3. Marketing Research and Market Intelligence

Introduction and Context

Hospitality marketing decisions are often made in conditions of uncertainty. A hotel may need to decide whether to renovate rooms, introduce a new restaurant concept, target a new source market, adjust prices, or invest in a loyalty programme. Each decision involves assumptions about customers, competitors, demand, distribution channels, economic conditions, and the experience that guests value. Marketing research provides a disciplined means of reducing this uncertainty. Market intelligence extends the process by continuously interpreting information about the market, competitors, customers, and the organisation itself.

In hospitality, research is particularly important because the product is perishable, experience-based, location-bound, and influenced by time, seasonality, reputation, and human interaction. An unsold room tonight cannot be stored for sale tomorrow. A dissatisfied guest may not complain directly but may communicate dissatisfaction through a review or social media post. Consequently, managers require more than occasional surveys. They need reliable evidence, timely interpretation, and a system for converting information into decisions.

Marketing research is not simply the collection of data. It is a purposeful process involving problem definition, research design, sampling, measurement, data collection, analysis, interpretation, and managerial action. Market intelligence is the broader organisational capability through which internal and external information is gathered, evaluated, shared, and used. Together, they support evidence-based marketing and sales management.

Learning Objectives

After studying this chapter, students should be able to:

1. Explain the meaning, scope, and strategic importance of marketing research and market intelligence in hospitality.

2. Distinguish between exploratory, descriptive, and causal research.

3. Design appropriate qualitative and quantitative research for hospitality problems.

4. Evaluate primary and secondary data for relevance, reliability, validity, and ethical use.

5. Apply sampling, measurement, questionnaire, interview, observation, and experimental methods.

6. Interpret customer, competitor, and market-potential information for managerial decisions.

7. Recognise the limitations, risks, and ethical responsibilities associated with hospitality research.

8. Use digital tools, analytics, and artificial intelligence responsibly in market intelligence.

The Nature and Scope of Hospitality Marketing Research

Marketing research is the systematic and objective identification, collection, analysis, and communication of information for use in marketing decision-making. The definition contains four important ideas. First, research is systematic rather than opportunistic. Second, it is objective, requiring researchers to control personal bias. Third, it connects information to a decision. Finally, research includes communication and application, not merely data collection.

Hospitality marketing research may address several interconnected questions:

• Who are the property’s most valuable customer segments?
• What motivates guests to choose a particular hotel, restaurant, resort, or event venue?
• How do customers evaluate value, service quality, convenience, safety, and authenticity?
• What is the size and growth potential of a target market?
• How do competitors position themselves and manage price, distribution, and experience?
• Why are occupancy, average daily rate, restaurant covers, or repeat visits changing?
• Which promotional message, offer, channel, or service innovation is likely to produce the desired response?

Research can be conducted before, during, or after a marketing programme. Before launch, it may identify needs and test concepts. During implementation, it can monitor campaign response and customer sentiment. After implementation, it can evaluate outcomes such as conversion, satisfaction, loyalty, revenue, and return on marketing investment.

Research must begin with a managerial problem rather than a vague desire to “collect information.” “We need more data about our guests” is not a research problem. “Weekend occupancy among domestic couples aged 25–40 has declined, and management needs to determine whether the cause is price, product relevance, distribution, or communication” is a decision-oriented problem.

From Managerial Problem to Research Question

A useful research process begins by translating a management concern into a researchable question. The process normally includes the following stages:

1. Problem definition: Clarify the decision, symptoms, causes that may be investigated, and information required.

2. Situation analysis: Review existing performance data, previous studies, customer feedback, competitor activity, and environmental conditions.

