Every week, Q.ai releases a "thematic screen" covering stocks that share commonalities – a common theme, if you will. But today's thematic screen is a little…different. Instead of sharing our AI's insights on top-rated consumer or momentum opportunities, we're going to reveal which big-name growth stocks have earned a big, fat F. Let the stock shaming begin. Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don't have to do the digging yourself.

In groundbreaking self-service solution from Malong and Dell Technologies, Walmart is showing the power and potential of artificial intelligence at the …

Chatbots, virtual assistants, and Interactive Voice Response (IVR) systems are key components of successful customer service strategies. We had the pleasure of hearing from three AWS Contact Center Intelligence (AWS CCI) Partners as part of our Best Practices in Customer Service Automation webinar, who provided valuable insights and tips for building automated, customer-service solutions. Customers expect great customer service. At the same time, enterprises struggle with the costs and resources necessary to provide high-quality, highly available, live-agent solutions. Automated solutions, like chatbots and IVR, enable enterprises to provide quality support, 24/7, while reducing costs and increasing customer satisfaction.

Snapchat's new augmented reality (AR) features allow real-fit try-on from major fashion brands Throughout the pandemic, virtual try-on has offered a sweet sense of relief to fashion and beauty brands unable to demo their products offline. From seeing how a new pair of sunglasses might look on your face to testing new lipstick shades out in anticipation of a mask-free future, these AR- and AI-enabled experiences have brought a small part of the traditional retail experiences to lockdown. Even retailers like Wayfair and IKEA rolled out 3D and augmented reality furniture visualization tools so shoppers could'try before they buy'. Only, they haven't all been brilliant--particularly where fashion is concerned. In'real life', dressing rooms are the heart of shopping experiences.

Did you know that companies with omnichannel customer engagement strategies retain 89% of customers compared to just 33% for those with a weak omnichannel strategy? Furthermore, Google research claims that 42% of in-store shoppers search for information online while in-store. Another extensive study by the Harvard Business Review studied the shopping behavior of just over 46,000 customers and focused on which channels they used and why. The writing is on the wall: Fusing the shopping experience across channels – or in other words, omnichannel marketing – is the future, and the future is now. To that end, let's look at four examples of brands that are offering a seamless omnichannel experience and blurring the lines between physical and digital shopping, one channel at a time.

Thanks to the COVID-19 pandemic, 2020 was a year in which consumers learned to do many things in a contactless manner and shopping was no exception. Retail industry has had the most crucial decision-making months during the pandemic, especially on their ability to predict the future. Retailers today are facing lots of concerns. These include how to increase sales, improve sell-through and profitability. Certain forward-thinking retailers are beginning to understand that from here on, no year will be like the one before. And as a result, to answer those questions, retailers are adopting technology to their advantage.

In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization. Firstly, we build a semi-parametric structural model to learn individual price elasticity and predict counterfactual demand. This semi-parametric model takes advantage of both the predictability of nonparametric machine learning model and the interpretability of economic model. Secondly, we propose a multi-period dynamic pricing algorithm to maximize the overall profit of a perishable product over its finite selling horizon. Different with the traditional approaches that use the deterministic demand, we model the uncertainty of counterfactual demand since it inevitably has randomness in the prediction process. Based on the stochastic model, we derive a sequential pricing strategy by Markov decision process, and design a two-stage algorithm to solve it. The proposed algorithm is very efficient. It reduces the time complexity from exponential to polynomial. Experimental results show the advantages of our pricing algorithm, and the proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.

Machine learning (ML) models have been deployed successfully across a variety of use cases and industries, but due to the high computational complexity of recent ML models such as deep neural networks, inference deployments have been limited by performance and cost constraints. To add to the challenge, preparing a model for inference involves packaging the model in the right format and optimizing the model for each target hardware such as CPU, GPU, or AWS Inferentia. ML acceleration technologies have evolved to close the gap between productivity-focused ML frameworks and performance-oriented and efficiency-oriented hardware backends. However, optimizing a model for target hardware still involves assembling a complex tool chain of framework-specific converters and hardware-specific compilers, each with their own dependencies and configuration choices that can be difficult to understand, and then using it to compile the model. Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy ML models at 50% lower total cost of ownership than self-managed deployments on Amazon Elastic Compute Cloud (Amazon EC2).

Marketing has come a long way since the days of John Wanamaker and his famous complaint that he didn't know which half of his marketing spend was useful and which wasn't. However, as senior Forbes contributor George Bradt contends in his article, Wanamaker Was Wrong -- The Vast Majority Of Advertising Is Wasted, attribution is extremely difficult to measure, and brands would be smarter to try to spot their most loyal customers, rather than try to figure out the exact steps that should be attributed to a purchase. Although there are plenty of tools available to collect purchasing behavior, piecing together a somewhat reliable path-to-purchase is not easy, and Bradt believes the money is better spent both finding loyal customers and providing those customers with a personalized experience they will learn to covet. Today, personalization is becoming the optimum word in a radically-new customer experience environment. In her article 3 AI-driven strategies for retailers in 2019, Giselle Abramovich states "Personalization is table stakes for today's retailers, who are increasingly competing to be relevant in the hearts and minds of shoppers."

Praise be to scientists, who work behind the scenes, with little fanfare, to make our lives better. Case in point: Apeel, a company dedicated to reducing global food waste. In 2018 the company introduced a plant-based coating that helps extend the shelf life of produce, including avocados. No more avocados going from rock-hard in the grocery store to mushy brown goo on our kitchen countertops the moment we take our eyes off of them! The coating has been used commercially on avocados, organic apples, and citrus fruits, and it has been helping grocers save over 20 million pieces of fruit annually.