructured questions – they are usually open ended questions that allow the respondents to answer in their own words.

f) Sampling plan – population, sample, sample size, sampling unit, sampling frame, sampling method

g) Statistical methods used

Simple Percentage Method, Correlation, Regression Analysis, Chi Square test,

t test, z test, F test , Analysis of Variance (ANOVA) etc

4) Field work or data collection

Field work involves the selection, training and supervision of persons who collect data. It involves evaluating field workers to provide them with feedback on their performance as well as to identify the better field workers and build a high quality field force.


5) Data Analysis & Interpretation

It can be done by simple tabulation and cross tabulation

Simple tabulation – it involves counting the number of responses in each category for a question and developing into a frequency table form. This can be used to compute percentages by dividing the responses with the sample size.

Cross tabulation – it is a result of counting simultaneously answers to two different questions on a questionnaire.

Types of Data Analysis

Univariate Analysis – Involving one variable at a time.

Ex: Simple Percentage Method, One variable- Chi-Square test etc

Bivariate Analysis : It involves two variables at a time.

Ex: Chi Square test (Cross Tabulation), Simple Regression(one dependent & one Independent variable), Correlation, One way Anova.

Multivariate Analysis – Involving more than two variables at a time

Ex: Multiple Regression, Two-way Anova, Manova(Multivariate analysis of variance), Factor Analysis, Cluster Analysis, Discriminant Analysis & Conjoint Analysis

Tests of Hypotheses

Parametric Tests – When the data is on continuous scale (interval and ratio scale) then parametric tests can be used

Ex : of Parametric test

t-test - for small samples (n<30)

Z-Test - for large samples ( n>30)

ANOVA – Analysis of Variance ( both one & two ways)

Non Parametric Tests – When the data is on categorical scale (Nominal & Ordinal) then Non parametric tests can be used

Ex : of Non Parametric test

Chi – Square test, Kolmogorov Smirnov D test, Wilcoxon Matched Pairs test etc


6) Report Preparation & Presentation

The presentation of the report may be in the form of either Oral or Written presentation. The report should emphasize on the following aspects

a) Summary of the results b) Nature of the study c) Sources of data d) analysis of data and presentation of findings e) Conclusions f) Bibliography g)Technical appendices h) Index


Sources of Data Collection – Primary & Secondary

Primary Research ( Primary data) - is originated by the researcher for specific purpose of addressing the problem in hand. It is originally collected afresh for the first time.

Classification of primary data

Experimental and Non Experimental data

Experimental data is classified into Laboratory and Field Studies

Experimental data – is collected when researcher controls and manipulates the elements of environment to measure the impact of each variable.

Ex : A group of respondents who were shown T.V commercials and were asked about their intentions to purchase the product advertised.

Experimental data is classified into Laboratory and Field Studies

Laboratory Studies – are carried in a highly controlled environment. Several variables are controlled and one variable of interest is manipulated in a particular situation.

Field studies – are carried in real world usually in the form of Test Marketing ( for testing new products on a sample basis

Non Experimental data is classified into Qualitative and Quantitative

Quantitative – Survey, Observation and Experimentation

Qualitative – Focus group Interview, In-depth interview and Projective Techniques

Quantitative Sources of Primary data – Survey, Observation and Experimentation.

Survey – involves a structured questionnaire given to respondents designed to elicit specific information. The information is obtained from the respondents through questionnaire and interview

In survey method, the information is obtained by questioning the respondents

Respondents are asked a variety of questions regarding their behavior, intentions, awareness, interest, lifestyle, motivation and their demographic characteristics

Types of Survey method – Telephone methods, Personal methods, Mail methods, Electronic methods etc

Telephone methods – involves a contacting a sample of respondents and asking them a series of questions over phone


Computer Assisted Telephone Interviewing ( CATI) – uses a computer questionnaire administered to respondents over telephone. It is the modern method of contacting the respondents.

Personal Methods – it involves the contacting the respondents personally or direct interview in their homes or when they visit the shop.

Ex of personal methods – Personal in home interview, Mall intercept interviewing, Computer Assisted Personal Interviewing (CAPI)

Personal – in – home interview – the respondents are interviewed in their homes. The researcher asks the questions and recording the responses.

