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 effectively.

Bottom line: n-grams help you isolate themes that warrant special attention. 

Once identified, it becomes much easier to build advanced paid search structures centered on high-impact n-grams and generate stronger ROI.

Dig deeper: How to uncover hidden gems in your paid search accounts

How to use the Levenshtein distance to improve keyword quality

The Levenshtein distance quantifies the minimum number of single-symbol edits – insertions, deletions, or substitutions – needed to transform one string into another.

It may sound complex, but the concept is actually straightforward.

The Levenshtein distance between “cat” and “cats” is 1 because you only need to add the “s.” Between “cat” and “dog,” the distance is 3. And so on.

A common use case is detecting brand and competitor misspellings that appear in your search terms. 

For example, “uber” and “uver” have a Levenshtein distance of 1, so you would confidently exclude the misspelled version from your non-brand campaigns.

You can apply the same logic to keyword relevance. 

If the distance between a keyword and the search terms it matches is too high – think 10 or more – those terms likely have little in common with the keyword and deserve review. 

A low distance, on the other hand, usually means those queries are safe and do not require manual checks.

Consolidating PPC keywords with the Levenshtein distance

After using n-grams to build initial keyword clusters, you may still end up with thousands of search terms to organize into a workable campaign structure. 

Sorting through 6,000 unigrams manually is not an option. This is where the Levenshtein distance becomes essential.

The goal is to merge ad groups that target nearly identical keywords to avoid an overly granular, SKAG-like structure. 

Too much granularity creates complicated reporting and account management, along with inefficient bidding and wasted spend.

Using the same dataset, calculate the Levenshtein distance between queries across different ad groups. 

Then identify the closest keyword and ad group using a predefined threshold – for example, 3 for high accuracy. 

This lets you consolidate keywords and ad groups safely. With a looser threshold, such as 6, you can also group or name ad groups by similarity or intent.

Here is a simple example demonstrating why the three keywords below can be grouped together:

Levenshtein distance	24/7 plumber	24 7 plumber	247 plumber
24/7 plumber	0	1	1
24 7 plumber	1	0	1
247 plumber	1	1	0

Dig deeper: How to use negative keywords in PPC to maximize targeting and optimize ad spend

Going further with the Jaccard similarity

In PPC, you can simplify the Jaccard similarity as a proxy to understand the overlap between two sets of n-grams. 

The calculation is straightforward: the number of common unigrams between two sets divided by the total number of unique unigrams across both sets.

It may sound technical, but it’s easy to visualize. Think of it as: 

• Jaccard similarity = Red / Green

Here are concrete examples:

• “new york plumber” and “plumber new york” = 1 (all three unigrams appear in both sets, just in a different order)
• “new york plumber” and “NYC plumber” = 0.25 (only “plumber” is shared, and there are four unigrams total)

The Jaccard similarity is a useful first step for deduplicating similar keywords. It essentially bridges the gap between old phrase match and broad match modified logic.

But it also has limitations because it does not account for meaning. 

In the example above, “new york” and “NYC” should be recognized as equivalent, yet the Jaccard calculation treats them as distinct. 

To handle that level of nuance, you need more advanced techniques (which I’ll cover in a later article).

Combining the Jaccard similarity and Levenshtein distance

Consider a cybersecurity course campaign with the following top 10 keywords:

Keyword	Semrush average monthly searches in the U.S.
cybersecurity courses	5,400
cybersecurity online course	1,900
free cybersecurity courses	1,300
online cybersecurity courses	1,300
cybersecurity course	1,000
cybersecurity courses online	880
google cybersecurity course	880
cybersecurity courses free	720
cybersecurity free courses	590
cybersecurity online courses	480

By combining plural and singular versions and reordered versions of these keywords, you could reduce the top 10 into a more actionable top four:

• “Cybersecurity courses.”
• “Cybersecurity courses online.”
• “Free cybersecurity courses.”
• “Google cybersecurity course.”

You could use n-grams to do this, but scaling n-gram analysis across thousands of keywords can be overwhelming. 

A more efficient approach is to use both similarity metrics in sequence.

• First, apply the Levenshtein distance to consolidate very similar queries. 
• Then use the Jaccard similarity to deduplicate reordered variants. 
• At each step, you’ll sum the usual KPIs – cost, conversions, and other metrics – so the n-gram analysis remains actionable.

The result is a clear, compressed structure that holds up even as your search term volume grows.

Restructuring paid search campaigns with advanced semantic techniques

With the right semantic techniques, you can restructure massive keyword sets quickly and with consistent, high-quality results. 

AI can absolutely help by providing an initial summary, but you should not rely on it entirely. 

Otherwise, it’s a classic case of “garbage in, garbage out.”

Broad match can be powerful, but it also introduces more noise. These techniques help you verify that your queries stay on track.

Use n-grams, the Levenshtein distance, and the Jaccard similarity to apply client context to raw search data and produce a stable structure that aligns with your campaign goals. 

It can feel overwhelming at first, so here’s a summary table to close out the article:

Scenario	Best technique	Why
Identify high-intent patterns in huge search-term exports	n-grams	Surfaces themes fast; reduces dimensionality
Clean duplicate / near-duplicate keywords at scale	Levenshtein distance	Captures spelling + structural similarity
Deduplicate reordered or slightly varied keyword strings	Jaccard similarity	Order-insensitive token-based comparison
Create scalable clusters for campaign rebuilds	Combo: Levenshtein → Jaccard → n-gram	Sequence gives accuracy + compression