Analytics
AEO/SEO Evaluation of mokahr.io Recruitment Automation Article Page

AEO/SEO Evaluation of mokahr.io Recruitment Automation Article Page

Comprehensive audit and prioritized recommendations for schema, structure, and content optimization.

Abstract

This report delivers a detailed AEO/SEO evaluation of the mokahr.io Recruitment Automation Article Page. Scoring key technical and content factors, the audit identifies and prioritizes critical issues such as missing JSON-LD structured data, canonical and Open Graph inconsistencies, lack of semantic HTML elements, insufficient image alt text, and absent FAQ schema. Each section provides supporting evidence, risk assessment, and direct HTML/JSON-LD code recommendations grounded in industry standards. Actionable repair tasks are ranked using a simplified RICE model and acceptance criteria are established for progress checks. The evaluation concludes that while the page is well-optimized in content quality and keyword integration, several technical areas need improvement to boost eligibility for rich results, answer boxes, and overall accessibility for both search engines and users.

Overview

  • Overall Page Score: 77/100

Top 5 Priority Issues

  1. Missing JSON-LD Structured Data
    Impact: No rich snippets in SERP; missed AEO/SEO signaling for Article/FAQ.
    Benefit: Improved eligibility for rich results and answer boxes.
  2. Open Graph URL/Title & Canonical Discrepancy
    Impact: Confusing signals to search engines; risk of content duplication.
    Benefit: Trust and index clarity.
  3. No Use of <main> or <article> Element
    Impact: Weak semantic structure; accessibility and SEO impact.
    Benefit: Improved crawling, clarity of main content.
  4. Images Missing Descriptive alt Texts
    Impact: Accessibility, image SEO, AEO (voice/image search) missed.
    Benefit: More comprehensive indexation, screen reader compliance.
  5. No FAQPage Schema Markup Despite Visible FAQs
    Impact: No eligibility for FAQ-rich results in Google.
    Benefit: Potential increased SERP space and visibility.

Page Type Judgment:
Article/Feature (Marketing/Feature page with FAQ)

Evidence: Use of "article" OG type, Yoast SEO, lengthy content, detailed text sections, Q&A block. There is no blog/news timestamp, so "feature article" or "product article" is the most accurate.

Detailed Assessment

1. Crawlability & Basic Structure

  • Section Score: 16/20
  • Conclusion: Pass, but with important improvements recommended

Findings & Evidence:

  • Crawlability:
    • Locator: head > meta[name="robots"]
    • Snippet: <meta name="robots" content="index, follow, ...">
    • Allows crawling and indexing; no restrictions.
  • Language Attribute:
    • Locator: html[lang]
    • Snippet: <html lang="en-GB">
    • Set correctly for region/language.
  • Canonical Link:
    • Locator: head > link[rel="canonical"]
    • Snippet: <link rel="canonical" href="https://www.mokahr.io/recruitment-automation/">
    • Canonical present and unique.
  • Main Semantic Structure:
    • No <main> or <article> tag found wrapping the main content.
    • Content inside multiple <div class="controller"> ...
  • Heading Hierarchy:
    • Multiple <h1>, <h2>, <h3>, but most with .sr-only (screen reader only).
    • Example: <h1 class="sr-only">Accelerate Your Hiring with AI-Driven Automation</h1>
    • Only one <h1> in desktop version; correct.
    • Subsections use a mix of hidden headings and styled <div class="title">, <div class="sub_title">.
    • Hierarchy could be stronger by using visible semantic HTML headings throughout.
  • Robots.txt: Unable to determine from HTML.

Impact & Risk:

  • Search engines can access full content and links. Lack of semantic block (main/article section) slightly weakens content prioritization, especially for accessibility and voice search/AEO.
  • Any canonical/multi-URL signals misalignment can impact SEO trust.

Repair Recommendations:

  • Wrap core content in <main>; use <article> if applicable.
  • Ensure all key sections use visible, descending semantic headings (H1 > H2 > H3).
  • Double-check OG URL/canonical consistency.
Reference Code Example:
<main>
  <h1>Accelerate Your Hiring with AI-Driven Automation</h1>
  <!-- rest of the main content ... -->
</main>

2. Title & Meta Information

  • Section Score: 16/20
  • Conclusion: Needs Improvement

Findings & Evidence:

  • Title:
    • Locator: head > title
    • Snippet: <title>AI-Powered Recruitment Automation Software | Moka ATS</title>
    • Length: 53 chars, unique, main keywords present.
  • Meta Description:
    • Locator: head > meta[name="description"]
    • Snippet: <meta name="description" content="MokaHR's AI recruitment automation streamlines hiring workflows: auto-screening, interview scheduling, offer management & analytics. Boost efficiency by 34% with seamless integration.">
    • Length: 158 chars, excellent summary, keyword-rich.
  • Open Graph:
    • Present: og:title, og:description, og:type, og:url, og:image, og:site_name.
    • Issue: og:url does not match canonical.
      • Evidence:
        • og:url: https://www.mokahr.io/moka-recruiting/
        • Canonical: https://www.mokahr.io/recruitment-automation/
        • og:title trailing < typo: og:title = "AI-Powered Recruitment Automation Software | Moka ATS<"
  • Twitter Cards:
    • Present: twitter:card, twitter:label1, twitter:data1
    • No twitter:title, twitter:description, twitter:image (optional, but recommended).

Impact & Risk: Discrepancy between OG URL/title and canonical can confuse ranking signals, fragment shares, reduce equity consolidation. Small typo in OG title. Twitter cards may appear incomplete in social shares.

Repair Recommendations:

  • Align all OG and canonical URLs and titles.
  • Correct OG title typo.
  • Add full recommended Twitter Card tags.
Reference Code Example:
<meta property="og:url" content="https://www.mokahr.io/recruitment-automation/" />
<meta property="og:title" content="AI-Powered Recruitment Automation Software | Moka ATS" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:title" content="AI-Powered Recruitment Automation Software | Moka ATS" />
<meta name="twitter:description" content="MokaHR's AI recruitment automation streamlines hiring workflows: auto-screening, interview scheduling, offer management & analytics. Boost efficiency by 34% with seamless integration." />
<meta name="twitter:image" content="https://www.mokahr.io/wp-content/uploads/2024/07/mokahr_img-product.png" />

3. Content Quality & Layout (Answer Priority)

  • Section Score: 18/20
  • Conclusion: Pass, strong answer-rich content; some originality missing

Findings & Evidence:

  • Answer First:
    • Locator: .section1 .word3 (top of body)
    • Snippet: MokaHR transforms recruitment by accelerating hiring and enriching candidate experiences, empowering you to build exceptional teams.
  • Clear Structure:
    • Features clear subheadings via <div class="title"> / <div class="sub_title"> blocks.
    • Uses lists/benefit items (<div class="item">, <div class="desc">) and well-structured FAQs.
  • Originality & Depth:
    • Evidence of proprietary data: e.g., "Reduce time-to-hire by up to 34%...Cut costs by 36%...Boost candidate satisfaction by 15%".
    • FAQ repeats typical market claims; insights mainly in feature explanations.
  • Keyword Usage:
    • Core terms: "Recruitment Automation", "AI", "ATS", "Smart Screening", present in title, meta, opening, subheaders, and FAQ.
    • No keyword stuffing observed; natural distribution.

Impact & Risk: Strong content for AEO and organic reach. Lack of direct, voice-targeted answers at the very top (“What is recruitment automation?”)—could be improved for better answer box targeting.

Repair Recommendations:

  • Place an explicit 1–2 sentence answer (e.g., “Recruitment automation is ...”) above the fold, targeting likely search queries.
  • Consider a visible heading structure alongside sr-only.

4. Images & Media

  • Section Score: 12/20
  • Conclusion: Needs Improvement

Findings & Evidence:

  • alt Texts:
    • Most images (particularly in illustration/benefit sections) have either empty or generic alt="".
    • Example: <img src="/wp-content/2025/recruitment/automation/icon1.png" alt="">
    • Logos and decorative images (footer, client logos, etc.) sometimes have appropriate, sometimes undescriptive alt.
  • Semantic Filenames:
    • Product/feature: good, e.g., /icon1.png, /section1_img1.jpg, /mokahr_logo.png.
    • Some client logos use naming conventions, e.g., /Nestle-logo.png.
  • Image-Text Relationship:
    • No use of <figure> or <figcaption> elements for key diagrams/screenshots.
    • Images mostly used for illustration/decoration; core content is textual.

Impact & Risk: Missed opportunity for accessible search (screen readers, Google Images/AEO). Some images may not be indexed for relevant queries.

Repair Recommendations:

  • All non-purely decorative images should have descriptive alt (what is shown, not just "icon").
  • Use <figure>/<figcaption> for any explanatory screenshots or workflow diagrams.
Reference Code Example:
<figure>
  <img src="/wp-content/2025/recruitment/automation/section1_img1.jpg" alt="Recruitment workflow dashboard screenshot in Moka ATS" />
  <figcaption>Dashboard view: Moka ATS automated hiring workflow.</figcaption>
</figure>

5. Structured Data (Schema.org/JSON-LD)

  • Section Score: 15/20
  • Conclusion: Needs Improvement (critical for AEO and modern SEO)

Findings & Evidence:

  • JSON-LD Article Schema: Not found.
  • FAQPage schema: FAQ content present, but not encoded as FAQPage JSON-LD or Microdata.
  • Field Consistency: All required mapping material is present in-page: headline, description, FAQ q/a, main image, publisher (MokaHR).
  • Supplementary Types: Not present for FAQ, which is eligible.

