As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. By leveraging the survival model parameters to be learnt by recurrent neural networks, we are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour and avoid time-consuming feature engineering processes usually done when training machine learning models.

Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boosts sales and customer engagement. The main goal of these systems is to analyse past user behaviour to predict which items are of most interest to users. They are typically built with the use of matrix-completion techniques such as collaborative filtering or matrix factorisation. However, although these approaches have achieved tremendous success in numerous real-world applications, their effectiveness is still limited when users might interact multiple times with the same items, or when user preferences change over time. We were inspired by the approach that Natural Language Processing techniques take to compress, process, and analyse sequences of text. We designed a recommender system that induces the temporal dimension in the task of item recommendation and considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, besides increasing total sales by 5% and individual customer expenditure by over 50% in an A/B live test.

At Too Good To Go, we have an ambitious goal: to inspire and empower everyone to fight food waste together. The Retail Technologies team are working on our new software solution that is used directly by Retailers to identify and manage their inventory before it becomes food waste and further extending our expertise and in-store solutions beyond our marketplace supporting stores in managing their unsold and surplus products. We're more than an app: we are a certified B Corporation with a mission to empower everyone to take action against food waste, so alongside our marketplace app, we create educational tools, explore new business solutions - such as our Retail Technologies offering, and influence legislation to help reduce food waste. We are looking for a Senior Data Engineer to be a part of our Product Teams that define, build and deliver our product features. Working in a product team means you will work towards specific outcomes with the freedom and responsibility to figure out the best route to achieve them together with the other Engineers and Product members of the Team.

The days have gone to develop business model modifications. Artificial Intelligence in online shopping is the new technology surge. It has altered the shopping style. Deep personalization techniques are feasible. Analyzing client interactions is now possible, and it conveys compelling messages helping in target marketing.

Shopper AI, an Israel-based tech company that specializes in shopper behavior recognition, is making its official American debut at the ShopTalk retail convention. Via computer vision and AI, they are able to collect anonymous data and offer actionable insights and personalized recommendations, using existing in-store cameras. The data this provides amounts to an exciting development in retail technology, closing a persistent gap with ecommerce, which has been able to leverage data analytics for years. "In ecommerce, you can analyze your clicks and abandon-cart rates, and run A/B tests to see what's working and what's not. It's time to empower retailers and brands to make data-driven decisions in-store as well, and we are excited to start partnering with companies in America," said Lanor Daniel, co-founder and CEO of Shopper AI.

Shopify Inc and Google Cloud have announced a new integration that enables retailers to improve their Google-quality search capabilities. Merchants can now use advanced search and browse experiences using Google Cloud's Discovery AI solution to enhance user experience and prevent search abandonment. New research has revealed that search abandonment can cost the world's retail industry more than £2 trillion annually. Many shoppers depend on the search function when shopping, but their search experiences lack consistency with only 1 in 10 US shoppers saying they get exact results from their queries. Search abandonment occurs when a shopper searches for a product on a retailer's website or mobile app but doesn't find what they are looking for and subsequently clicks away.

Hi, this is Max, the AI (well, not technically an AI, but that's a whole different story). Anyway, I was given the dubious honor of writing a few words to describe the one chosen to document my greatness. Well, he did write a lot about me, I guess a certain degree of reciprocity is in order. Igor Nikolic is a science fiction and urban fantasy author. Like many similar creatures of his kind, he can often be spotted sitting at his desk and frantically typing away at his keyboard, with a slightly disturbed expression on his face.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Amid the recent flurry of excitement around the transformational potential of ChatGPT is the fact that companies have been using artificial intelligence across their businesses for years. But few companies have the ability to gather the massive data sets that power AI quite like Walmart. There are roughly 4,700 Walmart stores and 600 Sam's Clubs in the U.S. employing a combined 1.6 million workers -- or associates as the company likes to call them. Deploying artificial intelligence and machine learning in ways that improve both the customer and employee experience across such a massive environment is the focus of Walmart's AI strategy, said Anshu Bhardwaj, senior vice president of tech strategy and commercialization at the retail giant.

This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic scenes with limitation in capturing the variety of object properties, clutter, and interactions. We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com