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DTC Podcast | Ep 615: Prepare Your Brand for Agentic Commerce (How LLMs Are Collapsing the Consideration Phase)

DTC Podcast
DTC Podcast
DTC Podcast | Ep 615: Prepare Your Brand for Agentic Commerce (How LLMs Are Collapsing the Consideration Phase)
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The consideration phase is collapsing thanks to LLM shopping.

Awareness still happens on Meta. Conversion still happens on a PDP. But the comparison and research middle, the part brands have spent a decade optimizing, is increasingly happening inside an LLM the customer already trusts.

20% of holiday shoppers used an LLM in their purchase path last Q4. Google I/O just demoed one-tap concert tickets from a photo. Amazon folded Rufus into Alexa for Shopping. The behavior is moving fast enough that operators need to start preparing for Q4 now.

Eric sits down with Aves and Daniel from Pilothouse to unpack what’s actually happening and the work brands can start this quarter.

Inside the episode:

  • Why customers trust their LLM more than your ad
  • Daniel on why he stopped going to Amazon to compare vitamins
  • The persona mismatch that hurts brands more than it used to
  • Why reviews and earned media matter more than your landing page
  • What changes for abandon cart and retargeting
  • Two operator-tested audits to see if your brand shows up in ChatGPT, Claude, and Gemini
  • For founders and operators who want to be recommended when the customer asks.

What to Steal:

  • Three things you can do this week.
  • Run the prompt audit. Take your top five Google queries, run them through ChatGPT, Claude, and Gemini. Track who gets recommended, who gets cited, and whether you show up at all. Daniel uses this as his baseline before any other AI-visibility work.
  • Pick one brand truth and repeat it everywhere. Scattershot positioning loses to consistent positioning. If every ad pitches a different angle to a different persona, an LLM has nothing coherent to summarize about you.
  • Add dates to your blog posts and PR pages. Recency factors into LLM citation. Old content gets de-prioritized even when it’s accurate.

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