Digital Consumer Behaviour and Customer Journeys

Introduction

Digital commerce has changed consumer behaviour from a largely linear activity into a connected, data-rich and often non-linear process. A customer may discover a brand through a creator video, compare prices on a marketplace, read reviews on a retailer’s website, ask an AI assistant for advice, purchase through a mobile application and seek support through social media. The firm therefore competes not only for the transaction but also for attention, trust, data permission and post-purchase advocacy.

For e-commerce and direct-to-consumer (D2C) managers, understanding behaviour requires more than describing demographic segments. Managers must explain how consumers recognise needs, search for information, evaluate alternatives, manage perceived risk, make channel choices, respond to technology and form relationships after purchase. Customer-journey analysis provides a managerial method for connecting these psychological and technological processes with measurable touchpoints.

This chapter integrates consumer decision theory, technology-adoption models, trust frameworks, segmentation and journey mapping. It treats the journey as an analytical construct rather than as a fixed funnel. The objective is to identify where value is created, where friction or uncertainty emerges, and which interventions improve both customer outcomes and commercial performance.

Learning Objectives

After studying this chapter, the reader should be able to:

1. Explain major theories of digital consumer decision-making.
2. Apply technology-adoption and trust models to e-commerce contexts.
3. Distinguish useful behavioural segmentation from superficial demographic profiling.
4. Construct and interpret a customer-journey map.
5. Diagnose friction across discovery, evaluation, purchase and post-purchase stages.
6. Select appropriate journey metrics and interpret their limitations.
7. Recommend ethical, data-informed interventions for improving conversion and loyalty.

1. Digital Consumer Behaviour: Scope and Distinctive Features

Consumer behaviour is the study of how individuals or groups select, acquire, use, evaluate and dispose of products, services, ideas and experiences. Digital consumer behaviour examines these activities when digital interfaces, platforms, algorithms, connected devices and electronically mediated relationships influence the process.

Several characteristics distinguish digital environments:

• Information abundance: Consumers can access product specifications, expert opinions, user reviews, price comparisons and social commentary at low marginal cost.
• Low switching and search costs: A consumer can move between sellers rapidly, although cognitive overload may increase.
• Social visibility: Ratings, reviews, likes, comments and creator endorsements make consumption socially observable.
• Personalisation: Algorithms tailor search results, recommendations, promotions and content, potentially increasing relevance while also creating filter bubbles.
• Data traceability: Clicks, searches, dwell time, purchases and service interactions generate behavioural records.
• Channel fluidity: Consumers move among marketplaces, brand websites, applications, physical stores, social platforms and messaging channels.
• Temporal compression: Discovery, evaluation and purchase can occur within seconds, but high-involvement decisions may still extend over weeks.
• Platform dependence: Visibility and customer access may be influenced by marketplace rules, ranking systems and advertising auctions.

A digital interaction is not automatically a positive customer experience. Convenience may be offset by privacy concerns, confusing navigation, hidden charges, unreliable delivery or weak service recovery. The managerial task is therefore to optimise the complete experience rather than a single conversion event.

2. Consumer Decision Theory in Digital Markets

2.1 The consumer decision process

The traditional decision process includes need recognition, information search, evaluation of alternatives, purchase, consumption and post-purchase evaluation. In digital commerce, these stages remain useful but should be interpreted as recursive rather than strictly sequential.

Need recognition occurs when a consumer perceives a gap between a current state and a desired state. A need may be internally triggered, such as replacing worn footwear, or externally triggered by advertising, social content, a recommendation or a replenishment reminder. D2C brands often stimulate latent needs through education, lifestyle narratives and problem framing.

Information search may be internal, using memory and prior experience, or external, using search engines, marketplaces, reviews, social networks, comparison tools and conversational assistants. Search behaviour varies with involvement, perceived risk, product knowledge, time pressure and the cost of being wrong.

Evaluation of alternatives involves comparing attributes, benefits, price, availability, credibility, delivery terms and social proof. Consumers rarely use a complete optimisation calculation. They use simplifying rules, or heuristics, such as choosing the highest-rated product, selecting a familiar brand, accepting a marketplace recommendation or rejecting any product with an unacceptable delivery date.

Purchase includes more than clicking a payment button. It involves selecting a seller, channel, variant, payment method, delivery option and sometimes subscription terms. Cart abandonment may result from price, forced account creation, shipping charges, payment failure, uncertainty or simple postponement.