Mall Intercept Personal Interview – respondents are intercepted while they are shopping. The respondents are interviewed in the shopping malls. This is very pbopular method used in retail outlets.

Computer Assisted Personal Interviewing ( CAPI) – the respondent is seated in front of computer terminal and answers questionnaire. CAPI used to collect data at shopping malls, conferences and trade shows

Mail methods – questionnaire is mailed to the potential respondents. It consists of outgoing envelope, cover letter, questionnaire, return envelope and possibly an incentive. There is no verbal interaction between the researcher and respondent.

Ex of mail methods – mail panel

Mail panel – A large & nationally representative sample of households who have agreed to periodically participate in product tests by mail.

Electronic methods – the interview is conducted through email and internet.

Ex: of electronic methods – E-mail interviews, Internet interviews ( in the form of web based languages like HTML,DHTML,ASP etc).

Schedule – is similar to questionnaire. The difference between questionnaire and schedule is that

In questionnaire – it is filled by the respondent

Schedule is a proforma containing a set of questions which are filled by the enumerator or researcher who is appointed for this purpose.

In schedule – the questionnaire is filled by the enumerator or researcher by asking the questions and recording the responses from the respondent.


Observation – involves recording the behavioral patterns of the people, events, objects in a systematic manner to obtain the information about phenomenon of interest.

Ex: Observing the group of people at the shopping mall while purchasing the goods

Observation may be direct or indirect

Direct observation – the respondents are aware that they are being observed.

Ex : observing the group of respondents at the fast food center.

Indirect observation – the respondents are unaware that they are being observed. The respondents are observed by some mechanical devices like camera, mirror etc

Experimentation – is to measure the effect of one/more variables by changing the level of some other variables. It is commonly used to infer causal relationships between the variables. It defines the cause and effect phenomenon. The variables like dependent, independent and extraneous etc are used in experimentation.

Qualitative Sources of Primary data– Focus group Interview, In-depth interview and Projective Techniques.

Focus group interview – conducted by a trained moderator in a natural manner with a group of respondents. A focus group is a sample of respondents from the specified target market. Focus group interview takes place in the form of free-flowing group discussion among various target groups. The moderator plays a key role to establish the rapport with the participants to keep the discussion moving forward and probe the respondents to elicit insights into the problem.

Applications of focus group interview

Understanding consumer perceptions, concerning a product category

Obtaining impression of new product concepts

Generating ideas about new and existing products

Developing creative concepts of advertising copy from the consumer

In depth interview – It is method of obtaining qualitative data. An unstructured, direct, personal interview in which a single respondent is probed by a skilled interviewer to knowing underlying motivations, attitudes and feelings on a topic. The interviewer encourages the respondent to talk freely


Applications of In depth interview

Discussion of confidential, sensitive or embarrassing topics or situations where the strong social norms exist

Detailed understanding the complicated behavior of the consumer and other groups

Situations where product consumption experience is sensory in nature ( which is emotional in nature)

Projective techniques – an unstructured and indirect form of questioning that encourages the respondent to project their underlying motivations, attitudes, belief etc regarding the issue of concern.

In projective techniques, the respondents are asked to interpret the behavior of others rather than their own behavior

Ex of projective techniques – word association tests, sentence completion tests, story completion tests, picture response or TAT ( Thematic Apperception Test), Role playing, Third person techniques etc.

Word association test – respondents are presented with the help of words one at a time and are asked to respond to each with the first word that comes in mind

Sentence completion test – respondents are provided with the part of story and required to give conclusion in their own words.

Picture response or TAT ( Thematic Apperception Test) – the respondents are shown a picture and asked to tell a story describing it.

Role playing – respondents are asked to assume the behavior of someone else and have to role-play their behavior.

Third person technique – in which respondents are presented with verbal or visual situation and are asked to related the beliefs and attitudes of a third person. The third person may be friend, neighbour or family etc

Secondary Data (Library Research)

Library Research – is done for solving problems in business and it relates to study of organisation’s records, magazines, journals, news papers, account books and other


documents. It helps in recording the business policies, documents and previous studies of same/similar problems.

Library research provides material/information already contributed by others (which is called as Secondary data).

