Job Posting Best Practices — What Works Sevilla La Nueva

Why Most Job Postings Underperform: The Research Evidence

The average job posting generates a fraction of the applications it could generate if written and structured more effectively. Research by the Recruitment & Employment Confederation (REC, UK, 2023) found that 63% of candidates report abandoning a job application after reading the job description — not because they were unqualified, but because the description failed to communicate enough relevant information for them to decide whether applying was worth their time. A separate analysis by Indeed of 200,000 job listings found that listings in the bottom quartile for description quality received 58% fewer applications than top-quartile listings for the same job title in the same market.

The performance gap between well-written and poorly-written job postings has widened in the era of Google for Jobs. Because Google's job listing carousel applies ranking signals based on structured data quality, description specificity, and data completeness — salary, location, employment type — the difference between a job posting written to communicate effectively and one written as a compliance document affects not only candidate response rate but also whether the listing appears in Google Search at all, and in what position relative to competitor listings for the same role.

Sources: Recruitment & Employment Confederation UK Job Ad Quality Report 2023; Indeed Job Description Research 2022; Appcast Recruitment Marketing Benchmarks 2022.

The Job Title: The Single Highest-Impact Element

The job title appears in Google Search results, job board listings, email alerts, and candidate bookmarks. It is the first and most frequently the only piece of information a candidate uses to decide whether to click on a listing. A job title that fails to communicate the role clearly and specifically is a fundamental distribution failure — the listing will either not be found by relevant candidates (poor search query match) or will be found but not clicked (failure to signal relevance before the click).

The research consistently supports standard, searchable job titles over creative or branded variations. A LinkedIn study (2022) found that job postings with industry-standard titles received 29% more views than equivalent postings with non-standard creative titles, even when the job description content was identical. The Google for Jobs algorithm also uses job title as a primary classification signal — non-standard titles are harder to classify accurately, which can reduce visibility in relevant search results.

Title length matters too: titles between 17 and 60 characters perform best in terms of click-through rate on Google for Jobs results (based on Google's own structured data documentation guidance). Titles shorter than 17 characters typically lack sufficient specificity; titles longer than 60 characters are truncated in search results and may contain the unnecessary additions that reduce signal quality.

Sources: LinkedIn Job Title Performance Study 2022; Google for Jobs developer documentation (structured data guidelines); Indeed Job Title Benchmark Data 2023.

Salary: The Element with the Strongest Evidence for Including

The evidence for including salary information in job postings is stronger and more consistent than almost any other element of job posting practice. Multiple independent studies across different markets and time periods have found that salary-inclusive listings generate more applications, attract better-qualified candidates, and reduce offer-stage drop-off due to salary expectation mismatch.

• Appcast (2022): Listings with salary ranges received 30% more applications than equivalent listings without salary information, across a dataset of 2.7 million job applications.
• LinkedIn (2022): 70% of candidates reported that salary information was "very important" in their decision to apply for a role; 25% reported they would not apply to a listing without salary information.
• Indeed (2023): Indeed's algorithm gives ranking preference to listings with salary data in markets where it is available, affecting both organic search visibility and sponsored job performance.
• REC (2023): Employers who include salary ranges report a 22% reduction in offer-stage rejection due to salary mismatch, compared to employers who reveal salary only at interview stage.

The common employer objection to including salary — that it reveals compensation strategy to competitors or constrains negotiation — is increasingly difficult to sustain in a market where salary transparency legislation is expanding rapidly (mandatory in multiple US states, imminent across the EU) and where the evidence for application rate improvement is unambiguous. The practical recommendation is to include a realistic salary range (not a vanishingly wide range that communicates nothing — "£40,000–£90,000" is not informative) and to position that range at or above market rate for the role.

The effective example communicates the total compensation package specifically enough that a candidate can immediately assess whether the role is financially relevant to them, reducing wasted applications from candidates who would never accept the offered salary and missed applications from candidates who would — but couldn't tell without the information.

Sources: Appcast Recruitment Marketing Benchmarks 2022; LinkedIn Global Talent Trends 2023; Indeed Transparency Report 2023; REC Job Ad Quality Report 2023.

Job Description Structure: What Research Shows About Length, Format, and Content

The optimal job description is not the most comprehensive one. Research on candidate reading behaviour — including eye-tracking studies commissioned by Indeed and analysis of apply-click patterns across large listing datasets — consistently shows that candidates spend less than 60 seconds reading a job description before deciding to apply, abandon, or save for later. The practical implication is that the most important information must appear early and be immediately scannable.

Optimal description length: LinkedIn research found that listings between 300 and 700 words generated 30% more applications than listings above 700 words. There is a threshold beyond which additional requirement listing reduces application rates — each additional requirement added to a job description beyond the genuine minimum reduces the eligible candidate pool without necessarily improving hire quality. This is the single most actionable finding from job description research: most job descriptions are too long, list too many requirements, and screen out qualified candidates unnecessarily.

