Anticipatory shipping—retail’s crystal ball?

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Can products be on their way to you even before you think of ordering them? Can a company accurately anticipate your order and ship before you even place it? Amazon and other online retailers are investing in machine learning to more accurately forecast consumer demand and reduce fulfillment and shipping costs

How Alibaba leverage machine learning to disrupt retail and create pathway to reimagine online-to-offline shopping experiences

Alibaba has leveraged Artificial Intelligence (AI) to disrupt China Retail Industry for the past 19 years. Nevertheless, 81% of retail consumption in China still comes from the offline channel [1]. Realizing the importance of offline presence, the giant online e-commerce shifts its gear towards Omnichannel strategy. In 2017, Alibaba opened futuristic groceries stores, Hema, offering 30-minute deliveries and facial-recognition payment. In the same year, T-mall pop-up stores are introduced in collaboration with over 100 domestic and international brands, promoting inventive "Retailtainment” shopping experiences [2]. While offline stores around the world are suffering, the giant e-commerce leveraged AI and made a brave move to enter the physical world in an innovative means. Or this will be another significant retail disruption, reinventing offline shopping experiences…

Flipkart: Using Machine Learning to solve unique problems in Indian E-commerce

Home addresses in India pose a uniquely Indian problem- lack of standardization. This poses a challenge to e-commerce players whose success relies on efficiencies in last-mile logistics. This post talks about how Flipkart, an Indian e-commerce major is using Machine Learning(ML) to make sense of complex Indian addresses to iron out associated inefficiencies. In addition, we also look at other key areas of ML application for e-commerce companies.