Sources of Secondary Data - Internal Sources, External Sources, Syndicated or Private Research agencies

Internal sources – Company invoices, Annual reports, Sales reports based on territory wise, product wise, customer wise, journals, magazines, brochures etc

External sources – Public libraries,

Census data, Private Institutions, Universities, colleges, Directories

Government sources ( web sites, published articles in various print media)

Trade associations like FICCI, ICC etc,

Syndicated sources or Private Research agencies like ORG (Operations Research Group), MARG( Marketing & Research Group), MRS( Marketing Research Society), MBA( Marketing & Business Associates), MRAS (Marketing Research & Advisory Services)

Retail, Wholesale, Auditing Institutions

Conclusion

Secondary data involves less time & cost when compared to primary data.

Secondary data familiarise the researcher about the findings of the previous study

The major problem with the secondary data is its reliability. The data may be outdated when it is to be used by the researcher.


Sample Design

Concepts of Sample Design

Population

Census

Sample

Sample Size

Sampling unit

Sampling frame

Sampling Method – Probability & Non Probability Method

Population – is the predefined set of potential respondents ( elements ) in a geographical area (or) it is the aggregate of all the elements sharing some common set of characteristics that compromise the universe for the purpose of the research problem.

Ex: proportion of consumers who are loyal to a particular brand of soft drinks, population may be all the mothers who buy the branded baby food in a given area.

Census – A complete enumeration of elements of population or study objects. It is a systematic and complete count of all who are living in specified place.

Sample – is a subgroup of population selected for the participation of the study.

Sample Size – the number of samples chosen from a target population

Sampling unit – is the basic unit containing all the elements of target population

Ex : employees, customers, dealers, outlets etc

Sampling frame – is the list of blocks and locations or city/map of all the sampling units

Sampling method – refers how sampling units are selected.

Types of sampling – Probability Sampling and Non Probability Sampling.

Methods of Probability Sampling- Simple Random Sampling, Systematic Random Sampling, Stratified Random Sampling, Cluster Sampling

Methods of Non Probability Sampling – Convenient Sampling, Judgment Sampling, Quota Sampling, Snow ball sampling


Probability Sampling – A sampling procedure in which each element has a known, non-zero chance of being included in the sample. It can specify the probability with which each element of the population will be included in the sample. It is unbiased

Methods of Probability Sampling

Simple Random Sampling – in which each element in the population has known and equal probability of selection.

Systematic Random Sampling – The method by which sample is chosen by selecting a random starting point and picking ith element in succession from sampling frame.

Ex : If a sample of 1000 is selected from 100,000, the sampling interval is considered as 100. if the starting random number is 20, the sampling elements are 120, 220, 320 etc till 1000

Stratified Random Sampling – Population is divided into mutually exclusive groups. called as strata Large population is divided into subgroups ( Age, income, education etc). Elements are selected from each stratum or strata by a random manner.

Cluster Sampling -It involves the target population is divided into mutually exclusive and collectively exhaustive sub-population or clusters. It is also called as Area sampling which consists of geographical areas, countries, housing localities or blocks etc

Difference between Stratified & Cluster Sampling – In stratified sampling, all the sub-population (strata) are selected for further sampling. In cluster sampling, only a sample of sub-population ( clusters) is chosen

Non Probability Sampling – The probability of any particular member of the population being chosen is unknown. There is no way of ensuring that sample is a representative of population and there is no way of estimating the probability that any population element will be included in the sample. The selection of sampling units rely heavily on the personal judgment of the researcher. It is biased

The selection may be opportunistic or purposive or on the basis of convenience and judgment of the researcher. It is highly biased

Methods of Non Probability Sampling

Convenient Sampling – The researcher selects the most accessible population members to obtain the information based on his/her convenience.

Judgment Sampling – in which an experienced individual selects the sample based upon some appropriate characteristic of the sample members.


Quota Sampling – the researcher finds and interviews a prescribed number of people in each of several categories ( Quotas). The researcher selects the sample from the fixed quotas like age, income etc which are specified.

Snow ball sampling – in which initial group of sample is selected randomly. Subsequent respondents are selected based on the referrals or information provided by initial respondent.

Ex: It is used in Industrial buyer-seller research to identify buyer-seller pairs.

