Customers, Markets and Opportunity Discovery

Introduction

Product management begins before a product is designed. It begins with understanding people, organisations, markets and situations in which a meaningful problem exists. Opportunity discovery is the disciplined process of identifying unmet or poorly served needs and determining whether they are sufficiently important, common and addressable to justify product investment.

A product opportunity is not simply a feature idea. It is a hypothesis about a customer problem, a target group, a context of use and a possible source of value. For example, “build an appointment reminder feature” is a solution idea. “Patients frequently miss appointments because existing reminders are easy to overlook and difficult to reschedule” is a problem-oriented opportunity statement.

This distinction is central to product management. Teams that begin with solutions may confirm their assumptions selectively, build unnecessary functionality or compete in markets with limited demand. Teams that investigate customers and markets systematically are better able to frame valuable problems, identify attractive segments, differentiate from competitors and test assumptions before committing substantial resources.

Learning Objectives

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

1. Explain the relationship between customers, markets, problems and product opportunities.
2. Design and conduct basic market research using primary and secondary evidence.
3. Plan customer interviews and observations while reducing bias.
4. Segment markets and construct evidence-based personas.
5. Apply the jobs-to-be-done perspective to understand customer motivation and context.
6. Frame problems clearly without prematurely selecting a solution.
7. Analyse competitors and substitutes using structured criteria.
8. Prioritise and validate opportunities using evidence, desirability, feasibility and viability.
9. Recognise ethical, privacy and governance issues in customer research.

1. Customers, Markets and Opportunities

1.1 Customers and users

A user is a person who directly interacts with a product. A customer is the person or organisation that obtains, pays for, authorises or influences the purchase of a product. These roles may be held by different people. In an organisational software purchase, an employee may be the user, a department manager may be the buyer and an information-security team may be an approver.

Other relevant stakeholders include:

• Economic buyer: controls or influences the budget.
• Decision-maker: selects or approves the product.
• Beneficiary: receives the intended value.
• Influencer: shapes preferences or requirements.
• Gatekeeper: can prevent access or adoption.
• Administrator: configures, maintains or governs use.

A product manager must understand the relationships among these roles. Optimising only for the end user may produce a desirable product that cannot be purchased. Optimising only for the buyer may produce a compliant product that users avoid.

1.2 Markets

A market is a system in which groups with a need or desired outcome interact with providers offering alternatives. A market is more than a demographic category. It includes customers, needs, purchasing power, competitors, substitutes, regulations, distribution channels, technologies and social conditions.

A useful market description specifies:

• the customer group;
• the job or problem being addressed;
• the context in which it occurs;
• existing alternatives;
• the value exchanged; and
• the forces that may change demand or supply.

For instance, “the education market” is too broad for practical discovery. “Part-time diploma students who need flexible assessment support while working full time” is more useful because it suggests a population, context and need.

1.3 Opportunity versus idea

An opportunity is a credible possibility for creating and capturing value. It normally includes four linked propositions:

1. A meaningful customer problem or desired outcome exists.
2. A sufficiently identifiable group experiences it.
3. Current alternatives leave important value unrealised.
4. The organisation can feasibly and sustainably serve the group.

An idea becomes an opportunity only after evidence supports these propositions. Discovery therefore involves uncertainty reduction rather than certainty creation. Research rarely proves that a product will succeed; it improves the quality of decisions about what to test next.

2. A Market-Discovery Logic

Opportunity discovery can be organised as a sequence, although in practice teams move backward and forward between stages:

1. Explore: learn about customers, contexts, behaviours and market forces.
2. Segment: identify groups with meaningfully different needs or behaviours.
3. Frame: express the underlying problem or job without prescribing a solution.
4. Compare: study competitors, substitutes and current workarounds.
5. Prioritise: select opportunities using explicit criteria.
6. Validate: run focused tests of the riskiest assumptions.
7. Decide: continue, revise, pause or stop the opportunity.

