Marketing Information, Research and Insight

Introduction

Marketing management depends on decisions made under uncertainty. Managers must decide which customers to serve, what value proposition to offer, how to position a brand, which channels to use, and how to evaluate performance. These decisions require more than data. They require well-defined problems, credible evidence, disciplined interpretation, and insight that can guide action.

Marketing research is the systematic process of identifying an information need, collecting and analysing relevant evidence, and communicating findings for marketing decision-making. Marketing information is broader than research: it includes internal records, market intelligence, customer feedback, competitive observations, research studies, and digital data. Insight is the meaningful interpretation of evidence that explains a customer or market situation and indicates what the organisation should do.

A useful distinction is:

Data → Information → Insight → Decision → Learning

Raw observations become information when organised and given context. Information becomes insight when it reveals a meaningful pattern, tension, or opportunity. A decision converts insight into action, and subsequent results create new learning. Poorly designed research can interrupt this chain by producing inaccurate, biased, irrelevant, or unusable findings.

Learning Objectives

By the end of this chapter, students should be able to:

1. Distinguish marketing data, information, research, and insight.
2. Define marketing problems and translate them into research objectives and questions.
3. Select appropriate secondary, qualitative, and quantitative research methods.
4. Explain sampling, measurement, reliability, validity, and common sources of bias.
5. Interpret research findings cautiously and connect them to managerial action.
6. Design research that respects privacy, informed consent, fairness, and professional ethics.
7. Recognise the limitations of research, including those associated with digital analytics and artificial intelligence.

1. The Marketing Information System

A marketing information system (MIS) is an organised arrangement of people, processes, technologies, and procedures used to collect, analyse, store, and distribute information for marketing decisions. It normally includes four interconnected sources.

An MIS should not be confused with a large database. More information is not automatically better. Information must be relevant to a decision, sufficiently accurate, timely, understandable, and available in an appropriate form. A dashboard showing thousands of website events may be less useful than a carefully designed study explaining why customers abandon a subscription.

Marketing information has several qualities:

• Relevance: It addresses the decision at hand.
• Accuracy: It represents the phenomenon adequately and contains few errors.
• Timeliness: It is available while action is still possible.
• Completeness: Important dimensions have not been omitted.
• Comparability: Measures can be compared across periods, segments, or markets.
• Accessibility: Authorised users can obtain and understand it.
• Actionability: It supports a realistic decision rather than merely describing a situation.

2. From Management Problem to Research Problem

The most important stage of research occurs before data collection. Managers often state symptoms rather than problems. For example, “sales have fallen” is an observation, not an explanation. Possible causes include a price increase, lower distribution, stronger competition, product dissatisfaction, changing customer needs, or inaccurate sales reporting. Each cause would require different evidence and a different response.

A management decision problem asks what the organisation should do. A research problem asks what must be understood to make that decision. For example:

• Management problem: Should a sportswear brand introduce a lower-priced running shoe?
• Research problem: What benefits, price expectations, usage occasions, and trade-offs characterise potential buyers, and how would demand vary across target segments?

A disciplined problem-definition process includes:

1. Decision context: What has happened, and why is the decision important now?
2. Stakeholders: Who will use the findings, and who may be affected?
3. Symptoms and possible causes: What is known, assumed, or uncertain?
4. Decision alternatives: What actions are realistically available?
5. Information gaps: What evidence would distinguish among alternatives?
6. Scope and constraints: What budget, time, market, population, and ethical limits apply?

A research brief should specify the background, decision to be supported, research purpose, target population, proposed methods, deliverables, timetable, budget, and ethical requirements.

Research objectives and questions

Research objectives state what the study will accomplish. Good objectives are specific, feasible, and linked to the decision. They may involve describing, exploring, explaining, comparing, or evaluating.

Examples include:

• To identify the attributes that consumers associate with trustworthy financial brands.
• To estimate the proportion of current users who intend to renew a service.
• To compare satisfaction between two delivery models.
• To explore why first-time buyers do not complete an online purchase.

Research questions convert objectives into answerable questions. A question should identify the population, concept, context, and sometimes the relevant comparison. “What do consumers think?” is too broad. “How do urban users aged 18–30 evaluate convenience, privacy, and price when choosing a mobile-payment application?” is more useful.

A hypothesis is a testable proposition about a relationship or difference, such as: “Customers who perceive delivery as more reliable report higher repurchase intention.” A hypothesis is not required for every study, particularly exploratory qualitative research, but it can focus quantitative analysis.