Post-purchase evaluation compares perceived performance with expectations. Satisfaction is likely when performance meets or exceeds expectations; dissatisfaction is likely when performance falls below them. However, satisfaction is not identical to loyalty. A consumer may be satisfied but switch for a lower price, greater convenience or novelty.

2.2 Involvement, perceived risk and heuristics

Involvement refers to the personal relevance and importance of a decision. High-involvement purchases, such as premium electronics or health-related products, usually involve greater search, deliberation and risk reduction. Low-involvement purchases, such as routine household replenishment, are more strongly shaped by convenience, availability and habit.

Digital risk is multidimensional. It may include:

• Performance risk: The product may not work as expected.
• Financial risk: The consumer may lose money or incur unexpected costs.
• Privacy risk: Personal or payment data may be misused.
• Security risk: Accounts or transactions may be compromised.
• Social risk: The purchase may attract disapproval or damage identity.
• Time risk: The consumer may waste time returning, repairing or replacing the item.
• Delivery risk: The order may arrive late, damaged or not at all.

Trust signals, guarantees, transparent policies, demonstrations, reviews and secure payment systems reduce perceived risk. Their value is greatest where the consumer cannot physically inspect the product or where seller quality is uncertain.

Heuristics are efficient but imperfect decision rules. Common digital heuristics include the availability heuristic, in which easily recalled information influences judgement; the representativeness heuristic, in which a product is judged by similarity to a familiar category; and social-proof heuristics, in which ratings or popularity are used as evidence. Managers should not assume that more information always improves decisions. Excessive choice and contradictory reviews may create overload and delay.

2.3 Expectation–confirmation and loyalty

Post-purchase satisfaction can be understood through expectation–confirmation logic. Consumers form expectations before purchase and compare actual performance with those expectations. Positive disconfirmation occurs when performance exceeds expectations; confirmation occurs when it meets expectations; negative disconfirmation occurs when it falls below expectations.

Loyalty has attitudinal and behavioural dimensions. Attitudinal loyalty includes preference, commitment and willingness to recommend. Behavioural loyalty includes repeat purchase, retention and share of wallet. These dimensions may diverge: a customer may repurchase because switching is difficult but hold a negative attitude, or may express strong preference but purchase infrequently because the category is occasional.

A useful managerial distinction is between retention and relationship quality. Automated reminders may generate repeat purchases without creating genuine trust. Sustainable loyalty depends on reliable value, fair treatment, consistent experience and effective recovery when failures occur.

3. Technology Adoption and Digital Interface Behaviour

3.1 Technology Acceptance Model

The Technology Acceptance Model (TAM) proposes that perceived usefulness and perceived ease of use influence attitude and behavioural intention, which in turn influence actual use. In an e-commerce setting, perceived usefulness may mean that a mobile application saves time, improves product discovery or provides order visibility. Ease of use includes understandable navigation, simple search, accessible checkout and low learning effort.

TAM is valuable because it links interface design with adoption. However, it can understate emotional, social, situational and trust-related factors. A payment wallet may be easy and useful but rejected because consumers distrust the provider.

3.2 Unified Theory of Acceptance and Use of Technology

The Unified Theory of Acceptance and Use of Technology (UTAUT) extends adoption analysis through constructs including performance expectancy, effort expectancy, social influence and facilitating conditions. Performance expectancy concerns expected benefits; effort expectancy concerns ease; social influence concerns perceived pressure or encouragement from important others; facilitating conditions concern resources and infrastructure.

For example, adoption of live-commerce shopping may depend on whether consumers believe it improves product understanding, whether the interface is easy, whether creators or peers endorse it, and whether payment, delivery and customer-support systems are reliable.

3.3 Innovation diffusion and perceived compatibility

Innovation Diffusion Theory explains adoption through relative advantage, compatibility, complexity, trialability and observability. A new shopping feature is more likely to spread when it offers clear advantage, fits consumer routines and values, is not difficult to understand, can be tried with limited commitment and produces visible benefits.

These models should be used diagnostically rather than mechanically. They explain adoption intention better than all actual behaviour. Context, habit, regulation, affordability, access and organisational execution can produce a gap between intention and use.

4. Trust, Credibility and Risk Governance

Trust is the willingness to accept vulnerability based on positive expectations of another party’s intentions or behaviour. In digital commerce, trust may be directed towards the brand, seller, marketplace, payment provider, delivery partner, review system and interface itself.

