The National Retail Federation's 2020 Big Show in New York was jam packed full of robots, frictionless store mock-ups, and audacious displays of the latest technology now available to retailers. Dozens of robots, digital signage tools, and more were available for retail representatives to test out, with hundreds of the biggest tech companies in attendance offering a bounty of eye-popping gadgets designed to increase efficiency and bring the wow factor back to brick-and-mortar stores. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic) Here are some of the biggest takeaways from the annual retail event. With the explosion in popularity of Amazon, Alibaba, and other e-commerce sites ready to deliver goods right to your door within days, many analysts and retailers figured the brick-and-mortar stores of the past were on their last legs. But it turns out billions of customers still want the personal, tailored touch of in-store experiences and are not ready to completely abandon physical retail outlets.

Customer segmentation is the technique of diving customers into groups based on their purchase patterns to identify who are the most profitable groups. In segmenting customers, various criteria can also be used depending on the market such as geographic, demographic characteristics or behavior bases. This technique assumes that groups with different features require different approaches to marketing and wants to figure out the groups who can boost their profitability the most. Today, we are going to discuss how to do customer segmentation analysis with the online retail dataset from UCI ML repo. This analysis will be focused on two steps getting the RFM values and making clusters with K-means algorithms.

This is a sponsored post written by Atomic Reach. The opinions expressed in this article are the sponsor's own. Artificial intelligence (AI) is one of the biggest trends in digital marketing today. A decade ago, AI was associated with robots, machinery, and complex sciences. It felt abstract and somewhat alien.

Walgreens Boots Alliance, Inc. and Tata Consultancy Services, a leading global information technology (IT) services, consulting and business solutions organization, announced that the companies have expanded their strategic partnership to advance WBA's transformation of its global IT function by launching a new operating model for IT Run and Operational services, enabling WBA to accelerate its digitalization program to advance innovation for customers, drive organizational efficiencies across the company and further reduce its cost base. "WBA is building a scalable, sustainable and globally unified IT operating model that is focused on digital transformation, automation of service delivery and innovation" The expansion of the WBA and TCS strategic partnership is based on WBA's review of its IT operating model and vendor landscape. As a result, the WBA global IT team will focus on leading and supporting strategic technology projects that create customer value through the development of new digital products and services on its business platforms. Following a period of transition, TCS will provide Run and Operational managed services, including application maintenance and support, infrastructure and security operations. The TCS approach will blend artificial intelligence, machine learning and advanced software engineering to enhance operational resilience and boost productivity.

Artificial Intelligence and Machine Learning enable Retailers to boost their bottom lines, in part by showing when it's not necessary to offer a discount.

The retail sector is the poster child for the use of artificial intelligence. Self-driving delivery robots, automated warehouses, intelligent chatbots, personalized recommendations, and deep supply chain analytics have been making significant impact on the bottom line -- if you're Amazon.com. Other retailers, however, are struggling to adapt. In fact, only 19 percent of large retailers in the U.S., UK, Canada and Europe have deployed AI and are using it in production, according to Gartner. Get the latest insights with our CIO Daily newsletter.

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Amazon Comprehend supports custom classification and enables you to build custom classifiers that are specific to your requirements, without the need for any ML expertise. Previously, custom classification supported multi-class classification, which is used to assign a single label to your documents from a list of mutually exclusive labels. Starting January 6, custom classification also supports multi-label classification. With multi-label classification you can train models and classify your documents with more than one label.

Two-thirds of retailers in France considered the use of AI indispensable for ecommerce, and a further 18.5% said it was very useful, per Octopeek. "Besides customer experience, some AI implementations operate at the back-end of retail operations," said Karin von Abrams, eMarketer principal analyst and author of our latest report, "Western Europe Ecommerce Trends 2020: Consumer-Facing AI, Social Commerce and Delivery/Returns on the Agenda." "For example, fashion store H&M uses AI to analyze purchases and returns at physical stores; these patterns help determine whether a specific store should promote or increase stock of certain items," she said. Stores can also analyze consumers' online search behaviors over time to identify products that regularly attract interest but could be marketed more effectively. Retailers typically see consumer-facing AI as a natural extension of traditional in-store activities.

At a warehouse on the outskirts of Berlin recently, a new addition to the warehouse, a robot, drew press attention. The New York Times called the component-sorting robot "a major advance in artificial intelligence and the ability of machines to perform human labor." A video demo of the robot in action revealed the robot placing various items, with different shapes, in different containers. "As millions of products move through warehouses run by Amazon, Walmart and other retailers, low-wage workers must comb through bin after bin of random stuff--from clothes and shoes to electronic equipment--so that each item can be packaged and sent on its way. Machines had not really been up to the task, until now," said The New York Times.