This chapter discusses exploratory research design using secondary data sources. It begins with an overview and outlines the key points that will be covered, including the differences between primary and secondary data, criteria for evaluating secondary data, and classifications of various secondary data sources. Examples of secondary data sources discussed include internal business data, published materials from businesses and governments, computerized databases, and syndicated data services providing household and institutional data.
The chapter discusses qualitative research methods including focus groups, depth interviews, and projective techniques. Focus groups involve interviewing groups of 6-12 people to explore views in a group setting. Depth interviews use open-ended questions to understand motivations and attitudes. Projective techniques indirectly explore subconscious motivations through activities like word associations. Online methods can reduce costs but lack control over environment.
The document outlines a chapter on descriptive research design, specifically survey and observation methods. It provides an overview and classification of various survey methods including telephone, personal, mail, and electronic methods. Evaluation criteria for different survey methods are presented such as flexibility, diversity of questions, use of stimuli, sample control, and response rate. Observation methods are also classified and evaluated based on structure, disguise, and ability to observe in natural settings.
The document discusses research design and different types of research designs. It covers exploratory research, descriptive research including cross-sectional and longitudinal designs, and causal research. Descriptive research aims to describe characteristics or functions, while causal research determines cause-and-effect relationships by manipulating variables. Exploratory research provides insights and understanding to define problems or generate hypotheses. Descriptive research such as surveys and panels can estimate behaviors or determine perceptions. Causal research uses experiments to test hypotheses by controlling variables. Cross-sectional designs collect one-time data from samples, while longitudinal designs track the same samples over time.
The document outlines a chapter on exploratory research design using secondary data. It discusses the advantages and uses of secondary data as well as the criteria for evaluating secondary data sources, such as specifications, error, currency, objectives, nature, and dependability. It also provides classifications of different types of secondary data sources, including internal sources, published sources, computerized databases, and syndicated services.
The marketing research problem involves examining how consumers evaluate products and make purchase decisions. Specifically, it aims to understand the impact of the self-reference criterion (SRC), which refers to judging other cultures based on one's own cultural standards and experiences.
1. Domestically, factors like preferences, values and economic conditions shape consumer behavior.
2. In foreign markets, additional considerations include differing cultural norms, beliefs and economic environments. Local consumer perspectives must be understood on their own terms without judgment.
3. The SRC complicates the problem by influencing how researchers from one culture study others. One's own cultural lens can distort the interpretation of foreign consumer behavior.
4. To address the problem, the SRC influence must be removed
This document discusses international marketing research methods. It provides an outline of topics including the marketing research environment, survey methods, measurement and scaling, questionnaire translation, and ethics. For survey methods, it describes techniques for different regions including telephone interviews, in-person interviews, mail surveys, and electronic surveys. It also addresses challenges with equivalence and translation when conducting international marketing research.
This document discusses research design and provides examples of exploratory, descriptive, and causal research. It defines key terms like research design, sampling error, and non-sampling error. It also provides examples of how Citicorp uses exploratory, descriptive, and causal research to design new financial packages for senior citizens. Finally, it describes Greenfield Online Research Center, which conducts online focus groups, surveys, and polls using a panel of registered internet users.
The chapter discusses various sampling techniques used in marketing research. It covers probability sampling methods like simple random sampling, systematic sampling, stratified sampling and cluster sampling as well as non-probability methods like convenience sampling, judgmental sampling, quota sampling and snowball sampling. The key steps in sampling design are defined as identifying the target population, determining the sampling frame, selecting a sampling technique, determining sample size and executing the sampling process. Factors to consider for determining sample size include importance of decision, nature of research, number of variables, analysis required, resources and more. [/SUMMARY]
The chapter discusses qualitative research methods including focus groups, depth interviews, and projective techniques. Focus groups involve interviewing groups of 6-12 people to explore views in a group setting. Depth interviews use open-ended questions to understand motivations and attitudes. Projective techniques indirectly explore subconscious motivations through activities like word associations. Online methods can reduce costs but lack control over environment.
The document outlines a chapter on descriptive research design, specifically survey and observation methods. It provides an overview and classification of various survey methods including telephone, personal, mail, and electronic methods. Evaluation criteria for different survey methods are presented such as flexibility, diversity of questions, use of stimuli, sample control, and response rate. Observation methods are also classified and evaluated based on structure, disguise, and ability to observe in natural settings.
The document discusses research design and different types of research designs. It covers exploratory research, descriptive research including cross-sectional and longitudinal designs, and causal research. Descriptive research aims to describe characteristics or functions, while causal research determines cause-and-effect relationships by manipulating variables. Exploratory research provides insights and understanding to define problems or generate hypotheses. Descriptive research such as surveys and panels can estimate behaviors or determine perceptions. Causal research uses experiments to test hypotheses by controlling variables. Cross-sectional designs collect one-time data from samples, while longitudinal designs track the same samples over time.
The document outlines a chapter on exploratory research design using secondary data. It discusses the advantages and uses of secondary data as well as the criteria for evaluating secondary data sources, such as specifications, error, currency, objectives, nature, and dependability. It also provides classifications of different types of secondary data sources, including internal sources, published sources, computerized databases, and syndicated services.
The marketing research problem involves examining how consumers evaluate products and make purchase decisions. Specifically, it aims to understand the impact of the self-reference criterion (SRC), which refers to judging other cultures based on one's own cultural standards and experiences.
1. Domestically, factors like preferences, values and economic conditions shape consumer behavior.
2. In foreign markets, additional considerations include differing cultural norms, beliefs and economic environments. Local consumer perspectives must be understood on their own terms without judgment.
3. The SRC complicates the problem by influencing how researchers from one culture study others. One's own cultural lens can distort the interpretation of foreign consumer behavior.
4. To address the problem, the SRC influence must be removed
This document discusses international marketing research methods. It provides an outline of topics including the marketing research environment, survey methods, measurement and scaling, questionnaire translation, and ethics. For survey methods, it describes techniques for different regions including telephone interviews, in-person interviews, mail surveys, and electronic surveys. It also addresses challenges with equivalence and translation when conducting international marketing research.
This document discusses research design and provides examples of exploratory, descriptive, and causal research. It defines key terms like research design, sampling error, and non-sampling error. It also provides examples of how Citicorp uses exploratory, descriptive, and causal research to design new financial packages for senior citizens. Finally, it describes Greenfield Online Research Center, which conducts online focus groups, surveys, and polls using a panel of registered internet users.