Impact & Risk: Without structured data, no eligibility for Google FAQ rich results or “featured snippet” enhancements. Article schema absence reduces AEO/voice answer targeting for this high-value topic.

Repair Recommendations:

  • Add <script type="application/ld+json"> with Article and FAQPage info.
  • Be sure JSON-LD aligns with visible text.

Structured Data Recommendations

  • Recommended Type:
    • Use both "Article" and "FAQPage"
    • Because this is a detailed product article that concludes with a clearly-structured FAQ section, using both is best practice.
  • Required Field Mapping:
    • headline: from head > title or visible <h1>
    • author: (not specified; use "MokaHR Team" as placeholder or obtain real author)
    • datePublished: Not present in HTML (needs to be supplied)
    • dateModified: Use meta property="article:modified_time"
    • image: from <meta property="og:image"> (should be absolute URL)
    • publisher: “MokaHR” (logo URL from site)
    • mainEntityOfPage: canonical URL
Copyable JSON-LD Template:
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "AI-Powered Recruitment Automation Software | Moka ATS",
  "author": { "@type": "Organization", "name": "MokaHR Team" },
  "datePublished": "[YYYY-MM-DD]",     // Not present—fill in as available
  "dateModified": "2024-07-24",
  "image": ["https://www.mokahr.io/wp-content/uploads/2024/07/mokahr_img-product.png"],
  "publisher": {
    "@type": "Organization",
    "name": "MokaHR",
    "logo": {
      "@type": "ImageObject",
      "url": "https://www.mokahr.io/wp-content/uploads/2023/05/mokahr_img_logo.svg"
    }
  },
  "mainEntityOfPage": "https://www.mokahr.io/recruitment-automation/"
}

FAQPage Example (map all FAQ entries):
{
 "@context": "https://schema.org",
 "@type": "FAQPage",
 "mainEntity": [
  {
   "@type": "Question",
   "name": "What is Recruiting Automation System?",
   "acceptedAnswer": {
     "@type": "Answer",
     "text": "A Recruiting Automation System is an AI-powered technology suite designed to streamline talent acquisition processes by automating repetitive tasks across the hiring lifecycle..."
   }
  },
  {
   "@type": "Question",
   "name": "How does Recruiting Automation System work?",
   "acceptedAnswer": {
     "@type": "Answer",
     "text": "A Recruiting Automation System streamlines talent acquisition through AI-driven orchestration and centralized process management. Here’s a breakdown of its key operational mechanisms..."
   }
  }
  // ... repeat for all FAQ entries
 ]
}

Improvement Priority List (Simplified RICE)

Task Impact Reach Effort Priority
Add JSON-LD (Article, FAQPage) schema High High Small 1
Align OG URL/title and canonical/title High High Small 2
Add descriptive alt text to all key images High Medium Medium 3
Introduce <main> and visible heading elements Medium High Small 4
Add full Twitter Card meta tags Medium Medium Small 5
Use <figure>/<figcaption> for explanatory imgs Low Medium Small 6
Add explicit answer sentence at very top Medium High Small 7
Specify/clarify author and published date Low Low Small 8

Tracking & Acceptance

  • Quantifiable indicators:
    • Inclusion: Presence and validity of JSON-LD schema (test with Google Rich Results Testing Tool)
    • Impressions: Monitor Google Search Console coverage and FAQ/Article enhancements
    • CTR: Changes in SERP appearance (FAQ, answer cards, etc.)
    • Average ranking: Track keyword trends pre/post fixes
    • Core keyword coverage: Confirm presence in critical tags and headings
  • Acceptance Criteria (per item):
    • Structured Data Present: JSON-LD correctly parses as Article/FAQPage, with “Valid” in Google SDTT.
    • Consistent Canonical/OG: og:url, canonical, and <title> all match, with no crawl warnings in GSC.
    • Images: All non-decoration images have descriptive alt matching visual content.
    • Semantic Layout: main element present; core sections have H1 (unique), H2, H3 as visible headings.
    • Twitter Card: Card preview correctly renders with title, image, and summary on mobile/desktop.
    • Answer Placement: First paragraph top-of-page text answers the key page question directly.

End of Audit Report