Customer Discovery, Problems and Market Opportunity

Introduction

A business blueprint is only as credible as the evidence behind its assumptions. Founders frequently begin with a solution, a technology, or a market-size estimate and then search selectively for confirmation. Customer discovery reverses this sequence. It begins with people, contexts, unmet needs, current alternatives, and the consequences of inaction. The purpose is not to prove that an idea is good; it is to improve the quality of decisions under uncertainty.

In the Lean Canvas, customer discovery informs several interdependent elements: the problem, customer segments, unique value proposition, existing alternatives, channels, revenue streams, and unfair advantage. A well-designed discovery process helps an entrepreneurial team distinguish a significant problem from a minor inconvenience, identify early adopters rather than an abstract mass market, and estimate a market opportunity without confusing theoretical demand with attainable sales.

This chapter presents a rigorous approach to ethical interviews, observation, secondary research, jobs-to-be-done analysis, problem prioritization, and estimation of total addressable market (TAM), serviceable available market (SAM), and serviceable obtainable market (SOM).

Learning Objectives

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

1. Design and conduct ethical customer interviews and observational studies.
2. Distinguish customer segments, users, buyers, beneficiaries, and early adopters.
3. Apply jobs-to-be-done reasoning to understand customer motivations and contexts.
4. Prioritize problems using explicit criteria and evidence quality.
5. Integrate primary and secondary research while recognizing bias and uncertainty.
6. Estimate TAM, SAM, and SOM using defensible top-down and bottom-up methods.
7. Translate discovery evidence into responsible Lean Canvas decisions.

1. Customer Discovery as Evidence-Based Entrepreneurship

Customer discovery is a structured process for learning how a defined group experiences a situation, attempts to make progress, evaluates alternatives, and makes purchasing or adoption decisions. It is not merely “talking to customers.” It is inquiry designed to reduce uncertainty about a business model.

Several types of uncertainty commonly coexist:

• Problem uncertainty: Is the problem real, frequent, costly, urgent, and sufficiently important?
• Customer uncertainty: Who experiences it, who pays, and who influences the decision?
• Solution uncertainty: Would a proposed intervention improve the situation?
• Market uncertainty: Is there a sufficiently large and accessible group of potential customers?
• Adoption uncertainty: Can customers discover, trust, purchase, implement, and continue using the offering?

A discovery project should identify which uncertainty matters most at the current stage. Interviews are useful for understanding perceptions, language, motivations, and experience; they are less reliable for predicting actual purchase volumes. Observation reveals behavior and workarounds; it may not fully reveal private motivations. Secondary research provides scale and context; it may be too general or outdated for a specific venture. Strong research therefore triangulates different forms of evidence.

A useful evidence hierarchy is not a universal ranking but a fit-for-purpose principle. If the question is “What do people do during a workflow?”, direct observation may be stronger than a stated preference. If the question is “How large is the licensed market?”, regulatory and industry data may be more useful than a small interview sample. If the question is “Will customers pay our proposed price?”, a real transaction, paid pilot, deposit, or procurement commitment is stronger than an intention expressed in an interview.

2. Defining the Unit of Analysis

Before recruiting participants, specify whose experience is being studied. A “customer” may refer to several distinct roles:

Confusing these roles creates misleading findings. Users may value convenience while buyers prioritize risk, integration, and measurable return. A discovery plan should therefore state whether it is investigating the user problem, the purchasing problem, or the complete adoption system.

The segment should be defined behaviorally and contextually rather than only by demographics. “Adults aged 25–45” is usually too broad to guide discovery. “Independent physiotherapy clinics with three to ten practitioners that currently schedule appointments by telephone and experience more than ten weekly no-shows” is more actionable because it identifies a setting, existing behavior, and a possible pain signal.

3. Ethical Interviews and Observation

Ethics is not an administrative addition to discovery; it affects the validity of evidence and the legitimacy of the venture. Participants should understand the purpose of the research, what participation involves, how information will be used, and whether participation is voluntary. In a university or regulated setting, institutional review requirements may apply. Commercial research may also be subject to privacy, consumer-protection, employment, health, or sector-specific rules.

