AI Is Driving Revenue. You’re Not Seeing It.
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
-
ナレーター:
-
著者:
概要
Your leads are coming from AI. Your attribution model has no idea.
As more consumers turn to ChatGPT, Google AI, and other large language models to research products and make decisions, a growing portion of your pipeline is being influenced by interactions that never show up in your analytics. No click. No UTM parameter. No referral source. Just a direct visit from someone who already made up their mind thanks to an AI recommendation you can't see.
In this episode, we break down why traditional attribution models are failing in an LLM-driven world and what marketers actually need to do about it.
We cover the "dark AI" problem and why so much LLM-influenced traffic looks like direct, the metrics that matter for AI visibility citation share, sentiment analysis, and brand voice consistency and how first-party data, schema markup, and RAG are becoming essential attribution infrastructure. We also get into multi-touch attribution for AI exposure, how to run incrementality studies to measure LLM-driven lift, and why asking customers directly is making a comeback.
If your attribution stack was built for a world where every touchpoint gets a click, this episode will show you what you're missing.