Advanced Query Analysis Techniques: N-grams, Levenshtein & Jaccard

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 = 1
• uber → uver = 1
• keyword → 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:

1. Consolidate with Levenshtein distance — group and merge misspellings and minor character variations.
2. Deduplicate with Jaccard similarity — collapse reordered variants that share intent.
3. 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.

Related resources

• Keyword Research Basics
• Topical Authority and Clustering
• AI-Powered Keyword Research