Table of Contents
Q.1. Define Marketing Research and discuss its possible application areas in Hospitality industry. (20)
Market research is the process of gathering, analyzing and interpreting information about a market, about a product or service to be offered for sale in that market, and about the past, present and potential customers for the product or service; research into the characteristics, spending habits, location and needs of your business’s target market, the industry as a whole, and the particular competitors you face.
Accurate and thorough information is the foundation of all successful business ventures because it provides a wealth of information about prospective and existing customers, the competition, and the industry in general. It allows business owners to determine the feasibility of a business before committing substantial resources to the venture.
Market research provides relevant data to help solve marketing challenges that a business will most likely face–an integral part of the business planning process. In fact, strategies such as market segmentation (identifying specific groups within a market) and product differentiation (creating an identity for a product or service that separates it from those of the competitors) are impossible to develop without market research.
Applications of marketing research
Bulk of research is done to measure consumer wants and needs. Also, marketing research is carried out to assess the impact of past marketing actions. Some research is done to understand the competitive, technological, social, economic cultural, political or legal environments of the market.
Another way of looking at the function of marketing research is to look at the particular decision area where research results are used.
1. Sales Analysis
Much research is done in the following areas which are broadly referred as sales analysis,
• Measurement of market potential/demand projection,
• Determination of market characteristics;
• Market share estimation;
• Studies of business trends.
In fact, some of the more detailed studies to be carried out under the broad ambit of sales analysis could be as follows,
• The types of consumers that constitute the potential market
• The size and location of the market
• The growth and. concentration of the market over certain period of time,
• The competitive picture for the product;
• The major strategies of leading competitors with respect to price, offerings distribution etc.
• The purchase habits of key market segments;
• What is the pattern of pre-purchase deliberations made by the consumers’?
The above list is not exhaustive. Here research is basically done with a view to know consumers’ motivation, attitude, cognition and perceptions etc. Thus information wilI be collected in a manner so that they have some implications for various marketing decisions.
2. Sales Methods and Policies
Marketing research studies are also conducted with a view to evaluate the effectiveness of present distribution system. Such studies are used in establishing or revising sales territories.. They are also helpful in establishment of sales quotas, design of territory boundary, compensation to sales force, physical distribution and distribution cost analysis etc. Marketing research is also done to assess the effectiveness of different promotional activities such as premiums, deals, coupons, sampling etc.
3. Product Management
Every marketer tries to formally or informally utilize information to manage the existing and new products. It examines market feedback about competitive product offerings. Also, some companies make use of marketing research to form market segments through choice of alternative bases. Companies also carry out different research studies to assess consumer feedback to new products and their likely potential. Of late, in India many consumer products have been launched after making rigorous amount of research. Moreover, researches have enabled to dragonise how consumers perceive various brands of a product. Such studies have enabled the companies to position their brands.
4. Advertising Research
a. Media research
Three National Readership Surveys (NRS) have so far been conducted in India. These studies have basically estimated the readership of leading newspapers. The last NRS has also assessed qualitatively, readers feedback on the editorial content. Moreover, some marketing research have evaluated the relative effectiveness of different media in specific product fields, and in context of achieving specific tasks such as creating brand awareness or a particular product benefit.
b. Copy research
Advertising agencies have been regularly engaged in this activity where they test out alternative copy designs by obtaining the feedback from to consumers. Studies of advertisement effectiveness – Advertising agencies regularly make use of marketing research studies to assess and monitor effectiveness of different advertising campaigns.
5. Corporate Research
Large scale corporate image studies among different target publics – They involve an assessment of knowledge about company activities, association of company with sponsored activities and company perceptions on specific dimensions. These types of corporate image studies are done periodically to monitor any change in image over time among different publics.
Social values research: Knowledge, attitude and practices on family planning, anti-dowry, smoking, drinking etc.
Political studies: In recent times marketing studies have been conducted to ascertain the public opinion about the election results.
Customer service studies: Many banks and large industrial houses have resorted to marketing research to know the consumers’ changing need for service and possible grievances about existing operations.
6. Syndicated Research
Several research agencies collect and tabulate marketing information on a continuing ‘basis. Reports are sent periodically (Weekly, monthly or quarterly) to clients who are paid subscribers. Such services are found specially useful in the ‘lie spheres of movement of consumer goods through retail outlets (ORG Retail Audit), incidence of disease and use of branded drugs (MARL- prescription audit), Television Program viewing (the Television Rating Points), Newspaper & Magazine readership (NRS – discussed earlier under media research), assessment of market potential of a city with population one lakh and above (Thompson Indices), study of nation’s attitudes and psychographics (PSNAP and IMRB’ life style research on the cigarette market)
Q.2. What are the major types of Research Design? Explain any two of them. (4+8+8)
A research design is a broad plan that states objectives of research project and provides the guidelines what is to be done to realize those objectives. It is, in other words, a master plan for executing a research project.
Types of Research Designs
The research design is a broad framework that describes how the entire research project is carried out. Basically, there can be three types of research designs – exploratory research design, descriptive research design, and experimental (or causal) research design. Use of particular research design depends upon type of problem under study.