Conclusion

Sample Design - Population, Census, Pilot Survey, Sample, Sample Size, Sampling Unit, Sampling frame

Sampling Method (Probability & Non Probability)

Probability Samplin g – Simple Random sampling, Stratified Random Sampling, Cluster Sampling

Non Probability Sampling – Convenient Sampling, Judgment Sampling, Quota Sampling, Snow ball Sampling


Multivariate techniques

They are appropriate when one or more of the variables can be identified as dependent variables and the remaining as independent variables. Multivariate statistical techniques can be classified as dependence techniques and independence techniques.

• Dependence techniques – are appropriate when one or more variables can be identified as dependent variables and the remaining as independent variables.

When there is only one dependent variable - the techniques like 2-way anova, multiple regression, two group discriminant analysis, logit analysis and conjoint analysis can be used.

When there are more than one dependent variable – Manova(multivariate analysis of variance), multiple discriminant analysis and cannonical correlation.

Interdependence techniques – the variables are not classified as dependent or independent; rather the whole set of interdependent relationships is examined. These variables are classified based on variable interdependence or interobject similarity. The examples of variable interdependence is factor analysis and the examples for interobject similarity are Cluster analysis and multidimensional scaling


Factor Analysis

Factor analysis is an interdependence technique in that an entire set of interdependent relationships is examined

An underlying dimension that explain correlation among the set of variables called as factors

Factor analysis is a general name denoting a class of procedures primarily used for data reduction and summarization. In this large mass of data is to be simplified and condensed

Factor analysis is used in a situation when there are large number of variables , most of which are correlated and which must be reduced to a manageable level.

Relationships among sets of many interrelated variables are examined and represented in terms of a few underlying factors.

It also helps in extracting the overlapping information by reducing the problem down to just a few core factors

If the product characteristics influencing the consumer preferences are not clear, factor analysis is used in revealing the most important characteristics of the product among preferences.

For example households consider certain characteristics or factors in buying furniture for their home, investing in a bank, automobile etc

Applications of factor analysis

a. Market Segmentation : It can be used in identifying the underlying variables to group the customers. For ex: new car buyers might be grouped based on the relative emphasis they place on economy, convenience, performance, comfort and luxury. This might result in five segments : economy seekers, convenience seekers, performance seekers, comfort seekers and luxury seekers

b. Product Research : to determine the brand attributes that influence consumer choice. Toothpaste brands might be evaluated in terms of protection against cavities, whiteness of teeth, fresh breath and price


For example : The marketing manager of a two wheeler company designed a questionnaire to study for customers feedback about its two wheeler and inturn keen in identifying the factors of the study

The identification of the variables are :

Fuel efficiency, life of two-wheeler, handling convenience, quality of the original spares, breakdown rate, price etc

The application of the factor analysis to group these variables into factors

The questionnaire for factor analysis can be designed in the form of statements by using Interval scale(1-5, 1-7 or 1- 10 where 1 = completely disagree and 10 = completely agree) or Likert rating scale ( 1 – 5 where 1 = strongly disagree and 5 = strongly agree)

Ex: Questionnaire for two wheeler in the form of statements by using Likert-rating scale (1=strongly disagree , 5 = strongly agree)

I use a two-wheeler because it is affordable

It gives me a sense of freedom to own a two-wheeler

Low maintenance cost makes a two-wheeler very economical in the long run

I feel very enthusiastic when I ride two-wheeler

I like to see the ads of two-wheeler on a hoarding

My vehicle gives me a comfortable ride

I think two-wheelers are safe to travel

Three people should be legally allowed to travel on a two-wheeler


Cluster Analysis

Cluster Analysis is an interdependence technique of Multivariate analysis.

Cluster analysis is a class of techniques used to classify objects or cases into relatively homogenous groups called clusters. Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters. Cluster analysis is also called as classification analysis, or numerical taxonomy.

Cluster analysis is useful in identifying aggregates of consumers who behave similarly in terms of quality consciousness and price sensitivity.

Applications of Cluster analysis

Understanding buyer behaviors – it can be used to identify homogenous group of buyer. Then the buyer behavior of each group may be examined which may be a useful application in the selection of retail stores. It has also been used to identify the kinds of strategies automobile purchasers use to obtain external information.

