Solving Challenging E-commerce Problems Using the Power of Data Science
The Retail industry has been disrupted by the e-commerce revolution more than any other industry. Some giant retailers went out of business or filed for bankruptcy as a result of that like Sears and Toys R Us. However, some verticals in the retail industry are still robust and not been disrupted due to the lack of e-commerce solutions that convinced customers to turn their back to the existing physical stores in favor of the online experience. Home improvement is the best example of such vertical where e-commerce has not “yet” disrupted the domain and caused problems to the leading companies which still rely heavily on physical stores. That being said, home improvement retailers recognized the risk of not investing in building a robust online business that supports their physical stores in a seamless experience so most of the leading retailers in this hundred-billion-dollar industry started building their in-house solutions for all the challenging problems to give their shoppers a seamless experience when they shop online. Search and Recommender systems play a crucial role in this industry like any other online retailer. Therefore, it is very important to invest in building personalized, scalable, and reliable systems that proactively help shoppers discover products that engage them and match their intent and interest while on the website then re-engage them with products and content that align with their interest after they leave the website via email or social media. As a Director of Core Data Science at TheHome Depot which is the largest home improvement retailer in the world, I deal with the challenges of building such systems utilizing the cutting-edge technologies in AI, machine learning, and data science. In this talk, I would like to discuss and highlight how we have leveraged different aspects of AI to solve challenging e-commerce problems for HomeDepot
Session ID: LI1614 Presentation Type: Live Session (Replay Available)
Date / Time: [Day 1] Mon. Sep. 14, 2020 @ 16:15 ET (US)
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