3. Research objectives: State what the study must discover, measure, compare, or test.

4. Research design: Select the research approach, data sources, sampling plan, instruments, and analysis procedures.

5. Fieldwork: Collect data according to the approved protocol.

6. Data preparation and analysis: Check, code, clean, organise, and analyse the evidence.

7. Interpretation: Relate findings to the original managerial problem and distinguish evidence from speculation.

8. Recommendation and action: Present feasible choices, implications, risks, and measures for monitoring results.

Research objectives should be specific. For example, a resort may establish the objective: “To identify the attributes that influence the booking intention of urban residents for short wellness breaks within a six-hour travel radius.” This objective identifies the population, context, and intended outcome more clearly than “to study customer preferences.”

A research question may be supported by hypotheses. A hypothesis is a testable statement about a relationship between variables, such as: “Perceived value is positively associated with intention to revisit.” Variables must be operationalised so that they can be measured. Perceived value might be measured through several statements rated on a five-point agreement scale, while revisit intention might be measured through stated likelihood of booking within the next twelve months.

Research Designs: Exploration, Description, and Causation

The research design should reflect the purpose of the study.

Exploratory research

Exploratory research is used when the problem is unclear or poorly understood. It seeks insights, meanings, possible explanations, and hypotheses rather than statistically representative estimates. Common methods include depth interviews, focus groups, observation, social listening, expert discussions, and analysis of online reviews.

For example, if a boutique hotel observes that guests praise its location but complain about “impersonal service,” exploratory interviews can investigate what guests mean by personal service. The result may reveal issues involving recognition, communication style, flexibility, or local recommendations that a structured questionnaire would not initially capture.

Descriptive research

Descriptive research measures characteristics, behaviours, attitudes, or market conditions. It may estimate the proportion of guests who book through online travel agencies, the average satisfaction score, the profile of event buyers, or awareness of a hotel brand. Surveys, structured observation, customer databases, and market reports are common sources.

Descriptive studies require careful sampling and measurement. A large sample does not compensate for a biased sample or a poorly worded question.

Causal research

Causal research examines whether a change in one factor produces a change in another. Experiments are the strongest design for testing causation because they involve manipulation and comparison. A hotel might show two different promotional messages to comparable audiences and compare booking conversion, while controlling for timing and offer conditions.

Correlation does not establish causation. If guests who spend more also report higher satisfaction, it does not necessarily mean that satisfaction caused higher spending. The relationship may be influenced by room type, length of stay, trip purpose, or income.

Qualitative Inquiry: Understanding Meaning and Experience

Hospitality experiences are subjective and context-dependent. Qualitative research is valuable because it explores how customers interpret service encounters, construct value, describe emotions, and explain behaviour.

Depth interviews involve guided conversations with individual participants. They are appropriate for sensitive topics, complex decision processes, luxury consumption, corporate travel, and expert perspectives. The interviewer should use open questions, probes, and active listening without leading the participant.

Focus groups bring a small number of participants together to discuss a topic under the guidance of a moderator. Group interaction can stimulate ideas and reveal shared language, but dominant participants, conformity, and social desirability may influence responses. Focus groups are therefore useful for concept development, not necessarily for estimating market size.

Observation records behaviour in natural or controlled settings. Researchers may observe queue formation, restaurant table usage, check-in interactions, signage, or guest movement through a property. Observation can reveal a gap between what guests say and what they do.

Netnography and digital listening examine online reviews, travel forums, social media conversations, and user-generated content. These sources can identify recurring themes, but online contributors are self-selecting and may not represent the entire customer base.

Qualitative analysis generally involves transcription, coding, categorisation, theme development, comparison across participants, and interpretation. Researchers should preserve participants’ meanings while avoiding unsupported generalisation. Concepts such as credibility, reflexivity, triangulation, and saturation are important. Saturation refers to the point at which additional interviews generate little substantially new insight, although it should not be treated as a mechanical rule.

Quantitative Research and Measurement

Quantitative research converts observations into numerical data so that patterns, differences, relationships, and estimates can be examined. It is useful when management requires measurable evidence, comparisons among segments, or statistical testing.

A questionnaire should follow a logical sequence. It commonly begins with screening questions, proceeds to behaviour and evaluation, then attitudes and intentions, and concludes with classification questions. Questions should be concise, specific, neutral, and limited to one idea. “How satisfied were you with the room and breakfast?” is double-barrelled because a guest may evaluate the two differently. Better questions measure each attribute separately.