Identifying new product opportunities : by clustering brands and products, competitive sets within the market can be determined. Brands in the same cluster compete more fiercely with each other than with brands in other clusters. A firm can examine its current offerings compared to those of its competitors to identify potential new product opportunities

Selecting test markets – by grouping cities into homogenous clusters, it is possible to select comparable cities to test the products and various marketing strategies.

Reducing data – it can be used as a general data reduction tool to develop clusters or subgroups of data that are manageable than individual observations. For ex: to describe differences in consumers’ product usage behavior, the consumers may first be clustered into groups. The difference among the groups may be then be examined using multiple discriminant analysis.

It is widely used in market segmentation studies. A firm segmenting its market is seeking to group potential customers into homogenous groups that are large enough to be profitably cultivated.

By determining the areas where they live and the demographics of those areas from census data, geo-demographic segments of the population can be formed. (people of similar groups). For ex: A sporting goods manufacturer was attempting to identify the market segments for all types of sporting equipment.


Other benefits of cluster analysis

1.Sort households demand patterns for electricity

2. Group T.V programs into similar types on the basis of viewers’ reports. Group other media in terms of the similarity of their audience appeal

4.Develop homogenous configurations of census e.g for consumer and political purposes

5. group brands and products on the basis of how similar to competitors’ products they are perceived to be, thus how likely they are to serve as substitutes

6. determine spheres of opinion leadership in word-of-mouth networks

Assess the similarity of countries and cultures in world markets.

The questionnaire for cluster analysis can be designed in the form of statements by using Interval scale(1-5, 1-7 or 1- 10 where 1 = completely disagree and 10 = completely agree) or Likert rating scale ( 1 – 5 where 1 = strongly disagree and 5 = strongly agree)

Ex: Questionnaire for a consumer durable goods company to know various features and services the consumers perceive when purchasing through catalogs can be designed in the in the form of statements by using Likert-rating scale (1=strongly disagree , 5 = strongly agree)

The company should provide toll-free numbers

The reputation of the company should be good

They should have discount schemes based on quantity

The company should provide guarantee for the product

The company should give a trial period

The sales catalog should be attractive

The company should make on-time delivery

Advertisements play a vital role in decision-making


Discriminant Analysis

Discriminant analysis is a multivariate statistical technique for analyzing data when the criterion or dependent variable is categorical and the predictor or independent variables are interval in nature. For ex, the dependent variable may be the choice of a brand of personal computer (brand A,B, or C) and the independent variables may be ratings of attributes of Personal computers on a 5 or 7 point Likert scale

DA is a method of constructing a linear combination of the variables (i.e., a weighted sum) in such a way that this newly created function optimally discriminates among the groups. We can then assess how the groups differ with respect to the linear combination score

DA techniques are described by the number of categories possessed by the criterion variable. When the criterion variable has two categories, the technique is known as two-group DA OR linear DA. For ex: LDA is used to study successful salesman and unsuccessful salesman in order to determine the characteristics possessed by successful salesman but not unsuccessful salesman. Once the characteristics have been identified, the information can be used to recruit individuals with characteristics similar to those possessed by successful salesman.

When three or more categories are involved, the technique is called multiple discriminant analysis. Ex: the potential buyers can be classified into light, medium & heavy users. The prize winners of the competition can be classified into grand prizewinner, consolation prizewinner & unsuccessful prizewinner.

DA can be used to answer questions such as :

In terms of demographic characteristics, how do customers who exhibit store loyalty differ from those who do not/

Do heavy, medium, and light users of soft drinks in terms of their consumption of frozen foods?

What psychographic characteristics help differentiate between price-sensitive and non price sensitive of groceries

Do the various market segments differ in media consumption habits?

In terms of lifestyles, what are the differences between heavy patrons of regional department store chains and patrons of national chains?

What are the distinguishing characteristics of consumers who respond to direct mail solicitations?


DA is useful in determining the characteristics that differentiate the following :

light and heavy users of a product

purchasers of our brand and those of competing brands

Customers who patronize every-day-low-pricing retail outlets those who shop at high-end, service-oriented ones

Good, mediocre, and poor sales representatives

Good and poor loan risks

To determine the characteristics that distinguish the listening audiences of radio stations

To differentiate among segments of automobile buyers

To predict adopters and non adopters of new products

To relate purchase behavior to advertising exposure

To determine the relationship between personality variables and consumer decisions.

