Owning Weekly AI Shopping Habits
Stuart Kearney, co-founder of Vetted, on AI agents in shopping
This reveals that Vetted is trying to win habit, not just transactions. Big ticket items like TVs generate occasional, high intent visits, but they do not create the repeated usage needed to become someone’s default place for buying advice. That is why Vetted targets everyday household purchases around a $40 order value, where users come back often enough to build trust through repeated successful recommendations.
-
Vetted’s own product history shows the tradeoff. Slant scaled with SEO and recommendation pages, but the company learned that SEO traffic is hard to sustain and weak at building a durable direct consumer relationship. The newer strategy is to earn a home screen habit around frequent purchase decisions.
-
The workflow also fits how AI shopping works best today. Users ask long, specific questions about routine products like skincare, towels, or household basics, then refine options through multi turn dialogue. That behavior is much better for retention than one shot searches for a TV every few years.
-
This is also a bet against the old publisher model. In an SEO driven commerce business, the goal is to catch one off search traffic at the moment of purchase. In Vetted’s model, the goal is to become the recurring research layer, more like a shopping companion than a review page.
The category winners in AI shopping are likely to be the products that turn sporadic search into weekly behavior. As OpenAI, Perplexity, and platform incumbents add shopping features, the durable advantage will come from owning repeated low stakes decisions, because that is where preference data, trust, and default usage compound fastest.