3.1 Core ethical principles

A responsible study should address the following:

• Informed consent: Participants receive understandable information before agreeing.
• Voluntary participation: They can decline or withdraw without penalty.
• Proportionality: The burden and risk of the study are justified by its learning value.
• Privacy and confidentiality: Collect only information needed for the research question and protect it appropriately.
• Data minimization: Avoid collecting sensitive personal data merely because it might be useful later.
• Non-deception: Do not misrepresent the venture, sponsor, or purpose unless a formally approved research design requires it.
• Fair selection: Do not systematically exclude or exploit vulnerable groups.
• Respect for power relationships: Employees, students, patients, and dependent customers may feel unable to refuse.
• Responsible reporting: Do not selectively quote participants or imply general conclusions from anecdotal evidence.

Consent is especially important when interviews are recorded. Participants should know whether audio, video, transcripts, names, quotations, or identifiable organizational details will be retained. A short consent script can state the purpose, duration, recording choice, confidentiality arrangements, voluntary nature of participation, and contact information for questions.

3.2 Interview design

A discovery interview should explore actual experience rather than invite speculative praise. The interviewer should ask about a recent, concrete episode:

• “Tell me about the last time this occurred.”
• “What happened first?”
• “What did you do next?”
• “What did that cost in time, money, risk, or frustration?”
• “How do you handle it today?”
• “What alternatives have you tried?”
• “Who else is involved?”
• “What would make a change difficult?”

Questions such as “Would you buy an app that solves this?” are weak because they encourage politeness, imagination, and hypothetical behavior. Better questions investigate existing behavior and commitments. If a participant claims that a problem is severe, the interviewer can ask what they have already done about it, what budget has been allocated, or what consequence followed the most recent occurrence.

A semi-structured guide is generally preferable to a rigid questionnaire. It preserves comparability across interviews while allowing the participant’s experience to surface unexpected issues. The interviewer should speak less than the participant, avoid defending the concept, and record observations separately from interpretations.

3.3 Observation and contextual inquiry

Observation is valuable when behavior is routine, tacit, distributed across tools, or different from what people remember. The researcher might observe how a restaurant manager handles stockouts, how a nurse documents a handover, or how a small business reconciles payments. A field protocol should specify what is observed, where, for how long, and how interruptions or confidential information will be handled.

Observation does not mean surveillance. Obtain permission, avoid recording bystanders without authorization, and stop when sensitive information appears. The researcher should distinguish:

• Descriptive notes: “The employee copied the order number into three systems.”
• Interpretive notes: “This may indicate duplicated data entry.”
• Hypotheses: “A shared integration could reduce re-entry.”

This separation reduces premature solution bias.

3.4 Sampling and saturation

Discovery samples are usually purposive rather than statistically representative. Participants are selected because they possess relevant experience, not because they reproduce population proportions. Include variation that could change the problem: organization size, workflow, geography, technical maturity, role, and current alternative.

The concept of saturation is useful but must be applied carefully. Practical saturation occurs when additional interviews produce few substantially new themes for the defined question and segment. Saturation is not proof that every customer agrees, nor does it establish market prevalence. A team should document which segment was studied, what themes stabilized, and which uncertainties remain.

4. Jobs-to-be-Done and Early Adopters

Jobs-to-be-done (JTBD) theory focuses on the progress a person is trying to make in a particular situation. A job is not identical to a product feature or a demographic category. A customer may “hire” a spreadsheet, consultant, manual process, or software application to accomplish a functional, emotional, and social job.

A useful job statement identifies:

1. The situation or trigger.
2. The desired progress.
3. Constraints and criteria for success.
4. The current solution or workaround.
5. The consequences of failure.

For example: “When a clinic receives a cancellation on short notice, the coordinator needs to fill the appointment quickly without violating patient preferences or spending excessive time calling people.” This statement is more informative than “clinics need scheduling software.”

