Complete Guide to ML Clustering for Marketing & SEO: Use Cases & Model Choices

Clustering algorithms excel at finding natural patterns and groupings in your data—but knowing which clustering method to use for your specific marketing challenge isn’t always obvious. This three-part Google Sheets resource, created by Lazarina Stoy for her Introduction to ML for SEOs course, connects the dots between real-world marketing problems, clustering methodologies, and the specific algorithms that solve them, giving you a complete roadmap from problem to implementation.
The guide spans three interconnected tabs that build your understanding systematically. The first sheet presents 12 practical SEO and marketing use cases—from keyword clustering and topic modeling to backlink analysis and search trend identification—each mapped to appropriate clustering algorithms and beginner-friendly APIs like KeyBERT, spaCy, and Google Cloud AutoML. The second sheet breaks down seven distinct clustering approaches (centroid-based, density-based, connectivity-based, and more), explaining when each type is most effective and connecting them to specific marketing scenarios like customer segmentation, content categorization, and social media audience analysis. The third sheet provides an algorithm reference library covering 20 different clustering methods, from classic approaches like K-Means and DBSCAN to advanced techniques like BERTopic and Transformer-based clustering, complete with descriptions, key features, and common use cases.
Use this for:
‧ Identifying which clustering approach fits your specific marketing data and objectives, whether you’re working with keywords, customer behavior, or content
‧ Understanding the differences between hard vs. soft clustering and centroid-based vs. density-based methods to choose the right technique
‧ Discovering specialized clustering algorithms for text analysis, like LDA for topic modeling or BERTopic for semantic document clustering
‧ Matching your input data type (numeric, text, images, or behavioral data) to compatible clustering algorithms and models
‧ Finding pre-built APIs and models (Amazon Rekognition, Google Vision API, Hugging Face Transformers) that can implement clustering without building from scratch
This is perfect for SEO professionals and digital marketers who want to use clustering algorithms to uncover patterns in their data—whether for content strategy, audience segmentation, or keyword research—and need guidance on which specific algorithms and tools will deliver results.