The chapter discusses various sampling techniques used in marketing research. It covers probability sampling methods like simple random sampling, systematic sampling, stratified sampling and cluster sampling as well as non-probability methods like convenience sampling, judgmental sampling, quota sampling and snowball sampling. The key steps in sampling design are defined as identifying the target population, determining the sampling frame, selecting a sampling technique, determining sample size and executing the sampling process. Factors to consider for determining sample size include importance of decision, nature of research, number of variables, analysis required, resources and more. [/SUMMARY]
The document outlines the process of defining a marketing research problem and developing an approach to solve it. It discusses important tasks like discussing the problem with decision makers, interviews with experts, and secondary data analysis. It emphasizes that properly defining the problem is crucial before analyzing any data collected. The problem definition leads to the development of specific research questions, hypotheses, and identification of necessary information to address the problem components. The summary provides an overview of the key steps and concepts discussed in the document.
This document outlines the key topics covered in Chapter 4 which explores exploratory research design using secondary data. It discusses the differences between primary and secondary data, the advantages and criteria for evaluating secondary data, and provides classifications of various secondary data sources including internal/external, published, computerized databases, and syndicated sources. It also covers international secondary data sources and ethics in marketing research.
This chapter discusses fieldwork and data collection in marketing research. It covers the selection, training, supervision, validation and evaluation of field workers who collect data. Field workers are selected based on qualifications like communication skills, appearance and experience. They undergo training in techniques for initial contact, asking questions, probing, recording answers and terminating interviews. Supervision involves quality control, sampling control and preventing cheating. Fieldwork is validated by re-contacting respondents. Evaluation looks at costs, response rates and data quality.
This chapter discusses measurement and scaling techniques. It defines measurement as assigning numbers or symbols to object characteristics according to standardized rules. Scaling is measuring quantitative attributes. There are four primary scales of measurement: nominal (labels), ordinal (rank order), interval (equal distances), and ratio (absolute zero). Comparative scaling involves directly comparing objects and yields only ordinal data, while non-comparative scaling scales objects independently and can produce interval or ratio data. Comparative techniques include paired comparisons (preference between pairs), rank ordering (simultaneously ranking all objects), and constant sum scaling (allocating a fixed number of points among attributes).
The document outlines a chapter on descriptive research design, specifically survey and observation methods. It provides an overview and classification of various survey methods including telephone, personal, mail, and electronic methods. Evaluation criteria for different survey methods are presented such as flexibility, diversity of questions, use of stimuli, sample control, and response rate. Observation methods are also classified and evaluated based on structure, disguise, and ability to observe in natural settings.
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This chapter outlines different research design methods for surveys and observation. It discusses various survey methods classified by administration mode such as telephone, personal, mail, and electronic surveys. Evaluation criteria for different survey methods like flexibility, sample control, response rates and costs are also presented. The chapter also covers structured vs unstructured observation, natural vs contrived observation, and observational methods classified by administration mode including personal, mechanical, audit and content analysis.
This chapter introduces marketing research. It defines marketing research as the systematic process of identifying, collecting, analyzing, and reporting information to improve marketing decision-making. The chapter outlines the marketing research process and different types of marketing research problems. It also discusses internal and external marketing research suppliers and the services they provide.
The document provides an overview of marketing research, including definitions, classifications, processes, roles, and career paths. It discusses problem identification and problem solving research, and how research is used to link consumers and customers to marketers through systematic collection and analysis of information to improve marketing decisions. The chapter also outlines the marketing research process and different types of marketing research suppliers and services.
The document discusses sample size determination and adjusting for non-response in marketing research. It provides definitions of key terms like population, parameter, statistic, and confidence interval. It presents methods for determining sample sizes needed to estimate means, proportions, and multiple characteristics based on desired precision levels and population variability. The document also reviews techniques for adjusting sample sizes based on incidence rates and improving response rates, as well as methods for adjusting estimates for non-response bias, like weighting and imputation. Finally, it provides an example of a company that bases its opinions on online surveys of 1,000 respondents.
This chapter outline covers frequency distributions, measures of location, variability and shape associated with frequency distributions. It introduces hypothesis testing and provides a general procedure for hypothesis testing. It discusses cross-tabulations for two and three variables and associated statistics like chi-square. It covers hypothesis testing related to differences and parametric and non-parametric tests for one sample, two independent samples and paired samples. It concludes with a focus on internet and computer applications.
The document provides an overview of the key steps in the data preparation process for statistical analysis, including questionnaire checking, editing, coding, transcribing, data cleaning, statistically adjusting the data through weighting and variable respecification, and selecting an appropriate data analysis strategy. Specific techniques for each step are described, such as assigning codes in a codebook and using software like SPSS to identify outliers and inconsistencies in the data.
The document outlines the key steps and considerations in defining a marketing research problem and developing an approach to address it. It discusses defining the management decision problem, conducting discussions with decision-makers and experts, analyzing secondary data and qualitative research. It emphasizes properly defining the specific marketing research problem and its components before developing the research approach, which includes establishing objectives, analytical models, research questions, hypotheses and specifying the necessary information.
This chapter discusses noncomparative scaling techniques used in marketing research to measure attitudes, opinions, and characteristics without direct comparison between objects. It describes continuous rating scales where respondents indicate their rating along a line, and itemized rating scales including the Likert scale involving levels of agreement, semantic differential scales using bipolar adjective pairs, and Stapel scales using a numbered unipolar format. The chapter also covers decisions in designing these scales and evaluating their measurement properties.
The document describes Tennis magazine conducting a mail survey of subscribers using systematic sampling. The sampling interval was 1,000 based on a subscriber list of 1,472,000 names. Every 1,000th subscriber was selected starting from a random number between 1 and 1,000. A dollar bill incentive and follow-up mailings yielded a 56% response rate of 778 completed questionnaires from the sample of 1,472 subscribers.
The key is to recognize how one's own cultural biases and assumptions (the SRC) can complicate defining problems in international contexts, and to redefine problems in a culturally neutral way without those biases.
The document outlines the key topics covered in Chapter 15, which include frequency distribution, measures of location, variability and shape related to frequency distributions, hypothesis testing procedures, and cross-tabulation. It provides examples of computing common statistics like the mean, median, range and standard deviation. The chapter cover introduces hypothesis testing methodology, outlining steps like formulating hypotheses, selecting a test, and determining significance levels and types of errors. Examples are given of computing test statistics and determining probabilities. Cross-tabulation and related statistics like chi-square are also listed as chapter topics.
The chapter discusses different types of causal research designs and experimentation. It covers key concepts like causality, conditions for causality, validity in experimentation, and ways to control extraneous variables. Various experimental designs are classified and defined, including pre-experimental, true experimental, quasi-experimental, and statistical designs. Specific designs like the one-shot case study, one-group pretest-posttest, and randomized block are described.