To understand the differences between households that save their money at commercial banks versus those who choose savings and loan institutions

To assess the differences in importance of various attributes where the same products are being purchased in different countries

To determine the factors that supermarket buyers use in deciding whether to stock a new product or not

Practical examples of DA

Discriminant analysis can be used by credit rating agencies to rate individuals or to classify them into good lending risks or bad lending risks

A soft drink company wants to study about soft drinkers who prefer different brands of soft drink. This information is useful how the respondents in their target market are different from the respondents not in their target market.

A bank can use DA to classify their credit card holders as defaulters or non defaulters.

A retail outlet can estimate their consumer behavioral pattern of the purchase of products by the consumers in two categories – national and international brand accepters

The fmcg goods company can discriminate their dealers as potential successful or potential unsuccessful.


Note : DA is similar to the multiple regression technique. The form of the equation in a two-variable DA is : Y = a + k1x1 + k2x2 which is called as the discriminant function. Y is dependent variable and x1 and x2 are the independent variables, k1 and k2 are the coefficients of the independent variables, and a is a constant. The difference between regression and DA is that in regression, the dependent variable is continuous whereas in DA it is categorical. All the other independent variables in DA are continuous

K1 and k2 are also called as unstandardized discriminant function coefficients

Conjoint Analysis (CA)


CA attempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the level of attributes. This information is derived from consumers’ evaluations of brands, or brand profiles composed of these attributes and their levels. The respondents are presented with stimuli that consist of combinations of attribute levels. They are asked to evaluate these stimuli in terms of their desirability. The underlying assumption is that any set of stimuli such as products, brands, or stores, is evaluated as a bundle of attributes. Ca seeks to develop the part-worth utility functions describing the utility consumers attach to the levels of each attribute.

Characteristics of CA

Determining the relative importance of attributes in the consumer choice process, a standard output from CA consists of derived relative importance weights for all the attributes used to construct the stimuli used in the evaluation task. The relative importance weights indicate which attributes are important in influencing consumer choice

Estimating market share of brands that differ in attribute levels. The utilities derived from conjoint analysis can be used as input into consumer choice simulator to determine the share of choices, and hence the market share, of different brands.

Determining the composition of the most preferred brand. The brand features can be varied in terms of attribute levels and the corresponding utilities determined. The brand features that yield the highest utility indicate the composition of the most preferred brand.

Segmenting the market based on similarity of preferences for attribute levels. The part-worth functions derived fro the attributes may be used as a basis for clustering respondents to arrive at homogenous preference segments.

Applications of CA have been made in consumer goods, industrial goods, financial, and other services.

The goal of CA is to determine the features that respondents most prefer. Consumers might use such attributes as mileage per k.m, seating capacity, price, length of warranty in making judgments about which automobile they prefer.

CA is also used in distribution decisions – to evaluate vendors, determine the rewards that a salesforce values etc

The specific applications also include new product/concept identification, competitive analysis, pricing, market segmentation, advertising and distribution.

CA is used in Green Marketing, where it is used to find out the preferences of environmentally friendly products


CA is used in market segmentation - for ex: consumers seeking luxury cars about safety and service, smooth ride, design etc

CA is also used in determining consumer preferences in services like airlines, health organizations, tourism etc.

CA may be viewed as a special application of dummy variable regression.

CA allows the introduction of nominal variables in the regression equation.

Practical application of CA

For ex: the marketer is considering to introduce a new coffee maker and wish to assess how consumers evaluate the following levels of each of these product attributes

Capacity – 4, 8, 10 cups

Price – 2000, 3000, 4000 Rs.

For all the above three attributes capacity, price and brewing time, most consumers would probably prefer either the most or least of each property – the largest capacity maker, the shortest brewing time, at the lowest time.

In CA consumers were asked to rank the preferences of the attributes and the relative importance for each level of attributes is calculated. The attributes with highest relative importance is considered as one of the most important feature to be included in the product design.

For ex : A paint industry identified the attributes which are important to the customers are classified as follows:

Life of the paint – 3, 4, 5 years

Price of the paint – 50, 60, 70 Rs/litre

Color – green, red, blue

The consumers were asked to consumers were asked to rank the preferences of the attributes and the relative importance for each level of attributes is calculated.

