{"id":1967,"date":"2026-10-09T07:31:24","date_gmt":"2026-10-09T13:01:24","guid":{"rendered":"https:\/\/www.propellum.com\/blog\/?p=1967"},"modified":"2026-10-09T07:31:25","modified_gmt":"2026-10-09T13:01:25","slug":"blog-measure-skills-demand-across-industries-occupations","status":"publish","type":"post","link":"https:\/\/www.propellum.com\/blog\/blog-measure-skills-demand-across-industries-occupations\/","title":{"rendered":"How to Measure Skills Demand Across Industries and Occupations"},"content":{"rendered":"\n<p>Skills demand analysis helps organizations understand which capabilities employers are looking for, where those requirements are concentrated, and how they change over time.<\/p>\n\n\n\n<p>A skill can be common in one industry but rare in another. It can become essential for one occupation while remaining optional for another. Some skills appear across increasingly diverse roles, while others remain concentrated in a small set of specialized jobs.<\/p>\n\n\n\n<p>Job posting data provides a way to study these patterns through the requirements employers advertise. By collecting job postings, standardizing skill information, and comparing records across occupations, industries, and time, researchers can turn individual vacancies into a more detailed view of the skills market.<\/p>\n\n\n\n<p>The important part is knowing what to measure, how to compare it, and what the results actually mean.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Skills Demand Analysis?<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.propellum.com\/skills-demand\">Skills demand analysis<\/a> is the process of identifying and measuring the skills employers request in job postings, then examining how those requirements differ across occupations, industries, locations, and time periods.<\/p>\n\n\n\n<p>It can help answer questions such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which skills appear most frequently in a particular occupation?<\/li>\n\n\n\n<li>Which industries are increasingly requesting the same capability?<\/li>\n\n\n\n<li>Are employers asking for new skills alongside established ones?<\/li>\n\n\n\n<li>How are skill requirements changing within an occupation?<\/li>\n\n\n\n<li>Which skills are spreading across different types of jobs?<\/li>\n<\/ul>\n\n\n\n<p>Job posting data is especially useful because it captures the requirements employers communicate when advertising roles. When researchers structure and compare these records consistently, they can study the distribution and movement of skill demand at a more granular level.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>1. Start With a Clear Research Question<\/strong><\/h2>\n\n\n\n<p>Before counting skills, define what you want to understand. Different questions require different measurements.<\/p>\n\n\n\n<p>For example, a workforce researcher might ask whether demand for data analytics skills is increasing across industries. An education provider might want to understand which capabilities appear most frequently in entry-level roles. A labour economist might investigate whether technical requirements are expanding within an occupation.<\/p>\n\n\n\n<p>Each question requires a clear comparison.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Research question<\/strong><\/td><td><strong>What to measure<\/strong><\/td><\/tr><tr><td>Which skills are most requested?<\/td><td>Number and share of unique job postings mentioning each skill<\/td><\/tr><tr><td>Which skills are gaining demand?<\/td><td>Change in skill prevalence across comparable periods<\/td><\/tr><tr><td>Which industries need a particular skill?<\/td><td>Skill prevalence within each industry&#8217;s job postings<\/td><\/tr><tr><td>How do skill requirements differ by occupation?<\/td><td>Skill distribution across standardized occupation groups<\/td><\/tr><tr><td>Which skills are spreading across the economy?<\/td><td>Number of occupations and industries in which a skill appears<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Defining the question first prevents a common mistake: collecting large amounts of job data without knowing which decision or hypothesis the analysis should support.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>2. Turn Job Descriptions Into Comparable Skill Data<\/strong><\/h2>\n\n\n\n<p>Employers rarely describe skills in exactly the same way.<\/p>\n\n\n\n<p>One posting might request &#8220;machine learning.&#8221; Another might mention &#8220;ML.&#8221; A third might ask for experience building predictive models. These descriptions may overlap, but researchers cannot reliably compare them by simply counting identical words.<\/p>\n\n\n\n<p>A useful skills demand dataset needs a consistent method for identifying skills and resolving genuine synonyms and terminology variations.