Why advanced semantic techniques still matter in PPC and SEO

Now that anyone can use AI to generate keywords and spin up a paid search campaign in minutes, it’s easy to assume the hard work is done. 

But creating structured, scalable performance still requires a genuine understanding of how search works. 

Techniques like n-grams, Levenshtein distance, and Jaccard similarity give search marketers the ability to interpret messy search term data, apply client context, and build reliable frameworks that AI alone can’t produce. Here’s how.

What n-grams reveal in PPC and SEO analysis

Think of n-grams as the “n” words that make up a keyword. For example, in the term “private caregiver nearby,” we have:

• 3 unigrams (one word): “private,” “caregiver,” and “nearby”
• 2 bigrams (two consecutive words): “private caregiver” and “caregiver nearby”
• 1 trigram (three consecutive words): “private caregiver nearby”

N-grams are useful for simplifying keyword lists. 

This week, I restructured several campaigns with more than 100,000 search terms. Using n-grams, I was able to reduce those lists to:

• ~6,000 unigrams.
• ~23,000 bigrams.
• ~27,000 trigrams.

With these smaller sets, you may find that all keywords containing the “free” unigram perform poorly, so you’d exclude “free” as a broad match negative. 

Conversely, you may see that “nearby” performs exceptionally well, prompting you to experiment with local variations and landing pages.

There are, however, clear limitations:

• You need a large volume of search terms, so this method is more applicable to bigger budgets.
• The larger your “n,” the less useful the method becomes because it produces larger outputs, which defeats the purpose. At that point, you’ll need more advanced methods such as the Levenshtein distance or Jaccard similarity.

Clustering keywords with n-grams

Analyzing SEO and PPC data often requires reviewing huge volumes of long-tail search terms, many of which appear only once and have very little data. 

n-grams help convert that chaotic long-tail data into clear, manageable intelligence. 

This allows you to reduce wasted spend, identify new opportunities, and build a scalable structure.

• Start by exporting your search term data. In PPC, this includes cost, impressions, clicks, conversions, and conversion value broken out by search term. 
• For each n-gram, sum cost, impressions, clicks, conversions, and conversion value. 
• Then calculate CPA, ROAS, CTR, CVR, and other relevant metrics.

With this shorter, more digestible dataset, you can rank top-spending n-grams that do not convert (your negatives) and those that do (your positives). 

From there, build ad groups around recurring n-grams that drive performance.

For example, you may find that emergency-related n-grams (“24/7,” “same day,” “urgent,” etc.) often deliver higher conversion rates. You’d segment these to control them more…