Marks & Spencer is using artificial intelligence to advise shoppers on their outfit choices based on their body shape and style preferences, as part of efforts to increase online sales. The 130-year-old retailer is using the technology to personalise consumers' online experience, and suggest items to buy. Stephen Langford, the company's director of online, said M&S was using AI to adapt the language used to address shoppers, tailoring to six different preferences such as emotional, descriptive language or more straightforward prose. One of its aims is to personalise online interactions with shoppers, he said, such as prioritising products most relevant for an individual. Male shoppers would be less likely to be offered the latest deals on bras, for example.

The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. However, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.

WhatsApp is the new buzzword for digital conversations as the post-pandemic world gets more digital-savvy and contactless shopping emerges as the new trend. According to a Statista report, WhatsApp is the most popular messaging platform with more than 2 billion active users outranking Facebook Messenger at 1.3 billion and WeChat at 1.2 billion users. Not only this, it has become the world's third-largest social networking platform after Facebook and YouTube. Given the popularity and demand of this new messaging channel, businesses too have started to turn their ships, the WhatsApp way. Also, recent innovations in artificial intelligence (AI) and machine learning (ML) make WhatsApp more profitable for businesses.

I warned that the usual draws for Black Friday had disappeared. Deep discounting had already been the primary strategy that companies had been using to try to recover from Covid-19, which meant the massive sales had become normal. Plus, 70% of American adults report struggling to pay bills, leaving less disposable income for Black Friday and Cyber Monday. It looked like price-sensitive customers had temporarily disappeared, making cutting prices less effective than in the past. I wrote an article forecasting that Black Friday and Cyber Monday were poised to disappoint.

The good news: You won't have to camp out in front of Best Buy for that big screen TV "doorbuster" deal. The bad: So many will, indeed, be gathered at the same time, but you'll all be online for the Black Friday super sales. Who has the quickest trigger finger? Black Friday sales start earlier this year, as soon as Sunday, if you're not counting the Black Friday-adjacent sales that have already been out there. The "doorbusters" of years past are now online specials, starting at staggered times.

A chatbot can benefit the biggest or the smallest eCommerce company. Whether you're selling clothes, shoes, makeup, electronics, art & crafts, or furniture, having a chatbot is a great investment for your store. Big brands benefit from chatbots because it allows them to engage with numerous customers in a timely and efficient manner. At the same time, small stores that cannot hire several employees can compete with bigger businesses by providing immediate service. Having a chatbot can help increase your revenue because it solves different customer problems that often lead to cart abandonment.

It is difficult to decide upon the efficacy of an online sale simply from the discount offered on commodities. Different features have different influence on the price of a product which must be taken into consideration when determining the significance of a discount. In this paper we have proposed a machine learning based heuristic to quantify the \textit{"significance"} of the discount offered on any commodity. Our proposed technique can quantify the significance of the discount based on features and the original price, and hence can guide a buyer during a sale season by predicting the efficacy of the sale. We have applied this technique on the Flipkart Summer Sale dataset using Support Vector Machine, which predicts the efficacy of the sale with an accuracy of 91.11\%. Our result shows that very few mobile phones have a significant discount during the Flipkart Summer Sale.

That's because the online pioneer thrives on disruption. Just look at what happened to FedEx Corp. In February, FedEx shares collapsed following a Wall Street Journal report that Amazon was about to launch a delivery service for businesses. Shipping with Amazon, the moniker for the new service, was supposed to kill incumbents. FedEx CEO Fred Smith appears at a signing ceremony where President Donald Trump signed an Executive Order that establishes a National Council for the American Worker in the East Room of the White House, Thursday, July 19, 2018, in Washington.

HANGZHOU, China/SHANGHAI, June 28 (Reuters) - In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralised computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.

HANGZHOU, China/SHANGHAI (Reuters) - In the eastern Chinese city of Hangzhou, an ambulance speeds through traffic on a wave of green lights, helped along by an artificial intelligence (AI) system and big data. The system, which involves sending information to a centralized computer linked to the city's transport networks, is part of a trial by Alibaba Group Holding Ltd. The Chinese tech giant is hoping to use its cloud and data systems to tackle issues hobbling China's healthcare system like snarled city traffic, long patient queues and a lack of doctors. Alibaba's push into healthcare reflects a wider trend in China, where technology firms are racing to shake up a creaking state-run health sector and take a slice of spending that McKinsey & Co estimates will hit $1 trillion by 2020. Tencent-backed WeDoctor, which offers online consultations and doctor appointments, raised $500 million in May at a valuation of $5.5 billion.