3. Secondary Data and Primary Data

Secondary data were collected previously for another purpose or for a purpose broader than the current study. Examples include census statistics, industry reports, company sales records, published academic research, social-media archives, and website analytics. Primary data are collected specifically for the current research problem, such as interviews, observations, surveys, or experiments.

Secondary research should normally precede primary research. It can clarify the market, reveal what is already known, prevent duplication, refine questions, and reduce cost. However, secondary data may use different definitions, outdated samples, unknown methods, or commercial incentives that affect credibility.

A source should be evaluated for:

• Authority: Who collected and sponsored it?
• Purpose: Why was it collected and published?
• Method: How were concepts measured and cases selected?
• Coverage: Which people, locations, periods, and channels are included?
• Currency: How recent is it for the present decision?
• Consistency: Do definitions match those used in the current study?
• Bias and transparency: Are limitations and exclusions disclosed?

Internal data are valuable but often incomplete. A CRM may record purchases while excluding non-buyers, informal purchases, cash transactions, or customers who have opted out of tracking. Digital metrics also measure behaviour within a platform, not necessarily attitudes, motives, or total market behaviour.

4. Research Designs and Methods

Research design is the plan connecting a research question to evidence and analysis. Three broad purposes are commonly distinguished.

Exploratory research

Exploratory research investigates an unfamiliar or poorly understood problem. It is flexible and often qualitative. Methods include literature review, expert interviews, social listening, ethnographic observation, diary studies, and focus groups. It can reveal language, motivations, tensions, and hypotheses, but it is not usually designed to estimate population proportions.

Descriptive research

Descriptive research measures characteristics, behaviours, attitudes, or market conditions. Surveys, structured observation, panels, and analysis of customer records are common. Descriptive research may answer questions such as: What proportion of users know the brand? How frequently do they purchase? Which segment reports the highest satisfaction?

Causal research

Causal research examines whether changing one factor produces a change in another. Experiments are the strongest design when feasible. A treatment group may receive a new advertisement and a control group an existing advertisement or no advertisement. If groups are comparable and exposure is controlled, differences in outcomes provide evidence about the effect of the intervention.

Causal claims require care. A correlation between advertising exposure and sales does not prove that advertising caused sales, because highly motivated customers may be both more likely to notice advertising and more likely to buy. Other explanations include seasonality, distribution, competitor activity, and price changes.

Qualitative methods

Qualitative research seeks depth, meaning, and context. Common methods include:

• Depth interviews: Individual conversations that explore experiences and motives.
• Focus groups: Moderated group discussions useful for interaction and language, but vulnerable to conformity and dominant participants.
• Observation and ethnography: Study behaviour in natural settings rather than relying only on what people report.
• Diary studies: Participants record experiences over time, reducing reliance on distant recall.
• Projective techniques: Activities such as metaphor elicitation can help participants express difficult-to-articulate associations; interpretation must remain cautious.
• Online communities: Moderated digital spaces can capture evolving experiences but may overrepresent highly engaged users.

Qualitative samples are generally purposive rather than statistically representative. Researchers assess richness, relevance, and thematic sufficiency rather than claiming a population percentage. Analysis often involves transcription, coding, comparison of cases, development of themes, and checking interpretations against the original evidence.

Quantitative methods

Quantitative research represents observations numerically so that patterns, differences, and relationships can be estimated. Surveys, experiments, panels, structured observations, and transaction analysis are common. Quantitative research supports generalisation when the design, measurement, and sample justify it.

A well-designed survey requires clear questions, appropriate response options, logical order, accessible language, and pilot testing. Leading questions, double-barrelled questions, ambiguous time periods, and emotionally loaded wording can distort responses. For example, “How satisfied are you with the price and quality of our service?” combines two concepts and should be separated.

5. Sampling and Representativeness

A population is the complete set of units relevant to the research question. A sampling frame is the operational list or procedure from which a sample is selected. A sample is the subset actually studied. The target population might be all current users of a service, whereas an available email list may exclude former users, offline customers, or people without recorded addresses.

Probability sampling

In probability sampling, each population unit has a known, non-zero probability of selection. Methods include:

• Simple random sampling: Units are selected randomly from a complete frame.
• Systematic sampling: Every kth unit is selected after a random starting point.
• Stratified sampling: The population is divided into relevant subgroups and sampled within each, often to improve representation.
• Cluster sampling: Naturally occurring groups are sampled, which can reduce cost but may increase sampling error when cluster members are similar.