A strong discovery process combines qualitative evidence, which explains meanings and causes, with quantitative evidence, which estimates prevalence, magnitude or relationships. Neither type is automatically superior. Ten interviews may reveal why a behaviour occurs, while a survey may indicate how common it is; both can be misleading if the sample or questions are poor.

2.1 Evidence and assumptions

Teams should maintain an opportunity hypothesis such as:

We believe that [target group] experiences [problem] in [context], causing [consequence]. Existing alternatives fail because [gap]. If we address this problem, [measurable behaviour or outcome] will improve.

Each part is an assumption that can be tested. A useful evidence log distinguishes:

This separation prevents opinions from being presented as facts and makes uncertainty visible to the team.

3. Market Research Methods

3.1 Secondary research

Secondary research uses information already collected by others. Sources include government statistics, regulatory publications, industry reports, academic research, company filings, product reviews, app-store comments, support tickets and search trends.

Secondary research is efficient for understanding market size, regulation, demographic patterns, technology adoption and competitor positioning. Its limitations include outdated information, unclear methodology, commercial bias and mismatch between the published population and the intended target segment. A market report may describe national demand while the product serves a narrow professional niche.

When assessing a source, ask:

• Who collected the data and for what purpose?
• What population and period does it represent?
• How were concepts measured?
• What exclusions or sampling limitations apply?
• Is the evidence consistent with other sources?
• What decision can the source legitimately support?

3.2 Primary research

Primary research collects new evidence for a specific decision. Common methods include interviews, observation, diary studies, surveys, concept tests, usability tests and behavioural experiments.

The method should follow the decision. If the question is “Why do small retailers abandon stock reconciliation?”, interviews and observation are appropriate. If the question is “How many target retailers experience this at least weekly?”, a well-designed survey may be more suitable.

3.3 Qualitative and quantitative evidence

Qualitative research seeks depth, variation and explanation. It does not normally estimate population percentages. Quantitative research seeks measurement and comparison, but numerical precision does not compensate for biased sampling or ambiguous questions.

A practical sequence is often:

• conduct exploratory interviews and observation;
• identify recurring behaviours, language and hypotheses;
• design a survey or behavioural test to estimate prevalence;
• return to qualitative research to explain surprising results.

This is sometimes called methodological triangulation: using multiple sources or methods to examine the same question. Agreement increases confidence, while disagreement signals a need for further investigation.

4. Customer Interviews and Observation

4.1 Planning an interview

An interview guide should contain a research objective, participant criteria, opening questions, behavioural prompts and closing questions. It should not be a sales script or a list of features to pitch.

Weak question: “Would you use an app that automatically solves this problem?”

Stronger questions:

• “Tell me about the last time this problem occurred.”
• “What were you trying to accomplish?”
• “What did you do first?”
• “What alternatives did you consider?”
• “What was difficult, costly or risky?”
• “How often does this occur?”
• “Who else is involved?”
• “What did the experience cost in time, money or effort?”

Past behaviour and concrete episodes are generally more reliable than predictions about future behaviour. Participants may express politeness, enthusiasm or aspirational preferences that do not translate into action.

4.2 Interview technique

A skilled interviewer listens more than speaks, uses neutral language, asks one question at a time and follows relevant clues. Probing questions such as “What happened next?” and “How did you handle that?” encourage detail without suggesting an answer.

Interviewers should avoid leading questions, double-barrelled questions, jargon, premature explanation and excessive note-taking that reduces rapport. With consent, recording can improve accuracy, but participants must know how recordings will be stored and used.

4.3 Observation and contextual inquiry

Observation examines behaviour in the environment where work occurs. It is especially valuable when tasks are habitual, distributed across tools or difficult to describe. A person may say that a process takes five minutes while observation reveals interruptions, duplicate entry and later correction.

An observation plan should define:

• the setting and activity;
• what may be observed;
• the time period;
• events or behaviours to record;
• questions to ask without disrupting work; and
• safety, privacy and consent procedures.

