2. Customer Value and Strategic Alignment

Introduction and Context

Lean and Six Sigma systems are often described as methods for reducing waste, variation, defects, and process cost. Such descriptions are incomplete unless they answer a prior strategic question: waste, variation, defects, and cost relative to what value? A process can become faster and cheaper while producing an outcome that customers do not need, stakeholders do not accept, or regulators will not permit. Customer value therefore provides the reference point for improvement, while strategic alignment determines whether improvement effort is directed toward the organisation’s most important objectives.

Customer value is not limited to product features or transaction price. It includes reliability, safety, accessibility, responsiveness, ease of use, service recovery, sustainability, trust, and the total experience of receiving and using an offering. Different stakeholders may define value differently. A patient values safe and timely care; a hospital may value clinical outcomes and capacity utilisation; a regulator values compliance and public safety; employees may value workable processes and psychological safety. Lean Six Sigma practitioners must reconcile these perspectives and convert them into measurable requirements.

This chapter examines how the voice of the customer (VOC) and broader stakeholder needs are transformed into critical-to-quality (CTQ) requirements. It focuses on Kano analysis, affinity methods, and quality function deployment (QFD), and then connects these tools to strategy, risk, sustainability, and competitive priorities. The central argument is that improvement portfolios should not be selected merely because a process contains visible inefficiency. They should be selected because improvement will protect or increase value while advancing strategic priorities.

Learning Objectives

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

1. Explain customer value, VOC, stakeholder requirements, and CTQs in a Lean Six Sigma context.

2. Distinguish explicit, implicit, latent, and negative requirements.

3. Apply interviews, surveys, observation, affinity analysis, and Kano analysis to structure customer information.

4. Translate qualitative requirements into measurable CTQ statements and operational definitions.

5. Explain the logic and application of QFD and the House of Quality.

6. Evaluate improvement opportunities against strategy, risk, sustainability, and competitive priorities.

7. Identify common errors, ethical issues, and governance risks in customer-driven improvement.

8. Construct a strategically balanced improvement portfolio and justify project selection.

From Customer Voice to Organisational Value

The voice of the customer is the systematic expression of customer expectations, experiences, preferences, frustrations, and desired outcomes. It may be gathered directly through interviews, complaints, focus groups, surveys, usability tests, and observation. It may also be inferred indirectly from returns, warranty claims, call-centre records, social-media comments, lost sales, service cancellations, and operational data.

VOC should not be confused with a list of customer statements. A statement such as “the delivery should be quick” is valuable evidence but is not yet a quality requirement. It must be interpreted carefully and translated into a measurable expectation, such as “95 percent of standard orders delivered within two business days.” The translation must preserve the customer’s intent without imposing an unjustified technical solution.

Stakeholder analysis broadens the field beyond paying customers. Stakeholders may include users, purchasers, employees, suppliers, owners, regulators, communities, and partners. In business-to-business settings, the user, economic buyer, technical approver, and end consumer may have different requirements. Public services frequently involve citizens who receive services, taxpayers who fund them, employees who deliver them, and authorities who govern them. A sound requirements process makes these interests visible rather than allowing the loudest group to dominate.

Customer value can be represented conceptually as the benefits perceived by a customer relative to the sacrifices required to obtain and use an offering. Sacrifices include price, time, effort, risk, inconvenience, uncertainty, and environmental or social concerns. The relationship is not a precise accounting formula, but it reminds managers that value is multidimensional. A feature that adds technical sophistication may reduce value if it increases complexity or failure risk.

Three distinctions are particularly important. First, stated importance is not always the same as actual importance; customers may report that a feature matters but choose suppliers primarily on another criterion. Second, satisfaction is not identical to importance; a requirement may receive low satisfaction but have little effect on choice, or it may be a hidden dissatisfaction that customers cannot easily articulate. Third, customer value is dynamic. Competitor improvements, technological change, regulation, and changing social expectations can turn a differentiator into a basic expectation.

Requirements, CTQs, and Operational Definitions

A requirement is an expected condition or performance level. A need is the underlying outcome sought by a customer, while a specification is a formal statement of an acceptable level. A critical-to-quality characteristic is a measurable attribute whose performance has a significant effect on customer satisfaction, safety, compliance, or strategic value.

The translation from VOC to CTQ generally follows this logic:

Customer statement → underlying need → quality characteristic → measurable indicator → specification or target.

