Digital, Social and Omnichannel Consumer Behaviour

Introduction

Consumer behaviour has been transformed by connected devices, digital platforms, social networks and data-driven retail systems. Consumers no longer move through a simple linear sequence of awareness, consideration, purchase and post-purchase evaluation. Instead, they may discover a product through a short video, search for independent reviews, compare prices across marketplaces, visit a physical store, purchase through a mobile application and seek service through social media. These activities may occur within minutes, across several devices and through platforms controlled partly by organisations outside the seller’s direct ownership.

For sales managers, this environment creates both opportunity and difficulty. Digital interactions generate behavioural data, but data does not automatically explain motivation. A click may indicate curiosity rather than purchase intention; a review may be sincere, incentivised or fraudulent; and a final purchase may be credited to a channel that merely completed a journey initiated elsewhere. Effective management therefore requires an integrated understanding of consumer psychology, platform design, channel interaction, measurement and ethics.

This chapter examines online search, social proof, electronic word-of-mouth, platform behaviour, mobile commerce, social commerce and omnichannel journeys. It also introduces attribution approaches and explains why channel performance must be interpreted in relation to the entire customer journey rather than isolated touchpoints.

Learning objectives

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

1. Explain how digital search and platform environments shape consumer decision-making.
2. Analyse social proof and electronic word-of-mouth (eWOM), including their credibility conditions.
3. Distinguish mobile commerce, social commerce, marketplaces and owned digital channels.
4. Map omnichannel journeys and explain interactions between channels.
5. Compare common attribution approaches and identify their limitations.
6. Recommend ethical, customer-centred practices for digital and omnichannel sales management.

1. The digitally enabled consumer decision process

Traditional consumer decision models describe need recognition, information search, evaluation of alternatives, purchase, consumption and post-purchase evaluation. Digital environments do not eliminate these stages, but they make them recursive, observable and socially influenced. A consumer can return to search after reading reviews, ask a community for advice after adding an item to a basket, or evaluate a product publicly while still using it.

A useful contemporary representation is a set of connected activities:

• Trigger and need recognition: an advertisement, recommendation, life event or problem activates interest.
• Exploration: the consumer searches, browses, watches demonstrations and consults communities.
• Evaluation: alternatives are compared using price, reviews, specifications, convenience, identity and risk.
• Conversion: the consumer purchases, subscribes, downloads or requests contact.
• Experience: delivery, onboarding, usage and service influence satisfaction.
• Advocacy or avoidance: the consumer recommends, reviews, complains, returns or silently defects.

Digital environments increase consumer agency. Consumers can compare sellers, switch platforms, block communications and publish their own evaluations. At the same time, platform interfaces influence what is visible, convenient and socially prominent. Behaviour is therefore shaped by an interaction between consumer goals, social influence, firm actions and platform architecture.

2. Online search and digital information behaviour

Online search is a major mechanism through which consumers reduce uncertainty. Search behaviour may be informational (learning about a category), navigational (finding a known brand or site), transactional (seeking a purchase opportunity) or local (finding a nearby product or service). Search terms often reveal the consumer’s stage of decision-making, although intent cannot be inferred perfectly from keywords alone.

Search, relevance and perceived risk

Consumers search more extensively when perceived risk, financial sacrifice or product complexity is high. Search may be reduced when the product is inexpensive, familiar or purchased habitually. Digital search also changes the cost of comparison: prices, specifications and user experiences can be accessed rapidly. However, abundant information can create overload. Consumers may use shortcuts such as brand familiarity, star ratings, rankings, recommendations or a trusted creator’s opinion.

Search results are not neutral inventories. Ranking systems may incorporate relevance, popularity, commercial bidding, personalisation, location, previous behaviour and platform objectives. Consequently, visibility can affect perceived legitimacy. A highly ranked result may be interpreted as more trustworthy even when ranking reflects advertising or engagement optimisation rather than objective quality.