The JTBD lens often reveals three layers:

• Functional job: What task must be completed?
• Emotional job: How does the person want to feel or avoid feeling?
• Social job: How does the person want to be perceived by others?

The framework is useful because it shifts discovery from feature requests to desired progress. It also exposes competition beyond a product category. A calendar application may compete with a telephone call, a notebook, a staff member, or simply accepting an empty appointment.

Early adopters

Early adopters are not simply the first people who express enthusiasm. They typically experience the problem acutely, have recognized it, possess motivation and authority to act, and are already using imperfect alternatives. They may tolerate an incomplete product because the value of improvement exceeds the cost of experimentation.

A practical early-adopter profile includes:

• a specific triggering situation;
• high frequency or severity of the problem;
• visible consequences of not solving it;
• an existing workaround or expenditure;
• access to budget or decision authority;
• willingness to participate in pilots and provide feedback.

The early-adopter segment is a beachhead, not necessarily the entire long-term market. A venture should avoid generalizing from early adopters to mainstream customers without testing differences in trust, procurement, switching costs, and required reliability.

5. From Interview Data to Problem Prioritization

Discovery produces messy qualitative material. The purpose of analysis is not to turn every comment into a feature but to identify recurring, consequential problems and the conditions under which they occur.

A disciplined process includes:

1. Transcribe or summarize interviews consistently.
2. Code statements by situation, behavior, consequence, workaround, and decision criterion.
3. Separate observations from interpretations.
4. Compare patterns within and across segments.
5. Search for disconfirming evidence and negative cases.
6. Formulate problem statements in the customer’s context.
7. Prioritize problems using explicit criteria.

A problem statement should avoid embedding the solution. “Users need an AI dashboard” is a solution hypothesis. “Operations managers cannot identify delayed orders until customers complain” describes a problem that could have multiple solutions.

A prioritization model

A simple scoring model can structure managerial judgment. Let each problem receive scores from 1 to 5 for severity, frequency, economic impact, urgency, reachability, and evidence strength. One possible weighted score is:

Here, P is the priority score; S is severity; F is frequency; E is economic impact; U is urgency; R is reachability; and Q is evidence quality. Each input is rated from 1 to 5. The weights are illustrative and should be changed to reflect the venture’s strategy. The score is a decision aid, not a validated law of customer behavior.

For example, suppose a team rates a problem as S=5, F=4, E=4, U=5, R=3, and Q=4. Then:

The result, 4.15 out of 5, suggests high priority under the stated assumptions. It does not mean that the problem is worth solving automatically. The team must still test willingness to change, competitive intensity, regulatory constraints, and whether a feasible solution can create value.

A major risk is false precision. A score of 4.15 should not be interpreted as objectively 17 percent better than a score of 3.55. Use ranges, confidence notes, and qualitative evidence alongside numerical rankings.

6. Secondary Research and Market Intelligence

Secondary research uses information collected by others, including government statistics, regulatory filings, trade associations, academic studies, company reports, procurement data, app-store information, job postings, patent databases, and credible market studies.

A source should be evaluated for:

• authority and methodological transparency;
• date and update frequency;
• geographic and sector relevance;
• definition of the population and variables;
• incentives or conflicts of interest;
• comparability with the venture’s target segment;
• uncertainty and sampling limitations.

Market reports often use attractive but poorly defined labels. “The global digital wellness market” may combine unrelated products, geographies, and revenue types. Before using a figure, ask what is counted, at what price level, in which currency and year, and whether revenue is recognized at the producer, distributor, or end-customer level.

Secondary research should inform interview sampling and hypotheses. It should not replace direct investigation of the customer’s workflow. Conversely, a few compelling interviews should not be used to claim population-wide prevalence.

7. Estimating TAM, SAM, and SOM

Market sizing is a structured estimate, not a discovery of a single hidden number.