For example:

“Mortgage approval should not be a hassle” → confidence and simplicity → clarity and process responsiveness → number of avoidable contacts, application error rate, and decision lead time → defined limits for each measure.

A CTQ tree is useful for making this translation explicit. The broad need is placed at the top, major drivers are identified beneath it, and measurable requirements are placed at the lowest level. A need for “safe medical treatment” may lead to drivers such as correct identification, accurate medication administration, and timely escalation. These may then be measured through verified identification compliance, medication error rate, and response time to abnormal observations.

An operational definition specifies exactly what is measured, how it is measured, where the data come from, the unit of measure, the sampling rule, and the conditions under which the measurement is valid. Without an operational definition, terms such as “on time,” “defect,” “responsive,” or “easy to use” produce inconsistent data. A CTQ should ideally include a direction of desirability, a target or tolerance, and a customer or stakeholder rationale.

A useful CTQ statement is specific without prematurely prescribing a solution. “Customers must receive an automated text message” is a solution. “Customers must receive accurate status information at each agreed milestone” is a requirement that leaves room for alternative solutions.

Discovering and Structuring the Voice of the Customer

Selecting Appropriate Listening Methods

No single VOC method is sufficient. Interviews provide depth and reveal language, motives, and emotional concerns, but they are time-consuming and vulnerable to interviewer bias. Surveys allow broader coverage and quantitative comparison, but response scales and question wording can distort results. Observation and contextual inquiry reveal workarounds and unspoken needs, particularly where customers cannot easily describe their behaviour. Complaints and service records expose failure points, although they overrepresent dissatisfied or highly motivated users.

Sampling should reflect relevant customer segments, usage conditions, and failure experiences. Averages can conceal important differences. For example, a delivery service may appear satisfactory overall while being unacceptable for rural customers or customers requiring temperature-controlled transport. Segmentation may be based on use case, customer value, geography, risk exposure, channel, or frequency of interaction.

Questions should explore outcomes rather than merely invite feature requests. Asking “What would make this process easier?” is generally more informative than asking customers to choose from a predetermined list of technologies. Critical incidents—specific occasions when the service worked particularly well or poorly—often produce more reliable insight than general opinions.

Affinity Analysis

Affinity analysis is a qualitative method for organising a large number of observations or statements into meaningful themes. Each statement is first recorded separately, preserving the respondent’s language. Similar statements are then grouped, and groups are given descriptive labels. The process can be conducted by a cross-functional team using physical cards or digital collaboration software.

The discipline of affinity analysis lies in discovering relationships without forcing data into an existing organisational structure. For example, statements such as “I had to repeat my information,” “the agent could not see my previous request,” and “different departments gave different answers” may be grouped under “loss of continuity,” rather than under separate departmental categories. This can reveal a systemic customer problem that no single department owns.

Affinity outputs should be validated. Team members should distinguish evidence from interpretation, identify minority but high-risk needs, and return to customers where ambiguity remains. A theme is not automatically a CTQ; it is an intermediate structure requiring further analysis.

Kano Analysis

The Kano model classifies requirements according to their relationship with customer satisfaction. Basic or must-be attributes are expected as a minimum. Their presence may not create strong satisfaction, but their absence causes disproportionate dissatisfaction. Performance attributes generate satisfaction in relation to the degree of performance: more speed, accuracy, or capacity may create more value. Excitement attributes are unexpected features that can delight customers, although their absence is not normally dissatisfying. Some attributes are indifferent, meaning that their presence has little effect; reverse attributes reduce satisfaction for some customers when provided.

The model is valuable because it challenges the assumption that every feature should be improved in the same way. Basic requirements deserve protection and prevention of failure. Performance requirements require competitive benchmarking and optimisation. Excitement requirements may support differentiation, but they should not be funded at the expense of essential reliability.

Kano classification is not permanent. As competitors imitate an excitement attribute, it may become a performance expectation and eventually a basic requirement. Classification also varies by segment. A mobile application’s biometric login may be an excitement feature for one group, a performance expectation for another, and a barrier for users with accessibility concerns.

Traditional Kano questionnaires use paired functional and dysfunctional questions. Respondents indicate how they would feel if an attribute were present and how they would feel if it were absent. The response combination supports classification. In practice, the results should be interpreted alongside importance, satisfaction, cost, risk, and strategic fit. Kano analysis is not a substitute for judgement or experimentation.

Quality Function Deployment: Linking Needs to Design and Process

Quality function deployment is a structured method for translating customer requirements into product, service, process, and control characteristics. Its best-known representation is the House of Quality (HoQ). QFD encourages cross-functional dialogue and makes trade-offs visible before resources are committed.

