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00:35 — Behind seemingly effortless shopping experiences, such as with Amazon, very intentional AI and machine learning models are built to personalize those retail experiences.
01:15 — As fashion industry trends can be so fickle and constantly changing, there is a physical cost of the unused inventory. The supply chain is costing retailers about $50 billion, leading to over 16 million tons of textile waste.
02:00 — This is due to difficulties in scaling and identifying consumer characterizations. This is where AI comes in to drive personalized items, geographically-specific and tailored to cultures, tastes, and more.
02:20 — There’s industry-specific content that’s feeding this niche versus data layers, which is compromised of three components.
02:35 — First is the market data. This includes the product, landscape, retail behavior, and point of sale that gives retailers insights into preferences on brands, cultures, and regions. Also within this layer are third-party data and private data.
03:35 — The second is knowledge. This involves industry-specific terminology being translated through natural language processing (NLP) which can fuel the insights for end-users. It also creates a sense of familiarity and can build a relationship between the customer and the company.
04:35 — The third is industry intelligence. A multitude of AI models is used to dig into massive volumes of data and convert that into decision-making insights.
05:02 — These models can produce Explainable AI which essentially provides an explanation for the results.
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