The chapter discusses different scales of measurement used in marketing research including nominal, ordinal, interval, and ratio scales. It compares primary methods of scaling such as paired comparisons, rank ordering, and constant sum scaling. These scaling techniques can be used to measure preferences, attitudes, and perceptions in both comparative and noncomparative ways.
This chapter discusses the design of questionnaires and observation forms. It outlines the key steps in the questionnaire design process, including specifying the necessary information, determining question structure and wording, and ordering questions properly. The chapter also addresses overcoming respondents' inability or unwillingness to answer questions and choosing the appropriate question type based on the interview method. Questionnaires designed for children aim to minimize response error by keeping them short, defining contexts, and using simple language.
Secondary research (magazine institution research and uses and grat)atm1996
The document discusses four magazine publishing companies in the UK - Bauer, IPC Media, Emap, and Future PLC. It provides details on when each company was established, who owns them, examples of the types of magazines they publish, and whether their magazines are mainstream or niche. The concluding paragraphs discuss how Blumler and Katz's uses and gratifications theory suggests that media users actively choose media sources that fulfill their needs, such as diversion, personal relationships, personal identity, and surveillance. Billboard magazine is used as an example of how it can fulfill these four needs.
The document outlines the process of defining a marketing research problem and developing an approach to solve it. It discusses important tasks like discussing the problem with decision makers, interviews with experts, and secondary data analysis. It emphasizes that properly defining the problem is crucial before analyzing any data collected. The problem definition leads to the development of specific research questions, hypotheses, and identification of necessary information to address the problem components. The summary provides an overview of the key steps and concepts discussed in the document.
This document outlines the key topics covered in Chapter 4 which explores exploratory research design using secondary data. It discusses the differences between primary and secondary data, the advantages and criteria for evaluating secondary data, and provides classifications of various secondary data sources including internal/external, published, computerized databases, and syndicated sources. It also covers international secondary data sources and ethics in marketing research.
This chapter discusses fieldwork and data collection in marketing research. It covers the selection, training, supervision, validation and evaluation of field workers who collect data. Field workers are selected based on qualifications like communication skills, appearance and experience. They undergo training in techniques for initial contact, asking questions, probing, recording answers and terminating interviews. Supervision involves quality control, sampling control and preventing cheating. Fieldwork is validated by re-contacting respondents. Evaluation looks at costs, response rates and data quality.
This chapter discusses measurement and scaling techniques. It defines measurement as assigning numbers or symbols to object characteristics according to standardized rules. Scaling is measuring quantitative attributes. There are four primary scales of measurement: nominal (labels), ordinal (rank order), interval (equal distances), and ratio (absolute zero). Comparative scaling involves directly comparing objects and yields only ordinal data, while non-comparative scaling scales objects independently and can produce interval or ratio data. Comparative techniques include paired comparisons (preference between pairs), rank ordering (simultaneously ranking all objects), and constant sum scaling (allocating a fixed number of points among attributes).
The document outlines a chapter on descriptive research design, specifically survey and observation methods. It provides an overview and classification of various survey methods including telephone, personal, mail, and electronic methods. Evaluation criteria for different survey methods are presented such as flexibility, diversity of questions, use of stimuli, sample control, and response rate. Observation methods are also classified and evaluated based on structure, disguise, and ability to observe in natural settings.
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This chapter outlines different research design methods for surveys and observation. It discusses various survey methods classified by administration mode such as telephone, personal, mail, and electronic surveys. Evaluation criteria for different survey methods like flexibility, sample control, response rates and costs are also presented. The chapter also covers structured vs unstructured observation, natural vs contrived observation, and observational methods classified by administration mode including personal, mechanical, audit and content analysis.
This chapter introduces marketing research. It defines marketing research as the systematic process of identifying, collecting, analyzing, and reporting information to improve marketing decision-making. The chapter outlines the marketing research process and different types of marketing research problems. It also discusses internal and external marketing research suppliers and the services they provide.
The document provides an overview of marketing research, including definitions, classifications, processes, roles, and career paths. It discusses problem identification and problem solving research, and how research is used to link consumers and customers to marketers through systematic collection and analysis of information to improve marketing decisions. The chapter also outlines the marketing research process and different types of marketing research suppliers and services.
The document discusses sample size determination and adjusting for non-response in marketing research. It provides definitions of key terms like population, parameter, statistic, and confidence interval. It presents methods for determining sample sizes needed to estimate means, proportions, and multiple characteristics based on desired precision levels and population variability. The document also reviews techniques for adjusting sample sizes based on incidence rates and improving response rates, as well as methods for adjusting estimates for non-response bias, like weighting and imputation. Finally, it provides an example of a company that bases its opinions on online surveys of 1,000 respondents.
This chapter outline covers frequency distributions, measures of location, variability and shape associated with frequency distributions. It introduces hypothesis testing and provides a general procedure for hypothesis testing. It discusses cross-tabulations for two and three variables and associated statistics like chi-square. It covers hypothesis testing related to differences and parametric and non-parametric tests for one sample, two independent samples and paired samples. It concludes with a focus on internet and computer applications.
The document provides an overview of the key steps in the data preparation process for statistical analysis, including questionnaire checking, editing, coding, transcribing, data cleaning, statistically adjusting the data through weighting and variable respecification, and selecting an appropriate data analysis strategy. Specific techniques for each step are described, such as assigning codes in a codebook and using software like SPSS to identify outliers and inconsistencies in the data.
The document outlines the key steps and considerations in defining a marketing research problem and developing an approach to address it. It discusses defining the management decision problem, conducting discussions with decision-makers and experts, analyzing secondary data and qualitative research. It emphasizes properly defining the specific marketing research problem and its components before developing the research approach, which includes establishing objectives, analytical models, research questions, hypotheses and specifying the necessary information.
This chapter discusses noncomparative scaling techniques used in marketing research to measure attitudes, opinions, and characteristics without direct comparison between objects. It describes continuous rating scales where respondents indicate their rating along a line, and itemized rating scales including the Likert scale involving levels of agreement, semantic differential scales using bipolar adjective pairs, and Stapel scales using a numbered unipolar format. The chapter also covers decisions in designing these scales and evaluating their measurement properties.
The document describes Tennis magazine conducting a mail survey of subscribers using systematic sampling. The sampling interval was 1,000 based on a subscriber list of 1,472,000 names. Every 1,000th subscriber was selected starting from a random number between 1 and 1,000. A dollar bill incentive and follow-up mailings yielded a 56% response rate of 778 completed questionnaires from the sample of 1,472 subscribers.