Non-probability sampling

Convenience, purposive, quota, and snowball sampling may be practical when no complete frame exists. They can be appropriate for exploratory research or hard-to-reach populations, but selection probabilities are unknown. Findings should not automatically be generalised to the whole population.

Sample size and uncertainty

For a simple random sample estimating a population proportion, an approximate large-population sample-size formula is:

Here, n₀ is the initial sample size, z is the standard-normal critical value for the chosen confidence level, p is the anticipated population proportion, and e is the desired maximum margin of sampling error expressed as a proportion. The formula assumes a probability sample, independent observations, and a proportion estimate under the normal approximation. When no prior estimate of p is available, p = 0.5 gives the largest required sample for a specified z and e.

For a 95% confidence level, z ≈ 1.96. If p = 0.5 and e = 0.05, then:

The required sample is therefore approximately 385 completed observations before allowance for non-response. This calculation does not correct coverage error, poor measurement, or a biased recruitment process. A large non-probability sample can be less credible than a smaller well-designed probability sample.

When the population is finite and relatively small, a finite-population correction may be used:

where N is the population size and n is the adjusted sample size. The correction is relevant when the sample is a substantial fraction of the population and the sampling design supports the assumptions.

6. Measurement and Scale Design

Measurement assigns values or categories to characteristics according to explicit rules. Researchers must define constructs before measuring them. “Brand loyalty,” for example, might mean repeat purchase, attitudinal commitment, willingness to recommend, or some combination. These are related but not identical.

Four traditional levels of measurement are useful:

Rating scales should match the construct and respondent capability. A five- or seven-point agreement scale can measure attitudes, but agreement is not the same as importance, satisfaction, or likelihood to buy. Question wording, scale labels, order effects, acquiescence, and social desirability can all influence answers. Randomising suitable response options and including balanced positive and negative wording can reduce some effects, although negatively worded items may create confusion.

Reliability and validity

Reliability concerns consistency. A measure may produce similar results across items, occasions, or raters. Validity concerns whether it measures the intended construct and supports the intended conclusion. A measure can be reliable but invalid: a badly calibrated scale may give consistent but inaccurate readings.

Important forms of validity include:

• Face validity: The measure appears sensible to knowledgeable observers; useful but weak evidence.
• Content validity: The measure covers the relevant dimensions of the construct.
• Construct validity: The measure behaves as theory predicts, including convergent and discriminant evidence.
• Criterion-related validity: The measure relates appropriately to an external outcome or criterion.
• Internal validity: In a causal study, observed effects are attributable to the treatment rather than confounding factors.
• External validity: Findings can reasonably apply beyond the studied conditions.

Reliability and validity are not guaranteed by sophisticated software. They depend on conceptual definitions, design, administration, and interpretation.

7. Analysing and Interpreting Evidence

Analysis should follow the research question, not merely the availability of a statistical technique. Descriptive analysis may include frequencies, percentages, means, medians, and cross-tabulations. The arithmetic mean is:

where xᵢ is observation i, n is the number of observations, and x̄ is the sample mean. The mean is sensitive to extreme values; the median may better represent a skewed distribution such as household spending.

For a probability sample, an approximate 95% confidence interval for a proportion is:

where p̂ is the sample proportion and n is the sample size. This approximation assumes a suitable probability sample and sufficiently large expected counts. It describes sampling uncertainty under the design; it does not include non-response, coverage, measurement, or processing errors.

Managers should distinguish statistical significance from practical significance. A tiny difference may be statistically detectable in a very large sample but irrelevant to a decision. Conversely, a strategically important difference may fail to reach conventional significance in a small study. Confidence intervals, effect sizes, costs, risks, and business consequences should be considered together.

Qualitative analysis requires an audit trail. Researchers should explain how material was selected, coded, compared, and interpreted. Useful practices include multiple analysts, searching for disconfirming evidence, respondent feedback where appropriate, reflexive notes, and preserving links between themes and quotations. A vivid quotation is illustrative, not proof of prevalence.

