We caught up with Dr. Mark Chrystal, Chief Analytics Officer at rue21 to understand more about how he perceives the role of analytics in retail today, his upcoming talk at NRF's Big Show and the future of retail. MANTHAN: In your role as the Chief Analytics Officer, what would you say is the biggest challenge facing rue21 in 2019? MARK: The biggest challenge I face is the ability to explain what is happening in the industry and more importantly, with our current, lapsed and potential customers. My job is to help the business navigate the environment and provide insights that help chart a course to success. This is particularly challenging in the current retail environment and for a company that is in the midst of a turnaround.

Have you heard of artificial intelligence? Experts from different areas get excited about the possible applications of AI. On the other hand, most marketers don't really understand how to use AI solutions in practice. AI has its specifics, it requires proper investment and resources. At the same time, some simple solutions are nevertheless effective and capable of improving your marketing campaigns dramatically. According to eMarketer statistics, more than 80% of retailers think that email is the most effective way to get new customers and to keep existing ones.

Amulya Bhuyan, 37 years old, lives in Dhowachala, in the northeastern state of Assam, and has few ways to buy new things. It takes hours to get to the nearest small town from the village of 1,000 people. Mr. Bhuyan, a teacher, made his first purchase on Amazon in 2016. After a recent delivery of a pair of jeans, he showed off other acquisitions: the shoes, socks, pants and shirt he was wearing; in his house, the curtains, glasses, flowery decals decorating the wall, a peacock clock and a painting of seven white horses running in the moonlight. "Before I didn't even know where to buy these things, and now they arrive on my doorstep," he said.

Too often today, retail stores are caught between two damaging extremes that can reduce revenue or poison customer experience: out-of-stock situations and waste. Without better customer insights and data, and with so little room for error, either of these extremes can spell an end to profitability for a retailer. But before retailers can avoid these extremes, however, they must deal with the increasing complexity caused by shifting customer demand. Customers now come to expect a personalised experience whenever and wherever they buy, and that expectation can't be satisfied by the traditional statistical methods retailers have applied to projecting demand and setting pricing. The retailers who've adopted those traditional methods have benefited from improved forecasts at aggregate levels, the limits of those "tried and true" statistical methods mean that retailers still have difficulty making day-to-day customer demand predictions.

I purchased two introductory e-books on Machine Learning. The $20 book had 399 pages but I got so completely lost before hitting page 100 that I gave up in defeat. Then I found this $3 book – Machine Learning for Absolute Beginners – was not only short but it came out as the clear winner because I was able to get through right to the end of it with ease and without confusion. So turns out I got more than my money's worth here with this book! And on top of that, this book was clear, well-written and a definite confidence builder.

I certainly can't speak about all books on the market. However, since the first edition was released, I engaged in countless discussions with my readers, to help them with particular questions and to get their opinion on the parts they found unclear or topics they wish I had covered. The connection between theory and praxis in particular was what readers found most helpful and somewhat lacking from other introductory texts (which, I heard, were either too theoretical or too practical). This constructive feedback has been invaluable for the second edition, helping me to focus on those parts that were still left unclear. In a nutshell, the second edition of Python Machine Learning provides a healthy mix of theory and practical examples that most people found so helpful in the first edition, and the second edition adds on top of it with many refinements and additional topics based on the large corpus of invaluable reader feedback.

Many shoppers seem to want technologies including augmented and virtual reality to be provided by the stores from which they shop, and retailers may have gotten the message. Although such technologies are not being used by most retailers, they are on the agenda for the not-too-distant future, according to a new study by BPR. Nearly half (48%) of shoppers would be more likely to shop at a retailer that utilizes augmented reality, though only a few retailers, such as Ikea, Wayfair and Overstock.com, Nearly a third (32%) of retailers plan to implement augmented reality within the next three years, 14% of them within the next 12 months, according to the online survey of executives at North American retailers, 46% of which had sales of $500 million to $5 billion. Retailers are also moving to adopt virtual reality, with 4% already having implemented some form of it and 32% planning to do so within the next three years.

British supermarkets are to start selling food farmed in the UK by robots for the first time in a project led by Waitrose, the Telegraph has learned. The supermarket will use autonomous farming robots to analyse, plant and protect crops from weeds at a farm near Stockbridge, Hampshire. In a three-year trial, the robots - known as Tom, Dick and Harry - will start cultivating fields used to grow wheat for bread and flour sold in Waitrose stores. The robots, developed by Shropshire-based start-up the Small Robot Company, uses artificial intelligence to scan thousands of pictures of a specific field. The images allow them to spot weeds and plant seeds in the best location.

I have been wanting to start cross-selling at my family's webshop Seniorshoppen.dk for a while. The problem is that we sell 500 different products, so I have been pushing it off as it would take a lot of time to manually select possible cross-sells for each product we sell. Therefore, I have been thinking a lot about how to automate this best using machine learning or other techniques, I just haven't got around to doing it. For Black Friday we decided to require our customers to buy a minimum of two products to get a 25% discount. So, it was a natural time to actually execute on this to "help" our customers choose a second product for their Black Friday shopping spree. This is how I did it.

In this book, we focus on applied machine learning for text analysis using the Python libraries just described. The applied nature of the book means that we focus not on the academic nature of linguistics or statistical models, but instead on how to be effective at deploying models trained on text inside of a software application. The model for text analysis we propose is directly related to the machine learning workflow--a search process to find a model composed of features, an algorithm, and hyperparameters that best operates on training data to produce estimations on unknown data. This workflow starts with the construction and management of a training dataset, called a corpus in text analysis. We will then explore feature extraction and preprocessing methodologies to compose text as numeric data that machine learning can understand. With some basic features in hand, we explore techniques for classification and clustering on text, concluding the first few chapters of the book.