Search engine and marketplace behaviour

Search engine optimisation, paid search, marketplace search and in-platform search differ in ownership, data access and commercial intent. A retailer may control its website content but not a marketplace’s ranking algorithm. A sales manager should therefore distinguish:

Search performance should not be evaluated only through traffic. Relevant measures include qualified visits, product-page engagement, add-to-cart rate, conversion, margin, repeat purchase and assisted conversions. A high click-through rate may be commercially weak if the traffic is poorly matched to the offer.

3. Social proof and electronic word-of-mouth

Social proof is the tendency to use the behaviour or judgments of others as evidence when deciding what is appropriate, popular or safe. It is particularly influential under uncertainty. Examples include ratings, review volume, testimonials, “best seller” labels, follower counts, expert endorsements, customer photographs and visible purchase activity.

Electronic word-of-mouth refers to consumer-generated statements about products, services or brands that are made available to other consumers through digital channels. eWOM includes reviews, forum posts, comments, social posts, videos, ratings and private-group recommendations that are later shared.

Social proof is not automatically persuasive. Its effect depends on perceived similarity, expertise, independence, recency, detail, consistency and relevance. A review from a consumer with similar needs may be more useful than one from a celebrity. Detailed mixed reviews may appear more credible than uniformly perfect ratings, which can signal manipulation.

Credibility assessment

Consumers commonly assess eWOM using several cues:

1. Source credibility: Is the communicator knowledgeable and honest?
2. Similarity: Does the communicator resemble the consumer in needs or circumstances?
3. Argument quality: Does the message provide specific, diagnostic evidence?
4. Consensus: Do multiple independent sources report similar experiences?
5. Recency: Does the information reflect the current product or service?
6. Commercial transparency: Is sponsorship, compensation or affiliation disclosed?

Managers should seek authentic advocacy rather than manufacture apparent popularity. Useful practices include requesting reviews from verified purchasers, publishing negative as well as positive feedback, responding constructively to complaints and clearly labelling incentivised content.

A simple rating average can conceal important information. Suppose a product has ten five-star reviews and ten one-star reviews. Its average is three stars, but the distribution communicates substantial disagreement. A manager should therefore examine rating distributions, review themes, verified status, time trends and product variants rather than relying on a single average.

4. Platform behaviour and digital choice architecture

A platform is a digital environment that facilitates interactions among users, sellers, creators, advertisers or service providers. Examples include marketplaces, social networks, app stores, delivery applications and search systems. Platform behaviour is shaped by affordances—the actions that a design makes easy, visible or difficult—and by rules governing ranking, access, moderation and monetisation.

Key platform mechanisms

• Personalisation: content or offers are selected using user, contextual and behavioural data.
• Ranking: items are ordered according to relevance, engagement, commercial terms or predicted outcomes.
• Recommender systems: products, creators or content are suggested based on inferred preferences.
• Network effects: a platform may become more valuable as more users, sellers or creators participate.
• Ratings and reputation systems: previous behaviour is converted into a public or semi-public trust signal.
• Friction design: registration, payment, delivery and returns may be simplified or complicated.
• Scarcity and urgency cues: countdowns, low-stock messages and limited offers may accelerate decisions.

These mechanisms can increase convenience and discovery, but they can also narrow consumer choice, reinforce popularity bias and encourage impulsive purchasing. A platform may optimise engagement rather than consumer welfare. Sales managers must understand that platform metrics such as impressions, views and engagement are intermediary indicators, not final evidence of customer value.

Consumer data and algorithmic influence

Digital platforms infer interests from searches, views, location, purchases and interactions. Inferences can be inaccurate or sensitive. Behavioural targeting may be useful when it reduces irrelevant communication, but it becomes problematic when consumers cannot understand, control or challenge the process. Ethical management requires proportional data collection, meaningful consent, security, access controls and compliance with applicable privacy and consumer-protection law.

5. Mobile commerce and social commerce

Mobile commerce (m-commerce) is the purchase or exchange of value through mobile devices, including smartphones and tablets. It includes mobile websites, applications, digital wallets, in-app purchases, mobile banking and location-enabled services.

Mobile contexts differ from desktop contexts because they are often immediate, location-sensitive, interruption-prone and dependent on small screens. Mobile consumers may be commuting, standing in a store or responding to a time-limited need. Consequently, usability and speed are central components of value.