1. Exploratory Research Design
This design is followed to discover ideas and insights to generate possible explanations. It helps in exploring the problem or situation. It is, particularly, emphasized to break a broad vague problem statement into smaller pieces or sub-problem statements that help forming specific hypothesis.
The hypothesis is a conjectural (imaginary, speculative, or abstract) statement about the relationship between two or more variables. Naturally, in initial state of the study, we lack sufficient understanding about problem to formulate a specific hypothesis. Similarly, we have several competitive explanations of marketing phenomenon. Exploratory research design is used to establish priorities among those competitive explanations.
The exploratory research design is used to increase familiarity of the analyst with problem under investigation. This is particularly true when researcher is new in area, or when problem is of different type.
This design is followed to realize following purposes:
1. Clarifying concepts and defining problem
2. Formulating problem for more precise investigation
3. Increasing researcher’s familiarity with problem
4. Developing hypotheses
5. Establishing priorities for further investigation
Exploratory research design is characterized by flexibility to gain insights and develop hypotheses. It does not follow a planned questionnaire or sampling. It is based on literature survey, experimental survey, and analysis of selected cases. Unstructured interviews are used to offer respondents a great deal of freedom. No research project is purely and solely based on this design. It is used as complementary to descriptive design and causal design.
2. Descriptive Research Design
Descriptive research design is typically concerned with describing problem and its solution. It is more specific and purposive study. Before rigorous attempts are made for descriptive study, the well-defined problem must be on hand. Descriptive study rests on one or more hypotheses.
For example, “our brand is not much familiar,” “sales volume is stable,” etc. It is more precise and specific. Unlike exploratory research, it is not flexible. Descriptive research requires clear specification of who, why, what, when, where, and how of the research. Descriptive design is directed to answer these problems.
3. Causal or Experimental Research Design
Causal research design deals with determining cause and effect relationship. It is typically in form of experiment. In causal research design, attempt is made to measure impact of manipulation on independent variables (like price, products, advertising and selling efforts or marketing strategies in general) on dependent variables (like sales volume, profits, and brand image and brand loyalty). It has more practical value in resolving marketing problems. We can set and test hypotheses by conducting experiments.
Test marketing is the most suitable example of experimental marketing in which the independent variable like price, product, promotional efforts, etc., are manipulated (changed) to measure its impact on the dependent variables, such as sales, profits, brand loyalty, competitive strengths product differentiation and so on.
4. Correlational research design
Correlational research is a non-experimental research design technique that helps researchers establish a relationship between two closely connected variables. This type of research requires two different groups. There is no assumption while evaluating a relationship between two different variables, and statistical analysis techniques calculate the relationship between them.
A correlation coefficient determines the correlation between two variables, whose value ranges between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables and -1 means a negative relationship between the two variables.
Q.3. What is secondary data? Enumerate their sources and advantages. (20)
Secondary data are already published data collected for purposes other than the specific research needs at hand. On the basis of location of sources, secondary data may again be classified as internal or external data.. The data originating within or available within the organisation as a by-product of the MIS or the routine reporting system is called internal data of any given marketing research problem, initial data collected for purposes other than that specific problem could be termed internal secondary data.
Secondary data generated outside the organisation is termed external secondary data and can be collected from a multitude of sources like government publication, trade association publications, official reports, journals and periodicals and publication of marketing research agencies. Secondary data can &ISO be thought from research agencies though this is a fairly expensive preposition.
Secondary data may also be classified on the basis of whether it is periodic data or ‘adhoc data. Periodic data characterises most statistics collected over fixed periods of time like the census data, data from statistical abstracts of trade and other sector’s, price indices and so on. Adhoc data, on the other hand refers to the data obtained from a certain project report. Such data necessitates an external search to find the source from, which the data can be availed of.
Sources of secondary data
1. Government Agencies and Official Publications
The largest single source of secondary data in macro terms are the’ publications by the Union government. Marketing researches have relied on this source of data for estimating market potential and sales forecasts, determining distribution penetration and location of intermediate and final outlets, as well as for defining sales territories and routing schedules. Estimates of income and expenditure patterns become good starting points for estimation of paying capacity for different products and services. Estimates of literacy levels become effective inputs in planning promotional strategies. Over a period of time, the variety and depth of government data has increased manifold and it relevance to marketing research function has consequently enhanced.
2. Library
The library sources of marketing data include the whole gamut of publicly circulated material i.e, government documents and reports, books, periodicals, journals, individuals research project reports and trade association publications. The library represents an easy, economic and efficient source of secondary data.
3. Research Agencies and Data Services
The growing demand for marketing data has brought forth several organisation which collect and sell standardized data. Also called syndicated sources, these agencies include the marketing research agencies as well as the data services which in addition to providing standardized data also undertake specific data collection research projects. In India, advertising agencies have also emerged as ‘good sources of data on readership media habits, attitudinal research and other communication related areas.
