Market, Audience, Competitor, and Keyword Research

Introduction

Search engine optimisation (SEO) and search engine marketing (SEM) begin before content is written or advertising budgets are assigned. They begin with evidence-based research into markets, audiences, competitors, and the language people use when solving problems or evaluating offers. Research converts a broad business objective—such as increasing sales, applications, or qualified leads—into a set of discoverable search opportunities.

Search research is not simply the collection of high-volume keywords. It is an interpretive process. The marketer must determine who is searching, what the searcher is trying to accomplish, how urgently the need exists, which competitors satisfy it, whether demand is commercially valuable, and whether the organisation can provide a credible answer. A valuable opportunity therefore lies at the intersection of audience need, search demand, competitive attainability, business value, and organisational capability.

This chapter presents a practical framework for applying customer personas, journey mapping, search-intent classification, keyword discovery, topic clustering, competitor analysis, demand estimation, seasonality analysis, and prioritisation.

Learning Objectives

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

1. Define a market and construct evidence-based customer personas.
2. Map search behaviour across the customer journey.
3. Classify keywords according to search intent and business purpose.
4. Discover, clean, expand, and cluster keywords into meaningful topics.
5. Analyse organic and paid competitors without confusing visibility with relevance.
6. Estimate demand using volume, trends, click potential, and conversion evidence.
7. Account for seasonality, geographic variation, device differences, and uncertainty.
8. Prioritise SEO and SEM opportunities using transparent decision frameworks.
9. Apply ethical, privacy-conscious, and practically defensible research methods.

1. Market Research as a Search Strategy Foundation

A market is more than a demographic category. It is a group of people or organisations with related needs, constraints, alternatives, and buying situations. For search planning, a useful market definition includes:

• the problem or job to be completed;
• the audience experiencing the problem;
• the context in which the problem occurs;
• competing solutions and substitutes;
• geographic, regulatory, and language boundaries; and
• the commercial outcome sought by the organisation.

For example, “people interested in fitness” is too broad for an actionable search strategy. “Urban professionals seeking short, instructor-led strength classes near their workplace” provides a stronger basis for keyword and content decisions because it identifies a need, audience, context, and likely search modifiers.

1.1 Market segmentation

Segmentation divides a broad market into groups that differ meaningfully in needs or behaviour. Common bases include:

Effective segmentation is not based on stereotypes. It should be supported by customer interviews, CRM data, website analytics, search-query data, sales conversations, customer-service records, and reputable market research.

1.2 Customer personas

A persona is a research-based representation of a significant audience type. It should not be an invented biography designed merely to make a document attractive. A useful persona records:

• goals and desired outcomes;
• problems, barriers, and anxieties;
• knowledge level and vocabulary;
• information sources and trusted authorities;
• triggers that initiate research;
• decision criteria and objections;
• purchasing authority and budget;
• likely devices and locations; and
• questions asked at different journey stages.

Personas should remain provisional. They are hypotheses that must be tested against behaviour and updated when evidence changes. A persona that says “the customer is a 35-year-old professional who likes quality” has limited operational value. A stronger statement is: “The customer compares three providers, checks independent reviews, asks about cancellation terms, and searches on a mobile device during commuting hours.”

2. Customer Journey and Search Behaviour

The customer journey describes the progression from recognising a need to evaluating solutions, acting, and potentially returning. The stages are not always linear; customers may repeat stages, enter through a recommendation, or move directly from a problem to a transaction.

A practical journey model is:

1. Recognition: The person notices a problem or opportunity.
2. Exploration: The person learns terminology, causes, and possible approaches.
3. Evaluation: Alternatives, providers, prices, evidence, and risks are compared.
4. Conversion: The person purchases, registers, contacts, downloads, or books.
5. Experience and retention: The customer uses the offering, seeks support, and may repurchase or recommend it.

Search terms change across these stages. A person at recognition may search “why does my laptop overheat,” while an evaluator may search “best laptop cooling pad for gaming,” and a conversion-oriented user may search “buy laptop cooling pad London.”

Journey mapping should connect four elements:

Attribution must be interpreted carefully. An informational article may influence a later conversion without receiving the final click. Conversely, a last-click conversion does not prove that every preceding interaction caused the sale.