The key is to recognize how one's own cultural biases and assumptions (the SRC) can complicate defining problems in international contexts, and to redefine problems in a culturally neutral way without those biases.
The document outlines the key topics covered in Chapter 15, which include frequency distribution, measures of location, variability and shape related to frequency distributions, hypothesis testing procedures, and cross-tabulation. It provides examples of computing common statistics like the mean, median, range and standard deviation. The chapter cover introduces hypothesis testing methodology, outlining steps like formulating hypotheses, selecting a test, and determining significance levels and types of errors. Examples are given of computing test statistics and determining probabilities. Cross-tabulation and related statistics like chi-square are also listed as chapter topics.
The chapter discusses different types of causal research designs and experimentation. It covers key concepts like causality, conditions for causality, validity in experimentation, and ways to control extraneous variables. Various experimental designs are classified and defined, including pre-experimental, true experimental, quasi-experimental, and statistical designs. Specific designs like the one-shot case study, one-group pretest-posttest, and randomized block are described.
The chapter discusses different scales of measurement used in marketing research including nominal, ordinal, interval, and ratio scales. It compares primary methods of scaling such as paired comparisons, rank ordering, and constant sum scaling. These scaling techniques can be used to measure preferences, attitudes, and perceptions in both comparative and noncomparative ways.
This chapter discusses the design of questionnaires and observation forms. It outlines the key steps in the questionnaire design process, including specifying the necessary information, determining question structure and wording, and ordering questions properly. The chapter also addresses overcoming respondents' inability or unwillingness to answer questions and choosing the appropriate question type based on the interview method. Questionnaires designed for children aim to minimize response error by keeping them short, defining contexts, and using simple language.
Secondary research (magazine institution research and uses and grat)atm1996
The document discusses four magazine publishing companies in the UK - Bauer, IPC Media, Emap, and Future PLC. It provides details on when each company was established, who owns them, examples of the types of magazines they publish, and whether their magazines are mainstream or niche. The concluding paragraphs discuss how Blumler and Katz's uses and gratifications theory suggests that media users actively choose media sources that fulfill their needs, such as diversion, personal relationships, personal identity, and surveillance. Billboard magazine is used as an example of how it can fulfill these four needs.
This document outlines the primary and secondary research plans for a documentary about shoes. The primary research includes a questionnaire, planned interviews with shoe retailers, a podiatrist, shoe collector, athlete, and Clarks employee. Interview locations include a basketball court, podiatrist office, shoe collector's house, and shoe store. Secondary research will involve statistics on shoe sales, promotional photos, articles, and archived footage of athletes, shoe making processes, and large shoe collections.
Have cooperative retailers and distributors.
9) Be willing to participate in test marketing programs.
10) Have adequate facilities for data collection and analysis.
11) Be economically accessible.
12) Be willing to accept new products on a test basis.
13) Have stable economic and social conditions.
The chapter discusses different scales of measurement used in marketing research including nominal, ordinal, interval, and ratio scales. It compares primary methods of scaling such as paired comparisons, rank ordering, and constant sum scaling. These scaling techniques can be used to measure preferences, attitudes, and perceptions in both comparative and noncomparative ways.
The chapter discusses qualitative research methods including focus groups, depth interviews, and projective techniques. Focus groups involve interviewing groups of 6-12 people to explore views in a group setting. Depth interviews use open-ended questions to understand motivations and attitudes. Projective techniques indirectly explore subconscious motivations through activities like word associations. Online methods can reduce costs but lack control over environment.
A measure cannot be valid if it is not also reliable. Reliability is a necessary but not sufficient condition for validity. A measure must be free of random error (be reliable) in order to accurately reflect the true score (be valid), but reliability alone does not guarantee validity, as systematic errors could also impact validity.
This chapter discusses questionnaire and observation form design. It outlines the key steps in the questionnaire design process, including specifying the information needed, determining individual question content, overcoming respondents' inability or unwillingness to answer, choosing question structure and wording, and determining question order. The chapter emphasizes that questions must be necessary, unambiguous, avoid bias, and account for respondents' limitations. It also provides examples of effective and ineffective question wording.
The chapter discusses different types of experimental designs used in causal research, including:
1) True experimental designs that use random assignment to control for extraneous variables like the pretest-posttest control group design.
2) Quasi-experimental designs that partially apply experimental methods like time series designs when full randomization isn't possible.
3) Pre-experimental designs with no random assignment like the one-shot case study design that are better for exploratory research.
This document discusses fieldwork and data collection for marketing research. It covers selecting, training, and supervising field workers. Key aspects include developing job specifications and qualifications for field workers, training them on techniques like probing and recording answers, and supervising them to ensure quality control. Supervision involves validating a portion of completed interviews to check for accuracy and quality in data collection.
1) Factor analysis is used to identify underlying dimensions, or factors, that explain correlations among a set of variables and reduce data.
2) Conducting factor analysis involves constructing a correlation matrix, determining the appropriate factor analysis method, extracting factors, and interpreting the results, which include factor loadings and scores.
3) The example shows the results of a principal components analysis with 6 variables, including communalities, eigenvalues, factor matrices before and after rotation, and factor score coefficients.
This chapter discusses noncomparative scaling techniques used in marketing research. It covers continuous rating scales where respondents rate objects along a line. It also discusses itemized rating scales including the Likert scale using degrees of agreement, the semantic differential using bipolar labels, and the Stapel scale using a unipolar 10-point scale without a neutral point. The chapter compares the advantages and disadvantages of these techniques and how they are used to measure attitudes, images, and perceptions in marketing research.
This chapter discusses international marketing research. It provides a framework for international marketing research that considers the environmental, marketing, government, legal, economic, structural, and socio-cultural factors. It also discusses various survey methods used internationally such as telephone interviews, in-person interviews, mall intercepts, mail surveys, and electronic surveys. The chapter emphasizes the importance of establishing measurement equivalence across countries and discusses approaches to translating questionnaires like back translation and parallel translation.
This chapter discusses multidimensional scaling (MDS) and conjoint analysis. It outlines the key steps in conducting MDS, including formulating the problem, obtaining input data through direct or derived approaches, selecting an MDS procedure, deciding on the number of dimensions, interpreting and labeling the dimensions of the spatial map, and assessing reliability and validity. It also covers assumptions, limitations, and the basic concepts of conjoint analysis.
This chapter outline describes one-way analysis of variance (ANOVA). It introduces key concepts like decomposing total variation into between- and within-group components to test if group means are equal. The chapter will cover conducting a one-way ANOVA, including identifying variables, decomposing variation, measuring effects with statistics like η2, testing significance with the F-ratio, and interpreting results. Examples and assumptions of one-way ANOVA will also be discussed.
