A9 Bot - How To Get The Most Out Of Your Listing Optimisation In this informative episode of Seller Sessions, host Danny McMillan gives listeners an inside look at the A9 Bot available exclusively through his website. This specialised bot aims to help sellers better grasp Amazon's ever-evolving A9 ranking algorithms and optimisation factors by synthesising key learnings from extensive scientific literature and patents. Laying the Groundwork Around A9 Understanding Before demonstrating the A9 Bot tool itself, Danny emphasises how this fits into his broader mission to equip Amazon sellers with more technical competency on the inner workings of Amazon search. He’s compiled and working through 1234 scientific papers and has written at depth on subjects sellers may find confusing or conflicting when trying to rank higher. Understanding Product Photos and How Attributes Really Work How Amazon Protects Answers to Product Questions Using Similar Products The Real Reason Why A10 is a Myth Improving Seasonal Relevance and Ranking on Amazon Search This scientific grounding informs the A9 Bot's capabilities for listing optimisation tied to ranking factors. Danny emphasises digesting this background will prove useful for sellers aiming to "level up" their Amazon search education. Introducing Key Match Types: Lexical vs. Semantic vs. BERT Contextual As Danny shifts into demonstrating the tool itself, he starts by outlining three key match types critical to understand: Lexical Matching Semantic Matching BERT Contextual Matching While lexical matching should remain core to any keyword targeting strategy, Danny urges sellers not to limit themselves to only indexing keywords. The semantic and BERT matches within A9 paint a fuller picture of what customers may search for — and how listings can evolve to reach more searchers. Seeing the A9 Bot in Action: Listing Rewrites To illustrate the bot's capabilities, Danny provides a demo using a beard oil product as an example. He prompts the bot to rewrite the listing bullets targeting first lexical matches, then semantic matches, and finally keyword matches informed by BERT's contextual analysis. The output shows clear differences highlighting how each match type shapes results. The lexical rewrite sticks closest to exact keyword matches in the original beard oil listing. Meanwhile the semantic match incorporates more contextual phrases that extend meaning like beard grooming and shaping tools. Finally, the BERT rewrite recognises entities and relationships to recommend additional keywords around skin conditioning, packaging format, and product feel during application. This small demo begins to showcase how tapping scientific advances in language AI can assist sellers in reaching more customers. While their manual testing and listing quality control remains imperative, leveraging innovations like BERT as an input can spur new optimisation ideas. Key Takeaways and Parting Thoughts In concluding his A9 Bot intro, Danny shares a few final recommendations: Use provided prompts to experiment rewriting your own listings with lexical, semantic, and BERT matches Check his site’s “A9 Algorithm” section for articles dispelling myths plus evolving science around ranking factors Recognise sellers optimise for conversion while Amazon algorithms focus on customer experience first Avoid overstuffing listings with keywords without considering user experience With innovations in contextual language understanding racing ahead, Danny emphasises sellers must stay on the pulse of Amazon advancements to remain competitive in organic search. Tools like the A9 Bot point to a future where semantic search capabilities will only grow more advanced. Though testing and high-quality listings remain essential, embracing these new frontiers in AI can help uncover more opportunities. Link to A9 Bot: https://sellersessions.com/a9-bot/ Prompts For A9 Bot Examples (adjust accordingly to your requirements): I am launching a beard oi