3. Search Intent Classification

Search intent is the likely purpose behind a query. Intent classification helps determine whether a query should be targeted with an article, product page, landing page, local profile, comparison page, or advertisement. Intent is inferred, not directly observed, and the same wording can have different meanings in different contexts.

3.1 Core intent categories

• Informational intent: The user seeks knowledge, explanation, instructions, or a definition. Examples include “how to clean solar panels” and “what is technical SEO?”
• Commercial investigation: The user is researching alternatives before acting. Examples include “best accounting software for freelancers” and “CRM comparison.”
• Transactional intent: The user is ready to complete an action such as purchase, booking, registration, or download. Examples include “buy noise cancelling headphones” and “book dentist appointment.”
• Navigational intent: The user seeks a particular website, brand, platform, or location. Examples include “Adobe login” and “University of Leeds library.”
• Local intent: The user seeks a nearby provider, place, or service. Examples include “emergency plumber near me” and “vegan restaurant Bristol.” Local intent can overlap with informational, commercial, or transactional intent.

Intent is best validated through the search engine results page (SERP). If results are dominated by product pages and shopping listings, a long general essay is unlikely to satisfy transactional intent. If results are dominated by definitions and educational guides, an aggressive sales page may perform poorly and damage trust.

3.2 Modifiers and intent signals

Words such as how, why, guide, and meaning often signal information seeking. Words such as best, review, comparison, and alternative often indicate commercial investigation. Words such as buy, price, quote, book, and discount often indicate transaction readiness. However, modifiers are only clues. “Best running shoes” may still be early research, whereas “best running shoes size 9 available today” is more action-oriented.

4. Keyword Discovery and Data Preparation

Keyword research combines human language understanding with tools and evidence. A defensible workflow begins with seed concepts derived from products, customer questions, sales terminology, competitors, and internal search data.

4.1 Discovery sources

Useful sources include:

• search-console queries and landing-page performance;
• internal website search terms;
• customer-service tickets and call transcripts;
• sales objections and frequently asked questions;
• autocomplete suggestions and related searches;
• keyword-planning platforms;
• competitor page titles and headings;
• industry publications, forums, and professional communities;
• review websites and social listening;
• product catalogues and technical documentation; and
• interviews with customers, sales staff, and subject experts.

No single tool provides the complete truth. Tool volumes are estimates, query databases differ, and low-volume terms may represent highly valuable needs.

4.2 Cleaning and normalisation

A keyword dataset should be normalised before analysis. Typical actions include:

1. Remove clearly irrelevant terms and accidental matches.
2. Record the original query as well as a normalised form.
3. Separate brand, product, location, audience, and use-case modifiers.
4. Identify duplicates, spelling variants, singular/plural variants, and close synonyms.
5. Assign intent, funnel stage, topic, market, language, and device where possible.
6. Mark sensitive, regulated, or legally restricted topics for specialist review.

Deduplication should not erase meaningful distinctions. “Running shoes” and “running shoes for flat feet” may belong to the same topic but express different needs and should not automatically be combined into one target.

5. Topic Clustering and Information Architecture

A keyword list is not a content strategy. Topic clustering groups related queries according to a shared underlying need, entity, or search result pattern. The purpose is to create a coherent information architecture and avoid producing many thin pages that compete with one another.

A cluster may contain:

• a primary topic;
• closely related subtopics;
• questions and definitions;
• comparison and evaluation terms;
• product or service variants;
• geographic modifiers; and
• supporting entities and concepts.

Two queries should not be merged merely because they share a word. They may be clustered when they have similar intent, audience, required evidence, and likely best page type. SERP overlap can provide a useful empirical signal: if substantially similar pages rank for several terms, those terms may be addressable by one comprehensive page. If results differ substantially, separate pages may be justified.

5.1 A practical clustering procedure

1. Group terms by the central problem or entity.
2. Label the probable intent of each term.
3. Compare SERP patterns and page types.
4. Decide whether one page can satisfy the combined need without becoming confusing.
5. Create a pillar page for the broad topic and supporting pages for distinct subtopics.
6. Design internal links that explain the relationship between pages.
7. Review cannibalisation: two pages should not compete for exactly the same intent without a strategic reason.

Clustering is partly algorithmic and partly editorial. Automated similarity can identify patterns, but subject experts must check whether the proposed cluster is conceptually valid and useful to users.