8. Bias and Research Limitations

Bias is systematic distortion that affects the design, collection, analysis, or interpretation of evidence. Common forms include:

• Coverage bias: Some population members cannot be reached through the sampling frame.
• Selection bias: Participation or selection is related to the outcome of interest.
• Non-response bias: Respondents differ meaningfully from non-respondents.
• Recall bias: Participants inaccurately remember past behaviour.
• Social-desirability bias: Participants give answers they believe are acceptable.
• Interviewer bias: An interviewer’s wording, identity, or reactions influence responses.
• Question-order and framing effects: Earlier questions or wording alter later answers.
• Survivorship bias: Analysis includes only those who remain observable, excluding failures or dropouts.
• Confirmation bias: Researchers notice evidence supporting prior beliefs and discount contrary evidence.
• Automation bias: Users accept algorithmic outputs without adequate scrutiny.

Limitations should be reported explicitly. A study may be limited by a small or non-representative sample, self-reported rather than observed behaviour, cross-sectional data, short fieldwork, low response rates, platform-specific measurement, or confounding variables. Stating limitations does not make research weak; it makes claims more credible and appropriately bounded.

9. Ethics, Privacy, and Governance

Ethical marketing research respects autonomy, minimises harm, and treats participants fairly. Core practices include informed consent, voluntary participation, the right to withdraw, clear explanation of purpose, secure data handling, and honest reporting. Participants should not be deceived unless a strong justification exists, the risk is minimal, and appropriate debriefing is provided.

Privacy is more than removing names. Identifiable information may include device identifiers, location traces, combinations of demographic attributes, voice recordings, or behavioural profiles. Researchers should apply data minimisation: collect only what is necessary, use it for specified purposes, restrict access, define retention periods, and securely delete it when no longer needed. Sensitive populations, including children and vulnerable consumers, require enhanced safeguards.

Legal requirements vary by jurisdiction. Depending on location and activity, organisations may need to address data-protection laws, consent requirements, cookies and tracking rules, consumer-protection law, sector regulation, and cross-border data transfers. Legal compliance is a minimum standard, not a substitute for ethical judgement.

Research reports must not hide unfavourable results, fabricate participants, manipulate exclusions, or present exploratory findings as pre-planned confirmation. Sponsors should disclose conflicts of interest. AI tools used for transcription, coding, synthetic data, survey generation, or predictive analysis require human oversight. Researchers should assess training-data provenance, hallucinated outputs, discriminatory patterns, re-identification risks, explainability, and whether participant data are being transferred to an unauthorised system.

10. An Integrated Research Process

A practical research workflow is iterative rather than purely linear:

1. Clarify the management decision and stakeholders.
2. Define the research problem, objectives, questions, and hypotheses where appropriate.
3. Review secondary evidence and existing measures.
4. Select a design: exploratory, descriptive, causal, or a mixed-method sequence.
5. Define the population, sampling frame, recruitment method, and sample plan.
6. Operationalise constructs and develop instruments or discussion guides.
7. Pilot the instrument and revise unclear or biased elements.
8. Obtain ethical approval or organisational governance clearance where required.
9. Collect, document, clean, and secure the data.
10. Analyse according to the design and report uncertainty and limitations.
11. Translate findings into options, recommendations, and decision criteria.
12. Monitor implementation and compare outcomes with expectations.

Mixed methods are especially useful when managers need both depth and scale. For example, interviews may identify reasons for service dissatisfaction, followed by a survey estimating how widespread each reason is. The methods should be integrated deliberately; simply placing qualitative and quantitative results side by side does not create a coherent mixed-method study.

Applied Case: Repositioning a Sustainable Personal-Care Brand

This is an illustrative case, not a report of an actual company.

Background and problem

“PureNest,” a fictional personal-care brand, sells refillable shampoo products. Sales growth has slowed despite favourable environmental attitudes in its target market. Managers initially propose increasing social-media advertising. However, the evidence does not establish whether awareness, perceived performance, price, refill inconvenience, or distrust of sustainability claims is the principal barrier.

Analysis and research design

The team first audits internal sales by channel, repeat purchase, returns, customer-service contacts, and stock availability. It reviews competitor prices, packaging claims, category reports, and relevant research on sustainable consumption. This secondary work reveals that many customers try refill products once but do not repurchase, suggesting that acquisition alone may not solve the problem.

The team then conducts 20 purposively sampled depth interviews with recent buyers, lapsed buyers, and category users who have not tried PureNest. Participants discuss routines, product performance, refill storage, price perceptions, and trust. Observation of bathroom routines and a short diary exercise reveal a practical issue: refills are perceived as inconvenient and messy, even by consumers who strongly support sustainability.

