Marketing Research PDF

Chapter 1: Introduction to Marketing Research  Definition & Scope: o Marketing research is the function that links consumers, customers, and the public to the marketer through information. o It involves specifying the information required, collecti...

Chapter 1: Introduction to Marketing Research  Definition & Scope: o Marketing research is the function that links consumers, customers, and the public to the marketer through information. o It involves specifying the information required, collecting and analyzing the data, and interpreting the results for actionable decisions.  Types of Marketing Research: o Problem Identification Research: Identifies underlying problems, such as market potential, market share, brand image, or forecast errors. o Problem-Solving Research: Focuses on finding solutions to specific issues like pricing strategies, product development, and promotional effectiveness.  Marketing Research Process: 1. Define the Problem: Accurate problem definition avoids irrelevant research. 2. Develop an Approach to the Problem: Involves formulating objectives, theoretical frameworks, and hypotheses. 3. Research Design Formulation: Selects the methods (quantitative or qualitative), sampling design, and tools. 4. Fieldwork or Data Collection: Involves data gathering through surveys, observations, or secondary data. 5. Data Preparation and Analysis: Data coding, cleaning, and statistical analysis using software (e.g., SPSS, SAS). 6. Report Preparation and Presentation: Includes a clear, actionable presentation of findings and recommendations, often supported by charts and graphs.  Ethics in Research: o Protecting respondent privacy. o Avoiding deceptive practices. o Ensuring data integrity and honesty in reporting. Chapter 2: Defining the Marketing Research Problem  The Critical Role of Problem Definition: A well-defined problem ensures the research is focused on relevant issues. o Example: If a company notices declining sales, the management decision problem (MDP) might be “How do we increase sales?” while the marketing research problem (MRP) could be “What factors are influencing the decline in sales?”.  Tasks in Problem Definition:  o Discussions with Decision Makers: Understand the business goals, constraints, and strategic objectives. o Interviews with Experts: Gain insights from industry experts who can provide a broader view of the market context. o Secondary Data Analysis: Leverage existing data to narrow down the research problem and uncover trends. o Qualitative Research: Conduct initial focus groups or interviews to further explore the issue.  Environmental Context of the Problem: o Past Information and Forecasts: Review previous data and sales trends. o Resources and Constraints: Consider budget limitations, time constraints, and available personnel. o Legal, Economic, and Technological Environments: Consider how external factors (e.g., regulatory changes) may influence the problem.  Formulating Hypotheses: Hypotheses are testable statements, such as “Brand awareness significantly affects customer loyalty.” o Null Hypothesis (H0): No effect or relationship. o Alternative Hypothesis (H1): There is an effect or relationship.  Research Questions: Break down the research into specific questions such as "What is the current brand perception among customers?" or "How do customer demographics influence purchasing behavior?" Chapter 3: Research Design  Definition of Research Design: A blueprint detailing the procedures for collecting the required information. o It specifies the method (surveys, observations), sample selection, and data collection.  Classification of Research Designs: o Exploratory Research: Used when little is known about the problem. Often involves qualitative methods (focus groups, in-depth interviews). o Descriptive Research: Aims to describe characteristics of a population or phenomenon. Example: A survey that details consumer preferences across different demographics. o Causal Research: Focuses on determining cause-and-effect relationships. Example: Testing whether a price reduction will lead to increased sales.  Cross-Sectional vs. Longitudinal Research: o Cross-Sectional: Data is collected at a single point in time, such as a survey taken once by a group of respondents.  o Longitudinal: Data is collected from the same respondents repeatedly over time, allowing for analysis of changes (e.g., consumer behavior over a year).  Potential Sources of Error: o Random Sampling Error: Occurs due to the natural variation when selecting a sample from the population. o Nonsampling Error: Includes interviewer bias, measurement errors, and nonresponse errors. o Systematic Error: Biases that consistently skew data in a certain direction (e.g., leading questions in a survey). Chapter 4: Exploratory Research Design: Secondary Data  Primary Data vs. Secondary Data: o Primary Data: Collected firsthand for the specific research objective (e.g., surveys, experiments). o Secondary Data: Pre-existing data, often collected for different purposes, but useful in addressing the current problem (e.g., government reports, academic studies).  Advantages of Secondary Data: o Saves time and money. o Provides a historical context for primary research. o Useful for exploratory phases and hypothesis development.  