6. Competitor Analysis

A competitor is not always the organisation that sells the same product. Search competitors include any page, platform, marketplace, publisher, directory, or public institution competing for the same query and user attention.

6.1 Identifying competitors

Use three perspectives:

• Business competitors: Organisations offering similar products or services.
• SERP competitors: Domains that repeatedly rank for target queries.
• Attention competitors: Sources competing for the audience’s time, including videos, social platforms, forums, marketplaces, and comparison sites.

A business may have strong brand competitors but weak SERP competitors, or vice versa. Both views matter.

6.2 What to analyse

Competitor research should examine:

• query and topic coverage;
• ranking pages and their search intent fit;
• content depth, freshness, originality, and evidence;
• titles, snippets, headings, and structured presentation;
• backlinks and authority indicators, interpreted cautiously;
• page experience, accessibility, and mobile usability;
• product range, pricing, guarantees, reviews, and trust signals;
• local visibility and business-profile completeness;
• paid-ad messaging, extensions, landing pages, and offers; and
• gaps, weaknesses, and underserved questions.

The objective is not to copy competitors. It is to understand the current standard, identify user expectations, and find a defensible source of differentiation. Differentiation may involve specialist expertise, clearer evidence, better tools, local knowledge, transparent pricing, faster service, or a superior customer experience.

6.3 Limitations of competitor metrics

Third-party authority scores, estimated traffic, and advertising-intelligence data are directional rather than exact. They may use incomplete samples, modelled click-through rates, or historical observations. Decisions should therefore use multiple evidence sources and distinguish observed facts from estimates.

7. Demand Estimation, Opportunity, and Seasonality

Search demand is multidimensional. Monthly query volume does not equal potential traffic, and potential traffic does not equal revenue. Important variables include:

• query volume and trend;
• percentage of searches producing clicks;
• organic ranking difficulty and SERP features;
• paid competition and cost per click;
• relevance to the offer;
• expected conversion rate;
• average order value or lead value;
• geographic and language scope; and
• organisational ability to create and promote a credible result.

For a defined period, a basic estimate of conversions from a search opportunity can be represented as:

Here, I is the number of relevant impressions available to the result, CTR is the proportion of impressions receiving a click, and CR is the proportion of resulting visits that convert. CTR and CR are proportions, not percentages, in the equation. The estimate assumes that impressions, click behaviour, and conversion behaviour are measured for the same audience, period, device mix, and landing-page experience. It is a planning estimate, not a guaranteed outcome.

For example, if a page is expected to receive 20,000 relevant impressions, has an estimated CTR of 4% and a conversion rate of 3%:

• clicks = 20,000 × 0.04 = 800;
• conversions = 800 × 0.03 = 24.

If each conversion is worth £80 in contribution margin, estimated contribution is £1,920 before content, media, and operational costs. The arithmetic does not establish causality; actual performance should be tested and measured.

For paid search, a simplified expected advertising cost is:

CPC means cost per click and must be expressed in the same currency unit as expected cost. Profitability requires additional variables such as conversion value, gross margin, fulfilment cost, and agency or platform fees. A high CPC can be acceptable when conversion value and margin are sufficiently high; a low CPC is not automatically profitable.

7.1 Seasonality

Seasonality is systematic variation in demand associated with time, such as holidays, weather, school terms, tax deadlines, or annual events. A monthly search-volume series should be interpreted over several comparable periods where possible. A single spike may reflect news, a platform change, or sampling noise rather than a stable seasonal pattern.

A simple seasonal index for period t can be calculated as:

The index is dimensionless. An index above 1 indicates demand above the selected overall average, while an index below 1 indicates demand below it. This method assumes that the overall average is an appropriate baseline and does not remove long-term growth or decline. More advanced forecasting should separate trend, seasonality, events, and random variation.

Marketers should plan before peak demand because content production, indexing, approvals, and campaign learning take time. They should also consider post-peak demand, when users may seek returns, maintenance, support, or complementary products.

8. Prioritising Search Opportunities

Research creates more opportunities than a team can implement. Prioritisation makes trade-offs explicit. A useful score can combine business value, relevance, demand, attainability, and effort, provided that the scale and assumptions are documented.

One illustrative framework is:

Where:

• V = business value score;
• R = relevance to the target audience and offer;
• D = demand potential;
• A = attainability or probability of achieving meaningful visibility; and
• E = implementation effort.