Researchers must choose appropriate measurement scales. A nominal scale classifies categories, such as booking channel. An ordinal scale ranks categories, such as low, medium, and high preference. An interval-like rating scale may measure perceived satisfaction from 1 to 5. A ratio scale has a meaningful zero, such as number of nights or amount spent.

Reliability concerns the consistency of measurement. If a scale produces highly different results under similar conditions, it may be unreliable. Internal consistency can be assessed for multi-item scales, while test–retest reliability examines stability over time. Validity concerns whether the instrument measures what it claims to measure. Content validity asks whether relevant dimensions are included; construct validity examines whether the measure behaves as theory suggests; criterion validity considers its relationship with an appropriate external indicator.

Pretesting is essential. A questionnaire should be tested with people similar to the intended respondents to identify confusing language, excessive length, missing response categories, technical problems, and unintended interpretations.

Sampling and Data Quality

A population is the full group about which the organisation wishes to draw conclusions, such as all guests who stayed at a property during a period. A sample is a subset of that population. The sampling frame is the operational list or method used to access potential participants.

Probability sampling gives population members a known, non-zero chance of selection. Simple random, systematic, stratified, and cluster sampling are common forms. Stratified sampling can be useful when the hotel wants representation from business, leisure, and group segments.

Non-probability sampling includes convenience, purposive, quota, and snowball methods. These may be practical for exploratory work or hard-to-reach groups, but their results should not automatically be generalised to the entire market.

Sample size depends on the research purpose, population variability, desired precision, confidence level, design, resources, and expected response rate. Statistical formulas cannot correct coverage error, non-response bias, or poor measurement. A large survey of only loyalty-programme members may provide precise information about that group while saying little about occasional guests.

Non-response should be examined. Respondents may differ systematically from those who decline. Incentives, concise instruments, appropriate timing, multiple contact modes, and clear privacy information can improve participation, but incentives must not pressure people to provide favourable answers.

Primary and Secondary Data

Primary data are collected specifically for the current research problem. Examples include guest surveys, interviews, observation, experiments, intercept studies, and mystery visits. Primary research offers relevance and control but can be costly and time-consuming.

Secondary data were collected for another purpose. Internal sources include property-management systems, point-of-sale data, customer relationship management records, booking histories, cancellation data, complaint logs, review scores, and campaign reports. External sources include tourism statistics, government publications, industry associations, destination reports, competitor websites, public financial information, and commercial databases.

Secondary data should be evaluated systematically. The researcher should ask:

• Who collected the data and for what purpose?
• How recent are they, and do definitions remain comparable?
• What population and method were used?
• Are the measures valid for the present decision?
• Are there missing values, commercial biases, or undisclosed assumptions?
• Can the source be independently verified?

Triangulation improves confidence by comparing different sources or methods. For example, a decline in restaurant satisfaction may be examined through review themes, complaint records, table-turn data, and interviews with servers. Agreement across sources is informative, but disagreement may also reveal a valuable difference between customer perception and operational measurement.

Market Intelligence and the Competitive Environment

Market intelligence is an ongoing capability rather than a one-time study. It integrates information about customers, competitors, intermediaries, suppliers, destinations, regulations, technology, and broader economic conditions. Its purpose is not surveillance for its own sake, but informed action.

A useful intelligence system has four functions. It gathers information, evaluates its quality, interprets implications, and distributes relevant insights to decision-makers. Information should reach the people who can act on it, including revenue managers, sales teams, operations managers, digital marketers, and senior executives.

Competitor intelligence may examine positioning, room categories, packages, rates, cancellation terms, distribution presence, review themes, loyalty benefits, service design, event offerings, and communication style. Ethical competitor analysis uses publicly available or legitimately obtained information. It does not involve deception, unauthorised access, industrial espionage, or misuse of confidential information.