The principal elements of a House of Quality are:

• Customer requirements, often called the “what”s.
• Importance ratings and, where useful, current satisfaction or competitive assessment.
• Technical or process characteristics, the “how”s, that can be controlled by the organisation.
• A relationship matrix showing the strength of links between requirements and characteristics.
• A correlation roof showing positive and negative interactions among technical characteristics.
• Target values and priorities for the technical characteristics.
• Competitive or benchmarking information.

The relationship matrix commonly uses qualitative weights such as 9 for strong, 3 for moderate, 1 for weak, and 0 for no relationship. A weighted priority for a technical characteristic can be estimated by multiplying the importance of each customer requirement by the relationship weight and summing across requirements. The resulting figures are decision aids, not objective truths. They depend on the quality of requirements, the selected scales, and the judgement of the team.

A simplified example concerns an online university enrolment service. Customer requirements may include accurate course information, rapid confirmation, low effort, secure payment, and accessible design. Technical characteristics may include data synchronisation frequency, system response time, number of form fields, payment verification controls, and accessibility conformance. The matrix may reveal that response time supports rapid confirmation and low effort, while data governance strongly supports information accuracy. The roof may show a conflict between extensive security checks and low effort. Management can then explore risk-based authentication rather than treating the conflict as an unavoidable trade-off.

QFD can be deployed in stages. The first matrix translates customer requirements into product or service characteristics. Subsequent matrices translate these into component characteristics, process operations, and control plans. This creates traceability from customer value to operational control. However, excessive formality can turn QFD into a scoring exercise. The method is most useful when teams debate evidence, expose conflicts, and identify decisions requiring leadership attention.

Aligning Improvement with Strategy, Risk, Sustainability, and Competition

A customer requirement becomes strategically important when it contributes to an organisation’s chosen position or protects its ability to operate. Strategy provides the criteria for deciding which needs deserve priority when resources are limited. For example, a low-cost provider may emphasise process stability, high asset utilisation, and low total cost, whereas a premium provider may prioritise personalisation, responsiveness, and service recovery. Both must meet basic safety and compliance requirements, but their performance priorities differ.

Strategic alignment can be assessed through a portfolio logic. Each proposed improvement should be examined for its contribution to strategic objectives, customer impact, financial value, risk reduction, sustainability, feasibility, and learning potential. A weighted scoring model may be used, but scores should not conceal non-negotiable constraints. A project that reduces cost but increases regulatory exposure should not rank highly merely because cost savings receive a large weight.

Risk-based prioritisation is especially important for CTQs related to safety, privacy, continuity, and legal compliance. Failure Mode and Effects Analysis (FMEA) can complement VOC and QFD by examining how a requirement may fail, the effects of failure, existing controls, and the need for action. Customer importance and risk severity should be considered together. A low-frequency failure may still deserve priority when its consequences are severe.

Sustainability expands the definition of value across the product or service life cycle. Customers and stakeholders may value lower energy consumption, responsible sourcing, repairability, reduced packaging, fair labour practices, and lower emissions. Lean initiatives should therefore avoid narrow interpretations of waste. A reduction in local processing time that increases transport, energy use, material waste, or supplier risk may not create net value. Life-cycle thinking and stakeholder consultation help prevent such sub-optimisation.

Competitive priorities commonly include cost, quality, delivery, flexibility, innovation, and sustainability. They interact rather than operate independently. A robust improvement may improve several priorities, while another may create a trade-off. For instance, standardisation can improve quality and cost but reduce flexibility if applied without segmentation. QFD’s correlation roof and portfolio review are useful for making such interactions explicit.

Building an Improvement Portfolio

Improvement portfolios should contain a balanced set of projects rather than a collection of locally attractive initiatives. A portfolio may include foundational projects that stabilise basic requirements, strategic projects that differentiate the offering, risk-reduction projects that protect continuity, and enabling projects that improve data, capability, or technology.

Project selection begins with a clear problem and a validated customer or stakeholder consequence. The team should identify the relevant CTQs, baseline performance, affected segments, process ownership, and strategic objective. Financial benefits should be estimated conservatively, distinguishing hard savings, avoided cost, capacity release, revenue protection, and intangible benefits. Dependencies and resource constraints must be visible.