The data that can be obtained from these syndicated agencies includes consumer data, retail data, wholesale data, industrial data, advertising evaluation data and, media and audience data.
Leading marketing research agencies like MARG and ORG regularly survey consumers attitude and opinions regarding consumption behaviour and a variety of contemporary issues relevant to marketing.
Advantages of Secondary Data
The major advantage of secondary data is the economy of resources that it offers both in terms of money and time. Primary research involves selecting the sample frame, determining the sample size, choosing the tools of data collection, getting data collection instrument printed in case of field studies as well as editing tabulating and analysing the results, which turns out to be expensive and time consuming. Secondary data on the other hand can be collected by researcher from published or compiled research at very little cost and usually very speedily.
Another important advantage which characterises some secondary data services is that they provide access to information which would not ordinarily be obtainable by an individual organisation. The census of wholesale and retail establishments for example can require them to furnish details of sales, expenses and profit information which would be inaccessible to an inch dual researcher. Also, as information like this is collected in the usual course of events, the data is less prone to be biased which may be the case when the data is collected with a specific purpose in mind.
Q.4. Discuss the various methods of data collection. (20)
Various methods of data collections are –
1. Interviews
Interviews in marketing research are by far the most common method of data collection. Interviews may be:
a. Structured and Direct
Involving the use of a structured formal questionnaire as well as an interviewer (e.g. surveys using questionnaires).
b. Unstructured and Direct
Not involving the use of a predecided questionnaire, only the interviewer (e.g. personal interviews)
c. Structured and indirect
Where the responded to a non-personal ambigous Data Collection situation which is later interpreted (e.g. projective techniques like word association)
d. Unstructured and indirect
where the respondent is asked to respond to non- personal ambigous situation but where the interviewer has considerable degree of freedom in modifying/altering the situation.
2. Observation
Observation involves collecting information without asking questions. This method is more subjective, as it requires the researcher, or observer, to add their judgment to the data. But in some circumstances, the risk of bias is minimal.
For example, if a study involves the number of people in a restaurant at a given time, unless the observer counts incorrectly, the data should be reasonably reliable. Variables that require the observer to make distinctions, such as how many millennials visit a restaurant in a given period, can introduce potential problems.
In general, observation can determine the dynamics of a situation, which generally cannot be measured through other data collection techniques. Observation also can be combined with additional information, such as video.
3. Documents and records
Sometimes you can collect a considerable amount of data without asking anyone anything. Document- and records-based research uses existing data for a study. Attendance records, meeting minutes, and financial records are just a few examples of this type of research.
Using documents and records can be efficient and inexpensive because you’re predominantly using research that has already been completed. However, since the researcher has less control over the results, documents and records can be an incomplete data source.
4. Focus groups
A combination of interviewing, surveying, and observing, a focus group is a data collection method that involves several individuals who have something in common. The purpose of a focus group is to add a collective element to individual data collection.
A focus group study can ask participants to watch a presentation, for example, then discuss the content before answering survey or interview-style questions.
Focus groups often use open-ended questions such as, “How did you feel about the presentation?” or “What did you like best about the product?” The focus group moderator can ask the group to think back to the shared experience, rather than forward to the future.
Open-ended questions ground the research in a particular state of mind, eliminating external interference.
5. Oral histories
At first glance, an oral history might sound like an interview. Both data collection methods involve asking questions. But an oral history is more precisely defined as the recording, preservation, and interpretation of historical information based on the opinions and personal experiences of people who were involved in the events.
Unlike interviews and surveys, oral histories are linked to a single phenomenon. For example, a researcher may be interested in studying the effect of a flood on a community. An oral history can shed light on exactly what transpired. It’s a holistic approach to evaluation that uses a variety of techniques.
As in interviewing, the researcher can become a confounding variable. A confounding variable is an extra, unintended variable that can skew your results by introducing bias and suggesting a correlation where there isn’t one.
The classic example is the correlation between murder rates and ice cream sales. Both figures have, at one time or another, risen together. An unscientific conclusion may be that the more people buy ice cream, the higher the occurrence of murder.
However, there is a third possibility that an additional variable affects both of these occurrences. In the case of ice cream and murder, the other variable is the weather. Warmer weather is a confounding variable to both murder rates and ice cream sales.
6. Questionnaires and surveys
Questionnaires and surveys can be used to ask questions that have closed-ended answers.
Data gathered from questionnaires and surveys can be analyzed in many different ways. You can assign numerical values to the data to speed up the analysis. This can be useful if you’re collecting a large amount of data from a large population.
To be meaningful, surveys and questionnaires need to be carefully planned. Unlike an interview, where a researcher can react to the direction of a respondent’s answers, a poorly designed questionnaire will lead the study nowhere quickly. While surveys are often less expensive than interviews, they won’t be valuable if they aren’t handled correctly.
Surveys can be conducted as interviews, but in most cases, it makes sense to conduct surveys using forms.
Online forms are a modern and effective way to conduct surveys. Unlike written surveys, which are static, the questions presented in online forms can change according to how someone responds.