A common trust framework distinguishes:

• Ability: The perceived competence of the firm to deliver the promised product and service.
• Benevolence: The belief that the firm will consider customer interests rather than exploit vulnerability.
• Integrity: The belief that the firm keeps promises and follows acceptable principles.
• Predictability: The expectation that the firm will behave consistently across transactions.

Trust is supported by institutional mechanisms such as consumer protection, secure payment standards, authentication, warranties, clear returns and dispute resolution. It is also supported by design cues: complete product information, verifiable reviews, visible contact details, realistic imagery, delivery estimates and explanations of data use.

Trust signals can be manipulated. Fake reviews, dark patterns, false scarcity, hidden fees and misleading influencer disclosures may increase short-term conversion while damaging long-term legitimacy. Review volume should not be treated as proof of quality without considering review authenticity, recency, distribution and product variation.

A useful principle is progressive disclosure: provide essential information at the moment it is needed, while allowing deeper detail for high-involvement decisions. The aim is not to conceal information but to make it understandable and accessible.

5. Segmentation for Digital and D2C Strategy

Segmentation divides a heterogeneous market into groups with meaningfully different needs, behaviours or responses. Digital data enables granular segmentation, but granularity is not the same as usefulness.

5.1 Major segmentation bases

5.2 RFM and customer value

A practical behavioural method is RFM analysis, based on recency, frequency and monetary value. Recency measures how recently a customer purchased; frequency measures the number of purchases in a specified period; monetary value measures spending in that period. Scores are often ranked within a customer base rather than interpreted as universal quantities.

Customer lifetime value (CLV) estimates the economic value of a customer relationship. A simplified, undiscounted contribution model is:

where m is contribution margin per order, f is expected orders per period, r is the expected number of periods retained, and CAC is customer acquisition cost. This form assumes constant margin, frequency and retention, and excludes discounting, service costs and changes in behaviour. A discounted model is more appropriate for long-lived relationships:

Here, R_t is revenue in period t, C_t is variable service cost, p_t is the probability of being active in period t, d is the discount rate per period, and T is the planning horizon. All monetary values must use the same currency and time basis. CLV is an estimate, not an accounting fact; model sensitivity should be tested.

5.3 Segment quality

A segment should be identifiable, substantial enough to serve, reachable through appropriate channels, distinguishable in its response and actionable. Managers should combine quantitative data with qualitative research. Analytics may show that a group abandons carts, but interviews may reveal that delivery uncertainty—not price—is the cause.

Segmentation also requires fairness review. Excluding customers from credit, offers or service based on protected characteristics or their proxies may create discriminatory outcomes. Personalisation should improve relevance without creating unjustified price differences, manipulation or unequal access.

6. Customer-Journey Mapping

A customer-journey map is a structured representation of a target customer’s experiences, actions, questions, emotions, touchpoints and pain points over time. It differs from a process map. A process map describes the organisation’s activities; a journey map describes the customer’s experience, including activities outside the firm’s direct control.

6.1 Stages and touchpoints

A practical journey may include:

1. Trigger and discovery: The customer becomes aware of a need, brand or solution.
2. Exploration: The customer gathers information and forms an initial shortlist.
3. Evaluation: Alternatives, evidence, risk and total cost are assessed.
4. Purchase: The customer chooses a seller, product, payment method and delivery option.
5. Fulfilment and use: The product is received, opened, installed and used.
6. Service and recovery: Questions, returns, complaints or warranty needs are handled.
7. Retention and advocacy: The customer repurchases, subscribes, reviews or recommends.

The same customer may enter at any stage, move backwards, pause, use multiple devices or abandon one journey and restart later. Therefore, the funnel is a useful reporting view but not a complete behavioural model.

6.2 Components of a robust map

A professional map should specify the persona or segment, scenario, goal, stage, customer actions, questions, emotions, touchpoints, backstage processes, pain points, evidence and opportunities. It should distinguish known evidence from assumptions. Customer interviews, usability tests, search data, service transcripts, cohort analysis and session recordings can provide evidence.

A service blueprint extends the map by linking frontstage interactions to backstage employees, systems, policies and partners. This is important in marketplaces, where a poor customer experience may originate with a seller, logistics provider or payment processor rather than the interface itself.

6.3 Measuring friction and performance

A basic conversion rate is:

The numerator and denominator must refer to the same population, period and definition. A purchase conversion rate should not be compared with an add-to-cart rate without acknowledging the different target actions.