Each factor may be rated on a consistent scale, such as 1–5. The score is ordinal: it helps rank options but should not be interpreted as a probability, revenue forecast, or universal scientific law. If effort is zero, the formula is undefined; in practice, every implementation requires some effort. Teams should also use judgement for legal, reputational, accessibility, and strategic considerations that a numerical score may omit.

A complementary matrix plots impact against effort. High-impact, low-effort opportunities are attractive quick wins; high-impact, high-effort opportunities require planning; low-impact, low-effort items may be opportunistic; and low-impact, high-effort items are generally deprioritised.

Prioritisation should distinguish:

• SEO opportunities: durable content, technical improvements, internal linking, digital PR, and structured information;
• SEM opportunities: paid testing, rapid demand capture, audience targeting, and controlled landing-page experiments; and
• combined opportunities: paid data informing organic messaging, or organic content supporting later paid conversion.

9. Integrated Applied Case: Northbridge Skills Academy

The following is an illustrative case, not a report of a real organisation.

Background

Northbridge Skills Academy, a fictional provider of professional digital-marketing training, wants to increase applications for evening courses. Its initial proposal is to target “digital marketing course,” a broad and competitive term.

Problem

The term has unclear intent. Some users seek free introductory material, some want university qualifications, and others want short practical courses. The academy has limited content resources and cannot compete immediately with national institutions for every broad term.

Analysis

Interviews with enquiries reveal three important segments:

1. career changers seeking flexible study and employability evidence;
2. small-business owners seeking practical skills they can apply immediately; and
3. junior marketers seeking specialist progression in SEO and paid media.

Search-query analysis identifies terms relating to evening study, online delivery, course duration, fees, accreditation, practical projects, and career outcomes. SERP review shows that broad informational queries are dominated by large educational publishers, while local and specific queries show a mixture of training providers, directories, and course pages.

Journey mapping indicates that career changers need explanatory content about role requirements and qualification differences before evaluating a course. Small-business owners need practical curriculum detail, time commitment, and evidence of immediate usefulness. Junior marketers need module-level information, tools covered, and progression pathways.

Approach and solution

The academy develops three topic pathways:

• an introductory guide explaining digital-marketing career routes;
• a comparison and evaluation hub covering course formats, duration, accreditation, and practical assessment; and
• conversion-focused pages for evening SEO, paid-media, and analytics courses, with local and online variants where the service genuinely differs.

The pages use real tutor expertise, transparent fees, entry requirements, student work samples, schedules, and clear calls to action. Paid search tests specific high-intent terms while excluding irrelevant searches such as free tutorials and unrelated degree programmes. Search-console data and enquiry language are reviewed monthly to identify new questions and improve page wording.

Outcome

The strategy creates a more focused portfolio than one page targeting every phrase. Success is assessed through qualified enquiries, application completion, cost per qualified application, enrolment rate, and student quality—not rankings alone. Early paid data identifies effective messages about flexibility and practical projects, which are then incorporated into organic titles and course pages.

Learning points

The case demonstrates that the strongest opportunity may be a set of connected, specific needs rather than one large keyword. Personas, journey stages, SERP evidence, competitor analysis, and business economics must be interpreted together. Content should make a distinctive promise and substantiate it with evidence.

10. Ethical, Legal, and Governance Considerations

Research must respect privacy and human dignity. Marketers should avoid inferring sensitive characteristics—such as health status, religion, ethnicity, or financial hardship—from search behaviour for inappropriate targeting. Personal data should be collected and processed lawfully, transparently, and only for legitimate purposes. Consent, access controls, retention limits, security, and applicable data-protection requirements must be considered.

Keyword research can also reproduce harmful stereotypes or exploit vulnerable situations. Emergency, medical, financial, and legal topics require heightened accuracy, qualified review, and cautious claims. Advertising must not mislead users through false scarcity, hidden fees, fabricated reviews, or unsupported performance promises. Competitor analysis should use lawful, publicly available information and must not involve unauthorised access, deceptive scraping, confidential-data theft, or impersonation.

Accessibility is part of search quality. Content should use clear language, meaningful headings, keyboard-accessible interactions, sufficient contrast, descriptive links, captions where needed, and accurate alternatives for non-text content. Ethical optimisation prioritises satisfying users rather than manipulating ranking systems.