Disadvantages: o May not be directly applicable or relevant to the current research problem. o Data accuracy, timeliness, or completeness may be in question.  Types of Secondary Data: o Internal Data: Data generated within the organization, such as customer databases, sales records, or loyalty programs. o External Data: Data from external sources, including government agencies, trade associations, and syndicated research providers (e.g., Nielsen, IRI).  Syndicated Data Services: o Household Panels: Tracks consumer purchases over time. o Retail Audits: Collects information on product movement and sales performance from retail outlets. o Scanner Data: Uses checkout scanners to gather real-time purchase data. Chapter 5: Exploratory Research Design: Qualitative Research   Qualitative Research: Focuses on understanding underlying motivations, attitudes, and perceptions. o Useful in the exploratory stage to gain insights that can guide further quantitative research.  Key Methods: o Focus Groups:  Group discussions led by a trained moderator.  Advantages: Group dynamics can stimulate ideas.  Disadvantages: Difficult to generalize findings due to small sample size and groupthink. o Depth Interviews:  One-on-one interviews allowing detailed probing.  Ideal for uncovering deeper insights into consumer behavior.  Disadvantages: Time-intensive and costly. o Projective Techniques:  Respondents reveal their subconscious thoughts through ambiguous stimuli.  Association Techniques: Word association exercises where respondents say the first word that comes to mind when presented with a stimulus.  Completion Techniques: Respondents complete a sentence or story.  Construction Techniques: Respondents are asked to create stories based on pictures or words.  Expressive Techniques: Respondents project their emotions onto others through tasks like role-playing. o Online Focus Groups: Increasingly popular due to convenience and lower costs. However, it may lack the richness of face-to-face interactions. Chapter 6: Descriptive Research Design: Survey and Observation  Descriptive Research: Aims to describe market phenomena by answering "who, what, when, where, and how" questions.  Survey Methods: o Telephone Interviews:  Advantages: Speed and cost-effectiveness.  Disadvantages: Limited depth due to time constraints and the absence of visual aids. o Face-to-Face Interviews:   Advantages: Rich data collection, visual aids can be used.  Disadvantages: Expensive, time-consuming, and interviewer bias may occur. o Mail Surveys:  Advantages: Low cost, respondents can complete the survey at their convenience.  Disadvantages: Low response rates, no control over respondent identity. o Online Surveys:  Advantages: Fast, scalable, cost-effective.  Disadvantages: Self-selection bias, lack of control over sample composition.  Observation Methods: o Personal Observation: Researcher observes behavior in a natural setting (e.g., observing consumer shopping habits in a store). o Mechanical Observation: Devices such as cameras or scanners are used to record behavior. o Audit: Physically counting inventory or measuring product placement in stores. o Ethnographic Research: In-depth study of people in their natural environments. For example, observing consumers in their homes or workplaces. Chapter 7: Causal Research Design: Experimentation  Causal Research: Designed to determine if one variable causes a change in another (e.g., does a price reduction lead to higher sales?).  Conditions for Causality: 1. Concomitant Variation: There must be a consistent relationship between the cause and effect. 2. Time Order: The cause must precede the effect. 3. Elimination of Other Factors: Other potential causes must be ruled out.  Types of Experimental Designs: o Pre-Experimental Designs: Do not use random assignment. For example, the one-shot case study (single group exposed to a treatment and then observed). o True Experimental Designs: Include random assignment to control and treatment groups. For example, the pretest-posttest control group design.  o Quasi-Experimental Designs: Lack random assignment, but include other controls (e.g., time series design).  Extraneous Variables: o History: External events occurring during the experiment may affect the results. o Maturation: Respondents may change over time, independent of the treatment. o Testing Effects: The act of taking a test may influence responses in future tests. o Selection Bias: If groups are not randomly assigned, there may be inherent differences between them. Chapter 8: Measurement and Scaling: Fundamentals and Comparative Scaling  Measurement Concepts: o Description: Assignment of labels to objects or events (e.g., 1 for male, 2 for female). o Order: The relative positioning of objects (e.g., ranking brands from most to least preferred). o Distance: The absolute difference between two objects (e.g., the temperature difference between 20°C and 30°C is 10°C). o Origin: A true zero point where the absence of the attribute is indicated (e.g., weight, sales).  Types of Scales: o Nominal: Categorization without any order (e.g., types of vehicles). o Ordinal: Rank-ordered categories (e.g., 1st, 2nd, 3rd place in a competition). o Interval: Equal intervals between values but no true zero (e.g., Likert scales). o Ratio: Has a meaningful zero (e.g., income, age).  