Cart-abandonment rate is commonly defined as:

This measure is sensitive to tracking design. For example, consumers who create carts for later comparison may inflate abandonment without indicating a checkout problem.

A retention rate for a cohort may be represented as:

The formula assumes that “active” is clearly defined and that the period and cohort boundaries are consistent. Managers should examine conversion, contribution margin, return rate, support contacts, delivery performance, satisfaction and repeat behaviour together. Optimising one metric can damage another; a discount may increase conversion while reducing margin and attracting low-retention customers.

7. From Journey Diagnosis to Intervention

Journey improvement should follow a disciplined sequence:

1. Define the target segment and business objective.
2. State the customer job, context and desired outcome.
3. Gather behavioural, attitudinal and operational evidence.
4. Map stages, channels, dependencies and pain points.
5. Quantify drop-offs and prioritise by customer impact, commercial value and feasibility.
6. Design interventions and specify a measurable hypothesis.
7. Test through controlled experiments, usability research or phased pilots.
8. Monitor unintended effects and institutionalise successful changes.

Examples of interventions include clearer delivery promises, richer product demonstrations, comparison tools, saved preferences, guest checkout, local payment methods, proactive order updates, self-service returns and human escalation for complex issues.

An experiment should distinguish a primary metric from guardrail metrics. If a redesigned checkout increases conversion but also increases payment disputes or returns, the result should not be judged as an unqualified success. Where randomised testing is not feasible, managers can use quasi-experimental comparisons, interrupted time series or matched cohorts, while recognising weaker causal inference.

8. Applied Case: Rebuilding the Journey for TerraSkin

The following is an illustrative case based on common D2C conditions; it is not a report of a specific company.

Background

TerraSkin, a D2C skincare brand, acquired traffic through short-form video, search advertising and creator partnerships. Website traffic grew rapidly, but first-purchase conversion remained modest. Repeat purchase was lower than management expected, and customer-service contacts frequently concerned product suitability, delivery timing and returns.

Problem

Management initially believed that price was the main barrier. A dashboard showed substantial product-page visits and a high proportion of mobile traffic, but it did not explain why visitors hesitated or why some first-time purchasers did not reorder.

Analysis

The firm combined four evidence sources: interviews with recent purchasers and abandoners, analysis of search terms, review coding and a cohort analysis of repurchase. The journey map revealed four problems:

• Discovery content generated interest but made broad efficacy claims without explaining which skin concerns each product addressed.
• Product pages contained many ingredients but lacked a simple routine-building guide.
• Delivery estimates appeared late in checkout, creating uncertainty.
• Post-purchase communication emphasised promotions rather than usage guidance, increasing the risk of disappointing results.

The firm also identified two segments. “Guided novices” had low product knowledge, high uncertainty and strong demand for reassurance. “Ingredient-led enthusiasts” had higher knowledge and wanted detailed formulation information. Treating both groups with the same page created either overload or insufficient detail.

Approach and solution

TerraSkin introduced a needs-based entry quiz with explicit consent and an option to browse without profiling. Results produced recommendations with explanations rather than opaque scores. Product pages used progressive disclosure: a concise suitability summary was followed by ingredient, evidence and routine details. Delivery dates and return conditions were shown before payment. A post-purchase sequence provided usage instructions, realistic time-to-result expectations and a simple support route.

The firm tested the changes against the original experience. Primary metrics were qualified add-to-cart rate and completed purchase rate. Guardrails included return rate, complaint rate, refund cost, unsubscribe rate and gross contribution per visitor. It separately monitored performance for new and returning customers and for the two segments.

Outcome

The intervention improved the quality of product selection and reduced “wrong product” support contacts. The important managerial insight was that conversion improvement did not come from adding urgency or discounting. It came from reducing uncertainty, matching information depth to needs and aligning post-purchase expectations with actual product performance.

Learning points

1. A journey problem may be caused by expectation failure rather than interface friction.
2. Segmentation should guide information architecture and service design, not only advertising.
3. Trust is strengthened by explanation, transparency and reliable fulfilment.
4. Conversion gains must be assessed alongside returns, complaints, margin and retention.
5. Personalisation should remain understandable, optional where appropriate and governed by consent.

9. Ethics, Privacy and Governance

Digital journey management raises significant ethical issues. Firms collect data about identity, behaviour, location, interests and transactions. Responsible practice requires a lawful basis, purpose limitation, data minimisation, security, retention controls and meaningful user choice, subject to applicable law. Consent should not be buried in manipulative interface design.