The attributes with highest relative importance is considered as one of the most important feature to be included in the product design by the marketer. In the above ex: a number of 3x3x3 = 27 combinations of preferences were generated on the given product levels

e.g of product design : 5 Years, Rs.50,green or 4 years, Rs.50, blue etc


Consumer Research

Consumer Research consists of


• Research on profile of consumer

• Research on buying roles

• Research on buying process

• Research on consumption patterns

Methods of conducting Consumer Research

Consumer Research is highly qualitative than quantitative. The methods used in the Consumer Research are as follows :

Focus Group Interview

In – depth Interview

Projective Techniques

Focus Group Interview – It is an interview conducted by a trained moderator with a group of respondents in the form of free association. The moderator establish the rapport with the participants to keep with the discussion moving

Applications of Focus group Interview

Understanding consumer perceptions, preferences & behaviour concerning a product category

Obtaining impressions of new product concepts

Developing creative concepts & copy material for advertisements

Securing price impressions

Securing preliminary Consumer reactions to specific marketing program

In-depth Interview – It is an direct or personal interview with the respondent in which the respondent is probed by a highly skilled interviewer to know underlying motivations, attitudes, feelings on a topic

Applications of In-depth Interview

Detailed understanding of the complicated behaviour of the respondent.


Discussion on confidential, sensitive issues with the respondent where the strong social norms exist.

Interviews with professional people(industrial marketing research)

Interviews with competitors who are unlikely to reveal the information is a group setting

Situations where the product consumption experience is sensory in nature which affects the moods and emotions of the people

Projective techniques – It is an indirect form of questioning that encourages the respondent to project their underlying motivations, belief, attitudes etc regarding the issues of concern

In projective techniques, the respondents are asked to interpret the behaviour of others than their own behaviour.

Types of Projective techniques

Association techniques- word association, sentence completion, story completion

Construction techniques – Picture response of Thematic Apperception Test(TAT)

Expressive techniques – Role playing, Third Party Method

Conclusion

Consumer research is highly qualitative

It focus in understanding consumer perceptions, motivations, attitudes etc where the behaviour of the consumers is highly sensitive

*Note – consumer buying decision process can also be considered as the major component of the consumer research

Format of the Report Preparation

Report Preparation involves the following format


Title Page

Inner Cover Page

Declaration

Certificate

Preface

Acknowledgements

Contents

List of tables/charts/exhibits

Executive summary

Chapter 1 (Introduction)

Industry Profile

Company Profile

Product Profile

Chapter 2

Scope of the study

Statement of the problem

Objectives

Statement of Hypothesis

Limitations

Chapter 3

Review of Literature

Chapter 4 (Research Methodology)

Research Design – type of research design

Research Instrument

Sources of Data


Sampling frame, Sampling method

Statistical tools used

Chapter 5 (Data analysis and Interpretation)

Tables, Charts, Inferences

Chapter 6

Findings

Suggestions

Conclusion

Annexure

Questionnaire

Bibliography

Glossary

Criteria for Report Writing

Accountability – the report should take into account the readers’ technical sophistication and interest in the project, as well as the circumstances under which they will read the report and how they will use it.

Easy to follow – the report should be structured logically and written clearly. The material particularly the body of the report, should be structured in logical manner so that the reader can easily see the inherent connections and linkages.

Presentable and Professional Appearance – the report should be professionally reproduced with quality papers, typing and binding

Objectivity – is a virtue that should guide report writing. The report should accurately present the methodology, results and conclusions of the project, without slanting the findings to conform to the expectations of the management

Reinforce text with tables and graphs – It is important to reinforce the key information in the text with tables, graphs, pictures, maps and other visual services. Visual aids facilitate communications and add to the clarity and impact of the report Tables in the report should have Title and number of the table, Arrangement of the data items, explanations and comments, headings, footnotes, sources of data etc. The graphs may be in the form of pie charts, Line charts, bar charts, histograms etc


Terse – A report should be terse and concise. Avoid lengthy discussions of common procedures. Anything unnecessary should be omitted.

Conclusion

Criteria of Report Writing – Accountability, Easy to follow, Presentable and Professional Appearance, Objectivity, Reinforce text with tables and charts, Terse.


Documents

Marketing Research quest and ans