Building a personal shopper for American Tourister Qatar

For American Tourister's shoppers, its personalized and guided shopping experiences played a key role in fostering their relationship with the brand. The brand's store associates are trained to understand every traveller's story and personal needs and help them make purchases based on that.

For American Tourister Qatar, the gap was clear: customers had real, specific needs (a stylish backpack, a cabin-friendly trolley, a gift for someone) but no easy way to communicate those needs on the website.

Without guidance, discovery felt flat and impersonal, the opposite of what the brand stands for.

They needed a shopping assistant that could understand a customer's style, purpose, and preferences, then recommend the right product with confidence.

American Tourister Qatar deployed Fynd Kaily as a personalized shopping assistant embedded in its website. The agent opens with a simple opening message, a promise to help customers shop for bags "Crafted For Your Own Journey".

• Understanding travel purpose, style preference, and occasion.
• Recommending products tailored to the customer's specific context.
• Surfacing detailed product information in natural language.
• Showing similar options and alternatives within the same conversation.
• Maintaining a friendly, brand-aligned tone throughout.

The result is a shopping experience that feels personalized for genuine, one-on-one interactions, even at scale.

When a customer described themselves as a style-conscious backpack user, Fynd Kaily did not just return a filtered list. It asked about their preferred aesthetic, then surfaced relevant options that were both functional and stylish with full product details, pricing, and next steps, all within the same conversation.

• 91% of the messages were about shoppers seeking detailed product information and guidance.
• 9% of the chats sought general support help.

Within its core use case, product discovery, Fynd Kaily reported 100% favourable customer sentiment and an average of 6.5 messages per thread showing deep, meaningful engagement.