Important mobile design considerations include:

• readable product information and accessible controls;
• fast page loading and stable checkout;
• trusted payment and authentication options;
• transparent delivery, returns and total cost;
• permission-based use of location and notifications;
• continuity between app, mobile web and physical store;
• accessibility for consumers with disabilities.

Social commerce integrates product discovery, evaluation, interaction and transaction within social or creator-led environments. It may involve shoppable posts, livestream selling, creator storefronts, direct-message selling, community buying and peer recommendations. Social commerce reduces the distance between inspiration and purchase, but the commercial nature of content must remain clear.

The distinction between content, recommendation and advertising is increasingly blurred. A creator may be simultaneously a cultural participant, salesperson, reviewer and affiliate. Disclosure, truthful claims, evidence for performance statements and responsible targeting are therefore essential.

6. Omnichannel consumer journeys

A multichannel firm uses several channels, such as stores, websites, applications, call centres and marketplaces. An omnichannel firm seeks to coordinate these channels so that the consumer experiences them as connected rather than independent. The distinction is not simply the number of channels; it concerns integration of information, inventory, service, identity, pricing and fulfilment.

A typical journey might be:

1. A consumer sees a social video.
2. They search for independent reviews.
3. They visit the brand website from a mobile device.
4. They inspect the product in a store.
5. They receive an abandoned-basket reminder.
6. They purchase online for store collection.
7. They contact customer service through messaging.
8. They post a review and receive a loyalty offer.

This journey contains both online-to-offline and offline-to-online movement. It may also involve different identities, devices and data permissions. The organisation’s challenge is to provide continuity without creating surveillance that feels intrusive.

Channel roles and interaction

Channels perform different roles. A social platform may create awareness; search may capture active demand; a store may reduce sensory or performance uncertainty; a contact centre may resolve risk; and a mobile application may support retention. A channel that assists a purchase may receive no last-click credit, even though it was commercially important.

Channel interaction can be:

• Complementary: one channel increases the effectiveness of another.
• Substitutive: increased use of one channel reduces use of another.
• Sequential: channels are used in a particular order.
• Reinforcing: consistent messages increase trust and confidence.
• Conflicting: price, stock, service or information differs across channels.

Effective omnichannel management requires a shared view of customer needs, but not necessarily unlimited centralisation of data. Integration should serve customer value and operational coordination rather than merely expand surveillance.

7. Attribution and channel performance

Attribution is the process of assigning credit for a conversion or other outcome to marketing and sales touchpoints. It is a measurement convention, not a direct observation of causality. Credit allocation depends on the chosen model, available data and assumptions about how channels influence one another.

Let a journey contain touchpoints indexed by i, with conversion value V. In a simple rule-based model, the credit assigned to touchpoint i is:

where C_i is credited value, w_i is the weight assigned to touchpoint i, and the weights satisfy ∑w_i = 1. This formula is applicable when the organisation deliberately specifies weights and when the credited value is being allocated rather than claimed as incremental causal value.

Common models include:

• Last-touch attribution: all credit goes to the final recorded touchpoint. It is simple but undervalues discovery and consideration.
• First-touch attribution: all credit goes to the first recorded touchpoint. It highlights acquisition but ignores later persuasion.
• Linear attribution: credit is divided equally among recorded touchpoints. It is transparent but assumes equal contribution.
• Position-based attribution: greater weight is assigned to the first and last touchpoints, with the remainder distributed among intermediate points.
• Time-decay attribution: more recent touchpoints receive greater credit according to a chosen decay rule. It may overvalue late-stage activity.
• Data-driven attribution: statistical or machine-learning methods estimate associations using observed journeys. These methods require sufficient quality data and remain vulnerable to confounding.

For example, if a £120 order contains four recorded touchpoints and a linear model is used, each receives £30 of attributed revenue. This does not prove that each touchpoint caused exactly £30 of value. It merely applies the model’s allocation rule.