The researcher should distinguish description from interpretation. “The employee copied the order number into three systems” is an observation. “The systems are poorly integrated” is an interpretation requiring further evidence.

4.4 Sampling for discovery

Discovery samples are selected for relevance and variation rather than statistical representativeness alone. Include participants who differ in experience, frequency of the problem, role, organisation size or use of alternatives. Extreme cases can reveal constraints, while ordinary cases indicate typical practice.

Recruitment through only enthusiastic existing users creates selection bias. Similarly, interviewing colleagues or friends can produce artificially positive responses. A recruitment screener should specify inclusion and exclusion criteria without revealing the desired answer.

5. Segmentation and Personas

5.1 Why segmentation matters

Segmentation divides a broad market into groups that differ in needs, behaviours, constraints or responses to a product. Useful segments are not merely easy to describe; they support different product, communication, pricing or service decisions.

Common bases include:

• Demographic: age, income, education or household structure.
• Geographic: location, climate, infrastructure or jurisdiction.
• Firmographic: industry, organisation size, revenue or operating model.
• Psychographic: attitudes, values and preferences.
• Behavioural: frequency, usage pattern, trigger, loyalty or switching behaviour.
• Needs-based: desired outcomes, constraints and unmet needs.
• Situational: context, urgency, device, time or occasion.

Needs-based, behavioural and situational segmentation are often especially useful for product decisions because they connect directly to customer experience and value creation.

5.2 Segment evaluation

A potential segment can be evaluated using questions such as:

• Is the group identifiable and reachable?
• Does it experience a sufficiently important problem?
• Does it have purchasing authority or influence?
• Is the problem frequent or costly enough to matter?
• Are current alternatives inadequate?
• Can the organisation serve the group effectively?
• Does the segment fit strategic, ethical and regulatory requirements?

Teams may score segments using a transparent ordinal scale, such as 1 to 5, for problem intensity, accessibility, strategic fit and commercial potential. Such scores are decision aids, not objective measurements. The criteria and evidence behind each score should be recorded.

5.3 Personas

A persona is a research-based representation of a significant customer pattern. It should synthesise evidence rather than invent a fictional character. A useful persona may include role, goals, context, behaviours, constraints, current alternatives, triggers and relevant quotations.

A persona is not a demographic stereotype. “Busy young professional” is weak unless linked to observed behaviour and a product decision. “Operations coordinator in a 20–50-person service firm who reconciles supplier invoices weekly using spreadsheets and email” is more actionable.

Personas should be treated as provisional models. They must be updated when evidence changes, and teams should avoid creating too many personas to satisfy every stakeholder. A persona is useful only when it helps answer questions such as: Which problem should be solved first? What language should the interface use? What adoption barrier must be addressed?

6. Jobs-to-be-Done and Problem Framing

6.1 The jobs-to-be-done perspective

Jobs-to-be-done (JTBD) focuses on what customers are trying to accomplish in a particular situation. Customers do not simply buy product attributes; they “hire” products to make progress. A job may be functional, emotional or social.

A job statement often follows this structure:

When [situation], I want to [motivation or progress], so I can [desired outcome].

For example: “When a delivery is delayed, I want to know the cause and revised arrival time, so I can adjust staffing and inform customers.” The statement does not assume a tracking dashboard, an automated message or any other particular solution.

JTBD encourages attention to triggers, desired outcomes, anxieties, habits and switching forces. It can reveal that competing products are not only direct rivals. A spreadsheet, telephone call, paper checklist or doing nothing may be a substitute.

6.2 Functional, emotional and social dimensions

The functional job concerns what must be accomplished. The emotional job concerns how the customer wants to feel, such as confident or in control. The social job concerns how the customer wants to be perceived, such as competent or responsible.

A product opportunity is stronger when it recognises the complete experience. A budgeting service may perform the functional job of categorising expenditure, while also helping users feel less anxious and appear financially responsible to a partner.