This chapter discusses discriminant analysis, a statistical technique used when the dependent variable is categorical and the independent variables are continuous. Discriminant analysis develops functions to discriminate between categories and examines differences among groups in terms of predictors. It determines important predictors and classifies new cases while evaluating accuracy. The chapter covers conducting discriminant analysis, associated statistics, and similarities and differences between discriminant analysis, regression, and ANOVA.
This document outlines the chapter on exploratory research design and secondary data. It discusses the different types of secondary data sources including internal sources, published external sources, computerized databases, and syndicated services. It describes the advantages and disadvantages of secondary data and important criteria for evaluating secondary data sources such as specifications, error, currency, objectives, nature, and dependability. Lastly, it provides examples of how secondary data from different sources can be combined and used for applications such as computer mapping, international marketing research, and focusing on specific companies.
This chapter discusses secondary data sources that can be used in marketing research. It describes the relationship of secondary data to other parts of the marketing research process. The chapter also covers the advantages and disadvantages of secondary data, criteria for evaluating secondary data sources, and various types of secondary data sources including internal sources, published sources, databases, and sources for international marketing research.
Exploratory research design secondary dataRohit Kumar
Secondary data are data that were collected for purposes other than the current research problem. There are two main types: internal secondary data that come from within an organization, and external secondary data from external published sources. Secondary data can help identify and define research problems, develop research designs, and interpret primary data. When evaluating secondary data, researchers consider criteria like the methodology, accuracy, currency, objectives, nature, and dependability of the data. Common sources of secondary data include published materials, computer databases, syndicated services, and internal records.
This document discusses secondary data collection and provides classifications of different types of secondary data sources. It begins by defining secondary data as information previously collected for other purposes that can be relevant to the current research problem. It then categorizes secondary data sources as internal or external. External sources include published materials like census data, government publications, indexes, guides and directories. They also include computerized databases that are online, offline, full-text, numeric, special-purpose or bibliographic. Syndicated services and international sources are also outlined. Throughout, advantages and limitations of secondary data are noted.
Data mining involves extracting hidden patterns from large databases. It helps companies analyze important information in their data. Some applications of data mining include financial data analysis, retail industry analysis, telecommunications analysis, biological data analysis, scientific applications, and intrusion detection. Data mining uses techniques like classification, clustering, and prediction.
This document discusses characteristics of big data and the big data stack. It describes the evolution of data from the 1970s to today's large volumes of structured, unstructured and multimedia data. Big data is defined as data that is too large and complex for traditional data processing systems to handle. The document then outlines the challenges of big data and characteristics such as volume, velocity and variety. It also discusses the typical data warehouse environment and Hadoop environment. The five layers of the big data stack are then described including the redundant physical infrastructure, security infrastructure, operational databases, organizing data services and tools, and analytical data warehouses.
Introduction to Data Analytics and data analytics life cycleDr. Radhey Shyam
The document provides an overview of data analytics and big data concepts. It discusses the characteristics of big data, including the four V's of volume, velocity, variety and veracity. It also describes different types of data like structured, semi-structured and unstructured data. The document then introduces big data platforms and tools like Hadoop, Spark and Cassandra. Finally, it discusses the need for data analytics in business, including enabling better decision making and improving efficiency.
The document discusses the use of secondary data in marketing analytics. It provides an overview of secondary data, including its purpose and advantages. Secondary data can help define problems, identify relevant variables, and test hypotheses. However, secondary data also has limitations, such as potential lack of relevance, accuracy, or currency for the current research problem. When using secondary data, researchers must evaluate it based on criteria like timeliness, sample characteristics, methodology, and definitions used.
This document discusses big data analytics. It covers the four V's of big data (volume, variety, velocity, and veracity), drivers of big data, an introduction to big data analytics including what it is and key technologies used, benefits including improved customer service and decision making, and applications in various industries like healthcare, manufacturing, and banking. The learning objectives are to understand the four V's of big data, drivers of big data, what big data analytics is, and its benefits and applications.
KIT-601-L-UNIT-1 (Revised) Introduction to Data Analytcs.pdfDr. Radhey Shyam
The document provides an overview of data analytics and big data concepts. It discusses the characteristics of big data, including the four V's of volume, velocity, variety and veracity. It describes different types of data like structured, semi-structured and unstructured data. The document also introduces popular big data platforms like Hadoop, Spark and Cassandra. Finally, it outlines key reasons for the need of data analytics, such as enabling better decision making and improving organizational efficiency.
The document discusses secondary data sources for research. It defines secondary data as data collected for other purposes than the current problem. Secondary data has advantages of quick and cheap collection but disadvantages of potential inaccuracies or lack of relevance. The document outlines internal sources like accounting records and sales reports, as well as external sources including databases, government agencies, directories, and experts. It also discusses using secondary data for international research.
The document discusses predictive analytics and data mining. It covers the data mining process, including common standard processes like CRISP-DM. CRISP-DM involves 6 steps - business understanding, data understanding, data preparation, model building, testing and evaluation, and deployment. It also discusses data mining applications in various industries like banking, retail, and government/defense. Key data mining tasks include prediction, classification, clustering, association rule learning, and outlier detection.
Empowering your Enterprise with a Self-Service Data Marketplace (EMEA)Denodo
This document outlines an agenda for an EMEA webinar about empowering enterprises with a self-service data marketplace. The agenda includes discussions of the data challenges facing users, how a data marketplace can help address those challenges, what constitutes a data marketplace, a demo of Denodo's data catalog tool, and a customer case study. Key benefits of a data marketplace mentioned are enabling self-service access to trusted data while maintaining governance over sensitive data and reducing dependency on IT.
An Introduction to Monitoring and Evaluation of Healthcare Projects. Monitoring and Evaluation is an integral component for the success of any donor-funded project as it provides accountability and well-informed decisions through the use of data and plan that guides implementation
CONTENTS: CASE STUDIES
CASE STUDY 1 Midsouth Chamber of Commerce (A): The Role of the Operating Manager in
Information Systems
CASE STUDY I-1 IMT Custom Machine Company, Inc.: Selection of an Information Technology
Platform
CASE STUDY I-2 VoIP2.biz, Inc.: Deciding on the Next Steps for a VoIP Supplier
CASE STUDY I-3 The VoIP Adoption at Butler University
CASE STUDY I-4 Supporting Mobile Health Clinics: The Children’s Health Fund of New York City
CASE STUDY I-5 Data Governance at InsuraCorp
CASE STUDY I-6 H.H. Gregg’s Appliances, Inc.: Deciding on a New Information Technology
Platform
CASE STUDY I-7 Midsouth Chamber of Commerce (B): Cleaning Up an Information Systems
Debacle
CASE STUDY II-1 Vendor-Managed Inventory at NIBCO
CASE STUDY II-2 Real-Time Business Intelligence at Continental Airlines
CASE STUDY II-3 Norfolk Southern Railway: The Business Intelligence Journey
CASE STUDY II-4 Mining Data to Increase State Tax Revenues in California
CASE STUDY II-5 The Cliptomania™ Web Store: An E-Tailing Start-up Survival Story
CASE STUDY II-6 Rock Island Chocolate Company, Inc.: Building a Social Networking Strategy
CASE STUDY III-1 Managing a Systems Development Project at Consumer and Industrial
Products, Inc.