• TAM (Total Addressable Market): The total annual revenue or volume that would exist if the relevant problem were served across the full defined market.
• SAM (Serviceable Available Market): The portion of TAM matching the venture’s product scope, geography, customer type, language, regulatory permissions, and delivery model.
• SOM (Serviceable Obtainable Market): The realistically attainable portion of SAM over a specified period, given competition, capacity, channels, adoption, and resources.

Always specify whether the estimate is revenue, units, users, or another measure, and specify the time period. A market in “customers” cannot be directly compared with a market in “annual revenue” without a conversion assumption.

Top-down estimation

A top-down estimate starts with a broad credible market figure and applies transparent filters:

Here, g is the proportion in the target geography, c is the proportion in the relevant customer category, and p is the proportion matching the product scope. These proportions must be defined and should not overlap inconsistently. The formula assumes the filters can be applied multiplicatively; if the categories overlap or the source reports different denominators, a segmented calculation is preferable.

Top-down analysis is useful for context but can conceal unrealistic assumptions. A broad market category does not prove that a particular venture can reach it.

Bottom-up estimation

Bottom-up analysis begins with identifiable customers, usage, price, and capacity:

N is the number of potential customer organizations or individuals; r is the proportion with the relevant need; u is the expected annual usage or subscription units per customer; and p is price per unit. Units must be consistent. If p is monthly price, multiply by 12 for a full year unless the usage variable already represents annual purchases.

For a subscription venture, a more direct form is:

This estimates revenue under stated customer and price assumptions; it is not a forecast of profit or cash flow.

Worked market-sizing example

The following is an illustrative example. Suppose a venture sells workflow software to independent clinics in one country.

• Potential clinics identified from a professional registry: 8,000.
• Clinics with the relevant workflow and compatible operations: 60%.
• Clinics within the initial geographic and regulatory scope: 50%.
• Annual subscription price: $1,200.
• Realistic three-year obtainable penetration: 4% of SAM clinics.

First, the SAM number of clinics is:

If every SAM clinic purchased, the annual SAM revenue at the stated price would be:

The obtainable customer count under the 4% assumption is:

Thus, the illustrative SOM revenue is:

The calculation assumes all customers pay the full annual price, churn is ignored, implementation capacity is sufficient, and the 4% penetration estimate is credible. A more advanced model would include ramp-up by year, churn, discounts, sales capacity, and implementation costs. The figures should therefore be presented as an assumption range rather than a promise.

Sanity checks and uncertainty

Use at least two methods where possible. Compare a top-down estimate with a bottom-up estimate, investigate major differences, and document the assumptions responsible. Sensitivity analysis can show how results change when a key input varies. For example, if the 4% SOM penetration is uncertain, calculate outcomes at 2%, 4%, and 6% rather than presenting one unsupported point estimate.

Do not add TAM, SAM, and SOM together: they are nested concepts. Do not call a global market TAM if the venture cannot legally or operationally serve that market. Do not infer SOM merely by choosing a convenient percentage such as 1% of TAM. SOM should follow from capacity, channel economics, competitive response, sales-cycle length, and adoption evidence.

8. Applied Case: ClinicFlow (Illustrative Case)

Background

ClinicFlow is a fictional graduate-team venture exploring software for independent physiotherapy clinics. The team initially proposed an AI scheduling assistant for “all healthcare providers.” Its first Lean Canvas claimed a large global market and assumed that clinic managers would rapidly adopt the product.

Problem and discovery design

The team narrowed its initial research to small physiotherapy clinics because the founders had access to professional networks. They conducted 18 semi-structured interviews: 12 clinic coordinators, four owners, and two practitioners. They also observed scheduling activity in three clinics, with permission, and reviewed professional-registry data and publicly available scheduling guidance.

The interviews revealed that “scheduling” was not one problem. Participants described three distinct situations: filling short-notice cancellations, coordinating practitioner availability, and reducing patient no-shows. The most acute problem was short-notice cancellations during high-demand periods. Coordinators used telephone calls, text messages, spreadsheets, and informal waiting lists. The cost was not only lost revenue; coordinators also experienced interruption, stress, and patient dissatisfaction.