Attribution versus incrementality

The stronger managerial question is not “Which channel received credit?” but “What additional outcome occurred because of this intervention?” Incrementality compares outcomes with an intervention to outcomes under a credible counterfactual, such as a control group or geo-experiment. A basic incremental effect can be expressed as:

where Y_T is the outcome for the treated group and Y_C is the outcome for a comparable control group. The units of Y must be the same in both groups—for example, purchases per 1,000 eligible customers over the same period. The estimate is interpretable as an incremental effect only when treatment and control are sufficiently comparable and other relevant conditions are controlled.

Attribution dashboards and incrementality studies serve different purposes. Attribution supports operational allocation within a defined measurement system; experiments provide stronger evidence about causal lift but may be slower, more expensive or difficult to implement.

8. Measurement framework for sales managers

A balanced measurement system should connect consumer experience, commercial outcomes and risk. Useful measures include:

Conversion rate is often calculated as:

The denominator must be defined consistently. A rate based on sessions is not directly comparable with a rate based on unique users or eligible visitors. Managers should also avoid treating correlation as causation. A campaign may reach consumers who were already likely to purchase.

Customer lifetime value (CLV) can be estimated in many ways. A simplified contribution approach is:

where m_t is expected contribution margin in period t, s_t is expected service or retention cost in period t, r_t is the probability of remaining active in period t, d is the discount rate per period, T is the planning horizon and A is acquisition cost. This is an estimate, not an accounting identity. It assumes the inputs are defined consistently and that future behaviour can be reasonably forecast. It should not be used to justify discriminatory treatment or excessive data collection.

9. Applied case: Northstar Outdoor Equipment

Background

Northstar Outdoor Equipment, an illustrative mid-sized retailer, sells hiking footwear through 28 stores, a website, a mobile application and a major marketplace. Its managers observe that paid social media generates many visits but few last-click purchases. Stores report that customers frequently examine footwear in person and later buy online. Customer service also receives complaints about inconsistent prices and stock information.

Problem

Senior management is considering reducing social-media spending because the channel appears weak under last-touch attribution. At the same time, the retailer lacks a reliable view of cross-channel journeys and cannot determine whether mobile reminders create additional purchases or merely remind existing buyers.

Analysis

Journey interviews and consented analytics reveal three recurring patterns:

1. Social video creates awareness among new hikers.
2. Search and independent reviews support technical evaluation.
3. Store visits reduce uncertainty about fit, while online purchase often provides greater size availability.

The company’s dashboard records different customer identifiers across the app, website, store tills and marketplace. Therefore, some journeys are fragmented. A review of customer comments shows that inconsistent stock messages reduce trust more than moderate price differences.

Approach and solution

Northstar takes five actions:

• It creates a channel-role framework rather than judging every channel by last-click sales.
• It improves consent-based identity resolution, with clear notices and an option to remain unidentified.
• It introduces “buy online, collect in store” and displays store-level inventory with stated accuracy limits.
• It runs a controlled test in selected regions to estimate the incremental effect of mobile reminders.
• It publishes creator sponsorship disclosures, verifies review purchases and responds to negative feedback using service recovery procedures.

The retailer reports social media as a discovery and assisted-consideration channel, while evaluating it through reach quality, branded search lift, new-customer rate and experimental evidence. Stores receive credit for assisted digital sales where the measurement design supports it, but managers are warned that allocated credit is not the same as causal contribution.

Outcome

The case illustrates how an apparently weak channel can be strategically important. The main improvement does not come from assigning more credit to social media; it comes from improving information continuity, inventory accuracy, experimentation and customer trust.

Learning points

1. Channel performance depends on the role a channel plays in a journey.
2. Fragmented identifiers can create measurement error and poor experiences.
3. Omnichannel integration must solve customer problems, not merely combine databases.
4. Incrementality testing is valuable when attribution results drive major investment decisions.
5. Authentic reviews and transparent commercial relationships strengthen long-term credibility.