A survey of current and potential category users is then designed. It measures awareness, perceived cleaning performance, convenience, trust in environmental claims, price sensitivity, repurchase intention, and demographic and usage variables. The questionnaire is piloted to identify confusing wording and completion problems. Because the recruitment panel does not represent the entire population, the report avoids claiming national prevalence and clearly describes the sample.

An online experiment tests three messages: environmental impact, convenience of the refill system, and performance proof. Participants are randomly assigned to message conditions, and outcomes include perceived relevance, brand credibility, and purchase intention. Purchase intention is treated as an indicator of attitude, not as equivalent to actual future sales.

Findings and solution

The combined evidence suggests that environmental concern attracts trial, but perceived inconvenience and uncertainty about product performance limit repeat purchase. The convenience message improves relevance, while performance evidence improves credibility. The team recommends redesigning the refill opening, publishing clearer usage instructions, offering a small trial size, and communicating independently verifiable performance evidence. Advertising investment is redirected from broad environmental claims toward demonstrations of convenience and performance.

Outcome and learning points

After implementation, management tracks repeat purchase, returns, customer complaints, and refill completion rather than relying only on impressions or stated intention. The case illustrates the value of sequencing secondary research, qualitative exploration, quantitative measurement, and controlled comparison. It also demonstrates that insight is not the statement “customers care about sustainability.” The actionable insight is that sustainability motivates trial, but convenience and credible performance determine continued use.

Professional Practice: Turning Findings into Insight

A useful insight has four elements:

1. Tension or need: What is difficult, unresolved, or changing for the customer?
2. Evidence: What observations support the interpretation?
3. Meaning: Why does the pattern matter in the customer’s life or market context?
4. Action implication: What could the brand, product, channel, or communication do differently?

A report should separate evidence from interpretation and recommendation. It should explain the method, sample, measures, key findings, uncertainty, limitations, and implications. Visualisations should use clear denominators, label missing data, avoid misleading scales, and distinguish counts from percentages. Decision-makers should be told what the research can support, what it cannot support, and what further evidence would reduce remaining uncertainty.

Chapter Summary

Marketing information systems integrate internal records, market intelligence, research, and analytical tools. Effective research begins by translating a management decision into a precise research problem, objectives, questions, and—when appropriate—testable hypotheses. Secondary data provide context and can reduce unnecessary primary research, but their authority, purpose, method, coverage, currency, and bias must be evaluated.

Exploratory, descriptive, and causal designs answer different questions. Qualitative methods provide depth and meaning; quantitative methods support measurement and comparison when sampling and instruments are appropriate. Sampling requires attention to the population, frame, selection process, non-response, and representativeness. Measurement must be reliable and valid, while analysis must distinguish sampling uncertainty from broader sources of error.

Research findings do not speak for themselves. Bias, confounding, poor wording, platform limitations, and overinterpretation can produce misleading conclusions. Ethical research protects autonomy, privacy, security, fairness, and truthful reporting. Insight emerges when evidence is interpreted in context and connected to a feasible decision. The strongest marketing research is therefore not merely technically correct; it is relevant, transparent, ethically governed, and actionable.

Key Terms

Marketing information system: Organised people, processes, technologies, and procedures for managing marketing information.

Marketing research: Systematic collection and analysis of evidence for a defined marketing decision.

Insight: A meaningful interpretation of evidence that explains a situation and suggests action.

Management decision problem: The action or choice facing a manager.

Research problem: The information issue that must be understood to support the decision.

Secondary data: Existing data collected previously for another or broader purpose.

Primary data: Data collected specifically for the current research problem.

Exploratory research: Flexible research used to investigate poorly understood issues.

Descriptive research: Research that measures characteristics, behaviours, or conditions.

Causal research: Research designed to assess whether one factor produces a change in another.

Qualitative research: Research focused on meaning, experience, context, and interpretation.

Quantitative research: Research that represents observations numerically for measurement and analysis.

Population: The complete set of units relevant to a research question.

Sampling frame: The operational list or procedure from which a sample is selected.

Probability sampling: Sampling in which selection probabilities are known and non-zero.

Reliability: The consistency of a measurement.

Validity: The extent to which evidence and measures support the intended interpretation.

Bias: Systematic distortion in design, collection, analysis, or interpretation.

Informed consent: Voluntary agreement based on adequate information about participation.