Q.5. Explain with suitable examples the various methods of Random Probability Sampling. (20)
Probability Sampling Methods
1. Simple Random Sampling
Under this sampling design, each member of the population has known and equal probability of being included in the sample. Simple random sampling is not widely used in marketing research because of the following reasons.
• In consumer research studies, we usually select individuals, households, shops or areas as the sampling units. It may not be easy to prepare a sampling frame as it is very difficult to get lists of households, individuals and shops, although areas may be completely represented through maps.
• We know that an industry comprises of various firms of different sizes. If one wants to study some aspects of an industry, one might like to choose a sampling design where there is a higher probability of a larger firm being selected. If that is the case, the very concept of simple random sampling becomes inapplicable in such situations. The simple random sampling has some applications in Industrial Marketing where generally purchasing agents or companies or areas are the sampling units which are usually not very big in number. Therefore, it becomes easy to prepare a sampling frame thus facilitating the use of simple random sampling.
2. Systematic Sampling
The mechanics of taking a systematic sample are very simple. Systematic sampling is a case of mixed sampling where both probabilistic and non-probabilistic methods of choosing a sample are used. This is because the first unit of the sample is selected at random between numbers 1 and K (probabilistic method) and then the rests of the units of the sample are fixed by the choice of the first member (non-probabilistic method).
It is very likely that systematic sampling would result into more representative sample than simple random sampling. In systematic sampling the elements of the population are ordered in a particular fashion.
Example – we want to estimate the sales of all the retail stores in Delhi. Under a simple random sampling, if we draw a random sample of size n, it is very likely that Most of the sampled stores might turn out to be low sales volume store. However, in systematic sampling we order these retail stores according to ascending or descending order of sales, therefore, a systematic sample would definitely contain some low volume and high volume retail stores. Thus, a systematic sample is likely to be more representative than a sample random sample. A systematic sample might also reduce the representativeness of the sample.
3. Stratified random sampling
It involves a method where the researcher divides a more extensive population into smaller groups that usually don’t overlap but represent the entire population. While sampling, organize these groups and then draw a sample from each group separately.
A standard method is to arrange or classify by sex, age, ethnicity, and similar ways. Splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.
Members of these groups should be distinct so that every member of all groups get equal opportunity to be selected using simple probability. This sampling method is also called “random quota sampling.”
In stratified sampling, the entire population is divided into various mutually exclusive and collectively exhaustive strata (groups). By mutually, exclusive it is meant that if an element of a group belongs to one strata, then it doesn’t belong to any other strata.
4. Cluster Sampling
If we divide all the elements of the population into suitable, clusters; and select few clusters randomly and all the elements of the selected clusters are used, then this method of sampling is called cluster sampling’.
This method of collecting data is cheaper since collection of data fear nearby units is easier, faster and more convenient than collecting data over units scattered over a region. For instance, it would not only be cheaper but also convenient to collect data on all households in a sample of few villages.(clusters) than to surrey a sample of the same number of households selected randomly from a list of all households.
The criteria for dividing the population into mutually exclusive and collectively exhaustive clusters is, that the elements in the clusters should be as heterogeneous as possible and elements between cluster should be as homogeneous as possible.
5. Area Sampling
In a marketing research study involving sampling of population which may be grouped according to geographical areas (blocks), Census tracts, Communities, constituencies etc., another version of cluster sampling namely Area Sampling is used.
The entire area is divided into various clusters. The cluster may or may not be of equal size. Below we will discuss a sampling scheme where sampling is done by taking into account the size of the cluster. This type of design is called probability proportional to size sampling.
Q.6. Highlight issues of ambiguities associated with Questionnaire method of data collection. Categorise formats of questionnaire and ways of administrating them. (20)
A questionnaire is a standardised format of data. collection. It is normally used when the data is collected from a large population about their awareness, attitudes, opinions, past and present behaviour.
Questionnaire Format
Questionnaire format depends upon the amount of structure and diguise required during data collection,
a. Structure
At the time of fronting the questionnaire the researcher must appropiiately determine the degree of structure to be imposed on the questionnaire. A highly structured questionnaire is one in which the question to be asked and the responses permitted are explicitly pre-specified. On the other hand in a non structured questionnaire the questions to be asked are kept flexible in their own words and also the respondents are allowed to answer the questions in a manner they like. The response pattern may vary from open-ended to closed-ended. In open-ended question the respondent is free to choose the possible response, where as in the closed ended from, the researcher pre specifies certain options and the respondent is allowed to choose the alternative(s) from the given options. For example, the structure of these two forms of response will be as follows:
• Open-ended
What brand of shampoo do you use?
• Close-ended
Mention the brand of shampoo you use from the list given below:
( ) Ponds ( ) clinic ( ) Tiara ( ) Palmolive
b. Disguise
Disguised questions is one where purpose is not made obvious to the respondents and is asked in an indirect manner. Non-disguised questions, on the other hand, are ones which are direct and the purpose of asking them is known clearly is the respondents.