11. Technology, Automation, and AI

Keyword platforms, clustering algorithms, natural-language processing, and generative AI can accelerate research. They can suggest synonyms, classify intent, identify recurring questions, summarise competitor themes, and draft research spreadsheets. However, automated outputs may contain fabricated terms, incorrect intent labels, duplicate ideas, cultural bias, stale information, or unsupported claims.

A responsible workflow is human-led:

1. define the research question and acceptable evidence;
2. collect data lawfully and record its source and date;
3. use automation for exploration and categorisation;
4. validate important findings against SERPs, customers, and subject experts;
5. document assumptions, exclusions, and uncertainty;
6. review generated content for accuracy, originality, accessibility, and disclosure requirements; and
7. monitor outcomes and revise the model.

AI should increase analytical capacity, not replace professional judgement or subject expertise.

12. Best-Practice Research Workflow

A repeatable professional workflow is:

1. Clarify the business objective and measurable outcome.
2. Define the market, geography, audience, and constraints.
3. Build and test evidence-based personas.
4. Map questions and barriers across the customer journey.
5. Generate seed terms from customers, experts, analytics, and competitors.
6. Expand the dataset using tools and qualitative sources.
7. Clean, label, and preserve the provenance of the data.
8. Classify intent and validate it through SERP observation.
9. Cluster queries into topics and page opportunities.
10. Audit competitors and identify genuine differentiation.
11. Estimate demand, click potential, conversion value, and seasonality.
12. Prioritise opportunities by value, attainability, effort, and risk.
13. Convert priorities into content, landing-page, technical, and media briefs.
14. Measure results, test assumptions, and update the research continuously.

Challenges and Limitations

Search research has unavoidable limitations. Search volume is estimated, not a direct count of unique people. Similar queries may represent different users, while one user may issue many queries. Rankings fluctuate by location, device, personalisation, language, and time. SERP features may reduce clicks even when a page ranks well. Conversion data may be sparse or affected by tracking loss, consent choices, offline interactions, or long buying cycles.

Competitor visibility may reflect historical investment rather than current quality. Correlation between a keyword and a sale does not prove that the keyword caused the sale. Automated clustering can confuse linguistic similarity with shared intent. Finally, demand is not the same as strategic fit: a high-volume query may attract users whom the organisation cannot serve profitably or ethically.

These limitations do not make research useless. They require transparent assumptions, triangulation, testing, and continuous revision.

Chapter Summary

Market, audience, competitor, and keyword research provide the evidence base for SEO and SEM decisions. Market definition and segmentation establish who is being served and under what circumstances. Personas become useful when grounded in observed needs, language, barriers, and behaviour. Journey mapping connects those needs to content and conversion stages.

Search intent determines the type of result most likely to satisfy a query. Keyword discovery should combine quantitative tools with customer, sales, service, and expert evidence. Cleaning and clustering transform an unmanageable list into a coherent topic and information architecture. Competitor analysis must consider business, SERP, and attention competitors while treating third-party metrics as estimates.

Demand estimation requires more than volume: click potential, conversion rate, value, competition, seasonality, and organisational capability all matter. Formulas such as estimated conversions and priority scores are planning aids whose assumptions must be documented. Ethical research protects privacy, avoids manipulation, supports accessibility, and uses AI with human validation. The professional outcome is not the largest keyword list, but a prioritised set of defensible opportunities connected to user needs and measurable business outcomes.

Key Terms

Market: A group of people or organisations with related needs, alternatives, and exchange conditions.

Market segmentation: Division of a broad market into groups with meaningful differences.

Persona: A research-based representation of an important audience type.

Customer journey: The sequence of needs and decisions from problem recognition through use and retention.

Search intent: The likely purpose behind a search query.

Informational intent: Intent to learn, understand, or solve a problem.

Commercial investigation: Research into alternatives before a possible action.

Transactional intent: Intent to purchase, book, register, download, or complete another action.

Navigational intent: Intent to find a particular website, brand, or destination.

Keyword: A word or phrase used to represent a search query or search opportunity.

Seed term: An initial concept used to generate a wider keyword set.

Keyword clustering: Grouping related queries according to shared topic and intent.

SERP: Search engine results page.

Search competitor: Any result or platform competing for visibility and user attention for a query.

Click-through rate (CTR): The proportion of impressions that result in clicks.

Conversion rate (CR): The proportion of relevant visits or interactions that produce a defined conversion.

