This episode explores the problem of non-converting ad spend in Amazon marketing campaigns, focusing on a technique called N-Gram Laddering to optimize ad performance. Against the backdrop of high percentages of non-converting spend (e.g., 50-70%), the host introduces N-grams as a method to analyze search terms with low click counts, which often contribute significantly to wasted ad budget. More significantly, the host details the "N-Gram Ladder" process: starting with single-word (1-gram) analysis, identifying potentially irrelevant terms, and then moving to 2-grams and 3-grams to gain a broader context and make informed decisions about negative keyword additions or targeted bidding. For instance, an initially irrelevant term like "goat" led to the discovery of profitable sub-categories like "Korean goat milk" through this laddering approach. The process also involves using AI tools to further analyze N-gram data, identifying potential negative keywords and refining campaign strategies. Ultimately, this method allows for a more nuanced understanding of search term performance, leading to improved efficiency and reduced wasted ad spend. What this means for Amazon marketers is a more data-driven approach to campaign optimization, leveraging readily available data to significantly improve ROI.
