You can save up to $8.63 off orders over $50 at Amazon.com today. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. Our picks and opinions are independent from any business incentives. If you've been waiting to stock up on a few things on Amazon, today is the best day to save some extra money. Amazon is having a massive site-wide sale, taking $8.63 off of (almost) any order over $50 with the code BIGTHANKS. Amazon set a new high score in the Harris Corporate Reputation Poll, with an 86.27.

Lowe's Home Improvement recently announced layoffs as a part of an effort to "overhaul its staffing model." The cuts, which will constitute less than one percent of the total staff, come in the context of a reshuffling of responsibilities, which a source told the Charlotte Observer is meant to provide an increase in "face time with customers." Some of the additional changes include reassigning job responsibilities and the elimination of the department manager title, a later Observer article reported. But Lowe's is also eyeing expansion. Lowe's plans to open 15 to 20 stores annually over the next three years, which the chain said will create 4,000 new store-level jobs. And despite the cutbacks, Lowe's continues to add staff as necessary to meet seasonal demands.

Leveraging data from disparate sources to provide more personalized promotions to consumers can seem like a fool's errand. The efforts, however, can be rewarded with higher key performance indicators (e.g., click throughs, conversions, average order values, loyalty). As part of our, "4 Companies You Didn't Know Worked in Artificial Intelligence" feature, Website Magazine caught up with Rubikloud CEO Kerry Liu to learn more about how his company helps retailers gather data from consumer behaviors and use it in meaningful ways. What are some common problems of retail promotions? Retailers tend to struggle with gaining a holistic view of their customers due to outdated legacy solutions.

The primary application of artificial intelligence in retail is customer service chatbots, intelligent search tools, and personalisation. Despite knowing where to put their money, only a handful of retailers in the UK have trialled AI due to it being prohibitively expensive. Most AI investment in 2017 will be targeted towards e-commerce -- though Amazon is something of an exception. Amazon intends to use AI to replace in store cashiers in its Amazon Go stores to detect products shoppers have picked up. Over the next five years a growing number of retailers will buy into AI as it becomes more affordable. Here's where some big players are putting their money: Most retailers use algorithms to suggest similar items or items bought by other customers already, but in 2016 John Lewis was one of the first retailers in the UK to implement an artificially intelligent visual search tool for its iPad app.

Prior to that, Siri made adding an event to your calendar or quickly looking up the weather as easy as a simple push of a button on your smart phone. At what point does "Siri, tell me the weather" or "Alexa, play music" become "I'll have a cheeseburger and fries" or "What aisle can I find the peanut butter on?" or "Does this shirt come in small"? As we move away from assistants who perform simple automated tasks, 2017 has the potential to turn voice assistant technology into a virtual customer service functionality. The convergence of chatbots and virtual voice assistants has the potential to completely change the retail experience. For years, we have heard rumors about quick service retail (or QSR) replacing humans with touch screen tablets.

Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Once your models are ready, Amazon Machine Learning makes it easy to obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure. Amazon Machine Learning is based on the same proven, highly scalable, ML technology used for years by Amazon's internal data scientist community. The service uses powerful algorithms to create ML models by finding patterns in your existing data.

January has been a month characterized by price cuts but low prices are not the only factor eating into profits. At a time when competition is increasing, not only from the discounters, but from new players with optimized supply chains and leading distribution networks, meeting the demands of customer experience has become far harder. Limited product availability, a longstanding challenge for retailers across the globe, has led to customers across the UK becoming dissatisfied with their grocery shopping. A bad customer experience ultimately eats sales figures and margins. Worse still, the vast majority of retailers are unaware of the scale of this problem.

The North Face, Macy's and 1-800 Flowers are some of the brands collaborating with the supercomputer to glean insights from the increasing mounds of data they collect from customers. Its name, "Gwyn," stands for "gifts when you need." Gwyn is not a chatbot, though, said vp of marketing and customer experience Jon Mandell. "No two conversations are the same." Here's what 1-800 Flowers has learned, nine months in.

Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.

On the flight back to San Francisco, I could not help wondering, will retail ever be the same? The National Retail Federation is the cream of the crop of retail events and New York never seems to disappoint. A massive and immersive experience, NRF seems to find the golden mean between human interaction, thought leadership and the ultimate display of retail tech booths, including a great mix between both big and small exhibitors. To say that #nrf17 is a global event would be an understatement, as retail professionals from over 90 countries attend. Our team at Star Cloud noticed a lot of innovation in tech this year (emphasis on data & tech) that's shifting the retail industry quickly into the 21st century.