Disguised questions are used in the conditions when the issues concerned are such that respondents may not give correct answer to direct questions.
Based on the above discussion, questionnaires could be classified into for categories.
• Structured, non-disguised questionnaire
• Structured, disguised questionnaire
• Non-structured, non-disguised questionnaire
• Non-structured, disguised questionnaire
Structured, non-disguised questionnaires are very popular in marketing research studies. These are more applicable when large sample sizes are there. Non-structured, non-disguised questionnaires, on the other hand, are used when a freehand is to be provided to the respondents so that an in-depth information on the subject could be solicited e.g. in industrial marketing research wherein number of respondents would also be low.
Non-structured, disguised questionnaires are mainly used in `motivation research’. ‘Wore Association Test’, ‘Sentence Completion Test’, `Thematic Appreciation Test’, ‘Cartoon Test’, etc. may be used in this category, Structured, disguised questionnaires are more appropriate where responses are required towards certain sensitive issues like attitude towards aids patients, abortion etc.
Questionnaire Administration
The questionnaire method may also vary depending ‘on the way it is administered:
These could be broadly classified into three different categories.
a. `Personal interview’, wherein there is a face to face interaction between interviewer (s) and respondents (s).
b. ‘Telephone survey`, in which survey is conducted over phone i.e. unlike personal interview there is only a voice contact.
c. `Mail survey’, as the name suggests, is conducted through mail and as such there are no interviewers.
Ambiguities related to Questionnaire Methods
a. Question being too long
Long questions comprising of complex and compound sentence structures become’ incomprehensible to respondents. As all words are potential sources of ambiguity, the longer the question, greater will be the possibility of its being misunderstood. Consider the following question which was actually used in a study on distribution network for automobile parts:
Ques- Under the new system, do you think spare part dealers would be independent businessmen like applicance dealers and furniture merchants who own their outlets, or they would be employees of the automobile companies?
This sort of question would pose problems of comprehension among most respondents. It could easily be rephrased as:
Ques- Under the new system, do you think spare part dealers would be owners of their business or employees of the automobile companies?
b. Question using vocabulary unfamiliar to be respondent.
The questions should, as far as possible, consist of words that are a part of the normal vocabulary of the respondent. For example consider the following question.
Ques- Do you think the pasteurisation process interferes with the lactogenic balance of milk’?
This question may be all right if put to doctors, chemists or biochemists but how many members of the general public would be a match to the vocabulary of this researcher?
c. Question using words that are ambiguous in context
Sometimes the words that are fairly understandable on their own may be used in a way that renders their ambiguous. In the following question:
Ques- Do you watch television programmes regularly?
The word `regularly` may have different meanings for different people. It does not specify whether regularly means the whole day long, seven times a week, five times a week, or certain programmes on every telecast. The question` therefore needs to be rephrased in the light of the information sought.
d. Combined questions
Poor question constriction sometimes results in two questions being asked as one. A question put to housewives is given below as an example:
Ques- What do you think is a healthier and economic medium for your cooking, refined oil or unrefined ones?
It is clear that the housewife who thought that one medium was more healthy and the other more economical would not be able to respond logically to this question: The simpler and more effective way to get this information would be to break this question into two, one dealing with nutritional value and the other with economy.
e. The multiple choice form is also prone to another kind of ambiguity.
Q.7. What are the ways of conducting qualitative research ? Elaborate giving examples. (20)
Methods of conducting Qualitative Research
1. Individual ‘Depth’ or ‘Intensive’ Interviews
The in-depth interviews could be classified as:
a. In a non-directive intend
the respondent is given maximum freedom to respond in a manner that he wishes to, within a reasonable limit of relevancy to the topic under discussion. However, the interviewer retains the initiative in the interview process, else the focus of the interview would be lost. Thus, with this technique, the respondent is given a chance to freely express his ideas and thoughts, which acts as an important feedback to the company regarding the company’s’ products/service.
b. In a semi-structured or focussed interview
The initiative is retained by the interviewer, and the interview has to cover specific list of points, which has been decided in advance. There is also a tighter control over the interview, in order to maximise data collection and also collect data relevant to the topic under consideration. The interviewer also has determined as to which questions are to be asked. The best example to highlight this interview process is the chat shows that take place on television. Even though the participant(s) is/are given maximum freedom with respect to his/her answers, the initiative is retained by the interviewer, and he/she has decided in advance the questions that would be asked in the e. nurse of the interview.
In-Depth interviews are appropriate in the following situations:
1. When detailed probing of an individual’s behaviour, attitude, and needs is required.
2. When-the subject matter-is of a highly confidential nature (e.g., how do you plan your investments: required for annual tax planning).
3. When the subject matter is an emotionally charged one or of an embarrassing nature (e.g., how do you spend when you go on a date).
4. When a step-by-step understanding is required of complicated decision making (e.g., how does a family plan its holiday – selection of vacation site, mode of travel and stay places, or how does a family decide when purchasing a house – which is normally a lifetime decision).
