Advanced Query Analysis Techniques for SEO and PPC
AI can generate keywords at scale, but campaign performance comes from structured, data-driven analysis. Advanced query analysis applies semantic and statistical techniques to interpret messy search-term data, add strategic context, and build campaign structures that generation alone cannot produce. Three techniques do most of the work: N-gram analysis, Levenshtein distance, and Jaccard similarity.
N-gram analysis for thematic clustering
N-grams are contiguous sequences of n words drawn from a text. They collapse massive keyword lists into their core components to reveal hidden patterns.
- Unigrams — single words (“private,” “caregiver,” “nearby”).
- Bigrams — two consecutive words (“private caregiver,” “caregiver nearby”).
- Trigrams — three consecutive words (“private caregiver nearby”).
Break thousands of long-tail queries into a smaller set of n-grams, aggregate performance (clicks, cost, conversions) for each, and high-impact themes surface quickly:
- Find negative keywords — n-grams that spend budget with no conversions (“free,” “jobs,” “reviews”).
- Discover positive themes — high-converting n-grams that deserve their own ad groups or content clusters (“24/7,” “emergency,” “local”).
- Reduce dimensionality — turn 100,000 unique search terms into a few thousand n-grams you can actually analyze.
Levenshtein distance for similarity matching
The Levenshtein distance is the “edit distance” between two strings — the minimum single-character edits (insertions, deletions, substitutions) needed to turn one into the other.
cat→cats= 1uber→uver= 1keyword→ad group= 8
Use it to clean and consolidate near-identical keywords, avoiding over-granular structures and cannibalization:
- Catch misspellings — identify common misspellings of brand or competitor terms to cover or exclude.
- Consolidate ad groups — merge low-distance variants (“24/7 plumber,” “24 7 plumber,” “247 plumber”) to simplify reporting and bidding.
- Assess relevance — a large distance between a keyword and the terms it matches flags a relevance problem to review.
Jaccard similarity for deduplication
The Jaccard similarity measures overlap between two sets: shared words divided by total unique words across both queries. It is order-insensitive.
new york plumber&plumber new york= 1.0 (3 shared / 3 unique)new york plumber&NYC plumber= 0.25 (1 shared / 4 unique)
It excels at deduplicating queries with identical intent but different word order, replicating phrase-match logic without the ambiguity:
- Deduplicate — merge database rows that are the same query (“seo agency london” vs. “london seo agency”).
- Limitation — it does not understand semantic equivalence; it treats “new york” and “NYC” as entirely different.
A combined workflow for campaign restructuring
The three techniques are strongest in sequence when reworking a large account or keyword set:
- Consolidate with Levenshtein distance — group and merge misspellings and minor character variations.
- Deduplicate with Jaccard similarity — collapse reordered variants that share intent.
- Analyze with N-grams — run n-gram analysis on the cleaned data to identify the performance themes for a new, scalable structure.
This layered approach is a repeatable process for turning raw search data into a logically structured, high-performing campaign.
