Data Science has become one of the most powerful technologies in the retail sector by providing fact-based and data-driven insights. Data Science technologies help retailers in enhancing their marketing strategies, operations, and financial performance. Retailers today are searching for ways to derive more customer intelligence and operational insights from their overflowing databases which are currently fulfilled by Data Science technologies. Data science plays a vital role in almost all sectors of retail such as assortment, recommendation, Logistics and Supply Chain Management, Demand Forecasting, Price Optimization for products, Predictive Maintenance, Churn prediction, and Data-Driven Product Management. Other products that are bought together with the required products by the customers lead to increase in sales.

In e-commerce, a user tends to search for the desired product by issuing a query to the search engine and examining the retrieved results. If the search engine was successful in correctly understanding the user's query, it will return results that correspond to the products whose attributes match the terms in the query that are representative of the query's product intent. However, the search engine may fail to retrieve results that satisfy the query's product intent and thus degrading user experience due to different issues in query processing: (i) when multiple terms are present in a query it may fail to determine the relevant terms that are representative of the query's product intent, and (ii) it may suffer from vocabulary gap between the terms in the query and the product's description, i.e., terms used in the query are semantically similar but different from the terms in the product description. Hence, identifying the terms that describe the query's product intent and predicting additional terms that describe the query's product intent better than the existing query terms to the search engine is an essential task in e-commerce search. In this paper, we leverage the historical query reformulation logs of a major e-commerce retailer to develop distant-supervised approaches to solve both these problems. Our approaches exploit the fact that the significance of a term is dependent upon the context (other terms in the neighborhood) in which it is used in order to learn the importance of the term towards the query's product intent. We show that identifying and emphasizing the terms that define the query's product intent leads to a 3% improvement in ranking. Moreover, for the tasks of identifying the important terms in a query and for predicting the additional terms that represent product intent, experiments illustrate that our approaches outperform the non-contextual baselines.

Amazon have come under criticism for new packaging that cannot be recycled. The California-based company has angered environmentalists for three items used to mail purchases: an air pillow, bubble-lined plastic bag and standard plastic bag. All three are deemed single-use only, which means they can't be refashioned for another purpose - and, crucially, will not degrade naturally. This is despite growing pressure from politicians, such as former Prime Minister Theresa May, plus a host of multi-million pound retailers who've dumped wasteful packaging. In a statement, Amazon said: 'We value our customers' feedback about our packaging, both the positive comments and the negative, as in this instance.

"Artificial intelligence is among the most consequential issues facing humanity, yet much of today's commentary has been less than intelligent: awe-struck, credulous, apocalyptic, uncomprehending. Gary Marcus and Ernest Davis, experts in human and machine intelligence, lucidly explain what today's AI can and cannot do, and point the way to systems that are less A and more I." --Steven Pinker, Johnstone Professor of Psychology, Harvard University, and the author of How the Mind Works and The Stuff of Thought "Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough. No matter how smart and useful our intelligent machines are today, they don't know what really matters. Rebooting AI dares to imagine machine minds that goes far beyond the closed systems of games and movie recommendations to become real partners in every aspect of our lives." Every CEO should read it, and everyone else at the company, too.

The SMART Forecasting team at Walmart Labs is tasked with providing demand forecasts for over 70 million store-item combinations every week! For example, just how much of every type of ginger needs to go to every Walmart store in the U.S., every week for the next 52 weeks, with the goal of improving in stocks and reducing food waste. Our algorithm strategy was to build a suite of machine learning models and deploy them at scale to generate bespoke solutions for (oh so many!) store-item-week combinations. Random Forests would be part of this suite. We went through the traditional model development workflow of data discovery, identifying demand drivers, feature engineering, training, cross validation and testing.

More than a quarter (29%) of respondents said they use artificial intelligence (AI) to help streamline customer experiences, per Isobar. An additional 46% anticipate using AI in the future. Similarly, 36% of respondents said they're currently using machine learning, with an additional 37% who anticipate using it in the future. To better understand how these technologies are being leveraged, we spoke with Mario Ciabarra, founder and CEO of digital intelligence platform Quantum Metric. What are some common flaws in the ecommerce customer experience?

Hierarchical clustering has been shown to be valuable in many scenarios, e.g. catalogues, biology research, image processing, and so on. Despite its usefulness to many situations, there is no agreed methodology on how to properly evaluate the hierarchies produced from different techniques, particularly in the case where ground-truth labels are unavailable. This motivates us to propose a framework for assessing the quality of hierarchical clustering allocations which covers the case of no ground-truth information. Such a quality measurement is useful, for example, to assess the hierarchical structures used by online retailer websites to display their product catalogues. Differently to all the previous measures and metrics, our framework tackles the evaluation from a decision theoretic perspective. We model the process as a bot searching stochastically for items in the hierarchy and establish a measure representing the degree to which the hierarchy supports this search. We employ the concept of Partially Observable Markov Decision Processes (POMDP) to model the uncertainty, the decision making, and the cognitive return for searchers in such a scenario. In this paper, we fully discuss the modeling details and demonstrate its application on some datasets.

Woolworths has given an Australian audience the first major look at Olive, a conversational artificial intelligence platform that its digital arm WooliesX has been developing since late last year. WooliesX's chief digital technology officer Nick Eshkenazi told the SAP e'ffect conference in Sydney that Olive - in an initial chatbot form - has been live since November last year. Olive currently appears as a chatbot on the Woolworths "Contact Us" page. It is launched when users click "Live Chat" under the Woolworths Online section. Eshkenazi said that "Olive does some impressive things".

Recent AI adopters include Sam's Club Inc., the warehouse retailer owned by Walmart Inc., and Giant Eagle Inc., a regional chain of grocery and convenience stores. Giant Eagle said last month that it would test a technology similar to Amazon Go's at a convenience store in Pittsburgh, where it is based. Several companies that sell cashierless technology--including Standard Cognition Inc. and Vcognition Technologies Inc., which does business as Zippin--said they are working with U.S. customers but declined to give details. Sam's Club plans to offer AI-powered cashierless shopping later this month at a 32,000-square-foot store in Dallas, a quarter of the size of its average store. Once the AI system is in place, customers will use their smartphone cameras to scan the product itself.

This technical report records the experiments of applying multiple machine learning algorithms for predicting eating and food purchasing behaviors of free-living individuals. Data was collected with accelerometer, global positioning system (GPS), and body-worn cameras called SenseCam over a one week period in 81 individuals from a variety of ages and demographic backgrounds. These data were turned into minute-level features from sensors as well as engineered features that included time (e.g., time since last eating) and environmental context (e.g., distance to nearest grocery store). Algorithms include Logistic Regression, RBF-SVM, Random Forest, and Gradient Boosting. Our results show that the Gradient Boosting model has the highest mean accuracy score (0.7289) for predicting eating events before 0 to 4 minutes. For predicting food purchasing events, the RBF-SVM model (0.7395) outperforms others. For both prediction models, temporal and spatial features were important contributors to predicting eating and food purchasing events.