6.3 Problem framing

A well-framed problem identifies a person or group, context, unmet need and consequence. One practical format is:

[Customer] needs a better way to [accomplish outcome] when [context], because [evidence-based barrier or consequence].

A problem statement should be:

• specific enough to guide research and design;
• broad enough not to prescribe one solution;
• grounded in observed evidence;
• meaningful to the customer; and
• bounded by a clear context.

Avoid solution language such as “Customers need a mobile app.” Instead state the underlying need: “Field technicians need to record service outcomes while working in locations with unreliable connectivity.” The latter permits several solutions and exposes important constraints.

7. Competitor and Substitute Analysis

Competitor analysis is the systematic study of alternatives that customers may choose. Direct competitors offer similar products to a similar group. Indirect competitors solve part of the same problem differently. Substitutes may involve manual work, internal processes, outsourcing or inaction.

A compact comparison can include:

A feature checklist alone is inadequate. Product managers should compare outcomes, total effort, trust, accessibility, integration, price, learning cost, switching cost and service quality. Public reviews and support discussions can reveal dissatisfaction, but they may overrepresent extreme experiences.

7.1 Positioning and differentiated value

A differentiated value proposition explains for whom the product is useful, what important outcome it improves and why the approach is credible. Differentiation may arise from performance, convenience, trust, workflow integration, service, price, network effects or a distinctive business model.

Differentiation is meaningful only if customers value it and competitors cannot immediately neutralise it. A technically impressive capability may not create advantage if it does not improve a priority outcome.

8. Opportunity Prioritisation and Validation

8.1 Desirability, feasibility and viability

An opportunity should be examined from three connected perspectives:

• Desirability: Do customers recognise the problem and want a better outcome?
• Feasibility: Can the organisation build, deliver and support an effective response?
• Viability: Can the response create sustainable economic or strategic value?

A fourth dimension, responsibility, is increasingly important. The product should not create unacceptable harm, discrimination, privacy intrusion or environmental cost.

These dimensions are not sequential gates. A desirable idea may be infeasible, and a feasible product may have no meaningful demand. Early discovery should expose the riskiest assumption rather than polish every aspect of a concept.

8.2 Opportunity scoring

A team may create a simple scoring model. For example, each opportunity can be rated from 1 to 5 for problem severity, frequency, reach, strategic fit and evidence strength. The scores should not be added mechanically without discussion; a serious safety risk or legal barrier may override a high total.

A more explicit weighted model is:

where S is the opportunity score, I is problem intensity, F is frequency, R is relevant reach, E is evidence strength, and w₁, w₂, w₃, w₄ are non-negative weights. The ratings must use the same defined scale, and the weights should sum to 1 if a weighted average is intended. This model is ordinal and comparative; it does not estimate probability of success or market revenue.

8.3 Riskiest-assumption testing

Validation tests should be linked to a decision. Examples include:

• interviews to test whether a problem is experienced;
• a landing-page experiment to test qualified interest;
• a concierge service to test whether customers value an outcome before automation;
• a prototype usability test to identify interaction barriers;
• a pre-order or paid pilot to test willingness to exchange money;
• a workflow integration test to test feasibility.

A test must define its participants, procedure, evidence threshold and decision rule before results are observed. Otherwise teams may reinterpret any result as support.

Worked Example: Prioritising Two Opportunities

An illustrative team is exploring tools for independent clinics. It rates two opportunities from 1 to 5 on intensity, frequency, reach and evidence. It assigns weights of 0.35, 0.25, 0.20 and 0.20 respectively.

For rescheduling:

For claim tracking:

The first opportunity currently ranks higher, but the result is not proof of demand. The team should investigate whether clinics will pay, whether existing practice-management systems can support the workflow and whether rescheduling authority lies with patients, staff or insurers.

9. Applied Case: ClearRoute Student Services

The following is an illustrative case, created for educational purposes.

Background

ClearRoute is a proposed digital service for diploma students who work during the day. The founding team initially proposes an AI study planner that generates weekly schedules and reminders.