<\/p>\n\n\n\n<p>This typically involves four steps:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Extract skills:<\/strong> Identify explicit skill requirements in job descriptions using natural language processing or other extraction methods.<\/li>\n\n\n\n<li><strong>Standardize terminology:<\/strong> Map equivalent terms to consistent skill labels where appropriate.<\/li>\n\n\n\n<li><strong>Preserve distinctions:<\/strong> Keep related but different capabilities separate. For example, SQL and NoSQL should not automatically count as the same skill.<\/li>\n\n\n\n<li><strong>Connect skills to job attributes:<\/strong> Associate each skill with the relevant occupation, industry, employer, location, and posting date.<\/li>\n<\/ol>\n\n\n\n<p>The result is a dataset in which researchers can compare skills across different employers without relying on the exact wording of each job description.<\/p>\n\n\n\n<p>This is where <a href=\"https:\/\/www.propellum.com\/\">structured job data<\/a> becomes particularly useful. Without consistent extraction and normalization, apparent differences in skills demand may reflect differences in writing rather than differences in employer requirements.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>3. Measure Skill Prevalence, Not Just Mention Counts<\/strong><\/h2>\n\n\n\n<p>Counting skill mentions is a useful starting point, but it can produce misleading results.<\/p>\n\n\n\n<p>Imagine that one industry publishes many more job postings than another. It may generate more mentions of a particular skill simply because it advertises more roles.<\/p>\n\n\n\n<p>To compare industries fairly, measure both the number of job postings mentioning a skill and the share of relevant postings that mention it.<\/p>\n\n\n\n<p><strong>Skill prevalence formula<\/strong><\/p>\n\n\n\n<p>[<br>\\text{Skill Prevalence} =<br>\\frac{\\text{Unique Job Postings Mentioning the Skill}}<br>{\\text{Total Unique Job Postings in the Group}}<br>\\times 100<br>]<\/p>\n\n\n\n<p>Consider this illustrative example:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td><strong>Technology<\/strong><\/td><td><strong>Financial services<\/strong><\/td><\/tr><tr><td>Unique job postings analyzed<\/td><td>10,000<\/td><td>5,000<\/td><\/tr><tr><td>Postings mentioning Python<\/td><td>3,000<\/td><td>1,000<\/td><\/tr><tr><td>Skill prevalence<\/td><td>30%<\/td><td>20%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>In this example, technology has both more Python-related postings and a higher share of postings mentioning Python.<\/p>\n\n\n\n<p>The distinction matters because the absolute count measures the size of observed demand, while prevalence measures how common the skill is within the selected group.<\/p>\n\n\n\n<p>Neither metric tells the whole story on its own. Use both to understand the scale and relative importance of a skill.<\/p>\n\n\n\n<p><em>These figures are illustrative, not actual market measurements.<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>4. Compare Skills Across Industries and Occupations<\/strong><\/h2>\n\n\n\n<p>Once skills are standardized and measured consistently, the next step is to compare them across groups.<br>This reveals patterns that individual job descriptions cannot show.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Across industries<\/strong><\/h3>\n\n\n\n<p>Compare the prevalence of a skill in technology, financial services, healthcare, manufacturing, or other industries.<\/p>\n\n\n\n<p>For example, data analysis may appear in technical roles across several sectors, but employers may request it for different purposes. A healthcare organization might emphasize clinical or operational analytics, while a financial services firm might emphasize risk analysis or financial modelling.<\/p>\n\n\n\n<p>The skill label alone does not explain the difference. Industry and occupational context help reveal how employers apply the capability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Across occupations<\/strong><\/h3>\n\n\n\n<p>Compare the requirements of different roles within the same industry or across industries.<\/p>\n\n\n\n<p>A data analyst, data engineer, and business analyst may all require SQL, but their broader skill profiles can differ considerably. Examining the full set of skills associated with each occupation provides a more useful view than ranking skills in isolation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Across industries and occupations together<\/strong><\/h3>\n\n\n\n<p>A two-dimensional comparison can show where a skill is concentrated.<\/p>\n\n\n\n<p>For example, a skill may appear frequently in software engineering roles across several industries but remain uncommon in other technical occupations. Another skill may have moderate prevalence in multiple roles and sectors, suggesting broader applicability.