Complete Guide Understanding ML Classification for Marketers: Marketing Use Cases & Model Choices

Machine learning classification can feel abstract and technical, but this three-part Google Sheets resource makes it immediately actionable for marketing professionals. Created by Lazarina Stoy for her Introduction to ML for SEOs course, this guide takes a reverse-engineering approach: instead of starting with algorithms and theory, it begins with the marketing problems you’re already trying to solve—like predicting ad engagement, categorizing content, or identifying high-value customers—and shows you exactly which classification methods and tools can help.
The guide is organized into three interconnected sheets that work together to give you the complete picture. The first sheet maps nine essential SEO use cases—from keyword categorization and search intent classification to sentiment analysis and schema markup suggestions—directly to the algorithms and beginner-friendly APIs that can handle them (like Google Natural Language API, Amazon Comprehend, and Hugging Face Transformers). The second sheet dives into classification types (binary, multi-class, multi-label, and imbalanced), connecting each to specific marketing scenarios across the customer journey, complete with the metrics you should track and the input data types you’ll need. The third sheet breaks down six core classification algorithms, explaining how each one works, what makes it unique, and which marketing problems it solves best.
Use this for:
‧ Matching your specific marketing or SEO challenge to the right classification approach and algorithm
‧ Understanding when to use binary vs. multi-class vs. multi-label classification in your campaigns
‧ Finding beginner-friendly, pre-trained APIs so you can implement ML solutions without building from scratch
‧ Learning which algorithms work best for common tasks like ad engagement prediction, content performance analysis, or customer segmentation
‧ Connecting technical ML concepts to real marketing metrics like click-through rates, conversion rates, and engagement data
This is perfect for marketing professionals and SEO specialists who want to leverage machine learning classification in their daily work, whether they’re optimizing content strategy, improving ad targeting, or enhancing customer segmentation—without needing a data science background.