Seasonality: Systematic variation in demand associated with time.

Topic authority: Demonstrated breadth, depth, relevance, and credibility within a subject area.

Cannibalisation: Unintended competition between an organisation’s own pages for similar search intent.

Opportunity prioritisation: Ranking potential initiatives according to value, attainability, effort, and risk.

Review Questions

Short Answer Questions

1. What distinguishes a persona from a demographic description?
2. Define search intent and name four common intent categories.
3. Why should keyword volume not be treated as expected revenue?
4. What is the purpose of keyword clustering?
5. Identify two ethical risks in audience or keyword research.

Long Answer Questions

1. Explain how customer journey mapping can influence SEO content architecture and SEM landing-page design.
2. Describe a complete keyword-research workflow from seed-term generation to prioritisation.
3. Compare business competitors, SERP competitors, and attention competitors.
4. Explain the assumptions and limitations of the estimated-conversions formula.
5. Discuss how seasonality and uncertainty should influence search planning.

Analytical / Application Questions

1. A local training provider has many searches for “digital marketing,” but its strongest enquiries mention evening study, practical projects, and flexible payment. Construct two personas, map their journey stages, and recommend three topic clusters.
2. A query receives 30,000 relevant impressions, an estimated CTR of 5%, and a conversion rate of 2%. Calculate estimated clicks and conversions, then explain why the result should be treated as a planning estimate rather than a forecast.
3. Select a real or hypothetical market and design a prioritisation framework for five search opportunities. State your scoring scale, assumptions, ethical constraints, and the evidence required before implementation.

Multiple Choice Questions with Answers

Question 1

Which statement best describes a customer persona?

A. A fictional biography created without research

B. A research-based representation of an important audience type

C. A list of every keyword used by a market

D. A ranking report for competing websites

Correct Answer: B. A research-based representation of an important audience type

Question 2

A query such as “best project-management software for freelancers” most commonly indicates which intent?

A. Navigational intent

B. Transactional intent only

C. Commercial investigation

D. Purely local intent

Correct Answer: C. Commercial investigation

Question 3

Which source is most useful for validating whether a keyword cluster reflects a shared search need?

A. The colour scheme of competitor websites

B. SERP patterns and the types of pages ranking

C. The age of the marketing team

D. The number of social-media followers alone

Correct Answer: B. SERP patterns and the types of pages ranking

Question 4

If relevant impressions are 20,000, CTR is 4%, and conversion rate is 3%, what are the estimated conversions?

A. 24

B. 60

C. 240

D. 800

Correct Answer: A. 24

Question 5

In the equation Expected cost = clicks × average CPC, what does CPC mean?

A. Conversion-per-customer

B. Cost per click

C. Content performance coefficient

D. Competitive positioning category

Correct Answer: B. Cost per click

Question 6

What is the main purpose of analysing seasonality?

A. To guarantee a first-place ranking

B. To eliminate the need for customer research

C. To identify systematic changes in demand over time

D. To replace conversion measurement

Correct Answer: C. To identify systematic changes in demand over time

Question 7

Which is an example of a SERP competitor?

A. Only a company selling an identical product

B. Any page or platform competing for visibility for the same query

C. An employee in the marketing department

D. A supplier that never appears in search results

Correct Answer: B. Any page or platform competing for visibility for the same query

Question 8

Why should high-volume keywords not automatically receive the highest priority?

A. Volume is unrelated to user behaviour

B. High-volume terms may have weak relevance, low attainability, or poor commercial value

C. Search engines do not record high-volume terms

D. High-volume terms are always navigational

Correct Answer: B. High-volume terms may have weak relevance, low attainability, or poor commercial value

Question 9

Which practice is most consistent with responsible use of AI in keyword research?

A. Publishing all generated suggestions without review

B. Using AI outputs as final evidence without recording sources

C. Using automation for exploration and validating important findings with humans and evidence

D. Inferring sensitive personal characteristics for targeting

Correct Answer: C. Using automation for exploration and validating important findings with humans and evidence

Question 10

What does keyword cannibalisation describe?

A. Two or more of an organisation’s pages competing for similar search intent

B. A competitor buying the organisation’s brand name

C. A seasonal fall in search demand

D. A page receiving traffic from several devices

Correct Answer: A. Two or more of an organisation’s pages competing for similar search intent