In today's world, data is the engine that powers every company. The potential benefits of the data are being pursued by many significant organizations from various industries. Thanks to the solutions that data scientists have offered, several economic sectors are undergoing a fundamental revolution. As tech behemoths like IKEA, Amazon, and Netflix already make use of all potential advantages, the application of data science in the retail industry has increased as well. In India, the retail industry is expected to reach a whooping height of US$ 2 trillion by the year 2032, according to a survey held by the Boston Consulting Group. There is too much potential for income and growth for retailers and consumer goods companies in particular, in this data-driven world than can be ignored.

The digital revolution is in full swing, and the insurance industry is no exception. Insurance companies are under pressure to modernize their operations and adopt new technologies to remain competitive. Digital transformation is bringing new opportunities for growth and efficiency, as well as challenges. In this blog post, we'll take a look at how the digital revolution is impacting the insurance industry and what insurers need to do to stay ahead of the curve. With media attention on the insurance industry and how companies interact with consumers rising, businesses are feeling the pressure to ensure their practices are more transparent and that they provide a fairer value for customers.

With dedicated regulation now emerging for fintech and digital banks in some jurisdictions, it's a case of adapt or die for incumbent players. But banks have one asset on their side - data. With around a billion credit card transactions every day, banks have access to one of the most significant volumes of customer data of any industry. Using AI, banks can harness this information to unlock unparalleled insights and growth. McKinsey estimates that AI technologies could deliver up to $1 trillion of additional value each year for the global banking industry, combining a deep understanding of customer needs with the composable cloud architecture to roll out hyper-personalised services at scale.

China is both a global leader in e-commerce and the world's manufacturing powerhouse. And yet the development of an industrial Internet is far behind the booming consumer Internet, which creates a tremendous divide between consumer demand and the supply side. A distinctive feature of the recent surge of digital economy in China is the strong push from the rising consumer demand for quality products, which has a major impact on the industrial digitization.4 China has over 1.3 billion mobile Internet users, the largest online shopper population, the largest amount and highest ratio of mobile payment in the world.1 According to China Statistical Yearbook (CSY) 2020, 25% of the national retail took place online in 2019, amounting to $1.8 trillion,3,11 over 90% of which was via mobile payment.11

The new robotic put wall helps to mitigate labor shortages and meet surging eCommerce demands. Berkshire Grey Inc, (Nasdaq: BGRY) launches a news AI-powered Robotic Shuttle Put Wall (RSPW) solution for eCommerce order fulfillment. This new solution helps to buffer and sort inventory items while increasing order processing speeds. The solutions helps retailers to meet the current eCommerce surge and manual labor shortages resulting from the pandemic, while handling holiday peak seasons. RSPW provides businesses the ability to increase customer order throughput and meet heightened customer expectations.

Zebra Technologies (NASDAQ: ZBRA), a Lincolnshire, IL-based provider of performance edge solutions, is to acquire antuit.ai, The amount of the deal – subject to customary closing conditions, including regulatory approval and expected to close in 2021 – was not disclosed. AI-powered demand forecasting solution into Zebra's SaaS portfolio will enable retailers and consumer products companies to combine planning and execution to optimize margins and drive revenue growth. The system enables retailers to deliver on their omnichannel strategy by increasing margins with effective prices and promotions, as well as optimizing inventory allocations and order fulfillment. With the platform, consumer products companies can maximize forecast accuracy; anticipate consumer demand to meet retailers' service level, shelf-level, store-level, and e-commerce orders; optimize pricing and trade promotions; and unify sales, trade and demand planning.

Data analytics helps marketers learn about their customers with target precision, from the movies they watch on Netflix to their favorite scoop of chocolate ice cream. Data is ubiquitous, essential and beneficial -- except when it's not. Experts warn that data analytics is at an inflection point. Growing concerns about security risks, privacy, bias and regulation are bumping up against all the benefits offered by machine learning and artificial intelligence. Layer those concerns on top of worries about the coronavirus pandemic and how it has rapidly changed consumer behavior, and the challenges become clear.

Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making. Improvement of the forecasting accuracy, even by a small percentage, may have a substantial impact on companies' production and financial planning, marketing strategies, inventory controls, supply chain management, and eventually stock prices. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motivated by tensor factorization methodologies for personalized context-aware recommender systems, we propose a novel approach called the Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS), which achieves accurate and individualized prediction for sales by building a single tensor-factorization model across multiple stores and products. Our contribution is a combination of: tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of tensor into future time periods using state-of-the-art statistical (seasonal auto-regressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category datasets collected by the Information Resource, Inc., where a total of 165 million weekly sales transactions from more than 1,500 grocery stores over 15,560 products are analyzed.

A lot of the AI-enhanced solutions that can help us get through this time rely on personal data, Simon said, and it's important that clients and consumers feel confident about the ethics around their use. For the new report, Capgemini surveyed 1,580 executives in 510 organizations and over 4,400 consumers around the world, in countries including the United States, the United Kingdom, China, Germany and France.

Media Mix Modeling attempts to estimate the causal effect of media spend on sales, solely based on observational data. And, as we all know estimating causal effects from observational data is fraught with challenges. Over time, two leading, and complimentary, frameworks have emerged for dealing with causal inference. This paper explores the use of Pearl's graphical framework to control for selection bias in media mix modeling, specifically in paid search ads. Suppose we are aiming to measure the causal impact of search advertising (PPC) on sales.