5. When interviews are conducted with highly qualified professionals (e.g., surgeous – on the usage/problems with various medical equipments), a normal questionnaire would not suffice for getting information, and a detailed probing is required which would come out only through an in-depth interview.
2. Focus-Group Discussion
There are broadly two ways in which a group discussion can be conducted:
a. Brain-Storming
In such a method, there is no moderator for the group, and the group freely expresses its ideas on the given topic. The ideas could be absolutely abstract, but then this would help in generating new product ideas and also better ways of conducting a particular business etc. In this, use is made of tape recorder to record the group discussion, video-taping of proceedings is also done in order to record the facial expressions of the, participants, as also the intensity of their feelings.
b. Focussed Group Discussion
In such a method, the group is given a topic and asked to discuss the topic. A moderator would also be involved in order to ensure that the group ‘discussion remains relevant and does not go off the track.
The moderator could stop the, discussion between time intervals to find out what conclusions are being drawn by the group after each time interval.
Size and Composition of a Group Discussion Panel
There is no correct size prescribed for a Group Discussion. The size of the group depends upon the subject matter under discussion & the type of participants. Normally, 8 to 12 individuals in a group discussion panel is an ideal size. However, for highly professional and articulate people, the ideal size is 5/6 participants in the group, as the participants would have more to contribute to the topic under discussion.
The group members should be such that it reflects the characteristics of a particular market segment under study. The sampling plan is drawn up first, which then helps in deciding the composition of the group discussion panel members.
3. Projective Techniques
When a researcher is conducting an in-depth interview, or conducting a survey through the questionnaire method, he might face problems in the form of language barriers with the respondent, or illiterate respondent (especially in social research and rural research), or social barriers (respondent is embarrassed to talk about a topic) or psychological barriers (recall of event or feelings at that moment of interview is not there or trying to avoid certain questions or “can’t say” answer). In order to overcome such barriers faced during an interview process, the researcher may replace the questionnaire with the projective techniques.
The following are the different types of projective techniques:
a. Word Association Test
In this method, the respondent is presented with a list of stimulus words, and for each word, is asked to respond with what he thinks about the word. The respondent is not given time to think of the responses. The ideas is that the `first thought’ responses are likely to reveal the true feelings of the respondent about the stimulus.
b. Sentence Completion Test
This is an extension of the word association test. In this method, the respondent is asked to finish an incomplete sentence with the first thought that comes to his mind. The idea is that the respondent projects his own feelings into the sentence.
c. Fantasy Situation
Here, the respondents are asked to imagine that they are converted into a product itself e.g., car, box of chocolate. This leads to the respondent imagining himself to be product itself and give the human characteristics to the product. This method is used for developing brand perception, brand personality.
d. Cartoon Completion
In this method the respondent is shown a cartoon that is similar to a comic strip, with “balloons” indicating speech. Usually, two people are shown talking to each other about a particular product/service/situation, but only one balloon contains the speech.
The situation that is shown in the cartoon is obviously of special interest to the researcher, and is part of the research project under hand. The respondent has to fill the other `balloon’ with his answer to what the other person is saying.
With this one tries to measure attitude towards a product or service. Analysis and interpretation of these results are highly subjective.
e. Picture Interpretation (Thematic Apperception Test)
Thematic Apperception Test (TAT), along with the Rorschach Inkblot test, is probably the most widely known and used projective technique in Clinical Psychology. The same basic technique used above is applied for marketing research-applications.
Here, the respondent is shown a picture – either a line drawing, illustration or photograph which is rather ambiguous, and is asked to describe what is going on – or tell a story about what is illustrated.
4. Observation Method
Observation method is another very powerful tool for getting information about the consumer. This method is used for recording behaviour of people, objects, events. Informal observations are extensively used for observing customer buying patterns, impact of competitive advertisement on buying, product availability etc.
Observation technique is always used in conjunction with other research techniques. The inherent danger in this method is that one could draw wrong conclusions as it is highly subjective, and a lot depends upon the observer’s perception of a situation.
Q.8. Define Cluster Analysis. Explain how an airline’s marketing manager use cluster analysis to segment his customer. (20)
Cluster analysis is a technique that is used in order to segment a market. The objective is to find out a group of customers in the market place that are homogeneous i.e., they share some characteristics so that they can be classified into one group. The cluster/group so found out should be large enough so that the company can develop it profitably, as the ultimate objective of a company is to serve the customer and earn profits. The group of customers that the company hopes to serve should be large enough for a company so that it is an economically viable proposition for the company. This is also true for the customer as customer would not be willing to pay beyond. a certain price for a particular product (price of course is a function of positioning of product, cost of production etc.).
As a example, let us consider the Watch Industry. There could be many ways in which the Watch Industry could be segmented which are as follows
a. Gender (Male/Female)
b. Technology (Digital/Analog)
c. Design Features
d. Occasion of Use (Formal/Casua/Party)
e. Price (Low/Medium/High/Jewellery)
Some of the above segmentation factors are demographic (price, gender) whereas some are psychographic factors (occasion to use.)