CASE STUDY III-2 A Make-or-Buy Decision at Baxter Manufacturing Company
CASE STUDY III-3 ERP Purchase Decision at Benton Manufacturing Company, Inc.
CASE STUDY III-4 The Kuali Financial System: An Open-Source Project
CASE STUDY III-5 NIBCO’s “Big Bang”: An SAP Implementation
CASE STUDY III-6 BAT Taiwan: Implementing SAP for a Strategic Transition
CASE STUDY III-7 A Troubled Project at Modern Materials, Inc.
CASE STUDY III-8 Purchasing and Implementing a Student Management System at Jefferson
County School System
CASE STUDY IV-1 The Clarion School for Boys, Inc.– Milwaukee Division: Making Information
Systems Investments
CASE STUDY IV-2 FastTrack IT Integration for the Sallie Mae Merger
CASE STUDY IV-3 IT Infrastructure Outsourcing at Schaeffer (A): The Outsourcing Decision
CASE STUDY IV-4 IT Infrastructure Outsourcing at Schaeffer (B): Managing the Contract
CASE STUDY IV-5 Systems Support for a New Baxter Manufacturing Company Plant in Mexico
CASE STUDY IV-6 The Challenges of Local System Design for Multinationals: The MaxFli Sales
Force Automation System at BAT
CASE STUDY IV-7 Meridian Hospital Systems, Inc.: Deciding Which IT Company to Join
CASE STUDY IV-8 Mary Morrison’s Ethical Issue
Managing Information
Technology
Prentice Hall
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Seventh Edition
Carol V. Brown
Howe School of Technology Management,
Stevens Institute of Technology
Daniel W. DeHayes
Kelley School of Business,
Indiana University
Jeffrey A. Hoffer
School of Business Administration,
The University of Dayton
E. Wa ...
This document discusses applications and trends in data mining. It provides examples of data mining applications in various domains including financial data analysis, retail industry, telecommunications industry, and biological data analysis. It also discusses selecting appropriate data mining systems and provides examples of commercial data mining systems. Finally, it introduces the concept of visual data mining and the role of visualization in the data mining process.
1. Marketing research is the systematic gathering and analysis of data to provide information for marketing decisions. It is more complex for international markets due to cultural differences.
2. The marketing research process involves defining the problem, developing a research plan, collecting data through primary or secondary sources, analyzing the data, and presenting findings.
3. Gathering accurate and relevant data from foreign markets can be challenging due to issues with data availability, reliability, comparability, and validating secondary sources. Primary data collection has issues with communication, sampling, and language.
This document provides an introduction to the concepts of data analytics and the data analytics lifecycle. It discusses big data in terms of the 4Vs - volume, velocity, variety and veracity. It also discusses other characteristics of big data like volatility, validity, variability and value. The document then discusses various concepts in data analytics like traditional business intelligence, data mining, statistical applications, predictive analysis, and data modeling. It explains how these concepts are used to analyze large datasets and derive value from big data. The goal of data analytics is to gain insights and a competitive advantage through analyzing large and diverse datasets.
Department of Commerce App Challenge: Big Data DashboardsBrand Niemann
The document summarizes Dr. Brand Niemann's presentation at the 2012 International Open Government Data Conference. It discusses open data principles and provides an example using EPA data. It also describes Niemann's beautiful spreadsheet dashboard for EPA metadata and APIs. Finally, it outlines Niemann's data science analytics approach for the conference, including knowledge bases, data catalog, and using business intelligence tools to analyze linked open government data.
CONTENTS CASE STUDIESCASE STUDY 1 Midsouth Chamber of.docxdonnajames55
CONTENTS: CASE STUDIES
CASE STUDY 1 Midsouth Chamber of Commerce (A): The Role of the Operating Manager in
Information Systems
CASE STUDY I-1 IMT Custom Machine Company, Inc.: Selection of an Information Technology
Platform
CASE STUDY I-2 VoIP2.biz, Inc.: Deciding on the Next Steps for a VoIP Supplier
CASE STUDY I-3 The VoIP Adoption at Butler University
CASE STUDY I-4 Supporting Mobile Health Clinics: The Children’s Health Fund of New York City
CASE STUDY I-5 Data Governance at InsuraCorp
CASE STUDY I-6 H.H. Gregg’s Appliances, Inc.: Deciding on a New Information Technology
Platform
CASE STUDY I-7 Midsouth Chamber of Commerce (B): Cleaning Up an Information Systems
Debacle
CASE STUDY II-1 Vendor-Managed Inventory at NIBCO
CASE STUDY II-2 Real-Time Business Intelligence at Continental Airlines
CASE STUDY II-3 Norfolk Southern Railway:The Business Intelligence Journey
CASE STUDY II-4 Mining Data to Increase State Tax Revenues in California
CASE STUDY II-5 The Cliptomania™ Web Store: An E-Tailing Start-up Survival Story
CASE STUDY II-6 Rock Island Chocolate Company, Inc.: Building a Social Networking Strategy
CASE STUDY III-1 Managing a Systems Development Project at Consumer and Industrial
Products, Inc.
CASE STUDY III-2 A Make-or-Buy Decision at Baxter Manufacturing Company
CASE STUDY III-3 ERP Purchase Decision at Benton Manufacturing Company, Inc.
CASE STUDY III-4 The Kuali Financial System: An Open-Source Project
CASE STUDY III-5 NIBCO’s “Big Bang”: An SAP Implementation
CASE STUDY III-6 BAT Taiwan: Implementing SAP for a Strategic Transition
CASE STUDY III-7 A Troubled Project at Modern Materials, Inc.
