Why Semantic SEO & PPC Are Still Essential for Success

While AI tools have made generating keywords and launching paid search campaigns remarkably fast, achieving structured, scalable performance demands a deeper, more nuanced understanding of search mechanics. True expertise lies in interpreting complex data and building reliable frameworks, tasks that go beyond simple automation. Advanced semantic techniques like n-gram analysis, Levenshtein distance, and Jaccard similarity empower marketers to transform chaotic search data into actionable intelligence, ensuring campaigns are both efficient and effective.

Understanding the power of n-grams is a fundamental step. An n-gram is simply a contiguous sequence of ‘n’ words from a given text. For instance, the phrase “private caregiver nearby” breaks down into three single-word unigrams (“private,” “caregiver,” “nearby”), two two-word bigrams (“private caregiver,” “caregiver nearby”), and one three-word trigram (“private caregiver nearby”). This method is incredibly useful for simplifying massive keyword lists. In a recent campaign overhaul involving over 100,000 search terms, n-gram analysis distilled the data down to roughly 6,000 unigrams, 23,000 bigrams, and 27,000 trigrams. This condensed view allows for smarter decisions; you might discover that all keywords containing the unigram “free” perform poorly, making it an ideal broad match negative, while the term “nearby” drives exceptional results, signaling an opportunity to optimize for local intent.

The primary application of n-grams is in clustering keywords from vast pools of long-tail search data, much of which appears infrequently. By exporting your search term report, including cost, impressions, clicks, and conversions, you can sum these metrics for each n-gram. Calculating key performance indicators like CPA, ROAS, and CTR for these n-grams then reveals clear winners and losers. You can quickly identify high-spending n-grams that don’t convert (negatives) and those that do (positives), enabling you to build ad groups around recurring, high-performing themes. For example, emergency-related terms like “24/7” or “urgent” might consistently show higher conversion rates, warranting their own dedicated segment for tighter control and better performance.

To refine your keyword organization further, the Levenshtein distance is an invaluable tool. This metric measures the minimum number of single-character edits needed to change one word into another. The distance between “cat” and “cats” is 1 (adding an ‘s’), while “cat” to “dog” is 3. In practice, this helps detect brand misspellings in your search queries; “uber” and “uver” have a distance of 1, so you’d confidently add “uver” as a negative in non-brand campaigns. It’s also crucial for assessing keyword relevance. If a keyword and the search terms it triggers have a high Levenshtein distance (say, 10 or more), they are likely unrelated and require review. A low distance generally indicates safe, relevant queries.

After initial clustering with n-grams, you might still face thousands of terms to organize. Manually sorting through them is impractical. Here, the Levenshtein distance aids in consolidating PPC keywords by merging ad groups that target nearly identical terms, preventing an overly fragmented and inefficient account structure. By calculating the distance between queries across ad groups and applying a threshold, like 3 for high accuracy, you can safely group similar keywords. For instance, “24/7 plumber,” “24 7 plumber,” and “247 plumber” have very small distances between them and can be consolidated, simplifying management and bidding.

Taking analysis a step further involves the Jaccard similarity, which acts as a proxy for understanding the overlap between two sets of words. It’s calculated by dividing the number of common unigrams by the total number of unique unigrams across both sets. For example, “new york plumber” and “plumber new york” have a Jaccard similarity of 1 because they share all three words, just in a different order. Meanwhile, “new york plumber” and “NYC plumber” have a similarity of only 0.25, as only “plumber” is shared. This metric is excellent for deduplicating reordered keyword variants, effectively bridging old phrase match and broad match modified logic. However, its limitation is that it doesn’t account for semantic meaning; it treats “new york” and “NYC” as entirely different.

The most powerful approach combines these techniques. Consider a campaign for “cybersecurity courses” with a top ten keyword list containing many plural/singular and reordered variations. Using n-grams alone to consolidate this list could be overwhelming at scale. A more efficient method is to apply the Levenshtein distance first to merge very similar queries (like “cybersecurity course” and “cybersecurity courses”). Next, use the Jaccard similarity to deduplicate reordered variants (like “cybersecurity courses online” and “online cybersecurity courses”). At each stage, you aggregate key performance data, ensuring the final, compressed structure remains actionable and aligned with your campaign goals, even as search volume grows.

Ultimately, restructuring paid search campaigns with these semantic techniques allows for the rapid and consistent organization of massive keyword sets. While AI can provide a useful starting summary, relying on it entirely risks the classic “garbage in, garbage out” scenario. Broad match introduces noise, and these methods help verify query relevance. By applying n-gram analysis, Levenshtein distance, and Jaccard similarity, you inject crucial client context into raw data, building a stable, goal-oriented campaign architecture. Mastering these techniques transforms overwhelming data into a clear roadmap for superior ROI.

(Source: Search Engine Land)