The Challenge
A UK-based retail brand with both physical high-street stores and a growing ecommerce presence faced a dual visibility challenge. Locally, when users asked AI assistants for shopping recommendations near their stores, the brand was absent. Globally, when users asked for product recommendations in their category, the brand was equally invisible.
The brand had invested heavily in local SEO for their physical stores and traditional ecommerce SEO for their online shop. Both channels performed well in conventional search results. But AI assistants were creating a new discovery layer that operated on different signals, and the brand had no presence in this layer.
The complexity of optimizing for both local and global AI visibility simultaneously required a structured approach that their existing marketing team hadn’t developed.
The Solution
CiteScore’s audit tested both location-specific queries (“best [category] shops in [city]”) and global queries (“best [category] brands in the UK”). The results revealed a 14% local citation rate and an 8% global citation rate.
Content
The team developed a dual-content strategy. For local AI visibility, they created detailed store-specific pages with local context, neighborhood guides, and location-specific product availability information. For global visibility, they built comprehensive category authority content including buying guides, product comparisons, and trend analysis that positioned the brand as an expert voice in their retail category.
Technical
CiteScore’s website audit revealed critical gaps in LocalBusiness schema, Store schema, and product availability markup. The team implemented location-specific structured data for each store, along with comprehensive Product schema for their online catalog. They also added FAQ schema to their most important category and location pages.
Strategy
Using CiteScore’s competitive benchmarking, the team mapped both local and global competitors’ AI visibility profiles. They discovered that brands with strong editorial content and local community involvement were disproportionately cited in AI answers. The team developed a content partnership strategy with local publications and industry authorities to build the citation signals AI models rely on.
The Outcome
- 91% AI citation coverage across target queries (up from 11%)
- 267% increase in local AI assistant mentions for store-specific queries
- 4x growth in global AI recommendation frequency
- Foot traffic to physical stores increased 18% from AI-influenced discovery
- Online sales from AI-referred sessions grew by 156% within four months
Frequently Asked Questions
How do AI assistants handle local retail queries?
AI assistants increasingly incorporate location context into product and store recommendations. Retail brands with strong local content signals, structured location data, and consistent NAP information are more likely to be cited in location-specific queries.
Can retail brands optimize for both local and global AI visibility?
Yes. CiteScore's audit framework tests both location-specific and general category queries, enabling a dual-strategy approach that addresses local AI citations and broader global recommendation visibility simultaneously.
