A heritage luggage brand defending category position against newer DTC entrants.

The challenge

The client was a heritage luggage brand with a strong retail presence and decades of brand equity. The threat was competitive encroachment from a new generation of DTC luggage brands — heavily funded, highly visible on social media, and increasingly appearing in the AI-generated product discovery answers that now influence purchase decisions for considered purchases like luggage.

When someone asked ChatGPT "what's the best luggage for frequent fliers" or Perplexity "best carry-on for business travel," the DTC entrants were appearing consistently. The heritage brand — despite having better products, longer track records, and significantly more press coverage — was appearing less frequently.

This was an interesting inversion of the traditional SEO dynamic, where heritage and domain authority are advantages. In AI search, the signal is different: recency of training data coverage, entity clarity, and direct-answer content architecture matter as much as historical authority.

Our approach

The diagnostic was revealing. The DTC entrants had several advantages in the AI search layer that their traditional search profiles didn't reflect:

Higher training data recency. DTC brands had been generating a steady stream of review coverage, comparison articles, and social content over the preceding two years — exactly the window most relevant to the training data that current LLMs draw on. The heritage brand's coverage was historically dominant but had thinned in recent years as the brand had focused on retail rather than digital.

More direct-answer content. The DTC brands had built content specifically designed to answer the questions their customers were asking — detailed "why our carry-on" pages, head-to-head comparisons, FAQ content around durability and warranty. The heritage brand's content was brand-led and product-catalogue-style — well-written, but not structured to answer questions.

Cleaner entity associations. AI systems had built clear models of what the DTC brands stood for: "lightweight," "lifetime warranty," "direct-to-consumer." The heritage brand's entity associations were broader and less differentiated — a symptom of a brand that meant different things to different audiences.

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The work

We ran a three-part programme focused on heritage positioning rather than trying to compete on the DTC brands' terms.

Entity repositioning. We worked to sharpen and reinforce the brand's entity associations with the signals that differentiate a heritage brand: craftsmanship, provenance, longevity, professional travel. This meant structured data that made these attributes machine-readable, a consistent vocabulary across all on-site content, and targeted outreach to strengthen co-citations with the sources AI systems associate with these qualities — travel press, business travel publications, long-form review content.

Content architecture. We built a content layer specifically structured for AI citation: direct-answer pages for the specific questions AI systems were responding to around luggage selection, comparison content that contextualised the brand's position against named competitors, and use-case content targeting the professional and frequent traveller segments the brand serves best.

Technical and structured data. We implemented Product schema comprehensively across the product range, with rich attribute coverage — materials, warranty terms, weight, dimensions, certification. These structured attributes feed directly into AI systems' ability to recommend products in response to specific criteria queries.

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The outcome

The programme stabilised citation share in the brand's core product-discovery queries and generated meaningful improvement in the business travel segment — the highest-value segment for the brand. At the end of the six-month engagement, citation share on target queries had improved from approximately 18% to 31%.

The heritage positioning angle proved more effective than expected. AI systems responded well to content that emphasised provenance and durability in specific, verifiable terms — claims that could be substantiated with structured data and third-party co-citations rather than brand assertion.

"We were concerned that heritage was a liability in this context — that AI systems would default to the newer, louder brands. The data showed something different. Heritage is an advantage if you give the AI system the right signals to work with."

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Related disciplines

This engagement draws on AI Search strategy, entity visibility, content strategy, and structured data. It is a good example of a defensive AI Search programme for an established brand facing competitive disruption from newer entrants.