<\/p>\n\n\n\n<p>This analysis can support occupational research, workforce planning, skills intelligence, and curriculum alignment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>5. Track How Skills Demand Changes Over Time<\/strong><\/h2>\n\n\n\n<p>A single dataset snapshot tells you which skills employers mention during a particular period. Repeated observations help you study how those requirements change. To measure skill trends, compare equivalent time windows using consistent definitions and sufficiently comparable data coverage.<\/p>\n\n\n\n<p>Three measurements are particularly useful:<\/p>\n\n\n\n<p><strong>Skill growth:<\/strong> How much did the number of unique postings mentioning a skill change between periods?<\/p>\n\n\n\n<p><strong>Change in prevalence:<\/strong> Did the share of postings mentioning the skill rise or fall?<\/p>\n\n\n\n<p><strong>Industry and occupational spread:<\/strong> Did the skill begin appearing across more industries or occupations?<\/p>\n\n\n\n<p>These metrics answer different questions. A skill&#8217;s posting count may rise because the overall volume of hiring increased, while its prevalence remains unchanged. Conversely, a skill&#8217;s prevalence may increase even when total job posting volume falls. Historical data also helps researchers distinguish short-term fluctuations from patterns that persist across multiple periods. However, a trend in advertised skill requirements does not automatically demonstrate that employers have adopted the skill in practice or that workers possess it. It shows how stated demand in job postings has changed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>6. Look at Skills in Combination<\/strong><\/h2>\n\n\n\n<p>Employers rarely request skills in isolation. The combination of capabilities in a job description can reveal more about the work than any single keyword. Consider a posting that combines Python, SQL, cloud infrastructure, and machine learning. Another might combine SQL, data visualization, and business reporting. Both contain data-related skills, but their combinations suggest different job requirements.<\/p>\n\n\n\n<p>Skills co-occurrence analysis helps researchers investigate questions such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which skills commonly appear together?<\/li>\n\n\n\n<li>Which technical and business skills are increasingly requested in the same roles?<\/li>\n\n\n\n<li>How do skill combinations differ between industries?<\/li>\n\n\n\n<li>Are employers requesting broader combinations of capabilities within an occupation?<\/li>\n<\/ul>\n\n\n\n<p>To conduct this analysis, count how often skills appear together in unique job postings and compare those patterns across occupations, industries, and time periods.<\/p>\n\n\n\n<p>Researchers should also distinguish genuine changes in skill combinations from changes caused by job descriptions becoming longer or more detailed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>7. Account for Data Limitations<\/strong><\/h2>\n\n\n\n<p>Job posting data provides valuable evidence of employer-stated demand, but researchers need to interpret it carefully. The<a href=\"https:\/\/www.oecd.org\/en\/publications\/skills-for-the-digital-transition_38c36777-en\/full-report\/component-5.html\"> OECD&#8217;s research on online job postings<\/a> highlights limitations including incomplete coverage of vacancies and differences in how skills appear in online advertisements.<\/p>\n\n\n\n<p>Several checks help improve the reliability of skills demand analysis:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Remove duplicates:<\/strong> Avoid counting the same opening multiple times across sources.<\/li>\n\n\n\n<li><strong>Use consistent skill definitions:<\/strong> Changes in terminology should not automatically look like changes in demand.<\/li>\n\n\n\n<li><strong>Compare equivalent periods:<\/strong> Account for seasonality and fluctuations in overall posting volume.<\/li>\n\n\n\n<li><strong>Monitor source coverage:<\/strong> New data sources can create an apparent increase in demand even when employer requirements have not changed.<\/li>\n\n\n\n<li><strong>Interpret missing skills carefully:<\/strong> Employers may omit capabilities that are still important to the role.<\/li>\n\n\n\n<li><strong>Keep the denominator clear:<\/strong> Specify which occupations, industries, locations, and postings are included in each comparison.<\/li>\n<\/ul>\n\n\n\n<p>These checks do not eliminate every limitation. They make the analysis more transparent and help researchers distinguish observed posting patterns from broader conclusions about the labour market.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Where Propellum Fits<\/strong><\/h2>\n\n\n\n<p>Skills demand analysis depends on more than extracting keywords from job descriptions. Researchers need job records that can be compared across employers, occupations, industries, locations, and time.