Analysis

The team coded evidence by trigger, behavior, consequence, alternative, and decision constraint. It found that clinics with one to three practitioners often lacked an integrated scheduling system, but many had limited budgets and low technical capacity. Clinics with four to ten practitioners reported more frequent coordination problems and had an owner or administrator able to approve a pilot. This group became the provisional early-adopter segment.

The team tested its assumptions by asking participants about recent cancellations, not hypothetical product interest. Seven of the 18 participants had recently paid overtime or lost appointment revenue because they could not fill a cancellation. Five had already tried a waiting list or messaging workaround. These findings supported problem relevance but did not yet prove willingness to pay.

Approach and solution experiment

Rather than immediately build an AI system, the team proposed a concierge pilot: coordinators would enter cancellation information into a simple form, and ClinicFlow staff would assist with targeted patient notifications. The pilot tested whether the workflow and outcome were valuable before investing in automation. Participants were informed that the service was experimental, how their data would be used, and that no medical decision would be automated.

The team defined success metrics in advance: time required to initiate outreach, proportion of cancellations filled, coordinator-reported workload, and willingness to continue under a stated price. It also recorded negative cases. Some clinics declined because patient communication policies required administrator approval; this became a product and governance constraint rather than an inconvenience to ignore.

Outcome

The pilot showed that the problem was strongest in clinics with recurring demand and a maintained patient waiting list. Clinics without a waiting list obtained little value. The team therefore revised its early-adopter definition and removed “all healthcare providers” from the initial canvas. It also recognized that the buyer was often the clinic owner, the user was the coordinator, and the patient was a beneficiary whose consent and communication preferences mattered.

Learning points

1. A broad solution category concealed several different problems.
2. Recent behavior and existing workarounds were more informative than enthusiasm for an AI concept.
3. Narrowing the segment improved both research quality and market-sizing assumptions.
4. A concierge experiment tested value while limiting technical investment.
5. Privacy, consent, and communication governance were part of the product design, not merely legal review.

9. Technology, AI, and Contemporary Practice

Digital tools can improve discovery by organizing transcripts, identifying repeated terms, mapping workflows, and testing market-sizing scenarios. AI-assisted coding can suggest themes, but researchers remain responsible for interpretation. Automated systems may misread sarcasm, minority experiences, context, or silence. Sensitive interview data should not be uploaded to a third-party system without authorization, contractual protection, and an assessment of privacy risk.

AI-generated market summaries may reproduce outdated sources, unsupported claims, or category inflation. Every important statistic should be traced to its original source. Teams should preserve an audit trail showing the prompt or method used, the source material, human checks, and changes made to the conclusion.

Contemporary discovery is also increasingly shaped by platform data, online communities, product analytics, and privacy regulation. Digital behavioral data can reveal frequency and retention but may not explain meaning or unmet needs. Consent, lawful purpose, data minimization, and user expectations remain essential. Ethical discovery should not treat publicly visible data as automatically unrestricted for commercial profiling.

10. Managerial Implications and Best Practices

For entrepreneurial managers, discovery is a portfolio of learning investments. Allocate effort to the assumption that could most seriously invalidate the business model. A team with strong evidence of problem severity but weak evidence of buyer authority should interview buyers next, not conduct more generic user interviews.

Best practices include:

• write assumptions before collecting evidence;
• recruit for relevant experience rather than convenience alone;
• ask about recent events and current alternatives;
• separate user, buyer, and beneficiary perspectives;
• seek disconfirming evidence;
• document refusals and negative cases;
• use explicit criteria for prioritization;
• triangulate interviews, observation, experiments, and secondary data;
• report ranges and assumptions in market sizing;
• update the Lean Canvas after evidence changes;
• protect participant data and honor withdrawal requests;
• distinguish evidence, inference, and speculation in internal decisions.

