Customer engagement and network connectivity are becoming more and more critical to the success of today's retailers. And with the soaring numbers of mobile and IoT devices, managing a distributed network of stores and engaging with customers is becoming more challenging. Join this session to hear how organizations such as top retailer, Petsmart, are investing in AI-driven technology to transform IT operations rather than being stuck in a technology refresh cycle, ultimately driving up profitability and in-store experience.

Walmart is embracing a robot takeover in order to compete with Amazon. The Arkansas-based firm is using robots to fulfill grocery orders in one of its Supercenters and is set to add shelf-scanning machines to 650 additional stores by the end of the summer. The shift is aimed at reducing costs, improving store performance and gaining credibility in its battle against to reign supreme as the king of retail. Walmart announced it would soon incorporate automated robotic carts, called Alphabots, in one of its superstores in Salem, New Hampshire in 2018. Walmart is using robots to fulfill grocery orders in one of its Supercenters and is set to add shelf-scanning machines (pictured) to 650 additional stores by the end of the summer.

Customers post online reviews at any time. With the timestamp of online reviews, they can be regarded as a flow of information. With this characteristic, designers can capture the changes in customer feedback to help set up product improvement strategies. Here we propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Then, inspired by the Kano model which classifies preferences of product attributes in five categories, conjoint analysis is used to quantitatively categorize the structured affordances. Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products. A case study based on the online reviews of Kindle e-readers downloaded from amazon.com shows that designers can use our proposed approach to evaluate their product improvement strategies for previous products and develop new product improvement strategies for future products.

"Anyone who wants to discuss how artificial intelligence is shaping the world should read this book. Tegmark, a physicist by training, takes a scientific approach. He doesn't spend a lot of time saying we should do this or that, and as a result, Life 3.0 offers a terrific baseline of knowledge on the subject." Tegmark successfully gives clarity to the many faces of AI, creating a highly readable book that complements The Second Machine Age's economic perspective on the near-term implications of recent accomplishments in AI and the more detailed analysis of how we might get from where we are today to AGI and even the superhuman AI in Superintelligence. . . . At one point, Tegmark quotes Emerson: 'Life is a journey, not a destination.'

January 9, 2020 – Check out how two Washington University researchers' debate resulted in winning the Olin Award which "recognizes scholarly research that has timely, practical applications for complex business management problems". Dennis Zhang and Jake Feldman's research was focused on machine learning and customer choice modeling (respectively) to create a better algorithm for online shopping choices. In the end, they combined their approaches into a new mathematical model for presenting product choices to customers which resulted in 28% higher revenue per visit providing $22M marginal increase in a week's time for Chinese online retail giant Alibaba. Guess what, Alibaba adopted the new algorithm! Congratulations to Zhang and Feldman on your Olin Award and job well done!

The retail industry is undergoing seismic shifts stoked by swift and unprecedented technology innovation. This change presents both a challenge and an opportunity for retailers faced with adapting to a shopping landscape being upended by a $2.3 trillion global ecommerce market, and the rise of savvy, ever connected consumers with the world's biggest virtual mall at their fingertips. For retailers and brands navigating this new world, digital intelligence and innovation--be it an artificial intelligence powered chatbot that fields customer service requests or a hyper-local store assortment informed by the Internet of Things--are critical to driving sales and margins in today's on-demand consumer economy. Digital technology is the connective tissue adding ease, convenience, customization, and automation to every aspect of a retail organization--from its business processes, operations, and how employees work, to the customer experiences provided and the very products and services offered. In this paper, we explore the opportunities birthed by digital transformation, and how Microsoft can help retailers reimagine their businesses and survive and thrive in today's competitive environment.

Businesses that rely on storage and warehousing are benefitting a lot since IoT happened. It can aid in effectively tracking and managing inventory as it gives you automatically-controlled options. You simply have to install IoT software and devices in your storage units and warehouses. They will assist you in managing inventory changes. In retail, businesses also link AI with RFID and cloud technology to track inventory.

The future where people can delegate mundane tasks to a machine is not far from happening. From starting the laundry down to cooking dinner after a long day is about to be over. Artificial Intelligence has really helped shape our internet today. After all, we can already communicate with virtual assistants like Apple's Siri and Amazon's Alexa for small things around the house, like calling Uber or ordering a pizza. Things that we only see on sci-fi movies may be closer than you think.

Connecting consumers with relevant products is a very important problem in both online and offline commerce. In physical retail, product placement is an effective way to connect consumers with products. However, selecting product locations within a store can be a tedious process. Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world. To address these challenges, we propose a stochastic model of spatial demand in physical retail. We show that the proposed model is more predictive of demand than existing baselines. We also perform a preliminary study into different automation techniques and show that an optimal product allocation policy can be learned through Deep Q-Learning.