Dark patterns include obstructing cancellation, disguising advertising as neutral content, preselecting costly options and making privacy-protective choices difficult. Such practices can undermine autonomy and expose firms to regulatory and reputational risk. Transparency should explain what data is collected, why it is used, how automated recommendations work at an appropriate level, and how customers can exercise available rights.

AI introduces additional concerns. Recommendation systems can improve discovery but may reproduce popularity bias, narrow consumer choice or disadvantage new sellers. Generative systems may produce inaccurate product claims or fabricated reviews if not governed. Managers should establish human accountability, content verification, model monitoring, bias testing, access controls and escalation procedures. The principle is not to reject automation but to ensure that convenience does not replace responsibility.

10. Contemporary Developments and Best Practices

Several developments are reshaping digital journeys:

• Conversational commerce: Messaging and AI assistants increasingly support discovery, comparison and service. Their recommendations should be explainable and commercially transparent.
• Social and creator commerce: Discovery, persuasion and purchase may occur within one platform, increasing the importance of disclosure and creator credibility.
• Retail media: Marketplaces monetise behavioural data and advertising inventory, creating opportunities but also concerns about self-preferencing and measurement independence.
• Composable and headless commerce: Firms can connect specialised systems across channels, but integration failures may create inconsistent customer experiences.
• Omnichannel fulfilment: Buy-online-pick-up-in-store, ship-from-store and unified inventory reduce friction when stock, pricing and service policies are coordinated.
• Privacy-constrained measurement: Reduced third-party tracking increases the importance of first-party data, consented identifiers, contextual signals and incrementality testing.
• Community and membership models: D2C firms increasingly use subscriptions, communities and educational content to deepen relationships beyond transactions.

Best practice is to begin with customer outcomes, not available technology. Use evidence-based personas, map the complete service, disclose important conditions, design for accessibility, test with representative users, measure financial and experiential outcomes, and maintain a clear mechanism for human support.

Chapter Summary

Digital consumer behaviour is shaped by psychological needs, perceived risk, social influence, technology characteristics, platform design and post-purchase experience. The consumer decision process remains useful, but digital journeys are recursive, multi-channel and often non-linear. Involvement and risk affect information search and reliance on heuristics. TAM, UTAUT and Innovation Diffusion Theory help explain technology adoption, while trust frameworks clarify the roles of ability, benevolence, integrity and predictability.

Effective segmentation combines demographic, psychographic, behavioural, needs-based, value-based and contextual evidence. RFM and CLV support prioritisation but depend on explicit assumptions and careful interpretation. Journey mapping places customer actions, emotions, touchpoints, backstage processes and pain points into one analytical view. Conversion, abandonment and retention metrics are useful only when definitions, populations and time periods are consistent.

Managers should diagnose journeys through mixed evidence, design interventions around customer uncertainty and friction, and evaluate results with primary and guardrail metrics. Ethical governance is essential because personalisation, AI, reviews and platform systems can affect autonomy, privacy, fairness and trust. Sustainable loyalty is built through reliable value, transparent communication, effective fulfilment and credible service recovery.

Key Terms

Consumer behaviour: The study of how people select, acquire, use, evaluate and dispose of offerings.

Digital consumer behaviour: Consumer behaviour influenced by digital interfaces, platforms, data and connected technologies.

Customer journey: The sequence of customer experiences and interactions associated with a need, purchase or relationship.

Customer-journey map: A visual or structured representation of customer actions, thoughts, emotions, touchpoints and pain points.

Touchpoint: Any interaction between a customer and a brand, seller, platform or service partner.

Involvement: The perceived personal relevance or importance of a decision.

Perceived risk: The consumer’s expectation of possible negative consequences from a decision.

Heuristic: A simplifying rule used to make a judgement or decision.

Expectation–confirmation: A process in which perceived performance is compared with prior expectations.

Technology Acceptance Model: A model linking perceived usefulness and ease of use with technology adoption.

UTAUT: A technology-adoption framework involving performance expectancy, effort expectancy, social influence and facilitating conditions.

Trust: Willingness to accept vulnerability based on positive expectations of another party.

Social proof: Reliance on the behaviour or opinions of others as evidence of value or credibility.

Segmentation: Division of a market into groups with meaningfully different needs or responses.

RFM analysis: Segmentation using recency, frequency and monetary value of customer behaviour.