The goal is not certainty. In entrepreneurial environments, certainty is rarely available before commitment. The goal is a progressively more accurate and ethically obtained basis for deciding what to test, whom to serve, and where to invest scarce resources.

Challenges and Limitations

Customer discovery has persistent limitations. Participants may misremember events, rationalize decisions, seek to please the interviewer, or lack authority to purchase. Access is often biased toward convenient networks. Observation can alter behavior. Secondary data may use incompatible definitions. Market estimates may be highly sensitive to price, inclusion criteria, and adoption assumptions.

Qualitative research is especially vulnerable to overgeneralization. A vivid quotation demonstrates that an experience occurred; it does not establish how common it is. Quantitative surveys can estimate prevalence when sampling and measurement are sound, but poorly designed surveys create an illusion of precision. Similarly, a successful pilot may demonstrate value for a narrow context without proving scalable demand.

These limitations do not make discovery ineffective. They require methodological humility, transparent assumptions, iterative research, and a willingness to revise the business model.

Chapter Summary

Customer discovery is an evidence-based process for reducing uncertainty about problems, customers, solutions, markets, and adoption. Ethical practice requires informed consent, voluntary participation, privacy protection, data minimization, fair recruitment, and responsible reporting. Interviews should focus on recent behavior and current alternatives, while observation reveals contextual work that participants may not articulate.

Jobs-to-be-done analysis defines the progress customers seek in particular situations and distinguishes functional, emotional, and social dimensions. Early adopters experience a problem acutely, have motivation and authority to act, and often use imperfect alternatives. Problem prioritization should combine severity, frequency, economic impact, urgency, reachability, and evidence quality, while avoiding false precision.

Secondary research supplies scale and context but must be evaluated for authority, relevance, definitions, and bias. TAM describes the full defined opportunity, SAM narrows it to the venture’s scope, and SOM represents the realistically attainable portion. Top-down and bottom-up estimates should use explicit assumptions, consistent units, sensitivity analysis, and capacity-based reasoning. Discovery findings should continuously revise the Lean Canvas and guide the next experiment.

Key Terms

Customer discovery: Structured learning about customer problems, contexts, behaviors, and adoption conditions.

Primary research: Information collected directly through interviews, observation, experiments, or surveys.

Secondary research: Information previously collected and published by another party.

Informed consent: Voluntary agreement based on understandable information about participation and data use.

Observation: Systematic study of behavior in its real or simulated context.

Customer segment: A defined group sharing relevant needs, behaviors, or purchasing conditions.

User: Person who operates or experiences an offering.

Buyer: Person or organization that authorizes or makes a purchase.

Beneficiary: Person or group receiving the principal benefit.

Jobs-to-be-done: Framework describing the progress a customer seeks in a particular situation.

Early adopter: Customer with acute need, motivation, authority, and willingness to try an emerging solution.

Workaround: Existing alternative used to manage a problem without a dedicated solution.

Problem statement: Contextual description of an unmet need without embedding a particular solution.

Triangulation: Comparing different evidence sources or methods to improve confidence.

Saturation: Point at which additional qualitative research yields few substantially new themes for a defined question.

TAM: Total addressable market under a specified definition and period.

SAM: Serviceable available market within the venture’s scope.

SOM: Serviceable obtainable market that can realistically be captured.

Bottom-up estimation: Market sizing from identifiable customers, usage, price, and capacity.

Evidence quality: The credibility, relevance, directness, and transparency of evidence for a decision.

Review Questions

5 Short Answer Questions

1. What is the difference between a user, a buyer, and a beneficiary?
2. Why are questions about recent behavior generally stronger than hypothetical purchase questions?
3. Define an early adopter and identify two characteristics that distinguish one from a curious observer.
4. What is the difference between TAM, SAM, and SOM?
5. Why should interview observations be separated from interpretations in research notes?

