--- title: 'Computing Simple Similarity: A Practical Guide for Digital Marketing & Dev Agencies' description: 'Learn how to use simple keyword and title overlap, TF-IDF, and cosine similarity on bag-of-words to improve content matching, SEO, and personalization.' date: '2025-08-23' author: 'Gabriel Luis' tags: ['digital marketing', 'development', 'similarity', 'TF-IDF', 'cosine similarity', 'SEO'] --- import Image from 'next/image'; # Computing Simple Similarity: A Practical Guide for Digital Marketing & Dev Agencies

Text similarity algorithms for digital marketing and SEO

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In digital marketing and development, one of the most overlooked yet powerful tools in your arsenal is **text similarity measurement**. Whether you're optimizing content for SEO, building a recommendation engine, or clustering related blog posts, being able to quickly determine how similar two pieces of text are can give you a serious competitive edge. In this post, we’ll break down **three simple but effective methods** for computing similarity: 1. **Keyword & Title Overlap** 2. **TF-IDF (Term Frequency–Inverse Document Frequency)** 3. **Cosine Similarity on Bag-of-Words** We’ll cover **what they are**, **how they work**, and **where you can apply them** in a real agency context. --- ## 1. Keyword & Title Overlap The most basic method for determining similarity between two pieces of text is **keyword and title overlap**. ### How It Works Imagine you have two blog articles: - _"10 Email Marketing Strategies for 2025"_ - _"Email Campaign Optimization: Best Practices for 2025"_ A **keyword overlap** approach would: 1. Break down each title into words. 2. Normalize the text (lowercase, remove punctuation, stem/lemmatize). 3. Count how many keywords they have in common. For example: - Article 1 keywords: `["email", "marketing", "strategies", "2025"]` - Article 2 keywords: `["email", "campaign", "optimization", "best", "practices", "2025"]` Common keywords: `["email", "2025"]` → **Overlap = 2 / (average length)** ### Pros - **Fast** — no heavy computation. - **Interpretable** — easy to explain to non-technical stakeholders. ### Cons - Doesn’t capture synonyms (e.g., “campaign” vs “marketing”). - Sensitive to variations in phrasing. ### Agency Use Cases - Quick **deduplication** of blog titles. - Fast **SEO keyword clustering**. - Filtering **content suggestions** that are too similar. --- ## 2. TF-IDF (Term Frequency–Inverse Document Frequency) When you want something more **content-aware** than simple overlap, **TF-IDF** is your friend. ### The Concept TF-IDF scores each word in a document based on: - **TF (Term Frequency):** How often the word appears in the document. - **IDF (Inverse Document Frequency):** How rare the word is across all documents in the corpus. **Formula:** ``` TF-IDF(term) = TF(term) × log(TotalDocs / DocsContainingTerm) ``` Words that appear frequently in a document but rarely in others get **higher scores**. Common words like “the” or “and” get **lower scores**. ### Example Suppose you have two service descriptions: 1. "We offer web development, UX design, and eCommerce solutions." 2. "Our agency provides web development, SEO services, and PPC advertising." Using TF-IDF, “web development” would get a strong score in both, but unique terms like “UX design” and “PPC advertising” would help differentiate them. ### Pros - Captures **important words** instead of just common ones. - Works well even for longer documents. ### Cons - Still **bag-of-words** based — ignores word order. - Requires computing on a **corpus** of documents. ### Agency Use Cases - Matching **client briefs** to internal case studies. - Improving **search ranking** in your site’s content hub. - Identifying **content gaps** in your marketing library. --- ## 3. Cosine Similarity on Bag-of-Words Once you have numeric representations of your text (via TF-IDF or raw term counts), **cosine similarity** is a great way to measure how close two texts are. ### The Idea Think of each document as a vector in a high-dimensional space where each dimension corresponds to a unique word. Cosine similarity measures the **cosine of the angle** between these vectors. **Formula:** ``` similarity = (A · B) / (||A|| × ||B||) ``` Where: - `A · B` = dot product of the vectors - `||A||` = magnitude of vector A - `||B||` = magnitude of vector B If two documents have exactly the same words in the same proportions, the angle is 0° and the similarity is **1.0**. ### Example If two blog posts share many keywords with similar frequencies, their cosine similarity might be **0.92** (very close). If they share few words, it might be **0.15** (not similar). ### Pros - Normalized — unaffected by document length. - Works seamlessly with **TF-IDF vectors**. ### Cons - Doesn’t understand **semantic meaning** (that’s where embeddings come in). - Requires some preprocessing to avoid skew from common words. ### Agency Use Cases - **Content recommendation engines** for your blog. - Clustering similar **landing pages** for A/B testing. - Matching **ad copy** to relevant landing pages. --- ## Putting It All Together A **digital marketing and dev agency** can integrate these methods into several workflows: ### 1. SEO & Content Strategy - Use **keyword overlap** for initial clustering of topic ideas. - Apply **TF-IDF** to identify high-value keywords in competitor content. - Use **cosine similarity** to avoid publishing near-duplicate blog posts. ### 2. Paid Media Optimization - Match **PPC ad copy** to landing pages with the highest content relevance. - Improve **Quality Score** by ensuring strong keyword alignment. ### 3. Development & Automation - Implement a **content similarity API** for your CMS. - Automate **recommendation widgets** based on cosine similarity. --- ## Quick Python Example Here’s a mini snippet showing how TF-IDF + cosine similarity might work: ```python from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity docs = [ "10 Email Marketing Strategies for 2025", "Email Campaign Optimization: Best Practices for 2025" ] vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(docs) similarity_score = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2]) print(f"Cosine Similarity: {similarity_score[0][0]:.2f}") ``` Output: ``` Cosine Similarity: 0.78 ``` --- ## Final Thoughts Computing text similarity doesn’t have to be complicated. By starting with **keyword & title overlap**, moving to **TF-IDF**, and then using **cosine similarity**, you can create practical, **production-ready tools** that directly benefit both your **marketing** and **development** efforts. In an age where **personalization, relevance, and speed** are key, these methods give your agency the ability to: - **Understand content relationships** at scale. - **Serve more relevant experiences** to users. - **Outperform competitors** who rely solely on intuition. --- **Next Steps:** - Audit your current content and measure similarity. - Build a simple internal tool using TF-IDF + cosine similarity. - Integrate it into your SEO, content recommendation, or ad targeting workflows. > 💡 _Pro Tip:_ These methods are a great starting point. But when you’re ready to go deeper, > explore **semantic similarity** with modern embedding models for even more accurate matching.

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