Governance should include a sponsor who can resolve cross-functional conflicts, a process owner accountable for sustained performance, and a review mechanism that checks whether benefits reach customers rather than remaining internal efficiency gains. Leading indicators may include adoption, process capability, complaint trends, and control adherence; lagging indicators may include retention, cost, safety events, and customer satisfaction.

Illustrative Case: Aligning a Hospital Outpatient Appointment System

Background

The following is an illustrative case. A regional hospital experienced frequent complaints about outpatient appointments. Patients reported long waiting times, repeated calls, unclear preparation instructions, and difficulty rescheduling. Internal departments proposed separate projects involving call-centre staffing, online booking, and scheduling software.

Situation and Problem

The hospital’s initial improvement response focused on reducing average call duration. This produced a modest productivity gain but did not resolve patient dissatisfaction. Some calls became shorter because staff transferred patients or provided incomplete answers. The leadership team therefore commissioned a cross-functional VOC and strategic alignment exercise.

Analysis

Interviews were conducted with patients across age groups and appointment types. Observation covered the booking, reminder, arrival, and rescheduling processes. Complaint records and missed-appointment data were reviewed. Affinity analysis produced themes including continuity of information, certainty about preparation, flexible rescheduling, waiting-time transparency, and accessibility.

Kano analysis suggested that accurate appointment details and safe preparation instructions were basic requirements. Waiting-time predictability and ease of rescheduling were performance attributes. A digital reminder containing personalised preparation guidance was potentially an excitement attribute for some patients, but it could not substitute for accessible telephone support.

The team developed CTQs. “I need to know what to do before the appointment” became the requirement that preparation instructions be accurate, understandable, available through appropriate channels, and acknowledged for high-risk procedures. “I do not want to waste a day waiting” became a requirement for a defined proportion of appointments to begin within an agreed interval or for patients to receive timely delay information. “I need to change the appointment easily” became a requirement for rescheduling without repeating information or losing clinical priority.

QFD linked these requirements to technical and process characteristics: integrated patient and appointment data, standardised instruction templates, risk-based reminder timing, real-time schedule status, rescheduling rules, and staff access to a common record. The correlation roof revealed that stronger identity verification could increase effort, while integrated data could improve both continuity and privacy risk if access controls were weak.

Approach and Solution

The portfolio was divided into three releases. The first stabilised basic requirements through standard instruction content, ownership of appointment information, and error-proofing of high-risk bookings. The second improved performance through a common scheduling view, delay notifications, and a controlled rescheduling workflow. The third tested optional digital reminders and self-service functions with patients who could use them, while retaining assisted channels.

The projects were prioritised using customer importance, safety risk, strategic contribution, feasibility, and sustainability criteria. The hospital rejected a proposal to make digital self-service mandatory because it would have disadvantaged some patients and shifted work to those with limited digital access. Measures included preparation-related errors, avoidable contacts, rescheduling completion time, waiting-time predictability, missed appointments, patient-reported effort, and accessibility complaints.

Outcome

The illustrative case demonstrates that the most valuable intervention was not necessarily the most visible technology. Clarifying ownership, standardising information, and integrating process controls addressed basic requirements and enabled later digital improvements. The portfolio also balanced efficiency with equity, safety, privacy, and patient experience.

Learning Points

First, internal productivity metrics can improve while customer value deteriorates. Second, Kano analysis helps distinguish minimum expectations from differentiating opportunities. Third, QFD exposes technical relationships and trade-offs. Fourth, strategic alignment requires consideration of risk and inclusion, not only financial return. Finally, a portfolio should sequence foundational stability before optional innovation.

Challenges, Limitations, and Ethical Considerations

VOC data are vulnerable to sampling bias, recall bias, social desirability, and the tendency of organisations to hear only convenient feedback. Averages can hide vulnerable segments. Managers may also manipulate importance weights to justify a preferred project. Transparent documentation of sources, assumptions, and uncertainty is therefore essential.

Customer language can be ambiguous. Translation teams may unintentionally replace a need with a technical solution or confuse a complaint with its root cause. QFD can create false precision when numerical weights are treated as scientifically exact. Kano results may vary with wording, culture, experience, and market maturity. These tools support disciplined judgement; they do not eliminate judgement.

Ethically, organisations must obtain informed consent where appropriate, protect personal information, and avoid collecting more data than necessary. Automated sentiment analysis can misclassify language, accents, sarcasm, or minority experiences. Customers should not be denied meaningful service because they cannot use a digital channel. Requirements involving safety, accessibility, privacy, and fairness should be treated as governance obligations rather than optional preferences.