Problem

Early conversations reveal that students do not primarily lack calendars. Many miss assessment milestones because requirements are distributed across a learning platform, email and messaging groups. Some are uncertain about assessment instructions and discover problems only shortly before submission.

Analysis

The team interviews twelve students, observes two study-planning sessions with permission and reviews common questions submitted to student support. The sample is not statistically representative, but it includes new and continuing students, different work patterns and varied levels of digital confidence.

Three patterns emerge:

1. Students spend time locating the authoritative version of assessment information.
2. Work commitments make fixed reminders ineffective.
3. Students hesitate to ask for clarification because they fear appearing unprepared.

The team also maps alternatives: personal calendars, screenshots, messaging groups, spreadsheets and contacting tutors. The alternatives are accessible but fragmented. The AI planner idea addresses scheduling but not information confidence or authoritative content.

Approach and solution direction

The team reframes the opportunity as follows:

Working diploma students need a trusted, low-effort way to understand upcoming assessment obligations and adjust study actions around changing work schedules, because information is fragmented and fixed reminders do not match their circumstances.

Instead of immediately building an AI planner, the team runs a concierge pilot. For four weeks, staff consolidate official assessment information into a weekly digest and help students convert it into flexible actions. Students can report changes in work availability. The team measures whether participants open the digest, complete planned actions and request fewer clarification messages.

Outcome

The pilot indicates strong interest in consolidated information, but limited use of generic motivational reminders. Students value links to authoritative sources and the ability to mark an action as deferred without feeling that they have failed. The team therefore prioritises information integrity, flexible planning and escalation to human support. AI-generated suggestions may be considered later, but only with clear source references and human oversight.

Learning points

• The initial feature idea was not the same as the underlying opportunity.
• Interviews became more useful when they focused on recent episodes.
• Competitor analysis included workarounds, not only commercial software.
• A small service experiment tested value before expensive automation.
• The case illustrates why trust, privacy and escalation matter in education products.

10. Ethics, Privacy and Governance

Customer research creates responsibilities. Participants should understand the purpose of research, what participation involves, whether recording occurs, how information will be stored and whether they may withdraw. Consent must be voluntary and proportionate to the sensitivity of the research.

Researchers should collect only information necessary for the stated purpose, restrict access, protect identifiers and establish retention and deletion practices. Sensitive information may include health, financial, educational, employment, location or identity data. Applicable privacy law varies by jurisdiction, so product teams must consult organisational policies and legal specialists where required.

Power relationships require special care. Employees may feel pressured by managers, students may feel pressured by institutions and patients may fear consequences for refusing. Recruitment and incentives should not make participation coercive. Researchers should avoid exposing confidential business information or recording bystanders without permission.

AI-assisted research introduces additional risks. Transcripts may be sent to external services, models may infer sensitive attributes, and generated summaries may omit minority experiences or convert uncertainty into confident statements. Teams should verify summaries against source material, disclose relevant processing, limit sensitive inputs and retain human responsibility for interpretation and decisions. Synthetic participants or generated survey responses must never be presented as real customer evidence.

11. Challenges and Limitations

Opportunity discovery is affected by several recurring problems:

• Confirmation bias: seeking evidence that supports an attractive idea.
• Recall bias: inaccurate memory of past frequency or effort.
• Courtesy bias: positive responses intended to please the interviewer.
• Sampling bias: hearing mainly from accessible or enthusiastic participants.
• Survivorship bias: studying current users while ignoring people who abandoned the product.
• Overgeneralisation: treating a few vivid stories as market prevalence.
• Feature fixation: asking customers to choose features instead of investigating outcomes.
• False precision: using numerical scores that conceal weak evidence.
• Research theatre: conducting activities without linking them to decisions.
• Changing conditions: assuming that needs, competitors and regulations remain stable.

These limitations cannot be eliminated completely. They can be reduced through neutral questioning, varied samples, triangulation, explicit assumptions, transparent decision rules and repeated learning cycles.