Customer lifetime value: Estimated economic contribution from a customer over a defined relationship horizon.

Service blueprint: A map connecting customer-facing interactions with backstage processes and systems.

Dark pattern: Interface design that manipulates users into choices they might not otherwise make.

Guardrail metric: A secondary measure used to detect harmful effects of an intervention.

Omnichannel: An integrated approach in which customers can move across channels with coordinated information and service.

Review Questions

Short Answer Questions

1. Why is the digital consumer journey described as non-linear?
2. Distinguish performance risk from privacy risk.
3. What are the principal constructs of the Technology Acceptance Model?
4. What is the purpose of a service blueprint?
5. Why should conversion rate be interpreted with guardrail metrics?

Long Answer Questions

1. Explain how involvement, perceived risk and heuristics influence digital information search and evaluation.
2. Compare TAM, UTAUT and Innovation Diffusion Theory for analysing adoption of a new shopping technology.
3. Discuss the role of trust in marketplace and D2C purchasing, including institutional and design-based trust signals.
4. Explain how behavioural and needs-based segmentation can be combined in a D2C strategy.
5. Critically evaluate customer-journey mapping as a managerial tool, including its limitations.

Analytical / Application Questions

1. A marketplace has high product-page traffic but low checkout completion. Design a journey investigation, identify at least four possible causes and propose suitable metrics.
2. A retailer’s AI recommendation engine increases average order value but also increases returns among new customers. Analyse possible causes and recommend an evaluation plan.
3. Construct a journey map for a high-involvement purchase of a laptop, identifying customer goals, questions, emotions, touchpoints, risks and possible interventions at each stage.

Multiple Choice Questions with Answers

Question 1

Which condition best describes a non-linear digital customer journey?

A. Every customer completes the same stages in the same order

B. Customers may move between channels, revisit evaluation and enter at different stages

C. Customers never use information after discovering a product

D. Post-purchase experience has no effect on future behaviour

Correct Answer: B. Customers may move between channels, revisit evaluation and enter at different stages

Question 2

In the Technology Acceptance Model, which pair most directly influences technology adoption?

A. Perceived usefulness and perceived ease of use

B. Income and geographic location

C. Brand age and inventory turnover

D. Return rate and delivery distance

Correct Answer: A. Perceived usefulness and perceived ease of use

Question 3

Which trust dimension concerns the belief that a firm keeps promises and follows acceptable principles?

A. Ability

B. Benevolence

C. Integrity

D. Convenience

Correct Answer: C. Integrity

Question 4

A customer compares ratings, delivery dates and return policies before selecting a seller. Which stage is primarily illustrated?

A. Need recognition

B. Evaluation of alternatives

C. Consumption disposal

D. Post-purchase advocacy

Correct Answer: B. Evaluation of alternatives

Question 5

Which segmentation basis focuses most directly on convenience, assurance or sustainability motivations?

A. Geographic segmentation

B. Needs-based segmentation

C. Demographic segmentation

D. Device-based segmentation

Correct Answer: B. Needs-based segmentation

Question 6

If a store records 800 carts and 600 completed orders, what is the cart-abandonment rate using the chapter’s formula?

A. 20%

B. 25%

C. 33.3%

D. 75%

Correct Answer: B. 25%

Question 7

What is the principal purpose of a guardrail metric in an experiment?

A. To replace the primary objective

B. To detect harmful or unintended effects

C. To guarantee statistical significance

D. To eliminate the need for customer research

Correct Answer: B. To detect harmful or unintended effects

Question 8

Which feature most clearly distinguishes a customer-journey map from an internal process map?

A. It records only employee activities

B. It focuses on customer experiences, questions and emotions

C. It excludes external touchpoints

D. It measures only financial transactions

Correct Answer: B. It focuses on customer experiences, questions and emotions

Question 9

Which practice is most likely to undermine ethical digital commerce?

A. Showing delivery conditions before payment

B. Providing an understandable privacy choice

C. Using false scarcity and making cancellation difficult

D. Offering human escalation for complex service cases

Correct Answer: C. Using false scarcity and making cancellation difficult

Question 10

A D2C firm wants to reduce product-related returns. Which intervention best addresses expectation failure?

A. Hide product limitations until after purchase

B. Provide suitability guidance, realistic usage expectations and clear product information

C. Increase advertising frequency without changing product information

D. Remove customer reviews from product pages

Correct Answer: B. Provide suitability guidance, realistic usage expectations and clear product information