A competitor map can position properties according to dimensions meaningful to customers, such as price and experiential distinctiveness, or convenience and personalisation. The map is a managerial interpretation rather than an objective fact. Its value depends on sound evidence and appropriate dimensions.

Customer intelligence combines transactional, behavioural, attitudinal, and contextual information. A hotel may know that a guest booked three times, travelled on weekdays, opened a promotional email, and rated breakfast highly. It should not assume that these facts alone explain the guest’s motivations. Quantitative patterns require qualitative interpretation and careful respect for privacy.

Evaluating Market Potential

Market potential is the possible level of demand for a product or service under specified conditions. It is different from actual sales, which reflect the organisation’s price, capacity, distribution, awareness, and competitive performance.

Managers may assess potential through market size, growth, accessibility, spending capacity, competitive intensity, seasonality, regulatory conditions, and strategic fit. A simple market-sizing logic is:

Potential demand = target population × incidence of relevant need × expected purchase frequency × attainable share

Each component requires assumptions. For a resort, the target population may be households within a defined travel time; incidence may refer to those interested in wellness breaks; frequency may reflect expected annual trips; and attainable share depends on competition, capacity, positioning, and distribution.

Top-down estimates begin with published market totals and narrow them to the relevant segment. Bottom-up estimates build from observable units such as rooms, seats, events, visitor flows, or accounts. Scenario analysis is preferable to a single precise forecast. Managers can construct conservative, base, and optimistic cases and identify the assumptions that most affect the result.

Market attractiveness should be separated from organisational capability. A large market may not be attractive if acquisition costs are high, competitors are powerful, service capabilities are weak, or the segment conflicts with the brand. Conversely, a modest niche may be strategically valuable if it produces profitable, loyal, and strategically compatible demand.

Turning Evidence into Managerial Decisions

The final value of research lies in decisions. A research report should not merely present tables and percentages. It should explain what the findings mean, how confident management can be, what alternatives exist, and what action is recommended.

A strong managerial report normally includes an executive interpretation, decision context, methodology, key findings, limitations, implications, recommendations, implementation responsibilities, and performance measures. Findings should be prioritised according to importance and feasibility. For example, if business travellers value reliable Wi-Fi but are indifferent to a proposed lounge redesign, investment may be directed first toward connectivity and service recovery.

Managers should distinguish descriptive findings from recommendations. “Forty-two per cent of respondents booked through an online travel agency” is a finding. “The hotel should reduce dependence on that channel by improving direct-booking value” is a recommendation requiring further consideration of commission, conversion, customer acquisition cost, and brand strategy.

Useful decision tools include customer journey maps, perceptual maps, segmentation profiles, conjoint or trade-off analysis, customer lifetime value analysis, campaign attribution, and test-and-learn programmes. However, tools should serve a decision rather than create analytical complexity without managerial value.

Digital Analytics, Automation, and Artificial Intelligence

Digital hospitality systems generate extensive behavioural data. Web analytics can show traffic sources, device use, search behaviour, abandonment, and conversion. Booking-engine data can reveal lead time, length of stay, cancellation patterns, and rate response. Social listening and review-mining tools can identify sentiment and recurring themes. Customer data platforms may integrate information across stays, campaigns, and channels.

Automation can improve speed by classifying reviews, identifying anomalies, producing dashboards, and triggering personalised communications. Predictive models may estimate cancellation probability, demand, propensity to book, or likelihood of churn. Generative artificial intelligence can assist with coding qualitative comments, summarising reports, and proposing survey questions.

These tools do not eliminate research judgement. Automated sentiment classification may misunderstand sarcasm, multilingual expressions, or hospitality-specific language. Predictive accuracy may decline when market conditions change. A model trained on historical bookings may reproduce historical exclusion or favour profitable segments while neglecting fairness. Managers should maintain human review, document data sources, validate outputs, monitor bias, and avoid presenting generated interpretations as unquestionable facts.