12. Professional Best Practices

An effective product manager should:

1. Start with a decision and identify what must be learned to make it.
2. Separate observations, interpretations, assumptions and decisions.
3. Recruit participants who represent relevant variation, not merely convenience.
4. Ask about recent behaviour before discussing future solutions.
5. Observe real workflows when tasks are complex or habitual.
6. Segment by meaningful differences in needs or behaviour.
7. Use personas as evidence-based decision tools, not decorative profiles.
8. Include direct competitors, substitutes, workarounds and inaction.
9. Prioritise the riskiest assumption and test it economically.
10. Record contradictory evidence and disconfirming cases.
11. Protect participant privacy and explain data use clearly.
12. Translate findings into a specific problem frame and measurable next step.

Chapter Summary

Opportunity discovery is the evidence-led process of identifying and validating meaningful customer problems before committing to product solutions. Customers, users, buyers and stakeholders may occupy different roles, so product managers must understand the complete value network. Markets include needs, alternatives, providers, purchasing conditions, regulation and change—not merely demographic groups.

Secondary research provides efficient contextual evidence, while interviews, observation, surveys, diary studies, usability tests and behavioural experiments answer different questions. Qualitative methods explain experience and motivation; quantitative methods estimate prevalence and compare patterns. Sound research requires appropriate sampling, neutral questioning, careful interpretation and triangulation.

Segmentation identifies groups with meaningfully different needs or behaviours. Personas synthesise evidence into actionable representations, while the jobs-to-be-done perspective focuses on the progress customers seek in particular situations. Strong problem statements describe a customer, context, need and consequence without prescribing a solution.

Competitor analysis must include direct products, indirect alternatives, manual workarounds and inaction. Opportunities can be assessed through desirability, feasibility, viability and responsibility. Scoring models can structure discussion but do not replace judgement or evidence. The best validation tests target the riskiest assumption and define a decision rule in advance.

Finally, product discovery is both a commercial and ethical practice. Privacy, consent, fairness, security and responsible use of AI are essential to trustworthy research and sustainable products.

Key Terms

Customer: A person or organisation that obtains, pays for, authorises or influences a product purchase.

User: A person who directly interacts with a product.

Market: A system of customers, providers, alternatives and conditions surrounding an exchange of value.

Opportunity: A credible possibility for creating and capturing value by addressing a meaningful need.

Secondary research: Analysis of information previously collected by another party.

Primary research: Collection of new evidence for a specific product decision.

Triangulation: Use of multiple evidence sources or methods to investigate the same question.

Observation: Systematic study of behaviour in its natural or relevant context.

Segmentation: Division of a market into groups with meaningful differences.

Persona: An evidence-based representation of a significant customer pattern.

Jobs-to-be-done: A perspective that examines the progress a customer seeks in a particular situation.

Problem framing: Defining a customer problem without prematurely specifying a solution.

Substitute: An alternative way of achieving a similar outcome.

Switching barrier: A cost, risk or difficulty that discourages changing from an existing alternative.

Desirability: The extent to which customers value and want an outcome.

Feasibility: The extent to which an organisation can build and deliver a response.

Viability: The extent to which a response can create sustainable value.

Riskiest assumption: The belief whose failure would most threaten the opportunity.

Confirmation bias: The tendency to favour evidence that supports existing beliefs.

Review Questions

Short Answer Questions

1. Distinguish between a customer, a user and an economic buyer.
2. Why should competitor analysis include substitutes and inaction?
3. State two differences between qualitative and quantitative research.
4. What makes a persona evidence-based?
5. Explain why a problem statement should avoid prescribing a solution.

Long Answer Questions

1. Explain how a product team could move from broad market exploration to a validated opportunity hypothesis.
2. Compare interviews, observation, surveys and usability tests, including the decision each method is best suited to support.
3. Discuss the role of segmentation and personas in product strategy, including their limitations.
4. Explain the jobs-to-be-done perspective and show how it can improve problem framing.
5. Discuss ethical and privacy responsibilities when conducting customer research and using AI-assisted analysis.

