Complete Guide to Fuzzy Matching - Methods, Limitations & SEO Use Cases

Our very own SEO & Data Science Manager, Lazarina Stoy, has released a 30-minute video on fuzzy matching and its applications for SEO. Her goal is to help readers demystify fuzzy matching and its applications.

What is string matching??

String matching in machine learning is a problem that dates back to as early as the 1980s. The problem essentially measures the distance between two strings and calculates, on the basis of that, a similarity score between the two strings, or makes an approximation match to classify the strings as either equivalent, similar or distant.

What can fuzzy matching be used for in the context of SEO work?

Fuzzy Matching for Internal Link Opportunities Identification

The recommendation here is to use this only for pages that are somewhat similar in nature in terms of the content structure (e.g. product pages), as otherwise, you will be comparing apples to oranges. Another important note is that this will not help you evaluate content semantics as part of the process, so ensure you are aligned on the limitations of fuzzy matching before proceeding.?

It’s also best to keep the comparisons relatively low in terms of volume, so better to compare paragraphs of a page, or titles, as opposed to the entire content of the page. Whilst this is great for experimentation, it’s also very important to review the provided recommendations and sense-check them after, to ensure the links made are sensible.

Fuzzy Matching for Competitor Research

The aim of these created tools is to find differences between your ranking URLs structure, titles, and use of keywords and those of your competitor, discover where they are outranking you (via the use of Semrush API), and highlight opportunities and quick wins.

Fuzzy Matching for Keyword Clustering – not recommended. Here’s why.

I do want to mention that Polyfuzz and fuzzy matching in general can be used for keyword clustering and grouping of keywords in the process of keyword research or backlinks in the process of backlink research. The code for doing that can be found in the API’s documentation itself.?

However, as Lee Foot has stated after his experiments in using PolyFuzz for keyword clustering, this is not at all an ideal or recommended way to do keyword clustering, for the reasons mentioned in the theoretical portion of this article. Namely, that fuzzy matching does simple shifts between characters, as opposed to seeking semantic relations between the words in the cluster.

Now that you have the understanding of how fuzzy matching works, what its benefits and limitations are regarding SEO, it’s time you go and test it out for yourself!?

To read the original article, or watch Lazarina’s video, click here.