Ethics, Privacy, and Governance

Ethical research respects autonomy, privacy, dignity, safety, and fairness. Participants should receive clear information about the purpose of the study, what participation involves, how data will be used, and whether participation is voluntary. Consent should be meaningful, especially when collecting sensitive information or recording interviews.

Hospitality organisations must follow applicable privacy and data-protection requirements. Personal data should be collected for legitimate purposes, limited to what is necessary, stored securely, retained only as long as justified, and accessed by authorised personnel. Customer data obtained for service delivery should not automatically be repurposed for unrelated research or promotion without an appropriate legal and ethical basis.

Researchers should avoid deceptive questions, fabricated respondents, manipulated results, undisclosed sponsorship, and selective reporting. Incentives should be proportionate. Findings that contradict management expectations must not be suppressed. Data security includes access controls, secure transfer, anonymisation or pseudonymisation where appropriate, and procedures for responding to breaches.

Ethics also applies to competitor intelligence, mystery shopping, employee research, and the use of online content. Public availability does not always remove ethical responsibility. Researchers should consider reasonable expectations of privacy, platform rules, and the potential harm of identifying individuals.

Illustrative Case: Repositioning the Harbour View Hotel

Background

The Harbour View Hotel is a 180-room independent property in a coastal business and leisure destination. Weekday occupancy remained satisfactory, but weekend occupancy had fallen for two consecutive seasons. Management assumed that the hotel’s price was too high and proposed broad discounts.

Situation and Research Problem

The management team reframed the issue: Why were potential weekend guests not choosing Harbour View, and which changes could improve profitable demand without damaging the brand? The research objectives were to understand weekend-trip motivations, compare competitor positioning, identify barriers to booking, and estimate the appeal of possible packages.

Approach

The team first analysed internal booking, cancellation, rate, review, and channel data. It then conducted twelve depth interviews with recent guests and prospective travellers, observed competitor digital booking journeys, and reviewed publicly available competitor packages. A structured online survey of leisure travellers in the hotel’s catchment area measured attribute importance, awareness, price perceptions, and booking intention. The survey used screening criteria and quotas to include couples, families, and small groups.

Analysis

The evidence did not support a simple price explanation. Harbour View was perceived as acceptable but generic. Prospective guests valued flexible check-in, local experiences, breakfast quality, and ease of planning. Competitors communicated distinctive neighbourhood experiences more effectively. Review analysis showed that the hotel’s location was praised, but the website did not explain how guests could use the location. The proposed discount attracted interest but generated weak incremental preference when compared with a package including breakfast, late checkout, and a locally guided activity.

Strategy and Solution

Management developed a weekend experience package rather than a permanent price reduction. The package was targeted at short-break couples and included flexible arrival, breakfast, late checkout, and a choice of a local activity. The hotel redesigned website content around neighbourhood itineraries, improved direct-booking information, and trained staff to recommend local experiences. A controlled digital campaign tested experience-led and discount-led messages against defined conversion and profitability measures.

Outcome

In this illustrative case, the experience-led offer generated stronger direct-booking intent and higher expected contribution per booking than the blanket discount. Management continued monitoring occupancy, average rate, package attachment, cancellation, review themes, and repeat booking. The result was not attributed solely to the campaign because seasonality and competitor actions remained possible influences.

Learning Points

The case demonstrates the importance of defining the problem before selecting a solution. It also shows the value of combining internal data, qualitative insight, competitor intelligence, and quantitative measurement. Most importantly, research did not make the decision automatically; it improved the quality of managerial judgement and enabled a controlled test.

Challenges, Limitations, and Common Mistakes

Research can fail even when technically sophisticated. Common errors include defining the problem around a preferred solution, asking leading questions, relying only on highly engaged loyalty members, confusing satisfaction with loyalty, treating online reviews as a representative sample, and reporting averages that conceal segment differences.

Other risks include seasonal distortion, small subgroup sizes, recall error, social desirability bias, non-response, inconsistent definitions across systems, duplicate customer records, and overinterpretation of correlations. Competitor prices may change before a report is acted upon. Forecasts may appear precise even though their assumptions are uncertain.