Analytical / Application Questions

1. A team proposes a mobile app because delivery drivers “need better route planning.” Rewrite this as a testable problem statement and identify three research questions.
2. Using the weighted scoring approach in this chapter, create criteria for comparing two opportunities in a market of your choice. Explain the assumptions and limitations of your scoring.
3. A survey reports that 80% of respondents like a proposed product, but only 5% sign up for a paid pilot. Analyse at least four possible explanations and recommend the next research action.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes an opportunity from a product idea?

A. An opportunity is always a new technology, whereas an idea is an existing technology.

B. An opportunity is an evidence-supported possibility for creating value around a meaningful need; an idea is a possible solution.

C. An opportunity is a feature specification, whereas an idea is a market segment.

D. An opportunity is determined only by competitor pricing.

Correct Answer: B. An opportunity is an evidence-supported possibility for creating value around a meaningful need; an idea is a possible solution.

Question 2

Which research method is most suitable for understanding how a complex task is actually performed in its work environment?

A. Observation

B. A brand-awareness survey

C. A demographic census

D. A press-release test

Correct Answer: A. Observation

Question 3

Which interview question is least likely to introduce leading bias?

A. Would you use our automated reminder feature?

B. You find the current process frustrating, don’t you?

C. Tell me about the last time this problem occurred.

D. How valuable would our solution be to you?

Correct Answer: C. Tell me about the last time this problem occurred.

Question 4

What is the primary purpose of market segmentation?

A. To ensure every customer receives an identical product experience

B. To divide a market into groups with meaningfully different needs or behaviours

C. To eliminate the need for customer research

D. To identify only the largest competitor

Correct Answer: B. To divide a market into groups with meaningfully different needs or behaviours

Question 5

Which statement best represents a jobs-to-be-done formulation?

A. Customers need a dashboard with five widgets.

B. Customers prefer our brand colour to competitors’ colours.

C. When a delivery is delayed, the user wants to understand the cause and revised timing so staffing can be adjusted.

D. The product must include artificial intelligence.

Correct Answer: C. When a delivery is delayed, the user wants to understand the cause and revised timing so staffing can be adjusted.

Question 6

Which of the following is a substitute rather than necessarily a direct competitor?

A. A competing product with the same target segment and feature set

B. A customer’s spreadsheet used to achieve the same outcome

C. A regulator overseeing the market

D. A product team’s internal roadmap

Correct Answer: B. A customer’s spreadsheet used to achieve the same outcome

Question 7

In the chapter’s weighted scoring model, what do the weights represent?

A. The number of customers interviewed in each segment

B. The monetary price of each product feature

C. The relative importance assigned to the scoring criteria

D. The probability that the product will succeed

Correct Answer: C. The relative importance assigned to the scoring criteria

Question 8

A problem statement says, “Field technicians need a mobile app with offline forms.” What is the main weakness of this statement?

A. It identifies too many customer groups.

B. It prescribes a solution instead of describing the underlying need and context.

C. It contains no technical terminology.

D. It focuses on a customer job rather than a feature.

Correct Answer: B. It prescribes a solution instead of describing the underlying need and context.

Question 9

Which result provides the strongest evidence of willingness to exchange money for a proposed outcome?

A. A participant says the concept sounds interesting.

B. A participant selects “very interested” in an unvalidated survey.

C. A qualified customer joins a paid pilot under defined conditions.

D. A competitor has a similar slogan.

Correct Answer: C. A qualified customer joins a paid pilot under defined conditions.

Question 10

Why should AI-generated research summaries be checked against source material?

A. AI summaries are always legally prohibited.

B. Generated summaries can omit minority experiences or present uncertain interpretations too confidently.

C. Source material is never useful after summarisation.

D. Checking summaries removes the need for participant consent.

Correct Answer: B. Generated summaries can omit minority experiences or present uncertain interpretations too confidently.