5 Long Answer Questions

1. Explain how informed consent, privacy, data minimization, and power relationships affect the ethics and validity of customer discovery.
2. Describe how jobs-to-be-done analysis can reveal competition from non-product alternatives.
3. Compare top-down and bottom-up market sizing, including the strengths and limitations of each approach.
4. Develop a research plan for a venture whose proposed customer segment is currently defined only by age and income.
5. Explain how a team can use qualitative interviews without overgeneralizing from a small, non-representative sample.

3 Analytical / Application Questions

1. A team has interviewed ten users who describe a problem as frustrating, but none has changed behavior, allocated budget, or tried a workaround. Evaluate what this evidence supports and design the next research step.
2. A venture estimates a TAM of $50 million and claims a SOM of 2% solely because 2% seems conservative. Critique the estimate and specify the operational assumptions required for a defensible SOM.
3. Using the prioritization model in this chapter, calculate the score for a problem with severity 4, frequency 5, economic impact 3, urgency 4, reachability 5, and evidence quality 2. Interpret the result cautiously and identify one limitation of the scoring method.

Multiple Choice Questions with Answers

Question 1

Which interview question best investigates actual customer behavior?

A. Would you buy an application that solved this problem?

B. How important is innovation to your organization?

C. Tell me about the last time this problem occurred and what you did next.

D. Would a cheaper solution interest you?

Correct Answer: C. Tell me about the last time this problem occurred and what you did next.

Question 2

Which statement best describes an early adopter?

A. A person selected only because of demographic similarity to the founder

B. A customer with an acute problem, motivation, and willingness to try an imperfect solution

C. Anyone who expresses enthusiasm for a product concept

D. The largest possible population that might eventually use the product

Correct Answer: B. A customer with an acute problem, motivation, and willingness to try an imperfect solution

Question 3

In jobs-to-be-done analysis, a job primarily describes:

A. A software feature requested by a user

B. The progress a person seeks in a particular situation

C. The demographic profile of a market

D. The legal duties of an employee

Correct Answer: B. The progress a person seeks in a particular situation

Question 4

Which practice is most consistent with ethical customer discovery?

A. Recording every interview without telling participants

B. Collecting sensitive data in case it becomes useful later

C. Allowing participants to understand the study and withdraw voluntarily

D. Omitting negative findings from the final report

Correct Answer: C. Allowing participants to understand the study and withdraw voluntarily

Question 5

A market has 8,000 potential clinics, of which 60% fit the workflow and 50% fit the geographic scope. What is the resulting SAM customer count?

A. 400

B. 1,600

C. 2,400

D. 4,800

Correct Answer: C. 2,400

Question 6

If 2,400 SAM clinics pay $1,200 annually, what is the corresponding annual SAM revenue under the stated assumptions?

A. $288,000

B. $2,880,000

C. $28,800,000

D. $3,600,000

Correct Answer: B. $2,880,000

Question 7

Which statement about SOM is most accurate?

A. It is always 1% of TAM.

B. It is the total global market for the problem.

C. It is the realistically attainable portion of SAM over a specified period.

D. It is the number of people who have heard of the venture.

Correct Answer: C. It is the realistically attainable portion of SAM over a specified period.

Question 8

A bottom-up annual subscription estimate is generally based on:

A. Global population multiplied by a subjective adoption percentage

B. Customers multiplied by monthly price multiplied by 12

C. TAM multiplied by SAM multiplied by SOM

D. Competitor valuation divided by market share

Correct Answer: B. Customers multiplied by monthly price multiplied by 12

Question 9

What is a major limitation of a vivid interview quotation?

A. It cannot demonstrate that the experience occurred.

B. It proves the experience is common across the whole market.

C. It demonstrates an experience but does not establish its prevalence.

D. It is unusable unless it contains numerical data.

Correct Answer: C. It demonstrates an experience but does not establish its prevalence.

Question 10

In the chapter’s illustrative prioritization model, which input is explicitly included?

A. Founder charisma

B. Evidence quality

C. Number of product features

D. Competitor logo count

Correct Answer: B. Evidence quality