This, therefore, presents a problem to the market researcher/company, as to how to identify combination of factors that can be used to segment the market place. It is not always possible to segment a market on the basis of one single factor. Thus, a combination of factors must be used to segment the market place. And this is where Cluster Analysis technique specifically deals with how objects (people, places, products) should be assigned to groups, so that there should be similarity within the groups; and as much difference between the groups, as possible.
Cluster analysis can be a powerful data-mining tool for any organisation that needs to identify discrete groups of customers, sales transactions, or other types of behaviors and things. For example, insurance providers use cluster analysis to detect fraudulent claims, and banks use it for credit scoring.
Cluster analysis, like reduced space analysis (factor analysis), is concerned with data matrices in which the variables have not been partitioned beforehand into criterion versus predictor subsets.
The objective of cluster analysis is to find similar groups of subjects, where “similarity” between each pair of subjects means some global measure over the whole set of characteristics. In this article we discuss various methods of clustering and the key role that distance plays as measures of the proximity of pairs of points.
Cluster analysis is widely used in market research when working with multivariate data from surveys and test panels. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers, and for use in market segmentation, product positioning, new product development and selecting test markets.
Clustering can be used to group all the shopping items available on the web into a set of unique products. For example, all the items on eBay can be grouped into unique products (eBay does not have the concept of a SKU).
Cluster Analysis in Airlines
The cluster analysis would help a airlines company in the following ways:
a. The Airlines company can study the various clusters of customers that have emerged, and decide which customer group it would like to serve, depending upon the company’s own resources and capabilities, the volume of business in each cluster group that will generate sufficient business for the company’s own survival.
b. Once a cluster has been selected by the company, it can tailor various tour programmes for its cluster of customers.
c. The company can keep profile of its customers, and identify any new emerging group of cluster.
d. The company can decide to serve either only one group of customers (Niche marketing), or serve all groups of customers, at one and the same time, but having a range of travel and tour programmes.
Q.9. Write short notes on the following: (5×4=20)
a. Conjoint Analysis
Conjoint Analysis is basically a data decomposition technique which tries to plot the output data on the joint space of the importance of each attribute are the attribute.
It seeks data from the consumers in the form of their overall response to the totality of products (or their descriptions) while the output is in terms of the scores that the consumer has implicitly assigned to each of the attribute and its levels.
The important thing to note is that the consumer is not asked to assign scores to different attributes separately. In fact, the consumer is presented the stimulus in the form of totality of the product like in the case of MD S. However, there is one difference between and Conjoint Analysis. In Conjoint Analysis, the stimuli are created by the researcher himself While in the case of PODS already existing products or brands are used.
Conjoint Analysis derives the importance weights (called “part worth utilities”) assigned by each consumer to respective levels of attributes in such a way that they are directly comparable. This feature of the technique allows to determine the trade-offs that the consumers make in their minds. The relative importance of the attributes can also be derived from the output of “part worth utilities”. Thus, starting with a very simple input data (just the ranking of some predesigned product alternatives)
Conjoint Analysis provides the part worth utilities for each of the product attributes levels for every consumer individually
The steps involved in the application of Conjoint Analysis
1. Determination of the salient attributes for the given product from the points of view of the consumers
2. Assigning a set of discrete levels or a range of continuous values to each of the attributes.
3. Utilising Fractional Factorial Design of Experiment for designing the stimuli for experiment.
4. Physically designing the stimuli
5. Ranking or Rating data collection
6. Conjoint analysis and determination of part worth utilities.
7. Applying conjoint analysis output for different marketing decisions
b. Regression Analysis
Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest.
While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable.
Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.
Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilized to assess the strength of the relationship between variables and for modelling the future relationship between them.
Regression analysis includes several variations, such as linear, multiple linear, and nonlinear.
The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship. Regression analysis offers numerous applications in various disciplines, including finance.
Regression Analysis – Linear model assumptions
Linear regression analysis is based on six fundamental assumptions:
• The dependent and independent variables show a linear relationship between the slope and the intercept.
• The independent variable is not random.
• The value of the residual (error) is zero.
• The value of the residual (error) is constant across all observations.
• The value of the residual (error) is not correlated across all observations.
• The residual (error) values follow the normal distribution.
Regression Analysis – Simple linear regression
Simple linear regression is a model that assesses the relationship between a dependent variable and an independent variable. The simple linear model is expressed using the following equation:
Y = a + bX + ϵ
Where:
Y – Dependent variable
X – Independent (explanatory) variable
a – Intercept
b – Slope
ϵ – Residual (error)
Regression Analysis – Multiple linear regression
Multiple linear regression analysis is essentially similar to the simple linear model, with the exception that multiple independent variables are used in the model. The mathematical representation of multiple linear regression is:
Y = a + bX1 + cX2 + dX3 + ϵ
Where:
Y – Dependent variable
X1, X2, X3 – Independent (explanatory) variables
a – Intercept
b, c, d – Slopes
ϵ – Residual (error)
Multiple linear regression follows the same conditions as the simple linear model. However, since there are several independent variables in multiple linear analysis, there is another mandatory condition for the model:
• Non-collinearity: Independent variables should show a minimum of correlation with each other. If the independent variables are highly correlated with each other, it will be difficult to assess the true relationships between the dependent and independent variables.
c. Discriminant Analysis
Discriminant analysis is statistical technique used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables.
