Stanley 1913 Adapts Marketing for AI Search Era with Shopify Integration

Muscle Memory, Not Hacking

Stanley 1913 could have chased quick wins — gaming its way into AI-generated product recommendations through shortcuts and tricks. Instead, the brand is treating AI search adaptation as a discipline that requires sustained organizational change, not a marketing hack.

"It's about ensuring our authentic brand experiences translate into natural-language answers without losing the human touch," said Kate Ridley, Stanley 1913's chief brand officer. The metaphor she uses is muscle memory: the brand needs to train for this the way an athlete trains for a marathon, building habits that become automatic over time.

The stakes are significant. Stanley 1913's website received 6.6 million global visits in July 2026, a 35.5% increase compared to July 2025, according to Similarweb data. As that digital audience grows, the number of platforms where consumers discover the brand is multiplying — making the question of how Stanley's product information travels across the web increasingly important.

The Occasion Marketing Gap

The gap that prompted Stanley's strategic shift was surprisingly specific: occasion-based marketing. The brand had built plenty of visual content around Mother's Day, Teacher Appreciation Week, and Nurse Appreciation. It was heavy on imagery, light on explanation.

"We weren't necessarily writing copy that was really detailing the use, the occasion, and why it was a great gift option," Ridley explained. "Now we recognize that's actually really important for LLMs."

The insight is broadly applicable. AI language models cannot infer meaning from a photograph the way a human can. They aggregate text from the open web. If a brand's product page doesn't explicitly state what a product is for, who it's for, and why it's the right choice for a specific occasion, the model has nothing to cite. No amount of influencer-driven visual storytelling will compensate for that structural gap.

This is a critical realization for any ecommerce brand: more than 42% of US adults now use AI chatbots to search for information, according to Pew Research Center. The content that was built for humans — who can infer context from a face, a vibe, a trusted creator — is architecturally invisible to the systems that now mediate product discovery.

Shopify's Universal Commerce Protocol

On the infrastructure side, Stanley 1913 has been testing Shopify and Google's Universal Commerce Protocol to bring its product catalog directly into chat conversations. The brand is also implementing structured data to keep product information consistent for both traditional search engines and AI agents.

Working with partners including Yotpo's Discovery product, Stanley is building a baseline for two key metrics:

• How often the brand appears in LLM conversations
• How frequently its products get cited as recommendations

These metrics represent an entirely new category of ecommerce analytics. Just as brands spent decades developing SEO measurement frameworks, they now need equivalent rigor for AI discoverability. Stanley 1913 is, by its own admission, building the measurement infrastructure from scratch — "actively building out dedicated LLM guidance and measurement frameworks so everyone stays aligned," according to Ridley.

AI Platforms as a New Type of Influencer

Debra Aho Williamson, founder and chief analyst at Sonata Insights, framed Stanley's shift in a way that clarifies the strategic priority: "Brands like Stanley are recognizing that they need to think of AI platforms as influencers of a different type. They are becoming important places where consumers discover new brands, learn about them, and then make purchase decisions — the same roles human influencers play."

This framing has practical implications. Brands invest heavily in creator partnerships to generate authentic content that reaches specific audiences. The same logic applies to AI platforms, but the mechanism is different: instead of paying a creator to feature your product, you structure your product data and content so that AI systems can accurately represent it.

Stanley's approach extends beyond its own website. Since LLMs lean on third-party sources — earned media, affiliate coverage, reviews — the team tracks which sources agentic search tools treat as authoritative. Culturally-led campaigns, like the recent Kacey Musgraves partnership, feed into that by generating press coverage and conversation beyond Stanley's owned channels.

What Shopify Merchants Should Do

Stanley 1913's strategy is ahead of most brands, but the core principles are accessible to any Shopify merchant:

1. Add natural-language product descriptions. For every product, write copy that explicitly describes the use case, the occasion, and the audience. Don't rely on images to carry meaning — AI models need text.
2. Implement structured data. Use product schema markup (JSON-LD) to provide machine-readable product information. This is the foundation for both traditional search rich results and AI platform citations.
3. Build product-level FAQ content. Answer the questions customers actually ask: How do I care for this? Is it dishwasher safe? What size should I buy? LLMs favor comprehensive, question-answer content.
4. Test Shopify's Universal Commerce Protocol. If you're on Shopify, explore UCP integration to get your product catalog directly into AI shopping conversations.
5. Measure AI discoverability. Regularly query ChatGPT, Gemini, and Perplexity with category-relevant questions. Document when your brand appears and when it doesn't. Track this over time.

The shift toward AI-mediated discovery is not replacing what brands were already doing — it's adding a new layer. As Ridley said, "We're not moving away from one and towards the other. We need to do both." But specifically for LLMs, "you have to have the information there for it to do its job."

For Shopify merchants, the takeaway is clear: the brands that invest in AI-ready product data and content today will have a structural advantage as AI platforms capture an increasing share of the product discovery journey.