10. Ethical, legal and managerial issues

Digital and omnichannel sales raise several governance responsibilities:

• Privacy and consent: collect only necessary data, explain purposes clearly and respect withdrawal choices.
• Transparency: disclose advertising, sponsorship, affiliate relationships, ranking influences and material conditions.
• Fairness: test targeting and recommendation systems for discriminatory effects.
• Manipulation: avoid deceptive scarcity, hidden charges, obstructive cancellation and interfaces designed to defeat informed choice.
• Security: protect account, payment, location and behavioural data.
• Children and vulnerable consumers: apply stronger safeguards where consumers may have reduced capacity to assess persuasion.
• Review integrity: prohibit fabricated reviews, suppressing legitimate criticism and undisclosed incentives.
• Accessibility: design digital journeys that can be used by people with varied sensory, cognitive and motor abilities.
• Human oversight: provide routes to challenge automated decisions and resolve service failures.

The exact legal obligations vary by jurisdiction. Managers should therefore consult current privacy, consumer-protection, advertising, accessibility and platform regulations rather than relying on general principles alone.

11. Contemporary developments and best practice

Artificial intelligence increasingly supports recommendation, conversational selling, customer-service automation, audience selection, content generation and demand forecasting. These applications can improve relevance and responsiveness, but they introduce risks of hallucinated product claims, opaque decisions, privacy invasion, bias and over-automation. Human review is especially important for high-risk claims, vulnerable consumers, complaints and consequential decisions.

Best practice for digital and omnichannel sales includes:

1. Begin with customer tasks and friction points, not with available technology.
2. Map journeys across devices, channels and post-purchase service.
3. Define channel roles before choosing performance metrics.
4. Combine descriptive dashboards with experiments where causal decisions matter.
5. Treat reviews and creator content as trust systems requiring governance.
6. Maintain consistent price, product, stock, delivery and returns information.
7. Use consent-based data practices and minimise unnecessary collection.
8. Monitor quality and contribution margin, not only volume and engagement.
9. Design for accessibility, transparency and easy correction of errors.
10. Review algorithms and automated content continuously as consumer and platform conditions change.

Chapter Summary

Digital consumers search, compare, communicate and purchase across interconnected platforms and devices. Online search reduces uncertainty but is shaped by ranking, personalisation and commercial visibility. Social proof and eWOM influence decisions when sources appear credible, relevant, independent and informative. Mobile commerce prioritises convenience, speed, context and secure payment, while social commerce combines discovery, interaction and transaction in community and creator environments.

Omnichannel behaviour involves movement between digital and physical touchpoints. Channels may complement, substitute for or reinforce one another, and their value cannot be understood through isolated last-click measures. Attribution allocates credit according to a chosen rule; it does not by itself demonstrate causation. Incrementality testing offers stronger evidence about additional effects when valid comparison conditions are available. Effective sales management therefore combines journey mapping, balanced metrics, experimentation, operational integration, ethical data governance and transparent communication.

Key Terms

Attribution: Allocation of credit for a conversion or outcome across touchpoints.

Channel interaction: The way one channel influences, supports or substitutes for another.

Conversion rate: The percentage of defined eligible visits or users completing a specified action.

Electronic word-of-mouth (eWOM): Digitally published consumer statements about products, services or brands.

Incrementality: The additional outcome caused by an intervention relative to a credible counterfactual.

M-commerce: Commercial transactions conducted through mobile devices.

Multichannel: Use of multiple customer or sales channels without necessarily integrating them fully.

Omnichannel: Coordinated channel system designed to provide a connected customer experience.

Platform: Digital environment that facilitates interactions among users, sellers, creators or service providers.

Personalisation: Adaptation of content, offers or interfaces using consumer or contextual information.

Ranking: Ordering of content, products or sellers according to algorithmic or commercial criteria.

Search intent: The consumer’s underlying purpose when conducting a search.

Social commerce: Use of social or creator-led environments to support product discovery, evaluation and transaction.

Social proof: Reliance on others’ behaviour or judgments as evidence in decision-making.

Touchpoint: A consumer interaction with a brand, channel, product or service.

Trust signal: Information that helps consumers judge credibility, reliability or risk.

User-generated content: Content created and shared by consumers rather than the organisation itself.

Choice architecture: The design of an environment that influences how options are presented and selected.

