In this paper, we study the assortment optimization problem faced by many online retailers such as Amazon. We develop a \emph{cascade multinomial logit model}, based on the classic multinomial logit model, to capture the consumers' purchasing behavior across multiple stages. Different from existing studies, our model allows for repeated exposures of a product, i.e., the same product can be displayed multiple times across different stages. In addition, each consumer has a \emph{patience budget} that is sampled from a known distribution and each product is associated with a \emph{patience cost}, which captures the cognitive efforts spent on browsing that product. Given an assortment of products, a consumer sequentially browses them stage by stage. After browsing all products in one stage, if the utility of a product exceeds the utility of the outside option, the consumer proceeds to purchase the product and leave the platform. Otherwise, if the patience cost of all products browsed up to that point is no larger than her patience budget, she continues to view the next stage. We propose an approximation solution to this problem.

When we got our beautiful puppy, Addy, I warned my boyfriend that golden retrievers shed--a lot. Despite my warnings, he didn't fully comprehend just how much hair one dog could produce. At first, he just kept saying it must be a seasonal shed, but after three months of daily Swiffering, he finally accepted that our apartment and clothing would simply be covered in dog hair forever. A few weeks later, it was Black Friday, and while I was browsing the daily deals on Amazon, I saw that the iRobot Roomba i7 was on sale for $700--that's $300 less than its list price. Even though I haven't been that impressed by robot vacuums in the past, I decided it would be a nice Christmas gift for our pet hair–infested household, and I made an impulse purchase. It's the end of January now, and both my boyfriend and I agree that Ruby, our darling Roomba, was an incredible purchase--seriously, the best $700 I've ever spent!

Artificial intelligence at its most basic level provides steroids for retail data. Suppose a regional fashion retailer with an eCommerce presence has 100,000 customers in its data base. And suppose those records are fairly basic: most recent transactions, average purchase per visit, demographic information and website traffic history. Now suppose those records are supplemented by a third-party anonymized database. AI will take that data and match it to an algorithm that then allows all the data to be supplemented by data within the category or for competitive purchases.

TL;DR: The Echo Dot (3rd Gen) smart speaker is on sale for £29.99 on Amazon, saving you 40% on list price. This isn't a typical time for deals on Amazon devices, but the online retail giant has thrown a curveball by putting its entire range on sale. This sort of thing usually only happens on Prime Day and Black Friday, but we've been treated to an early taste of what's to come with discounted e-readers, smart speakers, video doorbells, tablets, and more. One of the standout deals from this selection is the discounted Echo Dot. This popular smart speaker is down to just £29.99 on Amazon, saving you 40% on list price.

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

He has extensive experience in the retail & marketing & e-commerce industries with skills that span customer-based strategies & digital transformation. Milton has had a long & successful career spanning a diverse portfolio of industries, including publishing & retail. He was formerly Chief Digital Officer of Retailwinds where he led digital media & e-commerce. Prior to Retailwinds, he was Chief Marketing Officer of Hudson's Bay Company where he oversaw the strategic direction & performance of all marketing efforts for the Company's North American retail banners & led the Marketing Center of Excellence (COE). He joined HBC as the Senior Vice President of Digital Marketing in September 2016.

New research using AI to match patent language with work shows that AI may reinvent high-skill work sooner than you think. When Amazon first arrived on the scene, books were its early focus for product selection, buyer insights, distribution and pricing. In those early days, retailers typically lamented the plight of bookstores, but relaxed in their conviction that products like clothing, food, jewelry and fashion would be safe. "Obviously," consumers would always want to experience such products by physically shopping, touching, smelling and trying them on. Now, we can see how the disruptive effects of Amazon-like technology transformed virtually every market, more quickly and differently than most anticipated.

Sustainability is at the forefront of all aspects of the supply chain in retail. From product development and production to shipping and returns, every aspect of the retail industry is working to become more sustainable. Increasing these efforts is challenging, as many retailers frequently introduce new products to meet the demands of the constantly evolving consumer. On average, up to 20% of clothing goes unsold and is often incinerated. Additionally, returned goods create more than 5 billion pounds of waste in U.S. landfills each year.

Micro-fulfillment that helps retailers meet service level agreements. Thinking back to a time when e-commerce was in its infancy and companies were setting up separate warehouses dedicated to this new sales channel, it was clear that harnessing the e-commerce monster wasn't going to be easy. Fast-forward to 2020 and retailers are still trying to figure out how to do it efficiently, effectively and without losing their shirts in the process. One viable option that more of them are leaning toward is micro-fulfillment, or the placement of smaller warehouses closer to the end consumer. "For most retailers, the operational driver right now is: 'how do I respond to the same-day, couple-of-hours service level agreements (SLAs) within a specific metro area?" Khodl explains.

Consumers want shopping experiences that feel personal to them, whether they are shopping online or in-store. Retailers are in a race to use technology to match consumer expectations and win more business. With new advancements in technology, retail will continue its state of constant disruption. Most consumers dread having to call a customer service phone number. Instead, 55% of Americans -- and 65% of millennials -- want chatbots involved in the customer service process, especially when it makes the customer service process more efficient.