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.propellum.com\/free-test-feed\">Propellum <\/a>provides job data infrastructure that collects job postings from employer career pages and supports the structuring and enrichment of those records. This creates a foundation for organizations building skills intelligence products, workforce analytics platforms, and labour market research datasets.<\/p>\n\n\n\n<p>With structured job data as the input, teams can focus their effort on the analytical questions that matter: which skills are growing, where they are needed, how requirements differ across occupations, and what those changes may mean for their users.<\/p>\n\n\n\n<p>The analysis still needs sound methodology and careful interpretation. The role of the underlying data is to make those comparisons more consistent and practical to perform.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The Value Lies in the Comparison<\/strong><\/h2>\n\n\n\n<p>Measuring skills demand is not simply a matter of identifying frequently mentioned skills. Useful analysis compares skill prevalence across occupations and industries, examines combinations of capabilities, and tracks how requirements change over time. It also accounts for data coverage, duplicate records, inconsistent terminology, and the limitations of job postings as a measure of labour market demand.<\/p>\n\n\n\n<p>When researchers combine these methods, job posting data becomes a practical source for studying employer skill requirements and how they evolve.<\/p>\n\n\n\n<p>The goal is not just to know which skills employers mention. It is to understand where those skills matter, how their demand differs, and how that demand changes.<\/p>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1791550070636\"><strong class=\"schema-faq-question\"><strong>What is skills demand analysis?<\/strong><\/strong> <p class=\"schema-faq-answer\">Skills demand analysis measures the skills employers request in job postings and examines how those requirements differ across occupations, industries, locations, and time periods.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1791550079042\"><strong class=\"schema-faq-question\"><strong>How can job posting data measure skills demand?<\/strong><\/strong> <p class=\"schema-faq-answer\">Researchers extract and standardize skills from job descriptions, associate them with structured job attributes, and calculate measures such as posting counts, skill prevalence, growth, and co-occurrence.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1791550103122\"><strong class=\"schema-faq-question\"><strong>How do you compare skills demand across industries?<\/strong><\/strong> <p class=\"schema-faq-answer\">Compare the number and share of unique job postings mentioning each skill within each industry. Use consistent skill definitions and account for differences in posting volume and data coverage.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1791550111718\"><strong class=\"schema-faq-question\"><strong>How can you identify emerging skills?<\/strong><\/strong> <p class=\"schema-faq-answer\">Track skills across comparable time periods and look for sustained increases in posting prevalence, new appearances across occupations, or expansion into additional industries. Validate the findings before calling a skill emerging.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1791550129194\"><strong class=\"schema-faq-question\"><strong>What are the limitations of job posting data for skills research?<\/strong><\/strong> <p class=\"schema-faq-answer\">Online postings do not capture every vacancy, and employers do not mention every skill required for a role. Posting patterns can also reflect differences in employer advertising practices, source coverage, and occupational mix.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1791550223770\"><strong class=\"schema-faq-question\"><strong>How does Propellum support skills demand analysis?<\/strong><\/strong> <p class=\"schema-faq-answer\">Propellum provides job data infrastructure to collect, structure, and enrich employer job postings. These records can support organizations developing skills intelligence, workforce analytics, and labour market research products.<\/p> <\/div> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>Skills demand analysis helps organizations understand which capabilities employers are [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1968,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[332],"tags":[],"class_list":["post-1967","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-job-data-automation"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Measuring Skills Demand With Job Posting Data | Propellum<\/title>\n<meta name=\"description\" content=\"Learn how to measure skills demand using job posting data. 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