Review Questions

Short Answer Questions

1. What is the difference between multichannel and omnichannel retailing?
2. Define electronic word-of-mouth and provide two examples.
3. Why may a high online click-through rate fail to produce strong commercial performance?
4. Identify three factors that influence the credibility of social proof.
5. What is the difference between attribution and incrementality?

Long Answer Questions

1. Explain how online search can reduce consumer uncertainty while also creating information overload.
2. Compare first-touch, last-touch, linear and data-driven attribution models, including one limitation of each.
3. Discuss how mobile commerce changes the design requirements of a consumer journey.
4. Explain how platform ranking and recommendation systems can influence consumer choice.
5. Evaluate the ethical responsibilities of a retailer using personalisation and behavioural data.

Analytical / Application Questions

1. A retailer’s social media produces many assisted journeys but few last-click purchases. Recommend a measurement plan that avoids prematurely reducing investment in the channel.
2. Map a likely omnichannel journey for a consumer buying technically complex equipment. Identify where search, eWOM, stores, mobile services and customer support add value.
3. A marketplace displays perfect five-star ratings but provides no verified-purchase information. Analyse the trust risks and propose actions for both the marketplace and participating sellers.

Multiple Choice Questions with Answers

Question 1

Which statement best describes omnichannel consumer behaviour?

A. Consumers use only the channel with the lowest price.

B. Consumers use several channels that are coordinated into a connected journey.

C. Consumers make all purchases through social networks.

D. Consumers avoid physical stores after visiting a website.

Correct Answer: B. Consumers use several channels that are coordinated into a connected journey.

Question 2

Which factor generally increases the credibility of electronic word-of-mouth?

A. Undisclosed sponsorship

B. A large number of identical, vague comments

C. Detailed information from a relevant and apparently independent source

D. A review with no connection to the product experience

Correct Answer: C. Detailed information from a relevant and apparently independent source

Question 3

What is the principal limitation of last-touch attribution?

A. It gives equal credit to every touchpoint.

B. It assigns all credit to the first interaction.

C. It ignores the final purchase interaction.

D. It may undervalue earlier discovery and consideration activities.

Correct Answer: D. It may undervalue earlier discovery and consideration activities.

Question 4

Social commerce is best defined as:

A. Selling only through physical social events

B. Integrating product discovery, social interaction and transaction in social or creator-led environments

C. Using search engines without advertising

D. Replacing all customer service with automation

Correct Answer: B. Integrating product discovery, social interaction and transaction in social or creator-led environments

Question 5

A product receives ten five-star and ten one-star reviews. What is its simple average rating?

A. 1 star

B. 2 stars

C. 3 stars

D. 4 stars

Correct Answer: C. 3 stars

Question 6

If a £120 order has four recorded touchpoints and linear attribution is used, how much value is allocated to each touchpoint?

A. £15

B. £30

C. £60

D. £120

Correct Answer: B. £30

Question 7

What does an incrementality test attempt to estimate?

A. The number of impressions purchased

B. The additional outcome caused by an intervention compared with a credible counterfactual

C. The average number of channels in a journey

D. The total number of reviews displayed

Correct Answer: B. The additional outcome caused by an intervention compared with a credible counterfactual

Question 8

Which is an example of complementary channel interaction?

A. A store visit helps a consumer assess fit before an online purchase

B. A website prevents a customer from contacting a store

C. Two channels display contradictory return policies

D. A marketplace removes all brand information

Correct Answer: A. A store visit helps a consumer assess fit before an online purchase

Question 9

Which practice is most consistent with ethical review management?

A. Publishing only positive reviews

B. Fabricating reviews to increase ranking

C. Clearly labelling incentives and retaining legitimate negative feedback

D. Removing all reviews older than one week

Correct Answer: C. Clearly labelling incentives and retaining legitimate negative feedback

Question 10

Why should conversion-rate denominators be defined consistently?

A. To ensure rates based on sessions, users and eligible visitors are directly comparable

B. To guarantee that every campaign produces the same result

C. To eliminate the need for customer research

D. To make attribution automatically causal

Correct Answer: A. To ensure rates based on sessions, users and eligible visitors are directly comparable