Managers should also beware of dashboard overload. More metrics do not necessarily produce better decisions. A limited set of decision-linked indicators is generally more useful than an expanding list of unexamined measures. Research should be proportionate to the value and reversibility of the decision: a major repositioning requires stronger evidence than a low-cost copy test.

Current Developments and Best Practices

Hospitality research is becoming more continuous, integrated, and experiential. Voice-of-customer systems increasingly combine surveys, reviews, service-recovery records, and operational data. First-party data is becoming strategically important as privacy restrictions and changing digital identifiers reduce dependence on third-party tracking. Mobile research, digital communities, geospatial analysis, and real-time demand signals can improve responsiveness.

At the same time, organisations are placing greater emphasis on responsible analytics, explainable models, data minimisation, and human oversight. The most effective practice is not to replace managerial experience with data, but to combine professional judgement with transparent evidence.

Best practice includes:

• Define the decision and success criteria before collecting data.
• Use the simplest method capable of answering the question well.
• Combine qualitative understanding with quantitative measurement when appropriate.
• Pretest instruments and document sampling decisions.
• Check data quality, missingness, bias, and alternative explanations.
• Triangulate important findings across sources.
• Separate evidence, assumptions, interpretation, and recommendation.
• Protect participant and customer privacy throughout the research life cycle.
• Present uncertainty and limitations honestly.
• Link recommendations to responsible owners, timelines, and measurable outcomes.

Chapter Summary

Marketing research is a systematic process for producing evidence that supports hospitality marketing decisions. It begins with a clearly defined managerial problem and progresses through objectives, design, sampling, data collection, analysis, interpretation, and action. Exploratory research develops understanding, descriptive research measures market characteristics, and causal research tests the effects of interventions.

Qualitative methods reveal meanings and experiences, while quantitative methods provide measurable patterns and comparisons. Primary data are tailored to the current problem; secondary data offer speed and context but must be evaluated for relevance and quality. Market intelligence is a continuing organisational capability that integrates customer, competitor, internal, and environmental information.

Market potential depends not only on demand but also on accessibility, competition, profitability, capacity, and strategic fit. Digital analytics and artificial intelligence can improve speed and prediction, but they require validation, privacy protection, bias monitoring, and human judgement. Ethical research requires informed participation, responsible data use, security, transparency, and honest reporting. The central managerial task is to transform credible evidence into feasible, measurable, and ethically responsible action.

Key Terms

Marketing research: Systematic collection and analysis of information for marketing decision-making.

Market intelligence: Ongoing organisational capability for gathering, interpreting, and using market and competitive information.

Research problem: A clearly defined decision-related issue requiring evidence.

Exploratory research: Research used to clarify problems, discover meanings, and develop hypotheses.

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

Causal research: Research examining whether one factor produces a change in another.

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

Secondary data: Existing information originally collected for another purpose.

Population: The complete group about which conclusions are intended.

Sample: A subset of the population selected for study.

Sampling frame: The list or procedure used to access population members.

Reliability: Consistency of a measurement instrument or process.

Validity: The extent to which a measure captures what it is intended to measure.

Triangulation: Comparison of multiple data sources or methods to strengthen interpretation.

Hypothesis: A testable statement about a relationship between variables.

Market potential: Possible demand under specified market and organisational conditions.

Customer intelligence: Integrated understanding of customer behaviour, attitudes, value, and context.

Competitor intelligence: Systematic analysis of competitors’ offerings, positioning, behaviour, and performance.

Non-response bias: Distortion arising when respondents differ systematically from non-respondents.

Data minimisation: Collecting and retaining only the personal data necessary for a legitimate purpose.

