Online retail is changing in other profound ways as consumers change their buying patterns and behaviors, with the shift to work-from-home and school-at-home changing the way people live, work, and socialize.
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The retail experience is certainly changing in the face of the global pandemic. A Rip Van Winkle who might have fallen asleep in January 2020 and woken up in September 2020 would find their retail experience to be a surreal experience with shoppers wearing masks, markings on the floor separating folks from one another by six feet, and plexiglass screens by registers in checkout aisles.
The online shopping experience has changed in many ways as well, with some items that had previously been taken for granted such as toilet paper, inflatable pools, and other commodities now being scarce commodities. Online retail is changing in other profound ways as consumers change their buying patterns and behaviors, with the shift to work-from-home and school-at-home changing the way people live, work, and socialize. Retail establishments that had previously counted on big Fourth of July and Labor Day celebrations, back-to-school specials, large social gatherings, and practically the whole travel and hospitality industry have had to throw out their usual sales, marketing, and supply chain practices and rethink their fundamental business strategies.
All this is making the focus on data and machine learning even more essential than ever. Previous process and program approaches have been challenged, resulting in organizations realizing the importance of data and data-driven decision-making. At the recent Data for AI 2020 conference, Khalifeh Al Jadda shared deep insights into how The Home Depot is tackling these existential retail issues and provided in-depth insights into the core of the company’s e-commerce systems. On a follow-up AI Today podcast, he shared insights into the changing data science organization and its increasingly strategic role in retail operations. In this article, he shares further insights into how major retailers like The Home Depot are approaching AI and data science.
What are some of the challenges retail operations face when it comes to AI adoption?
Khalifeh: There are many challenges facing AI adoption in retail. The most important one is building the data science organization with the right talents given the shortage in data science leadership in the job market. Also, the placement of data science is another challenge since retail companies are not technical companies and as such they tend to not have R&D organizations where they can place data science. Sometimes the data science group becomes part of an existing IT org and they try to manage the data science team with the same strategy they use to manage the other IT teams but that is not right. The other challenge they face in adopting AI is the mindset of the business leaders that don’t necessarily believe in automation and machine learning. Many people in retail companies will feel threatened by AI and thus they will push back in any initiative or opportunity which the data science teams may present. Organizations need the ability to create a research and discovery culture which is essential for the success of any data science organization.
How is Home Depot solving challenging E-commerce problems using the power of AI and Data Science?
Khalifeh: Home Depot has a mature data science organization with world-class data scientists that came from top schools and research labs. This organization leverages different aspects of data science to solve challenging problems like search relevancy, query understanding, personalized recommendations, and other applications of data science. One of the areas where [Home Depot] leveraged data science is in automation of collection recommendation. The customer pain point was to find all the products that form a collection when they shop for bathroom renovation or kitchen renovation or patio furniture. The customer can find one product, such as a faucet, which they like and wants to complete the bathroom set with shower head, towel bar, towel ring, and other coordinated items which have the same style, color, color finishing, and brand. At this point the customer has to conduct a separate search for each of the other products to find them in our catalog which is a time consuming and frustrating experience. Our deep learning multi-modal algorithm was designed to automate the process of finding all the products in our catalog that form a collection and provide those as recommendations whenever the customer lands on the product page. This work was published in the ACM RecSys 2019 and we have many other use cases which you can read about in these published research papers. […]
Read more: www.forbes.com
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Online retail is changing in other profound ways as consumers change their buying patterns and behaviors, with the shift to work-from-home and school-at-home changing the way people live, work, and socialize.
Copyright by www.forbes.com
The online shopping experience has changed in many ways as well, with some items that had previously been taken for granted such as toilet paper, inflatable pools, and other commodities now being scarce commodities. Online retail is changing in other profound ways as consumers change their buying patterns and behaviors, with the shift to work-from-home and school-at-home changing the way people live, work, and socialize. Retail establishments that had previously counted on big Fourth of July and Labor Day celebrations, back-to-school specials, large social gatherings, and practically the whole travel and hospitality industry have had to throw out their usual sales, marketing, and supply chain practices and rethink their fundamental business strategies.
All this is making the focus on data and machine learning even more essential than ever. Previous process and program approaches have been challenged, resulting in organizations realizing the importance of data and data-driven decision-making. At the recent Data for AI 2020 conference, Khalifeh Al Jadda shared deep insights into how The Home Depot is tackling these existential retail issues and provided in-depth insights into the core of the company’s e-commerce systems. On a follow-up AI Today podcast, he shared insights into the changing data science organization and its increasingly strategic role in retail operations. In this article, he shares further insights into how major retailers like The Home Depot are approaching AI and data science.
What are some of the challenges retail operations face when it comes to AI adoption?
Khalifeh: There are many challenges facing AI adoption in retail. The most important one is building the data science organization with the right talents given the shortage in data science leadership in the job market. Also, the placement of data science is another challenge since retail companies are not technical companies and as such they tend to not have R&D organizations where they can place data science. Sometimes the data science group becomes part of an existing IT org and they try to manage the data science team with the same strategy they use to manage the other IT teams but that is not right. The other challenge they face in adopting AI is the mindset of the business leaders that don’t necessarily believe in automation and machine learning. Many people in retail companies will feel threatened by AI and thus they will push back in any initiative or opportunity which the data science teams may present. Organizations need the ability to create a research and discovery culture which is essential for the success of any data science organization.
How is Home Depot solving challenging E-commerce problems using the power of AI and Data Science?
Khalifeh: Home Depot has a mature data science organization with world-class data scientists that came from top schools and research labs. This organization leverages different aspects of data science to solve challenging problems like search relevancy, query understanding, personalized recommendations, and other applications of data science. One of the areas where [Home Depot] leveraged data science is in automation of collection recommendation. The customer pain point was to find all the products that form a collection when they shop for bathroom renovation or kitchen renovation or patio furniture. The customer can find one product, such as a faucet, which they like and wants to complete the bathroom set with shower head, towel bar, towel ring, and other coordinated items which have the same style, color, color finishing, and brand. At this point the customer has to conduct a separate search for each of the other products to find them in our catalog which is a time consuming and frustrating experience. Our deep learning multi-modal algorithm was designed to automate the process of finding all the products in our catalog that form a collection and provide those as recommendations whenever the customer lands on the product page. This work was published in the ACM RecSys 2019 and we have many other use cases which you can read about in these published research papers. […]
Read more: www.forbes.com
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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