A further risk is metric fixation. Once a CTQ becomes a target, employees may optimise the measure rather than the underlying value. For example, reducing average handling time may increase repeat contacts. Balanced measures, audit sampling, qualitative feedback, and customer outcomes help counter this effect.

Digital Applications and Emerging Developments

Digital platforms can consolidate VOC from surveys, service tickets, web behaviour, contact-centre transcripts, product reviews, and operational data. Natural-language processing can identify themes and emerging complaints, while dashboards can connect CTQs with process performance. Digital QFD tools support collaborative matrices, version control, traceability, and scenario analysis.

Artificial intelligence can assist with clustering comments, detecting sentiment, predicting churn, and identifying relationships between experience variables and outcomes. Its use requires human validation, explainability, privacy protection, and monitoring for biased representation. A high volume of digital comments should not outweigh quieter evidence from customers with limited digital access.

Emerging practice is moving toward continuous discovery rather than occasional VOC studies. Organisations increasingly combine real-time experience signals with journey analytics, digital twins, sustainability data, and risk intelligence. The strategic challenge is to avoid becoming data-rich but insight-poor. Technology should improve the quality and timeliness of decisions, not replace direct engagement or accountable leadership.

Best-Practice Principles

Effective customer-value deployment generally follows several principles:

• Begin with customer and stakeholder outcomes, not available technology.
• Use multiple listening methods and segment the evidence.
• Preserve customer language before translating it into technical terms.
• Define CTQs operationally and connect them to process measures.
• Treat basic requirements, performance requirements, and differentiators differently.
• Use QFD to expose relationships and trade-offs, not merely to calculate scores.
• Integrate risk, sustainability, accessibility, and regulatory requirements.
• Prioritise portfolios rather than isolated projects.
• Validate assumptions with customers and process data.
• Establish ownership, controls, and post-implementation review.

Chapter Summary

Customer value is the foundation for deciding what Lean Six Sigma should improve. VOC methods reveal expressed and unexpressed needs, while stakeholder analysis ensures that users, employees, regulators, communities, and other affected groups are considered. Affinity analysis organises qualitative evidence into themes; Kano analysis distinguishes basic, performance, excitement, indifferent, and reverse attributes; and CTQ trees translate broad needs into measurable requirements.

QFD links customer requirements to technical and process characteristics and makes relationships, priorities, and trade-offs visible. Its value depends on sound evidence and thoughtful interpretation rather than mechanical scoring. Improvement portfolios should be aligned with organisational strategy, competitive priorities, risk exposure, sustainability, inclusion, and resource constraints. The strongest Lean Six Sigma decisions protect basic requirements, improve performance attributes, develop meaningful differentiation, and maintain governance over safety, privacy, fairness, and long-term value.

Key Terms

Voice of the Customer (VOC): Systematic information about customer expectations, experiences, needs, and perceptions.

Stakeholder: An individual or group that affects, is affected by, or has an interest in an organisation’s activities.

Customer value: Perceived benefits relative to the monetary, time, effort, risk, and other sacrifices required.

Requirement: An expected condition, outcome, or level of performance.

Critical-to-Quality (CTQ): A measurable characteristic that significantly affects customer, stakeholder, compliance, safety, or strategic outcomes.

Operational definition: A precise specification of what is measured, how, when, and under what conditions.

CTQ tree: A hierarchical method for decomposing broad needs into drivers and measurable requirements.

Affinity analysis: A method for grouping related qualitative statements into meaningful themes.

Kano model: A framework classifying requirements according to their effect on satisfaction.

Basic attribute: A minimum expected requirement whose absence causes strong dissatisfaction.

Performance attribute: A requirement for which greater performance generally produces greater satisfaction.

Excitement attribute: An unexpected feature that can create satisfaction but whose absence is not normally dissatisfying.

Quality Function Deployment (QFD): A structured method for translating customer requirements into design, process, and control characteristics.

House of Quality: The matrix-based representation commonly used in the first stage of QFD.

Competitive priority: A dimension such as cost, quality, delivery, flexibility, innovation, or sustainability on which an organisation competes.

Improvement portfolio: A coordinated collection of improvement projects selected and governed together.

Sub-optimisation: Improving one part of a system while harming overall performance or stakeholder value.

Life-cycle thinking: Considering impacts and value across the stages of an offering’s life.

Requirement traceability: The ability to follow a requirement from customer need through design, process, measurement, and control.