Essential vs desirable requirements: The distinction between "essential" and "desirable" requirements is standard practice in many job description frameworks, but research by LinkedIn (2019) found that women, on average, apply for jobs only when they meet 100% of the listed requirements, while men apply when they meet approximately 60%. A job description that lists 15 requirements without distinguishing essential from desirable will structurally under-attract from demographic groups that apply conservatively — which is both a diversity outcome problem and a talent acquisition problem (stronger candidates who do not meet one or two peripheral requirements may not apply).

Recommended structure (evidence-based):

Sources: Indeed Eye-Tracking Study on Job Listing Reading Behaviour; LinkedIn Talent Solutions Inclusion Insights 2019; SHRM Job Description Research 2022; REC UK Job Ad Quality Report 2023.

Technical Optimisation: Google for Jobs, Schema Markup, and AI Matching

Job posting performance is not only a copywriting problem — it is also a technical optimisation problem. The channels through which candidates discover job listings (Google Search, job board algorithms, AI matching systems) all apply ranking signals that are affected by data quality factors beyond writing style.

Google for Jobs technical requirements: For a listing to appear in Google's job carousel, the hosting page must carry valid schema.org/JobPosting structured data. Required fields include: title, description, hiringOrganization, jobLocation, datePosted, and employmentType. Salary data (baseSalary field) is not required but significantly improves ranking position within Google for Jobs results. The validThrough field (listing expiry date) is important: listings without an expiry date are eventually treated as potentially stale by Google's algorithm. Expertini implements all required and recommended schema fields automatically for every job posting across its 251-country network — employers posting on Expertini do not need to manage schema markup themselves.

AI matching system optimisation: Expertini's semantic NLP matching engine, and AI matching systems generally, perform better on job descriptions that contain specific, professional language rather than generic marketing copy. The following practices improve matching quality:

• Name specific technologies and tools explicitly: "Experience with Python, SQL, and dbt" will match more accurately than "programming experience" or "data skills"
• Use standard job title terminology: The matching engine's occupational taxonomy recognises industry-standard titles; branded or creative titles may be classified less accurately
• Include seniority signals explicitly: "5–8 years of experience" or "manages a team of 3" provide seniority signals that improve matching accuracy
• Mention industry context: "B2B SaaS sales environment" or "NHS clinical setting" provide industry context that improves candidate alignment
• Specify qualification requirements precisely: "ACCA or CIMA qualified" matches more accurately than "qualified accountant"

Job board algorithm optimisation: Most major job boards — including Expertini — apply freshness signals to job listings. Listings that have been live for more than 30 days typically receive reduced visibility in search results unless they are renewed or updated. Keeping listings current — updating the date posted when the role is still genuinely open — maintains search visibility. Listings that accumulate large numbers of applications without the employer reviewing or responding to them may also be downranked on platforms that track employer responsiveness.

The Language and Tone Dimension: Bias, Accessibility, and Candidate Signal

The language used in job descriptions affects which candidates apply — and not always in ways employers intend. Three well-documented patterns from the academic and practitioner literature:

Gendered language: Research by Gaucher, Friesen & Kay (2011), subsequently replicated multiple times, found that job descriptions containing words traditionally associated with masculine dominance ("competitive," "aggressive," "dominant," "ambitious" in their coded form) reduced application rates from women without affecting application rates from men. Words traditionally associated with communal values ("collaborative," "supportive," "nurturing") showed the opposite pattern. Most job descriptions include unintentional gendered language that affects the demographic composition of the applicant pool. Tools such as the Gender Decoder (based on the Gaucher et al. research) allow employers to check listing language against this finding.

Exclusionary requirements: Requirements that are not genuinely necessary for the role — specific degree requirements when a degree is not needed to perform the work; driving licence requirements when travel is not part of the role; "must be able to lift 25kg" for an office-based role — reduce the accessible candidate pool without improving hire quality. The Equality Act 2010 (UK) and equivalent legislation in most jurisdictions requires that requirements that indirectly discriminate against protected characteristics be demonstrably justified by the role's actual demands.

Culture signalling language: Phrases like "we work hard and play hard," "fast-paced environment," and "no such thing as a 9-5 here" are read differently by different candidate groups. Research by Harvard Business Review found that these phrases — intended to communicate energy and commitment — frequently signal to candidates with caring responsibilities, disability, or health conditions that the role may not accommodate their needs, reducing applications from these groups without the employer intending any exclusionary signal.

Sources: Gaucher D., Friesen J., Kay A.C. (2011) Evidence That Gendered Wording in Job Advertisements Exists and Sustains Gender Inequality. Journal of Personality and Social Psychology; UK Equality Act 2010; Harvard Business Review "Why Women Don't Apply for Jobs Unless They're 100% Qualified" (2014).

Platform-Specific Optimisation: Expertini, Indeed, Google for Jobs, LinkedIn

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