Each week I read a number of customer service and customer experience articles from various resources. Here are my top five picks from last week. I have added my comment about each article and would like to hear what you think too. My Comment: So many customer experience strategies (CX) seem to focused on B2C. I've been a strong advocate for B2B companies to understand their customers' expectations are being influenced by consumer behavior (mostly from retailers who deliver an outstanding experience).

The idea that automation is going to be a key driver of transformation in the retail industry in 2019 isn't a prediction, it's a certainty. From FedEx automating its sorting and distribution facilities, to Amazon Go putting pressure on retailers to reinvent the shopping experience by, as the New York Times reported, "testing robots that help keep shelves stocked, apps that let shoppers ring up items with a smartphone, and high-tech systems [that] completely automate the checkout process," automation will be a fact of life that retailers simply can't afford to ignore any longer. Particularly when it comes to customer service. Here are three ways AI and bots will fundamentally transform retail in the next year. In 2019, rapid adoption of messaging-based bots will be the number one trend in customer service -- in retail and across industries.

Retail giant Carrefour has announced that it has become the first in France to implement artificial intelligence to optimise supply chain management, and reduce food waste. The project is a part of the'Carrefour 2022' transformation plan to meet customer expectations through the introduction of advanced technologies. Carrefour will integrate a software developed by advanced analytics leader SAS into its supply chain. The software, SAS Viya, will collect and process data from stores, warehouses, and e-commerce sites. Analysis of the data will help the retailer to understand the nature of demand in various outlets, and accordingly arrange for the supply of products – thereby reducing stock outage and overstocking.

Maria Johnsen holds a degree in political economy from Kharkov University in Ukraine, Beauty Arts from Sorbonne University in Paris, BA in Information technology,BA in computer science and a Master of Science degree in computer engineering from university of science and technology in Norway and master degree in filmmaking and television from Royal Holloway University of London. Her professional background and education is diverse and includes skills in areas such as sales, multilingual digital marketing, content writing, business intelligence, software design and development. In addition, she possesses the experience and education in the management of complex Information Systems. Maria knows eighteen languages and possesses experience in language instruction, tutoring, and translation. She has also developed a unique teaching method for fast learning "Implications for Upgrading Accelerated Learning Practices In Educational Systems" This method is applied in China and Norway.

Many 0/1 datasets have a very large number of variables; on the other hand, they are sparse and the dependency structure of the variables is simpler than the number of variables would suggest. Defining the effective dimensionality of such a dataset is a nontrivial problem. We consider the problem of defining a robust measure of dimension for 0/1 datasets, and show that the basic idea of fractal dimension can be adapted for binary data. However, as such the fractal dimension is difficult to interpret. Hence we introduce the concept of normalized fractal dimension. For a dataset $D$, its normalized fractal dimension is the number of columns in a dataset $D'$ with independent columns and having the same (unnormalized) fractal dimension as $D$. The normalized fractal dimension measures the degree of dependency structure of the data. We study the properties of the normalized fractal dimension and discuss its computation. We give empirical results on the normalized fractal dimension, comparing it against baseline measures such as PCA. We also study the relationship of the dimension of the whole dataset and the dimensions of subgroups formed by clustering. The results indicate interesting differences between and within datasets.

Throughout the history of ecommerce, machine learning has helped online retailers often know what their customers want before they want it. In fact, one article estimated that 35% of the revenue of one of the world's largest ecommerce platforms came from their product recommendations.1 That's a staggering statistic when you consider that these were purchases that people made, without intentionally setting out to! It's the digital equivalent to making that "impulse buy" when you're checking out at your local grocery store, except on a much larger scale. The reason behind shoppers' willingness to buy recommended products is personalization. By using machine learning to analyze the buying patterns of millions of users, common themes tend to show up that group people who often buy the same products.

Walmart uses 100s of bots to automate back-office processes. Western Digital reduces CapEx by using artificial intelligence to optimise test equipment. Bank of America and Harvard University's Kennedy School collaborate on responsible AI development. At Pearson artificial intelligence is at the heart of the latest product innovations. April 11th 2018, Catherine Bessant, chief operations and technology officer at Bank of America, discusses the bank's partnership with Harvard Kennedy School to establish the Council on the Responsible Use of Artificial Intelligence and how Bank of America uses AI.

The first Chinese artificial intelligence (AI) powered robotic shopping guide showed up in a Beijing supermarket on November 30, to provide inquiry service, shopping guidance and goods recommendations to customers. Gain limited and restricted access to China Money Network. Enjoy comprehensive and exclusive data you can't find elsewhere!

Artificial Intelligence (AI) may look like something out of the pages of a sci-fi book, yet you'd be surprised how often you use it daily. As the technology continues to improve, AI will become even more common with more widespread utilization among diverse industries. To start with, let's begin with the basic definition of Artificial Intelligence (AI) and what it includes. Seeking Alpha gives a very apt description of the same in their article- At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence. Applied AI: Machines designed to complete very specifics tasks like navigating a vehicle, trading stocks, or playing chess – as IBM's Deep Blue demonstrated in 1996 when it defeated chess grand master Gerry Kasparov. General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of General AI requires machines to "learn" as they encounter new tasks or ...