CASE STUDY III-8 Purchasing and Implementing a Student Management System at Jefferson
County School System
CASE STUDY IV-1 The Clarion School for Boys, Inc.– Milwaukee Division: Making Information
Systems Investments
CASE STUDY IV-2 FastTrack IT Integration for the Sallie Mae Merger
CASE STUDY IV-3 IT Infrastructure Outsourcing at Schaeffer (A):The Outsourcing Decision
CASE STUDY IV-4 IT Infrastructure Outsourcing at Schaeffer (B): Managing the Contract
CASE STUDY IV-5 Systems Support for a New Baxter Manufacturing Company Plant in Mexico
CASE STUDY IV-6 The Challenges of Local System Design for Multinationals: The MaxFli Sales
Force Automation System at BAT
CASE STUDY IV-7 Meridian Hospital Systems, Inc.: Deciding Which IT Company to Join
CASE STUDY IV-8 Mary Morrison’s Ethical Issue
Managing Information
Technology
Prentice Hall
Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
Seventh Edition
Carol V. Brown
Howe School of Technology Management,
Stevens Institute of Technology
Daniel W. DeHayes
Kelley School of Business,
Indiana University
Jeffrey A. Hoffer
School of Business Administration,
The University of Dayton
E. Wain.
CONTENTS CASE STUDIESCASE STUDY 1 Midsouth Chamber of.docx
Malhotra04
1. Chapter Four
Exploratory Research Design:
Secondary Data
2. 4-2
Chapter Outline
1) Overview
2) Primary versus Secondary Data
3) Advantages & Uses of Secondary Data
4) Disadvantages of Secondary Data
3. 4-3
Chapter Outline
5) Criteria for Evaluating Secondary Data
i. Specifications: Methodology Used to Collect the
Data
ii. Error: Accuracy of the Data
iii. Currency: When the Data Were Collected
iv. Objective(s): The Purpose for Which the Data
Were Collected
v. Nature: The Content of the Data
vi. Dependability: Overall, How Dependable
are the Data
4. 4-4
Chapter Outline
6) Classification of Secondary Data
7) Internal Secondary Data
8) Published External Secondary Sources
i. General Business Sources
a. Guides
b. Directories
c. Indexes
d. Non-governmental Statistical Data
5. 4-5
Chapter Outline
s
ii. Government Sources Censu
Data
a. Census Data
b. Other Government Publications
9) Computerized Databases
i. Classification of Computerized Databases
ii. Directories of Databases
10) Syndicate Sources of Secondary Data
6. 4-6
Chapter Outline
11) Syndicated Data from Households
i. Surveys
a. Psychographics & Lifestyles
b. Advertising Evaluation
c. General Surveys
d. Uses of Surveys
e. Advantages & Disadvantages of Surveys
ii. Panels
a. Purchase Panels
b. Media Panels
c. Uses of Panels
d. Advantages & Disadvantages of Panels
7. 4-7
Chapter Outline
12) Electronic Scanner Services
i. Volume Tracking Data
a. Scanner Diary Panels
b. Scanner Diary Panels with Cable TV
c. Uses of Scanner Services
d. Advantages & Disadvantages
13) Syndicated Data from Institutions
i. Retailers & Wholesalers
a. Uses of Audit Data
b. Advantages & Disadvantages of Audit Data
8. 4-8
Chapter Outline
ii. Industry Services
a. Uses of Industry Services
b. Advantages & Disadvantages of Industry
Services
14) Combining Information from Different Sources:
Single-Source Data
15) Applications of Secondary Data
i. Computer Mapping
9. 4-9
Chapter Outline
16) International Marketing Research
17) Ethics in Marketing Research
18) Internet and Computer Applications
19) Focus on Burke
20) Summary
21) Key Terms & Concepts
10. 4-10
Primary vs. Secondary Data
Primary data are originated by a researcher
for the specific purpose of addressing the
problem at hand. The collection of primary
data involves all six steps of the marketing
research process (Chapter 1).
Secondary data are data which have
already been collected for purposes other
than the problem at hand. These data can be
located quickly and inexpensively.
11. 4-11
A Comparison of Primary & Secondary Data
Table 4.1
Primary Data Secondary Data
Collection purpose For the problem at hand For other problems
Collection process Very involved Rapid & easy
Collection cost High Relatively low
Collection time Long Short
12. 4-12
Uses of Secondary Data
Identify the problem
Better define the problem
Develop an approach to the problem
Formulate an appropriate research
design (for example, by identifying the
key variables)
Answer certain research questions and
test some hypotheses
Interpret primary data more insightfully
13. 4-13
Criteria for Evaluating Secondary Data
Specifications: Methodology Used to
Collect the Data
Error: Accuracy of the Data
Currency: When the Data Were Collected
Objective(s): The Purpose for Which the
Data Were Collected
Nature: The Content of the Data
Dependability: Overall, How Dependable
Are the Data
14. 4-14
Criteria for Evaluating Secondary Data
Table 4.2
Criteria Issues Remarks
Specifications Data collection method, response Data should be reliable,
& Methodology rate, quality & analysis of data, valid, & generalizable to
sampling technique & size, the problem.
questionnaire design, fieldwork.
Error & Examine errors in approach, Assess accuracy by
Accuracy research design, sampling, data comparing data from
collection & analysis, & reporting. different sources.
Currency Time lag between collection & Census data are updated
publication, frequency of updates. by syndicated firms.
Objective Why were the data collected? The objective determines
the relevance of data.
Nature Definition of key variables, units of Reconfigure the data to
measurement, categories used, increase their
relationships examined. usefulness.
Dependability Expertise, credibility, reputation, &
trustworthiness of the source. Data should be obtained
from an original source.
15. 4-15
A Classification of Secondary Data
Fig. 4.1
Secondary Data
Internal External
Ready to Requires Published Computerized Syndicated
Use Further Materials Databases Services
Processing
16. 4-16
Internal Secondary Data
Department Store Project
Sales were analyzed to obtain:
Sales by product line
Sales by major department (e.g., men's wear, house
wares)
Sales by specific stores
Sales by geographical region
Sales by cash versus credit purchases
Sales in specific time periods
Sales by size of purchase
Sales trends in many of these classifications were
also examined.
17. Type of Individual/Household Level Data
4-17
Available from Syndicated Firms
I. Demographic Data
- Identification (name, address, telephone)
- Sex
- Marital status
- Names of family members
- Age (including ages of family members)
- Income
- Occupation
- Number of children present
- Home ownership
- Length of residence
- Number and make of cars owned
18. Type of Individual/Household Level Data
4-18
Available from Syndicated Firms
II. Psychographic Lifestyle Data
- Interest in golf
- Interest in snow skiing
- Interest in book reading
- Interest in running
- Interest in bicycling
- Interest in pets
- Interest in fishing
- Interest in electronics
- Interest in cable television
There are also firms such as Dun & Bradstreet and American
Business Information which collect demographic data on businesses.
