<\/p>\n
Agentic shopping presents ecommerce marketers with a familiar problem in a new form.<\/p>\n
The promise is simple enough. AI agents act on behalf of shoppers to search, compare, select, and even purchase products. These agents will use a shopper\u2019s preferences \u2014 stated and inferred \u2014 rather than browsing products from digital shelves.<\/p>\n
McKinsey & Company describes<\/a> it this way: \u201cCompanies have spent decades refining consumer journeys, fine-tuning every click, scroll, and tap. But in the era of agentic commerce, the consumer no longer travels alone. Their digital proxies now navigate the commerce ecosystem.\u201d<\/p>\n Ecommerce marketers will target both people and AI in the era of agentic commerce.<\/p>\n<\/div>\n In effect, this means ecommerce marketers have two targets: a human and a machine.<\/p>\n It\u2019s a familiar scenario. Marketers seeking organic traffic have long sought shoppers and appeased machines, e.g., search engines.<\/p>\n An online pet supply company wants Google to place its dripless water bowls at the top of search results and humans to click the listing.<\/p>\n In much the same way, this retailer now wants an AI shopping agent to offer that dripless bowl when a consumer asks a genAI platform how to keep a Doberman puppy from sloshing water all over the kitchen.<\/p>\n This two-prong approach paints a helpful picture, as many ecommerce businesses wonder how they will drive sales when chatbots do most of the shopping.<\/p>\n For merchants, the most important component \u2014 shopping agents \u2014 will likely come via platforms.<\/p>\n Few ecommerce businesses will integrate their catalogs directly into every LLM or shopping agent. Instead, commerce platforms and marketplaces will be the conduits. Merchants will publish structured product data<\/a> once and let those intermediaries distribute it into agentic ecosystems.<\/p>\n This is already happening. Shopify, for example, is building an agentic shopping infrastructure<\/a> that allows agents to tap merchant catalogs and build carts.<\/p>\n Marketplaces will play a similar role. Amazon and Walmart already serve as product discovery engines<\/a>\u00a0and have no incentive to surrender that position.<\/p>\n A recent dispute<\/a> between Amazon and Perplexity over agentic shopping tools underscores how aggressively marketplaces may defend their infrastructure and customer relationships.<\/p>\n The implication for ecommerce marketers is practical. Marketing to machines will be a lot of structured data work. Product feeds, catalog hygiene, and API-ready commerce systems will become part of the visibility strategy, much as technical search engine optimization was necessary when Google dominated.<\/p>\n With agentic commerce, marketers aim to influence the AI. The second tactic is influencing the person typing<\/a> the prompt.<\/p>\n AI agents select products based on users\u2019 stated needs and inferred preferences. Merchants, then, have a clear objective: Shape what shoppers want, how they describe it, and which brands or shops they trust before asking.<\/p>\n This, too, is not new. It resembles brand demand<\/a> in Google search results. A shopper will get one set of results from typing \u201cbest dog bowl\u201d and another for \u201cbest dripless dog bowl Chewy.\u201d<\/p>\n In agentic commerce, brand-building<\/a> and preference-setting become even more valuable because they guide the shopper\u2019s intent. And that intent, in turn, influences the agent.<\/p>\n Here\u2019s how merchants exert that influence.<\/p>\n Advertising.<\/strong> Social and video ads foster familiarity, define product categories, and introduce specific terminology.<\/p>\n In time, that language becomes prompt phrasing. A merchant may not control the AI\u2019s model, but it can control whether its product name, differentiator, or problem statement becomes part of a shopper\u2019s vocabulary.<\/p>\n Content marketing.<\/strong> Buying guides, comparisons, and problem-solving articles seed the concepts that shoppers recall later in prompts.<\/p>\n Personalized lifecycle marketing and email marketing<\/a> may become even more critical because it represents an owned audience and an opportunity to identify shopper preferences.<\/p>\n Merchant systems, including AI, can use purchase history, browsing signals, and customer data to anticipate needs and recommend actions. The better a merchant is at retention<\/a>, the more likely it influences the prompt. Or, for that matter, bypass it altogether.<\/p>\n Personalized lifecycle marketing<\/strong> emphasizes\u00a0individuals, according to Matthew Fanelli, chief revenue officer at Digital Remedy<\/a>. Shopppers, Fanelli said, are like snowflakes: beautiful and unique in their own ways.<\/p>\n Influencer marketing<\/strong> is another prompt-shaper. Fanelli described it as a third prong, driven by peer behavior and social proof. \u201cWhat is my peer group doing? What are they buying? How do I get in with them?\u201d he said.<\/p>\n Fanelli expects a trifecta of forces to reshape ecommerce: more choice, shorter attention spans, and more connected devices. \u201cThat\u2019s when you start to get agents,\u201d he said. For marketers, the response is not panic but discipline. Create demand from humans and structure data for machines.<\/p>\n<\/p><\/div>\n2 Targets<\/h3>\n
Marketing to Machine<\/h3>\n
Marketing to People<\/h3>\n