{"id":3295,"date":"2024-05-10T16:02:50","date_gmt":"2024-05-10T23:02:50","guid":{"rendered":"https:\/\/www.scamper.com\/cv\/?page_id=3295"},"modified":"2025-09-20T11:07:19","modified_gmt":"2025-09-20T18:07:19","slug":"ai-shopping","status":"publish","type":"page","link":"https:\/\/www.scamper.com\/cv\/ai-shopping\/","title":{"rendered":"AI-based Shopping Assistant"},"content":{"rendered":"
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AI-based Shopping Assistant for Online Stores<\/span><\/p>\n

Utility design, UX\/UI <\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Role<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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UX\/UI Designer<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Team<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Stealth startup team<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Duration<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Feb-May 2024<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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As AI-enhanced consumer-facing assistants rose in popularity, I was tasked to visualize a low-key interface to provide guidance for the shopper who wasn’t sure what they were looking for. The goal was to present the assistance in a familiar chat-based format to make the customer feel comfortable as they were guided toward their goal.<\/span><\/p>\n

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The chatbot would be tied to the SKUs of each particular store, to ensure that suggestions presented to the customer reflected inventory that best met the needs of customer and store alike.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Starting a search query with the AI assistant<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The customer could use natural language during their search, as they might with in-person staff. And the AI assistant could field a broad variety of high-ambiguity questions, and not just questions about specific store items. This situational or contextual awareness could be achieved through product metadata tags and more broadly through training datasets, but the results would always be tied to specific local inventory.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The customer shares their preference with the AI assistant<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The assistant could be responsive to the user’s preferences over time, encouraging the user toward the product that best fit their needs with a minimum of friction, and keeping a conversational, inviting tone.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The AI assistant offers options that best meet the customer’s need<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Using a conversational approach, the user could be guided to a specific product, even if they began with little information about what they were looking for. The same AI assistant could highlight high-value sales items, or draw on a personalized profile to best meet the needs of the returning customer.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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The customer is delivered to the product page<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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