19. A Classification of Published Secondary
4-19
Sources
Fig. 4.2
Published Secondary
Data
General Business Government
Sources Sources
Guides Directories Indexes Statistical Census Other
Data Data Government
Publications
20. InfoUSA: :
4-20
Here, There, Everywhere
InfoUSA (www.infousa.com) markets subsets of its data in
a number of forms, including the professional online
services (LEXIS-NEXIS and DIALOG), the general online
services (CompuServe and Microsoft Network), the Internet
(look-ups), and on CD-ROM. The underlying database on
which all these products are based contains information on
113 million residential listings and 14 million business
listings, as of 2003. These are verified with over 16 million
phone calls annually. The products derived from these
databases include sales leads, mailing lists, business
directories, mapping products, and also delivery of data on
the Internet.
21. 4-21
A Classification of Computerized Databases
Fig. 4.3
Computerized
Databases
Online Internet Off-Line
Bibliographic Numeric Full-Text Directory Special-
Databases Databases Databases Databases Purpose
Databases
22. 4-22
Published External Secondary Sources
Guides
An excellent source of standard or recurring information
Helpful in identifying other important sources of directories,
trade associations, and trade publications
One of the first sources a researcher should consult
Directories
Helpful for identifying individuals or organizations that collect
specific data
Examples: Consultants and Consulting Organizations Directory,
Encyclopedia of Associations, FINDEX: The Directory of Market
Research Reports, Studies and Surveys, and Research
Services Directory
Indices
Helpful in locating information on a particular topic in several
different publications
23. 4-23
Classification of Computerized Databases
Bibliographic databases are composed of
citations to articles.
Numeric databases contain numerical and
statistical information.
Full-text databases contain the complete
text of the source documents comprising the
database.
Directory databases provide information
on individuals, organizations, and services.
Special-purpose databases provide
specialized information.
24. 4-24
Syndicated Services
Companies that collect and sell common
pools of data of known commercial value
designed to serve a number of clients.
Syndicated sources can be classified based
on the unit of measurement
(households/consumers or institutions).
Household/consumer data may be obtained
from surveys, diary panels, or electronic
scanner services.
Institutional data may be obtained from
retailers, wholesalers, or industrial firms.
25. 4-25
A Classification of Syndicated Services
Fig. 4.4
Unit of
Measurement
Households/
Institutions
Consumers
26. 4-26
Syndicated Services: Consumers
Fig. 4.4 cont.
Households /
Consumers
Panels
Electronic scanner
Purchase Media services
Surveys Volume Scanner Diary Scanner Diary
Tracking Data Panels Panels with
Cable TV
Psychographic Advertising
General
& Lifestyles Evaluation
28. 4-28
Overview of Syndicated Services
Table 4.3
Type Characteristics Advantages Disadvantages Uses
Surveys Surveys conducted at Most flexible way of Interviewer errors; Market
regular intervals obtaining data; respondent errors segmentation,
information on advertising theme
underlying motives selection and
advertising
effectiveness
Purchase Households provide Recorded purchase Lack of Forecasting sales,
Panels specific information behavior can be representativeness; market share and
regularly over an linked to the response bias; trends; establishing
extended period of demographic/ maturation consumer profiles,
time; respondent psychographic brand loyalty and
asked to record characteristics switching; evaluating
specific behaviors as test markets,
they occur advertising, and
distribution
Media Panels Electronic devices Same as purchase Same as purchase Establishing
automatically panel panel advertising rates;
recording behavior, selecting media
supplemented by a program or air time;
diary establishing viewer
profiles
29. 4-29
Overview of Syndicated Services
Table 4.3 cont.
Type Characteristics Advantages Disadvantages
Scan er V m
n olu e H ousehold purchases D reflect actual
ata D m not be
ata ay
T ack g D
r in ata are recorded through purchases; tim data, representative; errors in
ely
electronic scanners in less expensive recording purchases;
superm arkets difficult to link
purchases to elem ents
of m arketing m other
ix
than price
Scan er D y Pan
n iar els Scanner panels of D reflect actual
ata D m not be
ata ay
w C le T
ith ab V households that purchases; sam ple representative; quality
subscribe to cable TV control; ability to link of data lim ited
panel data to household
characteristics
30. 4-30
Overview of Syndicated Services
Table 4.3 cont.
Characteristics Advantages Disadvantages Uses
V erification of product R elatively precise C overage m be
ay M easurem ent of
m ovem ent by inform ation at the incom plete; matching consum sales and
er
exam ining physical retail and w holesale of data on com petitive m arket share,
records or perform ing levels activity m be
ay com petitive activity,
inventory analy sis difficult analy zing distribution
patterns; tracking of
new products
D banks on
ata Im portant source of D are lacking in
ata D eterm ining m arket
industrial inform ation on term of content,
s potential by geographic
establishm ents created industrial firm s, quantity and quality
, area, defining sales
through direct inquiries particularly useful in territories, allocating
of com panies, clipping initial phases of the advertising budget
services, and corporate projects
reports
31. 4-31
Single-Source Data
Single-source data provide integrated information on
household variables, including media consumption and
purchases, and marketing variables, such as product
sales, price, advertising, promotion, and in-store
marketing effort.
Recruit a test panel of households and meter each
home's TV sets.
Survey households periodically on what they read.
Grocery purchases are tracked by UPC scanners.
Track retail data, such as sales, advertising, and
promotion.
32. The NYT on the Web: A New Way
4-32
To Target Customers
To handle alternate forms of interaction and updates, The
New York Times created a separate unit, The New York
Times Electronic Media Co. The New York Times on the
Web (www.nytimes.com) has drawn over 10 million
registrants as of 2003. The database contains
demographic information, such as age, gender, income,
and zip code, that ties to an e-mail address for each of the
members. This new database marketing system can
identify and customize user groups, target Web messages
to specific segments of the population, and adjust the
message based on audience reaction. It can also increase
targeting opportunities through third-party data or additional
information supplied by the user.
33. The NYT on the Web: A New Way
4-33
To Target Customers
For example, the database enables an automobile firm to
emphasize safety to older customers, luxury to affluent
ones, and roominess to families. The system is set up so
that near real-time data can be received from the Web that
indicates how well ads are performing relative to age,
gender, and income characteristics. Thus, this system
allows a firm to maintain up-to-date information on
audiences in order to position its products effectively.
34. 4-34
A Classification of International Sources
Fig. 4.5
International Secondary Data
Domestic International Organizations in
Organizations in Organizations in Foreign Countries
the United States the United States
Government Nongovernment International Trade
Governments Organizations Associations
Sources Sources