SERP Intent Analyzer — Mining SERPs for Intent Classification and Gaps

Purpose

Mine a SERP for a target keyword and produce a structured competitive intelligence report: every result classified by intent, PAA questions extracted 3 levels deep, keyword table with intent tags, and gap identification. This is the research phase before any content brief is written.

Step 1: Deep SERP Mining

For the target keyword, collect from multiple sources:

Google (primary):

• Top 20 organic results: title, description, URL
• PAA questions — expand 3 levels deep by clicking each PAA card
• Autocomplete suggestions for the keyword and related phrases
• Featured snippet content if present
• Local pack results if present

Bing, DuckDuckGo (secondary):

• Top 10-20 results from each
• Flag any results appearing in these but NOT in Google (intent classification differences)

AI search (tertiary):

• What answer does Perplexity generate for the query?
• What sources does it cite?
• What sub-questions does it suggest?

Minimum: 50-100 total data points before proceeding.

Step 2: Intent Classification

For each of the top 20 Google results, classify intent:

| Intent | Definition | Pattern | |--------|-----------|---------| | Informational | Knowledge acquisition | how, what, why, explanation, guide | | Commercial | Pre-purchase research | best, top, review, comparison, vs | | Transactional | Immediate action | buy, price, discount, hire, quote, schedule | | Navigational | Locating specific destination | brand name, login, official, near me |

Mark each result. Calculate the intent distribution of the SERP (e.g., 60% informational, 25% commercial, 15% transactional). The dominant intent = what Google thinks most searchers want for this query.

Step 3: Intent Mismatch Analysis

Compare the SERP intent distribution to the intent of the page you are planning:

• If SERP is 60% informational and you want to rank a transactional page → mismatch, reconsider
• If SERP shows mixed intent → opportunity for a page that serves multiple intents
• If all top results are same intent as your page → viable, focus on depth and differentiation

Step 4: PAA Mining (3 Levels Deep)

For each PAA question shown on the SERP:

1. Record the question text
2. Click to expand and note the featured answer source
3. Click each answer to trigger 2nd-level PAAs — record all
4. Select 3-5 from level 2 and expand to level 3

Output: A tree of 25-50 PAA questions, organized by parent question. These become content brief headings.

Step 5: Keyword Intent Table

Produce a table of all discovered keywords with intent classification and estimated search volume:

| Keyword | Intent | Est. Volume | PAA Position | Content Gap? | |---------|--------|------------|-------------|-------------| | [keyword] | [intent] | [range] | [yes/no] | [yes/no] |

Mark "Content Gap: Yes" for any keyword where:

• No top-10 result comprehensively answers the query
• All top results are from directories or aggregators (not primary businesses)
• PAA answers are pulled from weak sources (thin content, old dates)

Step 6: Gap Report

Summarize gaps in priority order:

1. Critical gaps — high-volume queries where no strong content exists
2. PAA gaps — PAA questions with weak or missing answers
3. Intent gaps — queries where SERP intent doesn't match what users actually need
4. Freshness gaps — queries where all top results are 2+ years old

Each gap becomes a content opportunity. Map gaps to content briefs using koray-content-brief.

Cross-References

• keyword-researcher — generates the seed keyword list to feed this analysis
• intent-analyzer — decodes the hidden psychological intent beneath SERP classification
• query-classification-content-mapper — Google patent-based query classification
• paa-content-engine — PAA content production from the extracted questions
• koray-content-brief — content briefs built from the gap report

#seo-sop #seo #intent #serp #competitive-analysis #keyword-research