Comparative Scaling Techniques: o Paired Comparison Scaling: Respondents choose between two items (e.g., Coke vs. Pepsi). o Rank Order Scaling: Respondents rank items in order of preference (e.g., favorite to least favorite brand). o Constant Sum Scaling: Respondents distribute a fixed number of points across attributes based on importance (e.g., assigning 100 points across product features). Chapter 9: Measurement and Scaling: Noncomparative Scaling Techniques   Noncomparative Scaling: Each object is scaled independently of others.  Popular Techniques: o Likert Scale: Respondents indicate the extent of their agreement or disagreement with statements on a 5- or 7-point scale. o Semantic Differential Scale: Measures the meaning of things using bipolar adjectives (e.g., clean-dirty, good-bad). o Stapel Scale: Uses unipolar ratings, usually from -5 to +5, without a neutral point.  Scale Decisions: o Number of Scale Points: More scale points provide greater sensitivity (e.g., 7- point scales are more sensitive than 5-point scales). o Balanced vs. Unbalanced Scales: Balanced scales have equal numbers of positive and negative response options, while unbalanced scales skew toward one side. o Forced vs. Non-Forced Scales: Forced scales require respondents to choose a position, while non-forced scales allow a neutral option.  Reliability and Validity: o Reliability: The consistency of a measurement (e.g., test-retest reliability, internal consistency). o Validity: Whether the scale measures what it is intended to measure (e.g., content validity, criterion validity, construct validity). Chapter 10: Questionnaire and Form Design  Questionnaire Design Process: 1. Specify Information Needed: Define the research objectives and information required. 2. Method of Administration: Choose the method (e.g., online, telephone, mail). 3. Individual Question Content: Ensure each question serves a purpose and avoid double-barreled questions (questions that ask about more than one thing). 4. Question Structure: Decide whether questions will be open-ended (qualitative) or close-ended (quantitative). 5. Question Wording: Use simple, clear language to avoid confusion. Avoid leading, ambiguous, and double-barreled questions. 6. Question Sequence: Use the funnel approach, starting with broad questions and narrowing down to more specific ones.  7. Pretesting: Test the questionnaire on a small sample to identify potential issues with wording, structure, or length.  Types of Questions: o Dichotomous Questions: Yes/no questions (e.g., "Did you purchase this product?"). o Multiple Choice: Offers respondents a set of predefined answers to choose from. o Rank-Order Questions: Asks respondents to rank items in order of preference. o Constant Sum Questions: Asks respondents to allocate points across different attributes. Chapter 11: Sampling: Design and Procedures  Sampling Process: 1. Define the Target Population: Who or what is being studied (e.g., all adults in the U.S. who own a smartphone). 2. Select the Sampling Frame: The list or database from which the sample is drawn (e.g., a customer mailing list). 3. Choose a Sampling Technique: Probability or nonprobability methods. 4. Determine the Sample Size: A balance between accuracy and cost. 5. Execute the Sampling Process: Ensure that the sample is representative of the target population.  Sampling Techniques: o Probability Sampling:  Simple Random Sampling: Each member of the population has an equal chance of selection.  Stratified Sampling: Population is divided into subgroups (strata), and random samples are taken from each.  Cluster Sampling: Population is divided into clusters, and a random sample of clusters is selected. o Nonprobability Sampling:  Convenience Sampling: Using respondents who are easily accessible.  Judgment Sampling: The researcher selects respondents based on their judgment.  Quota Sampling: Dividing the population into groups and sampling a fixed number from each group.   Snowball Sampling: Respondents are asked to refer others in their network to participate. Chapter 12: Sampling: Initial and Final Sample Size Determination  Sampling Distribution: The distribution of a statistic across multiple samples. The larger the sample size, the closer the statistic will reflect the population parameter.  Confidence Interval Approach: Uses confidence levels (e.g., 95%) and margin of error to determine sample size. o Example: To estimate customer satisfaction with a margin of error of ±5% at a 95% confidence level.  Formulas for Sample Size Determination: o For Means: n=(Z⋅σE)2n = \left( \frac{Z \cdot \sigma}{E} \right)^2n=(EZ⋅σ)2  Where nnn is the sample size, ZZZ is the Z-score for the confidence level, σ\sigmaσ is the population standard deviation, and EEE is the margin of error. o For Proportions: n=(Z2⋅p⋅(1−p)E2)n = \left( \frac{Z^2 \cdot p \cdot (1-p)}{E^2} \right)n=(E2Z2⋅p⋅(1−p))  Where ppp is the estimated proportion of the population.  Adjusting for Nonresponse: If the response rate is lower than expected, increase the sample size to account for nonrespondents.  Response Rates: The percentage of the sample that actually participates in the study. Strategies like follow-up reminders and incentives can improve response rates.