For example, a doctor could perform a discriminant analysis to identify patients at high or low risk for stroke. The analysis might classify patients into high- or low-risk groups, based on personal attributes (e.g., cholesterol level, body mass) and/or lifestyle behaviours (e.g., minutes of exercise per week, packs of biscuits per day.
Two-Group Discriminant Analysis
A common research problem involves classifying observations into one of two groups, based on two or more quantitative, predictor variables.
When there are only two classification groups, discriminant analysis is really just multiple regression, with a few tweaks.
• The dependent variable is a dichotomous, categorical variable (i.e., a categorical variable that can take on only two values).
• The dependent variable is expressed as a dummy variable (having values of 0 or 1).
• Observations are assigned to groups, based on whether the predicted score is closer to 0 or to 1.
• The regression equation is called the discriminant function.
• The efficacy of the discriminant function is measured by the proportion of correct assignments.
Multiple Discriminant Analysis
Multiple discriminant analysis (MDA) is a statistician’s technique used by financial planners to evaluate potential investments when a number of variables must be taken into account. This technique reduces the differences between some variables so that they can be classified in a set number of broad groups, which can then be compared to another variable.
In finance, this technique is used to compress the variance between securities while screening for several variables.
Multiple discriminant analysis is related to discriminant analysis, which helps classify a data set by setting a rule or selecting a value that will provide the most meaningful separation.
The biggest difference between discriminant analysis and standard regression analysis is the use of a categorical variable as a dependent variable. Other than that, the two-group discriminant analysis is just like standard multiple regression analysis. The key steps in the analysis are:
• Estimate regression coefficients.
• Define regression equation, which is the discriminant function.
• Assess the fit of the regression equation to the data.
• Assess the ability of the regression equation to correctly classify observations.
• Assess the relative importance of predictor variables.
d. Factor Analysis
Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors, plus “error” terms. Factor analysis aims to find independent latent variables.
The theory behind factor analytic methods is that the information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Factor analysis is commonly used in biology, psychometrics, personality theories, marketing, product management, operations research, and finance. It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables. It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.
Factor analysis is related to principal component analysis (PCA), but the two are not identical. There has been significant controversy in the field over differences between the two techniques (see section on exploratory factor analysis versus principal components analysis below). PCA can be considered as a more basic version of exploratory factor analysis (EFA) that was developed in the early days prior to the advent of high-speed computers. Both PCA and factor analysis aim to reduce the dimensionality of a set of data, but the approaches taken to do so are different for the two techniques. Factor analysis is clearly designed with the objective to identify certain unobservable factors from the observed variables, whereas PCA does not directly address this objective; at best, PCA provides an approximation to the required factors. From the point of view of exploratory analysis, the eigenvalues of PCA are inflated component loadings, i.e., contaminated with error variance.
Q.10. Design a Marketing Research Plan to determine the feasibility of opening an up-scale restaurant in a metropolitan city. (20)
A marketing plan is an important part of the growth and development of a company, since it defines the key marketing elements of the company and clarifies the objectives and directions for the company and the employees.
Marketing plan is a document describing a company’s advertising and marketing plans for the upcoming years. It can include a company’s marketing situation, positioning, target markets and activities to achieve marketing objectives.
The main idea of a marketing plan is to identify and create a competitive advantage . A marketing plan has usually a number of different parts. The number depends on how detailed the company’s top management wants the marketing plan to be.
Marketing Plan for the Restaurant
1. Develop the unique selling proposition, which identifies the qualities of the product that sets you apart from the competition.
So the USP of this is that it a Restaurant Located at a beautiful location, the best place to spend the time .
2. Turn the business features into benefits. Customer don’t buy the product, and then buy what your product will do for them. When creating your marketing plan, you want to focus on the customers’ benefits, not on what your product offers.
So, the Restaurant at the best location with 24hr transportation facilities.
3. Identify your target market, which is the group of people who want or need what you’re selling.
Target Market for the Restaurant-
Couples
Families
Groups
Businessmen
Young people
4. Determine the places your target market visits and the media outlets they read or watch. When it comes time to promote your business, you’ll want to put your promotional materials directly in front of your market.
Advertising will be done on Social Media Sites, E- newspaper, websites, etc.
5. Create marketing materials that speak to your target audience. If you have more than one target group, tailor your materials to fit each of them.
Ads will be displayed on Social Media, YouTube, etc
6. Create a plan to position your marketing materials where your market will see them. Run ads or submit paid articles in the magazines or websites members of your target market visit.
Put business cards or fliers in locations your potential customers frequent. Create a website or blog that provides current news and information your market will want to stay informed about. The strategies you use should focus on your customer–where they are and what types of marketing to which they respond.