From “Personalisation” to “Vibe Shopping”

The next frontier in personalisation isn’t just about what a shopper likes—it’s about how they feel in this moment. Emotional AI—systems that read intent, sentiment, and vibe from behaviour—lets fashion brands create experiences that are not only personalised, but empathetic and intuitive. This isn’t just about selling clothes; it’s about helping people express themselves and find joy in what they wear.

But there’s a catch: feelings don’t convert without the right product data. If your catalogue can’t describe items the way customers speak and search, even the best recommendation engine will miss the match. And while many shopping agents today read text much better than images, the winners will be those that fuse both. That’s why the new performance battleground is the commerce data layer—continuously optimising product language and attributes for discovery, relevance, and conversion across every surface (your site, marketplaces, and AI chat).

At Selectika, we’ve been building for this future from day one.

Why Fashion Needs Its Own Data Language

Generic solutions for online shopping were built for books and gadgets, not blazers and ballet flats. Fashion requires attributes that reflect fit, silhouette, material behaviour, styling context, and mood—the things shoppers actually care about when they’re getting dressed.

We developed our taxonomy not to “sell a taxonomy,” but to generate the right features—the features that matter when recommending:

• Product → Person: what fits their style, mood, and context
• Product → Product: how to complete a look, substitute, or upsell

And because style evolves, our taxonomy does too. We scan trends and social signals to add or refine attributes (e.g., “quiet luxury,” “coastal cowgirl,” “lug sole,” “soft tailoring”) so your catalogue always speaks the current language of fashion.

Vibe Shopping: From Signals to Feelings to Looks

What it is. Shopping by how you want to feel—not just by product specs. A discovery experience centred on a shopper’s mood, context, and aesthetic vibe (e.g., “cosy weekend,” “quiet luxury,” “edgy night out,” “rainy-day commute”) that then curates items or full looks to create that feeling.

How it differs from classic personalisation. Beyond size/brand/history fit, it prioritises emotion and occasion. A shopper might say: “I need something confident but comfortable for a client meeting in rainy Berlin (12°C).”
The AI stylist returns a complete look plus alternates, explains why it fits the vibe, and lets you tweak (“more playful,” “under £150,” “lean into navy”).

How it works. Emotional AI analyses behavioural signals—browsing patterns, dwell time, search and chat queries, oscillation between categories, and even local weather and time of day—to infer what a shopper needs right now (e.g., “comfy after a long day,” “confident for a presentation,” “protected for a rainy commute”).

The Emotional Wardrobe Curator. Instead of returning only items, the curator suggests complete outfits designed to evoke a specific feeling—happy, relaxed, confident, cosy, protected, polished—and adjusts for context (e.g., rainy Berlin at 12°C vs sunny Lisbon at 18°C).

GEO: The New Performance Marketing

GEO (Generative Experience Optimisation) is where the real competition happens before the click. Discovery is a zero-sum game with limited digital shelf space. If a shopper searches “navy satin slip dress, midi, under £120” and your catalogue reads “women’s dresses – blue”, you may never be seen.

Agentic commerce data layer = the ongoing discipline of:

• Describing products in the language customers actually use
• Testing and iterating attributes and copy with the rigour of performance marketing
• Optimising for discovery, relevance, and conversion across search, on-site navigation, marketplaces, and AI chat

It isn’t a one-time clean-up—it’s a living layer that demands continuous tuning.

How Selectika Connects It All

1. Signal & click profiling
   Map signals and vibe to feelings (cosy, protected, confident, polished, playful, prepared) and to conditions (temperature bands, wind, rain).
2. Natural-language understanding
   Understand shopper language and connect it to your product catalogue.
3. Real-time context
   Fuse weather and timing with behavioural patterns (e.g., outerwear ↔ boots), plus semantics from search/chat (“windy,” “commute,” “date night”) and trend signals from social.
4. Feature generation (our taxonomy)
   Auto-enrich products with attributes that matter for fashion: silhouette, rise, fit, fabric behaviour, water resistance, lining/insulation, toe/heel shape, knit density, closure, etc., plus style context (“office smart,” “weekend city break,” “evening elevated”).
5. Search & vendor integration
   Connect to your search engine and existing vendor stack for seamless activation.
6. Emotion & context layer
   Translate feelings and conditions into ranked intents that steer recommendations and content.
7. Recommendations & content
   Outfit sets, substitutes, and narratives that match mood + context, rendered across PDP, PLP, and chat.
8. Agentic commerce data layer (GEO)
   Continuous A/B tests on attributes, synonyms, and phrasing to improve discovery and CTR—on your site and across external surfaces (marketplaces, AI chat).

Selectika powers the next shopping layer: an AI that understands feelings, vibe, and intent—and translates that into better search, complete looks, and higher-margin baskets. Our ever-evolving fashion taxonomy compounds into a defensible data asset with every SKU and session.