Review Questions

Short Answer Questions

1. What distinguishes a customer statement from a CTQ requirement?

2. Explain the difference between a basic attribute and a performance attribute in the Kano model.

3. What is the purpose of affinity analysis in VOC work?

4. Identify four common elements of a House of Quality.

5. Why should improvement projects be evaluated for sustainability as well as cost and quality?

Long Answer Questions

1. Explain how an organisation can translate VOC into operationally defined CTQs, using an example.

2. Discuss the strengths and limitations of interviews, surveys, observation, complaints, and operational data as VOC sources.

3. Describe the construction and managerial use of a House of Quality.

4. Explain how strategy, risk, sustainability, and competitive priorities influence improvement-portfolio selection.

5. Critically evaluate the statement: “Digital self-service always increases customer value.”

Analytical / Application Questions

1. A bank receives complaints that customers find loan applications “slow and confusing.” Construct an affinity structure, propose CTQs, and identify suitable measures.

2. A manufacturer can fund either a project reducing changeover time or a project improving traceability of safety-critical components. Develop a portfolio decision using customer value, risk, strategy, and sustainability criteria.

3. Using the hospital appointment case as a model, design a QFD-based approach for improving a public transport service while protecting accessibility and privacy.

22. Multiple Choice Questions with Answers

Question 1

Which statement best describes a CTQ requirement?

A. A customer’s general emotional reaction to a service

B. A measurable characteristic that materially affects valued outcomes

C. A technical solution selected before customer research

D. An internal efficiency target unrelated to customers

Correct Answer: B. A measurable characteristic that materially affects valued outcomes

Question 2

In Kano analysis, an attribute whose absence causes strong dissatisfaction but whose presence is taken for granted is classified as:

A. Excitement

B. Indifferent

C. Basic or must-be

D. Reverse

Correct Answer: C. Basic or must-be

Question 3

What is the primary purpose of affinity analysis?

A. To calculate process capability

B. To group related qualitative observations into themes

C. To establish financial project benefits

D. To replace customer interviews with automation

Correct Answer: B. To group related qualitative observations into themes

Question 4

Which sequence best represents the translation of VOC into a CTQ?

A. Technical solution → complaint → specification → need

B. Need → quality characteristic → measure → target

C. Target → survey → technology → stakeholder

D. Measure → strategy → need → complaint

Correct Answer: B. Need → quality characteristic → measure → target

Question 5

In a House of Quality, the relationship matrix primarily shows:

A. The links between customer requirements and technical characteristics

B. The organisation’s hierarchy and reporting relationships

C. The sequence of financial approvals for projects

D. The statistical distribution of process data

Correct Answer: A. The links between customer requirements and technical characteristics

Question 6

A feature that was once novel becomes expected by customers as competitors adopt it. In Kano terms, this illustrates that:

A. Requirements remain permanently in one category

B. Excitement attributes can evolve into performance or basic attributes

C. Basic attributes always become indifferent

D. Reverse attributes become regulatory requirements

Correct Answer: B. Excitement attributes can evolve into performance or basic attributes

Question 7

Which project-selection decision best demonstrates strategic alignment?

A. Selecting the project with the largest apparent cost saving regardless of risk

B. Selecting the project preferred by the most senior individual

C. Evaluating customer impact, strategic contribution, risk, sustainability, and feasibility together

D. Funding only projects involving new technology

Correct Answer: C. Evaluating customer impact, strategic contribution, risk, sustainability, and feasibility together

Question 8

Why can reducing average call-handling time fail to improve customer value?

A. Average measures are always invalid

B. Shorter calls may result from transfers, incomplete answers, or repeat contacts

C. Customers never value speed

D. Call-centre measures cannot be connected to CTQs

Correct Answer: B. Shorter calls may result from transfers, incomplete answers, or repeat contacts

Question 9

Which is the most appropriate ethical response when digital self-service disadvantages some customers?

A. Make digital use compulsory to maximise efficiency

B. Exclude those customers from the service analysis

C. Retain accessible assisted channels and evaluate inclusion as part of value

D. Treat accessibility as unrelated to quality

Correct Answer: C. Retain accessible assisted channels and evaluate inclusion as part of value

Question 10

What is a principal limitation of numerical scoring in QFD?

A. It prevents cross-functional discussion

B. It creates false precision if judgement and evidence behind scores are ignored

C. It cannot represent technical characteristics

D. It is useful only for manufacturing products

Correct Answer: B. It creates false precision if judgement and evidence behind scores are ignored