Data minimisation: Collecting and retaining only data necessary for a specified purpose.

Review Questions

Short Answer Questions

1. Distinguish marketing data, information, research, and insight.
2. Why should a management symptom not automatically be treated as a research problem?
3. State two strengths and two limitations of secondary data.
4. What is the difference between reliability and validity?
5. Identify three common sources of bias in marketing research.

Long Answer Questions

1. Explain how a marketing manager can move from a decision problem to research objectives, questions, and hypotheses.
2. Compare exploratory, descriptive, and causal research, giving an appropriate marketing application for each.
3. Discuss probability and non-probability sampling and explain why sample size alone does not guarantee representativeness.
4. Explain the ethical responsibilities involved in collecting and using customer data.
5. Describe how qualitative and quantitative methods can be integrated in a mixed-method research project.

Analytical / Application Questions

1. A brand’s sales have declined for three consecutive months. Design a staged research programme that distinguishes possible causes and specify the evidence required at each stage.
2. A company reports that 72% of respondents intend to purchase its new service, based on an online convenience sample. Evaluate what the company may and may not conclude.
3. Develop a research brief for improving repeat purchase of a sustainable consumer product. Include the decision problem, research questions, target population, method, ethical safeguards, and likely limitations.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a management decision problem from a research problem?

A. A decision problem asks what action should be taken; a research problem asks what must be understood to support that action.

B. A decision problem uses qualitative data; a research problem uses quantitative data.

C. A decision problem is always broader than the market; a research problem is always narrower than a brand.

D. A decision problem describes sampling; a research problem describes budgeting.

Correct Answer: A. A decision problem asks what action should be taken; a research problem asks what must be understood to support that action.

Question 2

Which method is most appropriate for exploring why customers find a refill system inconvenient?

A. A depth interview study

B. A national sales forecast only

C. A stock-count audit only

D. A randomised price experiment without customer discussion

Correct Answer: A. A depth interview study

Question 3

In probability sampling, which condition is required?

A. Every selected respondent must be a loyal customer.

B. Each population unit has a known, non-zero probability of selection.

C. The sample must contain exactly 1,000 respondents.

D. Participants must volunteer through social media.

Correct Answer: B. Each population unit has a known, non-zero probability of selection.

Question 4

Using z = 1.96, p = 0.5, and e = 0.05, the approximate initial sample size for a large population is:

A. 96

B. 196

C. 385

D. 1,960

Correct Answer: C. 385

Question 5

A measure that consistently produces the same incorrect result is best described as:

A. Reliable but potentially invalid

B. Valid but necessarily unreliable

C. Both representative and causal

D. Randomly sampled but unmeasurable

Correct Answer: A. Reliable but potentially invalid

Question 6

Which statement about a vivid quotation from a focus group is most accurate?

A. It proves that the quoted view is held by the majority.

B. It can illustrate a theme but does not establish population prevalence.

C. It eliminates the need for quantitative research.

D. It is automatically more valid than behavioural data.

Correct Answer: B. It can illustrate a theme but does not establish population prevalence.

Question 7

Which practice best reflects data minimisation?

A. Collecting every available identifier in case it becomes useful later

B. Retaining all raw data indefinitely

C. Collecting only data necessary for a specified purpose

D. Selling identifiable data to unrelated partners

Correct Answer: C. Collecting only data necessary for a specified purpose

Question 8

Why does a correlation between advertising exposure and purchase not by itself prove advertising caused the purchase?

A. Advertising can never affect purchase.

B. The relationship may be explained by confounding factors such as prior motivation.

C. Correlation is possible only in qualitative research.

D. Purchase data cannot be measured numerically.

Correct Answer: B. The relationship may be explained by confounding factors such as prior motivation.

Question 9

Which feature most directly improves the credibility of a quantitative survey instrument?

A. Using complex language to appear professional

B. Pilot testing questions with members of the relevant population

C. Removing all neutral response options

D. Increasing the number of questions without testing them

Correct Answer: B. Pilot testing questions with members of the relevant population

Question 10

What is the most actionable insight in the PureNest case?

A. Consumers have heard of sustainability.

B. Environmental issues are important in society.

C. Sustainability attracts trial, but convenience and credible performance support repeat use.

D. Advertising impressions are the main measure of brand success.

Correct Answer: C. Sustainability attracts trial, but convenience and credible performance support repeat use.