Content Gap Analysis with AI

Manual content audits were draining Zubin Daver’s 100+ member team. Manual tracking, messy spreadsheets, and siloed tools delayed every campaign. That’s when they partnered with Appkodes AI software development. In just one sprint, Appkodes automated their entire gap analysis workflow from crawling competitors to clustering topics using AI in under 10 minutes. This case study brings out why the automating content gap analysis was needed and how it worked for them.

The Client

Zubin Daver, Head of Digital Growth at a large content-driven company, oversees a diverse marketing operation of 100+ people. His team manages multiple domains across B2B and B2C industries, publishing hundreds of content pieces per month across blogs, landing pages, and knowledge bases.

Core SEO Responsibilities

• Monitor competitor content strategies
• Run weekly keyword audits
• Identify high-opportunity gaps
• Report to the content creation team

The Problem: A Time-Consuming Content Gap Workflow

Despite having advanced SEO tools, Zubin’s team still spent 12–18 hours per week per domain on content gap analysis. Here’s what their manual workflow looked like:

Stage 1: Competitor Selection

The team started by selecting top competitors based on market share, industry relevance, and strong visibility on Google’s SERP (Search Engine Results Page). This step required deep research and internal discussions to finalize the right benchmark domains.

Stage 2: URL Extraction

Using tools like Ahrefs and Semrush, the team manually extracted high-ranking blog URLs from each competitor site. This involved filtering by organic traffic, backlinks, and keyword reach, one URL at a time.

Stage 3: Keyword Compilation

For every selected URL, the team downloaded comprehensive keyword lists. This included ranking keywords, volume, keyword difficulty, and related terms. Again, each report was handled individually and stored manually.

Stage 4: Topic Mapping

In large Excel sheets, they grouped keywords by search intent: informational, transactional, or navigational. This clustering helped them map out each blog’s core theme and supporting topics, though the process was slow and prone to duplication

Stage 5: Gap Identification

Once all data was collected, the team manually compared competitor topics with their own content inventory to find:

• Missing content (topics not covered at all)
• Weak coverage (low-ranking or outdated content)

This step took the longest as it required both analytical thinking and content knowledge.

Step 6: Report Creation

The findings were then compiled into PDF reports or presentation slides for internal use. These reports were used by content strategists to decide which blogs to update or create new content for.