Review Questions

Short Answer Questions

1. Why must a hospitality research project begin with a managerial problem rather than a general desire for data?

2. Distinguish exploratory, descriptive, and causal research.

3. What is the difference between reliability and validity?

4. Identify two advantages and two limitations of secondary data.

5. What is triangulation, and why is it useful in hospitality research?

Long Answer Questions

1. Explain the stages involved in designing and implementing a hospitality marketing research project.

2. Discuss the contribution of qualitative methods to understanding hospitality experiences.

3. Explain sampling approaches and the major sources of sampling and non-response error.

4. Examine how customer and competitor intelligence can support positioning and revenue decisions.

5. Discuss ethical, privacy, and governance responsibilities in the use of hospitality customer data.

Analytical / Application Questions

1. A hotel’s review scores have declined, but occupancy and revenue have remained stable. Design a mixed-method research project to identify the causes and recommend appropriate action.

2. A resort plans to target a new international market. Explain how you would estimate market potential, evaluate strategic fit, and construct alternative scenarios.

3. A hotel wants to use artificial intelligence to predict cancellations and personalise offers. Analyse the potential benefits, data requirements, ethical risks, and controls you would recommend.

22. Multiple Choice Questions with Answers

Question 1

A hotel wants to understand why guests describe its service as “impersonal” before designing a survey. Which method is most appropriate initially?

A. Depth interviews

B. Large-scale probability survey

C. Price experiment

D. Occupancy forecasting

Correct Answer: A. Depth interviews

Question 2

Which statement best distinguishes market intelligence from a single marketing research study?

A. Market intelligence is limited to competitor prices

B. Market intelligence is an ongoing capability integrating multiple information sources

C. Market intelligence uses only qualitative data

D. Market intelligence replaces managerial judgement

Correct Answer: B. Market intelligence is an ongoing capability integrating multiple information sources

Question 3

A survey asks, “How satisfied were you with the room and breakfast?” What is the principal measurement problem?

A. It uses a nominal scale

B. It has a double-barrelled question

C. It guarantees non-response bias

D. It measures a causal relationship

Correct Answer: B. It has a double-barrelled question

Question 4

A hotel surveys only loyalty-programme members and generalises the findings to all potential guests. Which problem is most likely?

A. Coverage and sampling bias

B. Perfect external validity

C. Experimental contamination

D. High internal consistency

Correct Answer: A. Coverage and sampling bias

Question 5

Which design is most suitable for testing whether a new promotional message increases booking conversion?

A. Unstructured observation only

B. Controlled experiment

C. Historical competitor review

D. Single depth interview

Correct Answer: B. Controlled experiment

Question 6

Which finding is descriptive rather than a recommendation?

A. The hotel should improve direct booking value

B. The hotel should introduce a weekend package

C. Forty-two per cent of respondents used an online travel agency

D. Management should reduce channel commissions

Correct Answer: C. Forty-two per cent of respondents used an online travel agency

Question 7

Why should a market-potential estimate be presented through scenarios rather than a single precise number?

A. Market estimates never require assumptions

B. Scenarios expose uncertainty and the assumptions affecting demand

C. Scenarios eliminate competitive risk

D. A single number is always statistically invalid

Correct Answer: B. Scenarios expose uncertainty and the assumptions affecting demand

Question 8

Which practice is most consistent with ethical hospitality research?

A. Using customer data for any purpose once it has been collected

B. Hiding the research sponsor to obtain more honest responses

C. Collecting only necessary data and explaining its intended use

D. Deleting negative responses before analysis

Correct Answer: C. Collecting only necessary data and explaining its intended use

Question 9

An automated review tool classifies sarcasm as positive sentiment. What is the most appropriate managerial response?

A. Accept the output because automation is objective

B. Stop all forms of customer research

C. Validate the tool and retain human review

D. Report the classification as a verified fact

Correct Answer: C. Validate the tool and retain human review

Question 10

In the Harbour View case, why was an experience package preferred to a blanket discount?

A. The research indicated that customers valued distinctive experiences and planning convenience

B. Discounts always reduce occupancy

C. The hotel had no competitors

D. The package required no performance measurement

Correct Answer: A. The research indicated that customers valued distinctive experiences and planning convenience