The thinking behind it.
Current search behaviour and service assumptions require verification.
- Fix search first: semantic search with synonyms (wedding, bridal, bridesmaid, wedding guest, black tie), zero-results monitoring every week, and merchandising rules that favour full-price, in-stock and high-potential pieces.
- Conversational shopping: an AI stylist that takes occasion, date, budget, comfort, fit and mood, and returns a styled edit across all three brands, with delivery or click and collect guaranteed before the date. The conversation should reflect how Aje team members are trained in stores: warm, personal and expert, asking the same questions a stylist would ask on the shop floor.
- Personal by default: for signed-in customers, the stylist knows her size, past purchases, wishlist and preferred store from the customer data platform.
- AI and human together: one tap from the AI stylist to a real Aje stylist (virtual or in store) for high-value occasions, so online clienteling uses the team customers already rate highly.
- Every conversation feeds the 24/7 programme: an occasion date she shares triggers a timed edit and a styling invitation; what she asks for becomes product, merchandising and keyword insight.
- "What are you dressing for?" An occasion hub by event, dress code, season and location, for customers who prefer to browse.
- Complete the look (and the week) on every product page, styled by the creative team, not just an algorithm.
- See it on someone like her: key pieces shown on a range of models, sizes and skin tones, with imagery localised by market.
- Video and creator content on product pages showing movement and fit.
- Found by AI assistants too: rich product data (occasion, fabric, fit, care) so Aje appears when customers ask AI tools for a wedding guest dress.
Measured by: search conversion and zero-result rate, chat-assisted conversion and order value, styling hand-offs, and occasion dates captured.